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Variation in mouse chemical signals is genetically controlled and environmentally modulated
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SYSNO ASEP 0573981 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Variation in mouse chemical signals is genetically controlled and environmentally modulated Author(s) Stopková, R. (CZ)
Matějková, T. (CZ)
Dodoková, A. (CZ)
Talacko, P. (CZ)
Žáček, P. (CZ)
Sedláček, Radislav (UMG-J) RID
Piálek, Jaroslav (UBO-W) RID, ORCID, SAI
Stopka, P. (CZ)Number of authors 8 Article number 8573 Source Title Scientific Reports. - : Nature Publishing Group - ISSN 2045-2322
Roč. 13, č. 1 (2023)Number of pages 13 s. Language eng - English Country US - United States Keywords Genetic Variation ; Mice ; Proteins ; Proteomics ; Signal Transduction OECD category Biochemistry and molecular biology R&D Projects ED1.1.00/02.0109 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) LM2018126 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) EF16_013/0001789 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) GA16-23773S GA ČR - Czech Science Foundation (CSF) Research Infrastructure CCP II - 90126 - Ústav molekulární genetiky AV ČR, v. v. i. Method of publishing Open access Institutional support UMG-J - RVO:68378050 ; UBO-W - RVO:68081766 UT WOS 001001070500060 EID SCOPUS 85160268236 DOI 10.1038/s41598-023-35450-8 Annotation In most mammals and particularly in mice, chemical communication relies on the detection of ethologically relevant fitness-related cues from other individuals. In mice, urine is the primary source of these signals, so we employed proteomics and metabolomics to identify key components of chemical signalling. We show that there is a correspondence between urinary volatiles and proteins in the representation of genetic background, sex and environment in two house mouse subspecies Mus musculus musculus and M. m. domesticus. We found that environment has a strong influence upon proteomic and metabolomic variation and that volatile mixtures better represent males while females have surprisingly more sex-biased proteins. Using machine learning and combined-omics techniques, we identified mixtures of metabolites and proteins that are associated with biological features. Workplace Institute of Molecular Genetics Contact Nikol Škňouřilová, nikol.sknourilova@img.cas.cz, Tel.: 241 063 217 Year of Publishing 2024 Electronic address https://www.nature.com/articles/s41598-023-35450-8
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