Principles of metabolome conservation in animals.

Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.

Trends in dental care utilisation among the elderly using longitudinal data from 14 European countries: A multilevel analysis.

BACKGROUND: The use of dental care among older people is low compared to other forms of health care, with significant health consequences. However, the evidence on the extent to which countries' welfare systems and socio-economic factors influence the uptake of dental care by older people is limited. This study aimed to describe trends of dental care utilisation, and to compare use of dental care with other types of healthcare services among the elderly, considering different socio-economic factors and welfare systems in European countries. METHODS: Multilevel logistic regression analysis was performed using longitudinal data from four waves (between Wave 5 and 8) of the Survey of Health, Ageing and Retirement in Europe database, with a follow-up period of 7 years. The study sample included 20,803 respondents aged 50 years or older from 14 European countries. RESULTS: The annual dental care attendance was the highest in Scandinavian countries (85.7%), however, improving trends of dental attendance was recognized in Southern and Bismarckian countries (p<0.001). The difference in use of dental care services between socio-economic groups was expanding over time regarding low- and high-income level and residential area. A more marked difference was observed between social groups in dental care utilisation compared to other forms of care. Income level and unemployed status had significant effect on forgoing dental care due to cost and unavailability. CONCLUSION: The observed differences between socioeconomic groups may highlight the health consequences of the different organization and financing of dental care. The elderly population could benefit from adopting policies aiming to reduce the financial barriers to dental care usage, especially in Southern and Eastern European countries.

TFLink: an integrated gateway to access transcription factor-target gene interactions for multiple species.

Analysis of transcriptional regulatory interactions and their comparisons across multiple species are crucial for progress in various fields in biology, from functional genomics to the evolution of signal transduction pathways. However, despite the rapidly growing body of data on regulatory interactions in several eukaryotes, no databases exist to provide curated high-quality information on transcription factor-target gene interactions for multiple species. Here, we address this gap by introducing the TFLink gateway, which uniquely provides experimentally explored and highly accurate information on transcription factor-target gene interactions (∼12 million), nucleotide sequences and genomic locations of transcription factor binding sites (∼9 million) for human and six model organisms: mouse, rat, zebrafish, fruit fly, worm and yeast by integrating 10 resources. TFLink provides user-friendly access to data on transcription factor-target gene interactions, interactive network visualizations and transcription factor binding sites, with cross-links to several other databases. Besides containing accurate information on transcription factors, with a clear labelling of the type/volume of the experiments (small-scale or high-throughput), the source database and the original publications, TFLink also provides a wealth of standardized regulatory data available for download in multiple formats. The database offers easy access to high-quality data for wet-lab researchers, supplies data for gene set enrichment analyses and facilitates systems biology and comparative gene regulation studies. Database URL https://tflink.net/.

SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks.

Signaling networks represent the molecular mechanisms controlling a cell's response to various internal or external stimuli. Most currently available signaling databases contain only a part of the complex network of intertwining pathways, leaving out key interactions or processes. Hence, we have developed SignaLink3 (http://signalink.org/), a value-added knowledge-base that provides manually curated data on signaling pathways and integrated data from several types of databases (interaction, regulation, localisation, disease, etc.) for humans, and three major animal model organisms. SignaLink3 contains over 400 000 newly added human protein-protein interactions resulting in a total of 700 000 interactions for Homo sapiens, making it one of the largest integrated signaling network resources. Next to H. sapiens, SignaLink3 is the only current signaling network resource to provide regulatory information for the model species Caenorhabditis elegans and Danio rerio, and the largest resource for Drosophila melanogaster. Compared to previous versions, we have integrated gene expression data as well as subcellular localization of the interactors, therefore uniquely allowing tissue-, or compartment-specific pathway interaction analysis to create more accurate models. Data is freely available for download in widely used formats, including CSV, PSI-MI TAB or SQL.