High-spatial resolution functional chemistry of nitrogen compounds in the observed UK meteorite fall Winchcombe.

Organic matter in extraterrestrial samples is a complex material that might have played an important role in the delivery of prebiotic molecules to the early Earth. We report here on the identification of nitrogen-containing compounds such as amino acids and N-heterocycles within the recent observed meteorite fall Winchcombe by high-spatial resolution spectroscopy techniques. Although nitrogen contents of Winchcombe organic matter are low (N/C ~ 1-3%), we were able to detect the presence of these compounds using a low-noise direct electron detector. These biologically relevant molecules have therefore been tentatively found within a fresh, minimally processed meteorite sample by high spatial resolution techniques conserving the overall petrographic context. Carbon functional chemistry investigations show that sizes of aromatic domains are small and that abundances of carboxylic functional groups are low. Our observations demonstrate that Winchcombe represents an important addition to the collection of carbonaceous chondrites and still preserves pristine extraterrestrial organic matter.

Bridging mouse and human anatomies; a knowledge-based approach to comparative anatomy for disease model phenotyping.

The laboratory mouse is the foremost mammalian model used for studying human diseases and is closely anatomically related to humans. Whilst knowledge about human anatomy has been collected throughout the history of mankind, the first comprehensive study of the mouse anatomy was published less than 60 years ago. This has been followed by the more recent publication of several books and resources on mouse anatomy. Nevertheless, to date, our understanding and knowledge of mouse anatomy is far from being at the same level as that of humans. In addition, the alignment between current mouse and human anatomy nomenclatures is far from being as developed as those existing between other species, such as domestic animals and humans. To close this gap, more in depth mouse anatomical research is needed and it will be necessary to extent and refine the current vocabulary of mouse anatomical terms.

GA4GH Phenopackets: A Practical Introduction.

The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. A detailed example is presented that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases.

Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium.

The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how those large-scale data sets are processed, analyzed, and interpreted. The 67th Annual Meeting of the Radiation Research Society featured a symposium on MI approaches to highlight recent advancements in the radiation sciences and their clinical applications. This article summarizes three of those presentations regarding recent developments for metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.

Klarigi: Characteristic explanations for semantic biomedical data.

Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such as biosample gene expression profiles or patient phenotypes, and is useful for a range of tasks including differential diagnosis and causative variant prioritisation. These approaches, however, usually consider only univariate relationships, make limited use of the semantic features of ontologies, and provide limited information and evaluation of the explanatory power of both singular and grouped candidate classes. Moreover, they are not designed to solve the problem of deriving cohesive, characteristic, and discriminatory sets of classes for entity groups. We have developed a new tool, called Klarigi, which introduces multiple scoring heuristics for identification of classes that are both compositional and discriminatory for groups of entities annotated with ontology classes. The tool includes a novel algorithm for derivation of multivariable semantic explanations for entity groups, makes use of semantic inference through live use of an ontology reasoner, and includes a classification method for identifying the discriminatory power of candidate sets, in addition to significance testing apposite to traditional enrichment approaches. We describe the design and implementation of Klarigi, including its scoring and explanation determination methods, and evaluate its use in application to two test cases with clinical significance, comparing and contrasting methods and results with literature-based and enrichment analysis methods. We demonstrate that Klarigi produces characteristic and discriminatory explanations for groups of biomedical entities in two settings. We also show that these explanations recapitulate and extend the knowledge held in existing biomedical databases and literature for several diseases. We conclude that Klarigi provides a distinct and valuable perspective on biomedical datasets when compared with traditional enrichment methods, and therefore constitutes a new method by which biomedical datasets can be explored, contributing to improved insight into semantic data.

X-ray-induced bio-acoustic emissions from cultured cells.

PURPOSE: We characterize for the first time the emission of acoustic waves from cultured cells irradiated with X-ray photon radiation. METHODS AND MATERIALS: Human cancer cell lines (MCF-7, HL-60) and control cell-free media were exposed to 1 Gy X-ray photons while recording the sound generated before, during and after irradiation using custom large-bandwidth ultrasound transducer. The effects of dose rate and cell viability were investigated. RESULTS: We report the first recorded acoustic signals captured from a collective pressure wave response to ionizing irradiation in cell culture. The acoustic signal was co-terminous with the radiation pulse, its magnitude was dependent on radiation dose rate, and live and dead cells showed qualitatively and quantitatively different acoustic signal characteristics. The signature of the collective acoustic peaks was temporally wider and with higher acoustic power for irradiated HL-60 than for irradiated MCF-7. CONCLUSIONS: We show that X-ray irradiation induces two cultured cancer cell types to emit a characteristic acoustic signal for the duration of the radiation pulse. The rapid decay of the signal excludes acoustic emissions themselves from contributing to the inter-organism bystander signal previously reported in intact animals, but they remain a potential component of the bystander process in tissues and cell cultures. This preliminary study suggests that further work on the potential role of radiation-induced acoustic emission (RIAE) in the inter-cellular bystander effect is merited.

A laboratory challenge model for evaluating enyterocytozoon hepatopenaei susceptibility in selected lines of pacific whiteleg shrimp Penaeus vannamei.

Here we report for the first time a laboratory challenge model for Enterocytozoon hepatopenaei (EHP) to determine the difference of two Specific Pathogen Free (SPF) lines of Penaeus vannamei shrimp. These lines were experimentally challenged using EHP-infected fecal strings as inoculum. Real-time PCR and histopathology assays were performed to confirm EHP infection and evaluate differences in EHP susceptibility in the two genetic lines screened. Although the histopathology of the hepatopancreas tissue showed EHP lesions in both challenged groups, the histological lesions were more pronounced in one of the SPF lines. Quantitative PCR results revealed that animals displaying less hepatocellular damage have lower EHP load compared to animals displaying more pronounced pathological changes. There was no significant difference in final survival at 36 days post-infection in these lines with survival ranging between 80 and 100%. The data showed that mortality as an endpoint metric is not a suitable parameter to determine genetic susceptibility to EHP. Instead, histopathological changes in hepatopancreas, EHP load of the same tissue, and growth retardation would be better metrics to screen EHP susceptibility in P. vannamei. The results show the feasibility of screening genetic lines of P. vannamei for EHP resistance/tolerance using fecal string as an inoculum and, assessing histopathological changes, EHP load, and weight as indicators of resistance.

Datasets of in vitro clonogenic assays showing low dose hyper-radiosensitivity and induced radioresistance.

Low dose hyper-radiosensitivity and induced radioresistance are primarily observed in surviving fractions of cell populations exposed to ionizing radiation, plotted as the function of absorbed dose. Several biophysical models have been developed to quantitatively describe these phenomena. However, there is a lack of raw, openly available experimental data to support the development and validation of quantitative models. The aim of this study was to set up a database of experimental data from the public literature. Using Google Scholar search, 46 publications with 101 datasets on the dose-dependence of surviving fractions, with clear evidence of low dose hyper-radiosensitivity, were identified. Surviving fractions, their uncertainties, and the corresponding absorbed doses were digitized from graphs of the publications. The characteristics of the cell line and the irradiation were also recorded, along with the parameters of the linear-quadratic model and/or the induced repair model if they were provided. The database is available in STOREDB, and can be used for meta-analysis, for comparison with new experiments, and for development and validation of biophysical models.

Contribution of model organism phenotypes to the computational identification of human disease genes.

Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype-phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene-disease associations. We found that mouse genotype-phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms' data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation. This article has an associated First Person interview with the first author of the paper.

Bio-acoustic signaling; exploring the potential of sound as a mediator of low-dose radiation and stress responses in the environment.

OBJECTIVES: This commentary reviews and evaluates the role of sound signals as part of the infosome of cells and organisms. Emission and receipt of sound has recently been identified as a potentially important universal signaling mechanism invoked when organisms are stressed. Recent evidence from plants, animals and microbes suggests that it could be a stimulus for specific or general molecular cellular stress responses in different contexts, and for triggering population level responses. This paper reviews the current status of the field with particular reference to the potential role of sound signaling as an immediate/early bystander effector (RIBE) during radiation-induced stress. CONCLUSIONS: While the chemical effectors involved in intercellular and inter-organismal signaling have been the subject of intense study in the field of Chemical Ecology, less appears to be known about physical signals in general and sound signals in particular. From this review we conclude that these signals are ubiquitous in each kingdom and behave very like physical bystander signals leading to regulation of metabolic pathways and gene expression patterns involved in adaptation, synchronization of population responses, and repair or defence against damage. We propose the hypothesis that acoustic energy released on interaction of biota with electromagnetic radiation may represent a signal released by irradiated cells leading to, or complementing, or interacting with, other responses, such as endosome release, responsible for signal relay within the unirradiated individuals in the targeted population.

From tangled banks to toxic bunnies; a reflection on the issues involved in developing an ecosystem approach for environmental radiation protection.

The objective of this paper is to present the results of discussions at a workshop held as part of the International Congress of Radiation Research (Environmental Health stream) in Manchester UK, 2019. The main objective of the workshop was to provide a platform for radioecologists to engage with radiobiologists to address major questions around developing an Ecosystem approach in radioecology and radiation protection of the environment. The aim was to establish a critical framework to guide research that would permit integration of a pan-ecosystem approach into radiation protection guidelines and regulation for the environment. The conclusions were that the interaction between radioecologists and radiobiologists is useful in particular in addressing field versus laboratory issues where there are issues and challenges in designing good field experiments and a need to cross validate field data against laboratory data and vice versa. Other main conclusions were that there is a need to appreciate wider issues in ecology to design good approaches for an ecosystems approach in radioecology and that with the capture of 'Big Data', novel tools such as machine learning can now be applied to help with the complex issues involved in developing an ecosystem approach.

Multi-faceted semantic clustering with text-derived phenotypes.

Identification of ontology concepts in clinical narrative text enables the creation of phenotype profiles that can be associated with clinical entities, such as patients or drugs. Constructing patient phenotype profiles using formal ontologies enables their analysis via semantic similarity, in turn enabling the use of background knowledge in clustering or classification analyses. However, traditional semantic similarity approaches collapse complex relationships between patient phenotypes into a unitary similarity scores for each pair of patients. Moreover, single scores may be based only on matching terms with the greatest information content (IC), ignoring other dimensions of patient similarity. This process necessarily leads to a loss of information in the resulting representation of patient similarity, and is especially apparent when using very large text-derived and highly multi-morbid phenotype profiles. Moreover, it renders finding a biological explanation for similarity very difficult; the black box problem. In this article, we explore the generation of multiple semantic similarity scores for patients based on different facets of their phenotypic manifestation, which we define through different sub-graphs in the Human Phenotype Ontology. We further present a new methodology for deriving sets of qualitative class descriptions for groups of entities described by ontology terms. Leveraging this strategy to obtain meaningful explanations for our semantic clusters alongside other evaluation techniques, we show that semantic clustering with ontology-derived facets enables the representation, and thus identification of, clinically relevant phenotype relationships not easily recoverable using overall clustering alone. In this way, we demonstrate the potential of faceted semantic clustering for gaining a deeper and more nuanced understanding of text-derived patient phenotypes.

Linking common human diseases to their phenotypes; development of a resource for human phenomics.

BACKGROUND: In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations are not comprehensive, and they often do not have broad coverage of the disease terminologies, particularly ICD-10, which is still the primary terminology used in clinical settings. METHODS: We developed two approaches to gather disease-phenotype associations. First, we used a text mining method that utilizes semantic relations in phenotype ontologies, and applies statistical methods to extract associations between diseases in ICD-10 and phenotype ontology classes from the literature. Second, we developed a semi-automatic way to collect ICD-10-phenotype associations from existing resources containing known relationships. RESULTS: We generated four datasets. Two of them are independent datasets linking diseases to their phenotypes based on text mining and semi-automatic strategies. The remaining two datasets are generated from these datasets and cover a subset of ICD-10 classes of common diseases contained in UK Biobank. We extensively validated our text mined and semi-automatically curated datasets by: comparing them against an expert-curated validation dataset containing disease-phenotype associations, measuring their similarity to disease-phenotype associations found in public databases, and assessing how well they could be used to recover gene-disease associations using phenotype similarity. CONCLUSION: We find that our text mining method can produce phenotype annotations of diseases that are correct but often too general to have significant information content, or too specific to accurately reflect the typical manifestations of the sporadic disease. On the other hand, the datasets generated from integrating multiple knowledgebases are more complete (i.e., cover more of the required phenotype annotations for a given disease). We make all data freely available at https://doi.org/10.5281/zenodo.4726713 .

Chromium (VI) Inhibition of Low pH Bioleaching of Limonitic Nickel-Cobalt Ore.

Limonitic layers of the regolith, which are often stockpiled as waste materials at laterite mines, commonly contain significant concentrations of valuable base metals, such as nickel, cobalt, and manganese. There is currently considerable demand for these transition metals, and this is projected to continue to increase (alongside their commodity values) during the next few decades, due in the most part to their use in battery and renewable technologies. Limonite bioprocessing is an emerging technology that often uses acidophilic prokaryotes to catalyse the oxidation of zero-valent sulphur coupled to the reduction of Fe (III) and Mn (IV) minerals, resulting in the release of target metals. Chromium-bearing minerals, such as chromite, where the metal is present as Cr (III), are widespread in laterite deposits. However, there are also reports that the more oxidised and more biotoxic form of this metal [Cr (VI)] may be present in some limonites, formed by the oxidation of Cr (III) by manganese (IV) oxides. Bioleaching experiments carried out in laboratory-scale reactors using limonites from a laterite mine in New Caledonia found that solid densities of ∼10% w/v resulted in complete inhibition of iron reduction by acidophiles, which is a critical reaction in the reductive dissolution process. Further investigations found this to be due to the release of Cr (VI) in the acidic liquors. X-ray absorption near edge structure (XANES) spectroscopy analysis of the limonites used found that between 3.1 and 8.0% of the total chromium in the three limonite samples used in experiments was present in the raw materials as Cr (VI). Microbial inhibition due to Cr (VI) could be eliminated either by adding limonite incrementally or by the addition of ferrous iron, which reduces Cr (VI) to less toxic Cr (III), resulting in rates of extraction of cobalt (the main target metal in the experiments) of >90%.

A primordial 15N-depleted organic component detected within the carbonaceous chondrite Maribo.

We report on the detection of primordial organic matter within the carbonaceous chondrite Maribo that is distinct from the majority of organics found in extraterrestrial samples. We have applied high-spatial resolution techniques to obtain C-N isotopic compositions, chemical, and structural information of this material. The organic matter is depleted in 15N relative to the terrestrial value at around δ15N ~ -200‰, close to compositions in the local interstellar medium. Morphological investigations by electron microscopy revealed that the material consists of µm- to sub-µm-sized diffuse particles dispersed within the meteorite matrix. Electron energy loss and synchrotron X-ray absorption near-edge structure spectroscopies show that the carbon functional chemistry is dominated by aromatic and C=O bonding environments similar to primordial organics from other carbonaceous chondrites. The nitrogen functional chemistry is characterized by C-N double and triple bonding environments distinct from what is usually found in 15N-enriched organics from aqueously altered carbonaceous chondrites. Our investigations demonstrate that Maribo represents one of the least altered CM chondrite breccias found to date and contains primordial organic matter, probably originating in the interstellar medium.

DDIEM: drug database for inborn errors of metabolism.

BACKGROUND: Inborn errors of metabolism (IEM) represent a subclass of rare inherited diseases caused by a wide range of defects in metabolic enzymes or their regulation. Of over a thousand characterized IEMs, only about half are understood at the molecular level, and overall the development of treatment and management strategies has proved challenging. An overview of the changing landscape of therapeutic approaches is helpful in assessing strategic patterns in the approach to therapy, but the information is scattered throughout the literature and public data resources. RESULTS: We gathered data on therapeutic strategies for 300 diseases into the Drug Database for Inborn Errors of Metabolism (DDIEM). Therapeutic approaches, including both successful and ineffective treatments, were manually classified by their mechanisms of action using a new ontology. CONCLUSIONS: We present a manually curated, ontologically formalized knowledgebase of drugs, therapeutic procedures, and mitigated phenotypes. DDIEM is freely available through a web interface and for download at http://ddiem.phenomebrowser.net.

PATHBIO: an international training program for precision mouse phenotyping.

Design and production of genetically engineered mouse strains by individual research laboratories, research teams, large-scale consortia, and the biopharmaceutical industry have magnified the need for qualified personnel to identify, annotate, and validate (phenotype) these potentially new mouse models of human disease. The PATHBIO project has been recently established and funded by the European Union's ERASMUS+ Knowledge Alliance program to address the current shortfall in formally trained personnel. A series of teaching workshops will be given by experts on anatomy, histology, embryology, imaging, and comparative pathology to increase the availability of individuals with formal training to contribute to this important niche of Europe's biomedical research enterprise. These didactic and hands-on workshops are organized into three modules: (1) embryology, anatomy, histology, and the anatomical basis of imaging, (2) image-based phenotyping, and (3) pathology. The workshops are open to all levels of participants from recent graduates to Ph.D., M.D., and veterinary scientists. Participation is available on a competitive basis at no cost for attending. The first series of Workshop Modules was held in 2019 and these will continue for the next 2 years.

Ontology-based prediction of cancer driver genes.

Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from non-driver mutations. We have developed a novel method for identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, functions, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In addition to confirming known driver genes, we identify several novel candidate driver genes. We demonstrate the utility of our method by validating its predictions in nasopharyngeal cancer and colorectal cancer using whole exome and whole genome sequencing.