The Animal Audiogram Database: A community-based resource for consolidated audiogram data and metadata.

Knowledge of hearing ability, as represented in audiograms, is essential for understanding how animals acoustically perceive their environment, predicting and counteracting the effects of anthropogenic noise, and managing wildlife. Audiogram data and relevant background information are currently only available embedded in the text of individual scientific publications in various unstandardized formats. This heterogeneity makes it hard to access, compare, and integrate audiograms. The Animal Audiogram Database (https://animalaudiograms.org) assembles published audiogram data, metadata about the corresponding experiments, and links to the original publications in a consistent format. The database content is the result of an extensive survey of the scientific literature and manual curation of the audiometric data found therein. As of November 1, 2021, the database contains 306 audiogram datasets from 34 animal species. The scope and format of the provided metadata and design of the database interface were established by active research community involvement. Options to compare audiograms and download datasets in structured formats are provided. With the focus currently on vertebrates and hearing in underwater environments, the database is drafted as a free and open resource for facilitating the review and correction of the contained data and collaborative extension with audiogram data from any taxonomic group and habitat.

Envisaging a global infrastructure to exploit the potential of digitised collections.

Tens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it is still used only on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if images of collection objects could be made accessible in a single corpus. In this paper, we make the case for infrastructure that could support image analysis of collection objects. We show that such infrastructure is entirely feasible and well worth investing in.

HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology.

We present HepatoNet1, the first reconstruction of a comprehensive metabolic network of the human hepatocyte that is shown to accomplish a large canon of known metabolic liver functions. The network comprises 777 metabolites in six intracellular and two extracellular compartments and 2539 reactions, including 1466 transport reactions. It is based on the manual evaluation of >1500 original scientific research publications to warrant a high-quality evidence-based model. The final network is the result of an iterative process of data compilation and rigorous computational testing of network functionality by means of constraint-based modeling techniques. Taking the hepatic detoxification of ammonia as an example, we show how the availability of nutrients and oxygen may modulate the interplay of various metabolic pathways to allow an efficient response of the liver to perturbations of the homeostasis of blood compounds.

SEE: structured representation of scientific evidence in the biomedical domain using Semantic Web techniques.

BACKGROUND: Accounts of evidence are vital to evaluate and reproduce scientific findings and integrate data on an informed basis. Currently, such accounts are often inadequate, unstandardized and inaccessible for computational knowledge engineering even though computational technologies, among them those of the semantic web, are ever more employed to represent, disseminate and integrate biomedical data and knowledge. RESULTS: We present SEE (Semantic EvidencE), an RDF/OWL based approach for detailed representation of evidence in terms of the argumentative structure of the supporting background for claims even in complex settings. We derive design principles and identify minimal components for the representation of evidence. We specify the Reasoning and Discourse Ontology (RDO), an OWL representation of the model of scientific claims, their subjects, their provenance and their argumentative relations underlying the SEE approach. We demonstrate the application of SEE and illustrate its design patterns in a case study by providing an expressive account of the evidence for certain claims regarding the isolation of the enzyme glutamine synthetase. CONCLUSIONS: SEE is suited to provide coherent and computationally accessible representations of evidence-related information such as the materials, methods, assumptions, reasoning and information sources used to establish a scientific finding by adopting a consistently claim-based perspective on scientific results and their evidence. SEE allows for extensible evidence representations, in which the level of detail can be adjusted and which can be extended as needed. It supports representation of arbitrary many consecutive layers of interpretation and attribution and different evaluations of the same data. SEE and its underlying model could be a valuable component in a variety of use cases that require careful representation or examination of evidence for data presented on the semantic web or in other formats.

A community-driven global reconstruction of human metabolism.

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

Metabolite profiling of Chlamydomonas reinhardtii under nutrient deprivation.

A metabolite profiling technique for Chlamydomonas reinhardtii cells for multiparallel analysis of low-molecular weight polar compounds was developed. The experimental protocol was optimized to quickly inactivate enzymatic activity, achieve maximum extraction capacity, and process large sample quantities. As a result of the rapid sampling, extraction, and analysis by gas chromatography coupled to time-of-flight mass spectrometry, more than 800 analytes from a single sample could be measured, of which more than 100 could be identified. Analyte responses could be determined mostly with ses less than 10%. Wild-type cells of C. reinhardtii strain CC-125 subjected to nitrogen-, phosphorus-, sulfur-, or iron-depleted growth conditions develop highly distinctive metabolite profiles. Individual metabolites undergo marked changes in their steady-state levels. Compared to control conditions, sulfur-depleted cells accumulated 4-hydroxyproline more than 50-fold, whereas the amount of 2-ketovaline was reduced to 2% of control levels. The contribution of each compound to the differences observed in the metabolic phenotypes is summarized in a quantitatively rigorous way by principal component analysis, which clearly discriminates the cells from different growth regimes and indicates that phosphorus-depleted conditions induce a deficiency syndrome quite different from the response to nitrogen, sulfur, or iron starvation.

The involvement of a multicopper oxidase in iron uptake by the green algae Chlamydomonas reinhardtii.

In the unicellular green algae Chlamydomonas reinhardtii, high-affinity uptake of iron (Fe) requires an Fe(3+)-chelate reductase and an Fe transporter. Neither of these proteins nor their corresponding genes have been isolated. We previously identified, by analysis of differentially expressed plasma membrane proteins, an approximately 150-kD protein whose synthesis was induced under conditions of Fe-deficient growth. Based on homology of internal peptide sequences to the multicopper oxidase hephaestin, this protein was proposed to be a ferroxidase. A nucleotide sequence to the full-length cDNA clone for this ferroxidase-like protein has been obtained. Analysis of the primary amino acid sequence revealed a putative transmembrane domain near the amino terminus of the protein and signature sequences for two multicopper oxidase I motifs and one multicopper oxidase II motif. The ferroxidase-like gene was transcribed under conditions of Fe deficiency. Consistent with the role of a copper (Cu)-containing protein in Fe homeostasis, growth of cells in Cu-depleted media eliminated high-affinity Fe uptake, and Cu-deficient cells that were grown in optimal Fe showed greatly reduced Fe accumulation compared with control, Cu-sufficient cells. Reapplication of Cu resulted in the recovery of Fe transport activity. Together, these results were consistent with the participation of a ferroxidase in high-affinity Fe uptake in C. reinhardtii.