Log In Sign up
The largest database of trusted experimental protocols
> Chemicals & Drugs > Biologically Active Substance > Musk

Musk

Musk: A natural substance derived from the scent glands of the male musk deer (Moschus spp.).
Historically used in traditional medicine and perfumery, musk has a distinctive, earthy aroma.
Elon Musk, the renowned entrepreneur and innovator, is not directly related to the musk substance, despite the similarity in names.
PubCompare.ai leverages Elon Musk's expertise in artificial intelligence to help researchers optimize their protocols and enhance reproducibility, rather than the musk substance itself.

Most cited protocols related to «Musk»

1
Comprehensive Analysis of T2D Genetic Associations
We obtained summary statistics (association P-values and Z-scores for direction of effect or allelic effects and standard errors) for lead T2D SNPs in GWAS meta-analyses of metabolic traits in European descent populations. Summary statistics were aligned to the T2D risk allele from the combined meta-analysis. We obtained summary statistics for lead SNPs in all newly discovered and established loci for glycemic traits in non-diabetic individuals from the MAGIC Investigators5 (link),34 . For fasting glucose and fasting insulin, the meta-analysis comprised up to 133,010 individuals, genotyped with GWAS arrays and imputed on up to ~2.5 million SNPs, or genotyped with Metabochip. We also considered surrogate estimates of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) derived by homeostasis model assessment in up to 38,238 individuals (from GWAS meta-analysis only since these traits were not investigated in the enlarged MAGIC Metabochip study). We obtained summary statistics for lead SNPs in the newly discovered T2D loci (also including GRB14 and HMG20A) for BMI in up to 119,600 individuals from the GIANT Consortium15 (link). To eliminate potential bias in BMI allelic effect estimates at T2D susceptibility loci54 (link), we restricted our attention to meta-analysis of population-based studies not ascertained for disease status for ~2.8 million directly genotyped and/or imputed SNPs. We obtained summary statistics for the same SNPs for plasma lipid concentrations from the Global Lipids Genetics Consortium16 (link). This meta-analysis comprised ~2.6 million directly genotyped and/or imputed SNPs assessed for association to plasma concentrations of: total cholesterol (up to 100,184 individuals); LDL (up to 95,454 individuals); HDL (up to 99,900 individuals); and triglycerides (up to 96,598 individuals).
We also examined T2D association summary statistics at lead SNPs for 37 established T1D susceptibility loci. For each of these SNPs, we reported the allelic OR (aligned to the T2D risk-allele) and P-values in: (i) our Stage 1 T2D meta-analysis; and (ii) a GWAS meta-analysis of 7,514 T1D cases and 9,045 population controls from European descent populations from the Type 1 Diabetes Genetics Consortium35 (link).
Morris A.P., Voight B.F., Teslovich T.M., Ferreira T., Segrè A.V., Steinthorsdottir V., Strawbridge R.J., Khan H., Grallert H., Mahajan A., Prokopenko I., Kang H.M., Dina C., Esko T., Fraser R.M., Kanoni S., Kumar A., Lagou V., Langenberg C., Luan J., Lindgren C.M., Müller-Nurasyid M., Pechlivanis S., Rayner N.W., Scott L.J., Wiltshire S., Yengo L., Kinnunen L., Rossin E.J., Raychaudhuri S., Johnson A.D., Dimas A.S., Loos R.J., Vedantam S., Chen H., Florez J.C., Fox C., Liu C.T., Rybin D., Couper D.J., Kao W.H., Li M., Cornelis M.C., Kraft P., Sun Q., van Dam R.M., Stringham H.M., Chines P.S., Fischer K., Fontanillas P., Holmen O.L., Hunt S.E., Jackson A.U., Kong A., Lawrence R., Meyer J., Perry J.R., Platou C.G., Potter S., Rehnberg E., Robertson N., Sivapalaratnam S., Stančáková A., Stirrups K., Thorleifsson G., Tikkanen E., Wood A.R., Almgren P., Atalay M., Benediktsson R., Bonnycastle L.L., Burtt N., Carey J., Charpentier G., Crenshaw A.T., Doney A.S., Dorkhan M., Edkins S., Emilsson V., Eury E., Forsen T., Gertow K., Gigante B., Grant G.B., Groves C.J., Guiducci C., Herder C., Hreidarsson A.B., Hui J., James A., Jonsson A., Rathmann W., Klopp N., Kravic J., Krjutškov K., Langford C., Leander K., Lindholm E., Lobbens S., Männistö S., Mirza G., Mühleisen T.W., Musk B., Parkin M., Rallidis L., Saramies J., Sennblad B., Shah S., Sigurðsson G., Silveira A., Steinbach G., Thorand B., Trakalo J., Veglia F., Wennauer R., Winckler W., Zabaneh D., Campbell H., van Duijn C., Uitterlinden89- A.G., Hofman A., Sijbrands E., Abecasis G.R., Owen K.R., Zeggini E., Trip M.D., Forouhi N.G., Syvänen A.C., Eriksson J.G., Peltonen L., Nöthen M.M., Balkau B., Palmer C.N., Lyssenko V., Tuomi T., Isomaa B., Hunter D.J., Qi L., Shuldiner A.R., Roden M., Barroso I., Wilsgaard T., Beilby J., Hovingh K., Price J.F., Wilson J.F., Rauramaa R., Lakka T.A., Lind L., Dedoussis G., Njølstad I., Pedersen N.L., Khaw K.T., Wareham N.J., Keinanen-Kiukaanniemi S.M., Saaristo T.E., Korpi-Hyövälti E., Saltevo J., Laakso M., Kuusisto J., Metspalu A., Collins F.S., Mohlke K.L., Bergman R.N., Tuomilehto J., Boehm B.O., Gieger C., Hveem K., Cauchi S., Froguel P., Baldassarre D., Tremoli E., Humphries S.E., Saleheen D., Danesh J., Ingelsson E., Ripatti S., Salomaa V., Erbel R., Jöckel K.H., Moebus S., Peters A., Illig T., de Faire U., Hamsten A., Morris A.D., Donnelly P.J., Frayling T.M., Hattersley A.T., Boerwinkle E., Melander O., Kathiresan S., Nilsson P.M., Deloukas P., Thorsteinsdottir U., Groop L.C., Stefansson K., Hu F., Pankow J.S., Dupuis J., Meigs J.B., Altshuler D., Boehnke M, & McCarthy M.I. (2012). Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nature genetics, 44(9), 981-990.
Publication 2012
Alleles Attention Cholesterol Diabetes Mellitus, Insulin-Dependent Europeans Genome-Wide Association Study Gigantism Glucose GRB14 protein, human Homeostasis Insulin Insulin Resistance Lipids Physiology, Cell Plasma Single Nucleotide Polymorphism Susceptibility, Disease Triglycerides
2
Polygene Analysis of Obesity Risk
We assessed the cumulative effects of the 97 GWS loci on mean BMI and on their ability to predict obesity (BMI ≥ 30 kg m−2) using the c statistic from logistic regression models in the Health and Retirement Study17 , a longitudinal study of 26,000 European Americans 50 years or older. The variance explained (VarExp) by each SNP was calculated using the effect allele frequency (f) and beta (β) from the meta-analyses using the formula VarExp = β2(1 − f)2f.
For polygene analyses, the approximate conditional analysis from GCTA19 (link),20 (link), was used to select SNPs using a range of P value thresholds (that is, 5 × 10−8, 5 × 10−7, …, 5 × 10−3) based on summary data from the European sex-combined meta-analysis excluding TwinGene and QIMR studies. We performed a within-family prediction analysis using full-sib pairs selected from independent families (1,622 pairs from the QIMR cohort and 2,758 pairs from the TwinGene cohort) and then SNPs at each threshold were used to calculate the percentage of phenotypic variance explained and predict risk (Extended Data Figs 2 and 3). We then confirmed the results from population-based prediction and estimation analyses in an independent sample of unrelated individuals from the TwinGene (n = 5,668) and QIMR (n = 3,953) studies (Extended Data Fig. 3 and Fig. 1c). The SNP-derived predictor was calculated using the profile scoring approach implemented in PLINK and estimation analyses were performed using the all-SNP estimation approach implemented in GCTA.
Locke A.E., Kahali B., Berndt S.I., Justice A.E., Pers T.H., Day F.R., Powell C., Vedantam S., Buchkovich M.L., Yang J., Croteau-Chonka D.C., Esko T., Fall T., Ferreira T., Gustafsson S., Kutalik Z., Luan J., Mägi R., Randall J.C., Winkler T.W., Wood A.R., Workalemahu T., Faul J.D., Smith J.A., Zhao J.H., Zhao W., Chen J., Fehrmann R., Hedman Å.K., Karjalainen J., Schmidt E.M., Absher D., Amin N., Anderson D., Beekman M., Bolton J.L., Bragg-Gresham J.L., Buyske S., Demirkan A., Deng G., Ehret G.B., Feenstra B., Feitosa M.F., Fischer K., Goel A., Gong J., Jackson A.U., Kanoni S., Kleber M.E., Kristiansson K., Lim U., Lotay V., Mangino M., Leach I.M., Medina-Gomez C., Medland S.E., Nalls M.A., Palmer C.D., Pasko D., Pechlivanis S., Peters M.J., Prokopenko I., Shungin D., Stančáková A., Strawbridge R.J., Sung Y.J., Tanaka T., Teumer A., Trompet S., van der Laan S.W., van Setten J., Van Vliet-Ostaptchouk J.V., Wang Z., Yengo L., Zhang W., Isaacs A., Albrecht E., Ärnlöv J., Arscott G.M., Attwood A.P., Bandinelli S., Barrett A., Bas I.N., Bellis C., Bennett A.J., Berne C., Blagieva R., Blüher M., Böhringer S., Bonnycastle L.L., Böttcher Y., Boyd H.A., Bruinenberg M., Caspersen I.H., Chen Y.D., Clarke R., Daw E.W., de Craen A.J., Delgado G., Dimitriou M., Doney A.S., Eklund N., Estrada K., Eury E., Folkersen L., Fraser R.M., Garcia M.E., Geller F., Giedraitis V., Gigante B., Go A.S., Golay A., Goodall A.H., Gordon S.D., Gorski M., Grabe H.J., Grallert H., Grammer T.B., Gräßler J., Grönberg H., Groves C.J., Gusto G., Haessler J., Hall P., Haller T., Hallmans G., Hartman C.A., Hassinen M., Hayward C., Heard-Costa N.L., Helmer Q., Hengstenberg C., Holmen O., Hottenga J.J., James A.L., Jeff J.M., Johansson Å., Jolley J., Juliusdottir T., Kinnunen L., Koenig W., Koskenvuo M., Kratzer W., Laitinen J., Lamina C., Leander K., Lee N.R., Lichtner P., Lind L., Lindström J., Lo K.S., Lobbens S., Lorbeer R., Lu Y., Mach F., Magnusson P.K., Mahajan A., McArdle W.L., McLachlan S., Menni C., Merger S., Mihailov E., Milani L., Moayyeri A., Monda K.L., Morken M.A., Mulas A., Müller G., Müller-Nurasyid M., Musk A.W., Nagaraja R., Nöthen M.M., Nolte I.M., Pilz S., Rayner N.W., Renstrom F., Rettig R., Ried J.S., Ripke S., Robertson N.R., Rose L.M., Sanna S., Scharnagl H., Scholtens S., Schumacher F.R., Scott W.R., Seufferlein T., Shi J., Smith A.V., Smolonska J., Stanton A.V., Steinthorsdottir V., Stirrups K., Stringham H.M., Sundström J., Swertz M.A., Swift A.J., Syvänen A.C., Tan S.T., Tayo B.O., Thorand B., Thorleifsson G., Tyrer J.P., Uh H.W., Vandenput L., Verhulst F.C., Vermeulen S.H., Verweij N., Vonk J.M., Waite L.L., Warren H.R., Waterworth D., Weedon M.N., Wilkens L.R., Willenborg C., Wilsgaard T., Wojczynski M.K., Wong A., Wright A.F., Zhang Q., Brennan E.P., Choi M., Dastani Z., Drong A.W., Eriksson P., Franco-Cereceda A., Gådin J.R., Gharavi A.G., Goddard M.E., Handsaker R.E., Huang J., Karpe F., Kathiresan S., Keildson S., Kiryluk K., Kubo M., Lee J.Y., Liang L., Lifton R.P., Ma B., McCarroll S.A., McKnight A.J., Min J.L., Moffatt M.F., Montgomery G.W., Murabito J.M., Nicholson G., Nyholt D.R., Okada Y., Perry J.R., Dorajoo R., Reinmaa E., Salem R.M., Sandholm N., Scott R.A., Stolk L., Takahashi A., Tanaka T., van ’t Hooft F.M., Vinkhuyzen A.A., Westra H.J., Zheng W., Zondervan K.T., Heath A.C., Arveiler D., Bakker S.J., Beilby J., Bergman R.N., Blangero J., Bovet P., Campbell H., Caulfield M.J., Cesana G., Chakravarti A., Chasman D.I., Chines P.S., Collins F.S., Crawford D.C., Cupples L.A., Cusi D., Danesh J., de Faire U., den Ruijter H.M., Dominiczak A.F., Erbel R., Erdmann J., Eriksson J.G., Farrall M., Felix S.B., Ferrannini E., Ferrières J., Ford I., Forouhi N.G., Forrester T., Franco O.H., Gansevoort R.T., Gejman P.V., Gieger C., Gottesman O., Gudnason V., Gyllensten U., Hall A.S., Harris T.B., Hattersley A.T., Hicks A.A., Hindorff L.A., Hingorani A.D., Hofman A., Homuth G., Hovingh G.K., Humphries S.E., Hunt S.C., Hyppönen E., Illig T., Jacobs K.B., Jarvelin M.R., Jöckel K.H., Johansen B., Jousilahti P., Jukema J.W., Jula A.M., Kaprio J., Kastelein J.J., Keinanen-Kiukaanniemi S.M., Kiemeney L.A., Knekt P., Kooner J.S., Kooperberg C., Kovacs P., Kraja A.T., Kumari M., Kuusisto J., Lakka T.A., Langenberg C., Marchand L.L., Lehtimäki T., Lyssenko V., Männistö S., Marette A., Matise T.C., McKenzie C.A., McKnight B., Moll F.L., Morris A.D., Morris A.P., Murray J.C., Nelis M., Ohlsson C., Oldehinkel A.J., Ong K.K., Madden P.A., Pasterkamp G., Peden J.F., Peters A., Postma D.S., Pramstaller P.P., Price J.F., Qi L., Raitakari O.T., Rankinen T., Rao D.C., Rice T.K., Ridker P.M., Rioux J.D., Ritchie M.D., Rudan I., Salomaa V., Samani N.J., Saramies J., Sarzynski M.A., Schunkert H., Schwarz P.E., Sever P., Shuldiner A.R., Sinisalo J., Stolk R.P., Strauch K., Tönjes A., Trégouët D.A., Tremblay A., Tremoli E., Virtamo J., Vohl M.C., Völker U., Waeber G., Willemsen G., Witteman J.C., Zillikens M.C., Adair L.S., Amouyel P., Asselbergs F.W., Assimes T.L., Bochud M., Boehm B.O., Boerwinkle E., Bornstein S.R., Bottinger E.P., Bouchard C., Cauchi S., Chambers J.C., Chanock S.J., Cooper R.S., de Bakker P.I., Dedoussis G., Ferrucci L., Franks P.W., Froguel P., Groop L.C., Haiman C.A., Hamsten A., Hui J., Hunter D.J., Hveem K., Kaplan R.C., Kivimaki M., Kuh D., Laakso M., Liu Y., Martin N.G., März W., Melbye M., Metspalu A., Moebus S., Munroe P.B., Njølstad I., Oostra B.A., Palmer C.N., Pedersen N.L., Perola M., Pérusse L., Peters U., Power C., Quertermous T., Rauramaa R., Rivadeneira F., Saaristo T.E., Saleheen D., Sattar N., Schadt E.E., Schlessinger D., Slagboom P.E., Snieder H., Spector T.D., Thorsteinsdottir U., Stumvoll M., Tuomilehto J., Uitterlinden A.G., Uusitupa M., van der Harst P., Walker M., Wallaschofski H., Wareham N.J., Watkins H., Weir D.R., Wichmann H.E., Wilson J.F., Zanen P., Borecki I.B., Deloukas P., Fox C.S., Heid I.M., O’Connell J.R., Strachan D.P., Stefansson K., van Duijn C.M., Abecasis G.R., Franke L., Frayling T.M., McCarthy M.I., Visscher P.M., Scherag A., Willer C.J., Boehnke M., Mohlke K.L., Lindgren C.M., Beckmann J.S., Barroso I., North K.E., Ingelsson E., Hirschhorn J.N., Loos R.J, & Speliotes E.K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197-206.
Publication 2015
Europeans Obesity Phenotype Prognosis
3
Genome-wide association meta-analysis of height
The primary meta-analysis (Stage 1) included 46 GWA studies of 133,653 individuals. The in-silico follow up (Stage 2) included 15 studies of 50,074 individuals. All individuals were of European ancestry and >99.8% were adults. Details of genotyping, quality control, and imputation methods of each study are given in Supplementary Methods Table 1-2. Each study provided summary results of a linear regression of age-adjusted, within-sex Z scores of height against the imputed SNPs, and an inverse-variance meta-analysis was performed in METAL (http://www.sph.umich.edu/csg/abecasis/METAL/). Validation of selected SNPs was performed through direct genotyping in an extreme height panel (N=3,190) using Sequenom iPLeX, and in 492 Stage 1 samples using the KASPar SNP System. Family-based testing was performed using QFAM, a linear regression-based approach that uses permutation to account for dependency between related individuals29 (link), and FBAT, which uses a linear combination of offspring genotypes and traits to determine the test statistic30 (link). We used a previously described method to estimate the amount of genetic variance explained by the nominally associated loci (using significance threshold increments from P<5×10-8 to P<0.05)18 (link). To predict the number of height susceptibility loci, we took the height loci that reached a significance level of P<5×10-8 in Stage 1 and estimated the number of height loci that are likely to exist based on the distribution of their effect sizes observed in Stage 2 and the power to detect their association in Stage 1. Gene-by-gene interaction, dominant, recessive and conditional analyses are described in Supplementary Methods. Empirical assessment of enrichment for coding SNPs used permutations of random sets of SNPs matched to the 180 height-associated SNPs on the number of nearby genes, gene proximity, and minor allele frequency. GRAIL and GSEA methods have been described previously20 (link),21 . To assess possible enrichment for genes known to be mutated in severe growth defects, we identified such genes in the OMIM database (Supplementary Table 10), and evaluated the extent of their overlap with the 180 height-associated regions through comparisons with 1000 random sets of regions with similar gene content (±10%).
Lango Allen H., Estrada K., Lettre G., Berndt S.I., Weedon M.N., Rivadeneira F., Willer C.J., Jackson A.U., Vedantam S., Raychaudhuri S., Ferreira T., Wood A.R., Weyant R.J., Segrè A.V., Speliotes E.K., Wheeler E., Soranzo N., Park J.H., Yang J., Gudbjartsson D., Heard-Costa N.L., Randall J.C., Qi L., Smith A.V., Mägi R., Pastinen T., Liang L., Heid I.M., Luan J., Thorleifsson G., Winkler T.W., Goddard M.E., Lo K.S., Palmer C., Workalemahu T., Aulchenko Y.S., Johansson Å., Zillikens M.C., Feitosa M.F., Esko T., Johnson T., Ketkar S., Kraft P., Mangino M., Prokopenko I., Absher D., Albrecht E., Ernst F., Glazer N.L., Hayward C., Hottenga J.J., Jacobs K.B., Knowles J.W., Kutalik Z., Monda K.L., Polasek O., Preuss M., Rayner N.W., Robertson N.R., Steinthorsdottir V., Tyrer J.P., Voight B.F., Wiklund F., Xu J., Zhao J.H., Nyholt D.R., Pellikka N., Perola M., Perry J.R., Surakka I., Tammesoo M.L., Altmaier E.L., Amin N., Aspelund T., Bhangale T., Boucher G., Chasman D.I., Chen C., Coin L., Cooper M.N., Dixon A.L., Gibson Q., Grundberg E., Hao K., Junttila M.J., Kaplan L.M., Kettunen J., König I.R., Kwan T., Lawrence R.W., Levinson D.F., Lorentzon M., McKnight B., Morris A.P., Müller M., Ngwa J.S., Purcell S., Rafelt S., Salem R.M., Salvi E., Sanna S., Shi J., Sovio U., Thompson J.R., Turchin M.C., Vandenput L., Verlaan D.J., Vitart V., White C.C., Ziegler A., Almgren P., Balmforth A.J., Campbell H., Citterio L., De Grandi A., Dominiczak A., Duan J., Elliott P., Elosua R., Eriksson J.G., Freimer N.B., Geus E.J., Glorioso N., Haiqing S., Hartikainen A.L., Havulinna A.S., Hicks A.A., Hui J., Igl W., Illig T., Jula A., Kajantie E., Kilpeläinen T.O., Koiranen M., Kolcic I., Koskinen S., Kovacs P., Laitinen J., Liu J., Lokki M.L., Marusic A., Maschio A., Meitinger T., Mulas A., Paré G., Parker A.N., Peden J.F., Petersmann A., Pichler I., Pietiläinen K.H., Pouta A., Ridderstråle M., Rotter J.I., Sambrook J.G., Sanders A.R., Schmidt C.O., Sinisalo J., Smit J.H., Stringham H.M., Walters G.B., Widen E., Wild S.H., Willemsen G., Zagato L., Zgaga L., Zitting P., Alavere H., Farrall M., McArdle W.L., Nelis M., Peters M.J., Ripatti S., van Meurs J.B., Aben K.K., Ardlie K.G., Beckmann J.S., Beilby J.P., Bergman R.N., Bergmann S., Collins F.S., Cusi D., den Heijer M., Eiriksdottir G., Gejman P.V., Hall A.S., Hamsten A., Huikuri H.V., Iribarren C., Kähönen M., Kaprio J., Kathiresan S., Kiemeney L., Kocher T., Launer L.J., Lehtimäki T., Melander O., Mosley TH J.r., Musk A.W., Nieminen M.S., O'Donnell C.J., Ohlsson C., Oostra B., Palmer L.J., Raitakari O., Ridker P.M., Rioux J.D., Rissanen A., Rivolta C., Schunkert H., Shuldiner A.R., Siscovick D.S., Stumvoll M., Tönjes A., Tuomilehto J., van Ommen G.J., Viikari J., Heath A.C., Martin N.G., Montgomery G.W., Province M.A., Kayser M., Arnold A.M., Atwood L.D., Boerwinkle E., Chanock S.J., Deloukas P., Gieger C., Grönberg H., Hall P., Hattersley A.T., Hengstenberg C., Hoffman W., Lathrop G.M., Salomaa V., Schreiber S., Uda M., Waterworth D., Wright A.F., Assimes T.L., Barroso I., Hofman A., Mohlke K.L., Boomsma D.I., Caulfield M.J., Cupples L.A., Erdmann J., Fox C.S., Gudnason V., Gyllensten U., Harris T.B., Hayes R.B., Jarvelin M.R., Mooser V., Munroe P.B., Ouwehand W.H., Penninx B.W., Pramstaller P.P., Quertermous T., Rudan I., Samani N.J., Spector T.D., Völzke H., Watkins H., Wilson J.F., Groop L.C., Haritunians T., Hu F.B., Kaplan R.C., Metspalu A., North K.E., Schlessinger D., Wareham N.J., Hunter D.J., O'Connell J.R., Strachan D.P., Wichmann H.E., Borecki I.B., van Duijn C.M., Schadt E.E., Thorsteinsdottir U., Peltonen L., Uitterlinden A., Visscher P.M., Chatterjee N., Loos R.J., Boehnke M., McCarthy M.I., Ingelsson E., Lindgren C.M., Abecasis G.R., Stefansson K., Frayling T.M, & Hirschhorn J.N. (2010). Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature, 467(7317), 832-838.
Publication 2010
Adult Europeans Genes Genetic Diversity Genome-Wide Association Study Genotype Iplex kaspar Metals Single Nucleotide Polymorphism Susceptibility, Disease
4
Investigating Genetic Heterogeneity Across Populations
To investigate potential sources of heterogeneity between groups we compared the effect estimates of our 97 GWS SNPs for men versus women of European ancestry and Europeans versus non-Europeans. To address the effects of studies ascertained on a specific disease or phenotype on our results we also compare the effect estimates of European ancestry studies of population-based studies with the following European-descent subsets of studies: (1) non-population-based studies (that is, those ascertained on a specific disease or phenotype); (2) type 2 diabetes cases; (3) type 2 diabetes controls; (4) combined type 2 diabetes cases and controls; (5) CAD cases; (6) CAD controls; and (7) combined CAD cases and controls (Supplementary Tables 10 and 11). We also tested for heterogeneity of effect estimates between our European sex-combined meta-analysis and results from recent GWAS meta-analyses for BMI in individuals of African or east Asian ancestry10 (link),41 (link) (Supplementary Table 9). Heterogeneity was assessed as described previously42 (link). A Bonferroni-corrected P < 5 × 10−4 (corrected for 97 tests) was used to assess significance. For heterogeneity tests assessing effects of ascertainment, we also used a 5% FDR threshold to assess significance of heterogeneity statistics (Supplementary Table 11).
Locke A.E., Kahali B., Berndt S.I., Justice A.E., Pers T.H., Day F.R., Powell C., Vedantam S., Buchkovich M.L., Yang J., Croteau-Chonka D.C., Esko T., Fall T., Ferreira T., Gustafsson S., Kutalik Z., Luan J., Mägi R., Randall J.C., Winkler T.W., Wood A.R., Workalemahu T., Faul J.D., Smith J.A., Zhao J.H., Zhao W., Chen J., Fehrmann R., Hedman Å.K., Karjalainen J., Schmidt E.M., Absher D., Amin N., Anderson D., Beekman M., Bolton J.L., Bragg-Gresham J.L., Buyske S., Demirkan A., Deng G., Ehret G.B., Feenstra B., Feitosa M.F., Fischer K., Goel A., Gong J., Jackson A.U., Kanoni S., Kleber M.E., Kristiansson K., Lim U., Lotay V., Mangino M., Leach I.M., Medina-Gomez C., Medland S.E., Nalls M.A., Palmer C.D., Pasko D., Pechlivanis S., Peters M.J., Prokopenko I., Shungin D., Stančáková A., Strawbridge R.J., Sung Y.J., Tanaka T., Teumer A., Trompet S., van der Laan S.W., van Setten J., Van Vliet-Ostaptchouk J.V., Wang Z., Yengo L., Zhang W., Isaacs A., Albrecht E., Ärnlöv J., Arscott G.M., Attwood A.P., Bandinelli S., Barrett A., Bas I.N., Bellis C., Bennett A.J., Berne C., Blagieva R., Blüher M., Böhringer S., Bonnycastle L.L., Böttcher Y., Boyd H.A., Bruinenberg M., Caspersen I.H., Chen Y.D., Clarke R., Daw E.W., de Craen A.J., Delgado G., Dimitriou M., Doney A.S., Eklund N., Estrada K., Eury E., Folkersen L., Fraser R.M., Garcia M.E., Geller F., Giedraitis V., Gigante B., Go A.S., Golay A., Goodall A.H., Gordon S.D., Gorski M., Grabe H.J., Grallert H., Grammer T.B., Gräßler J., Grönberg H., Groves C.J., Gusto G., Haessler J., Hall P., Haller T., Hallmans G., Hartman C.A., Hassinen M., Hayward C., Heard-Costa N.L., Helmer Q., Hengstenberg C., Holmen O., Hottenga J.J., James A.L., Jeff J.M., Johansson Å., Jolley J., Juliusdottir T., Kinnunen L., Koenig W., Koskenvuo M., Kratzer W., Laitinen J., Lamina C., Leander K., Lee N.R., Lichtner P., Lind L., Lindström J., Lo K.S., Lobbens S., Lorbeer R., Lu Y., Mach F., Magnusson P.K., Mahajan A., McArdle W.L., McLachlan S., Menni C., Merger S., Mihailov E., Milani L., Moayyeri A., Monda K.L., Morken M.A., Mulas A., Müller G., Müller-Nurasyid M., Musk A.W., Nagaraja R., Nöthen M.M., Nolte I.M., Pilz S., Rayner N.W., Renstrom F., Rettig R., Ried J.S., Ripke S., Robertson N.R., Rose L.M., Sanna S., Scharnagl H., Scholtens S., Schumacher F.R., Scott W.R., Seufferlein T., Shi J., Smith A.V., Smolonska J., Stanton A.V., Steinthorsdottir V., Stirrups K., Stringham H.M., Sundström J., Swertz M.A., Swift A.J., Syvänen A.C., Tan S.T., Tayo B.O., Thorand B., Thorleifsson G., Tyrer J.P., Uh H.W., Vandenput L., Verhulst F.C., Vermeulen S.H., Verweij N., Vonk J.M., Waite L.L., Warren H.R., Waterworth D., Weedon M.N., Wilkens L.R., Willenborg C., Wilsgaard T., Wojczynski M.K., Wong A., Wright A.F., Zhang Q., Brennan E.P., Choi M., Dastani Z., Drong A.W., Eriksson P., Franco-Cereceda A., Gådin J.R., Gharavi A.G., Goddard M.E., Handsaker R.E., Huang J., Karpe F., Kathiresan S., Keildson S., Kiryluk K., Kubo M., Lee J.Y., Liang L., Lifton R.P., Ma B., McCarroll S.A., McKnight A.J., Min J.L., Moffatt M.F., Montgomery G.W., Murabito J.M., Nicholson G., Nyholt D.R., Okada Y., Perry J.R., Dorajoo R., Reinmaa E., Salem R.M., Sandholm N., Scott R.A., Stolk L., Takahashi A., Tanaka T., van ’t Hooft F.M., Vinkhuyzen A.A., Westra H.J., Zheng W., Zondervan K.T., Heath A.C., Arveiler D., Bakker S.J., Beilby J., Bergman R.N., Blangero J., Bovet P., Campbell H., Caulfield M.J., Cesana G., Chakravarti A., Chasman D.I., Chines P.S., Collins F.S., Crawford D.C., Cupples L.A., Cusi D., Danesh J., de Faire U., den Ruijter H.M., Dominiczak A.F., Erbel R., Erdmann J., Eriksson J.G., Farrall M., Felix S.B., Ferrannini E., Ferrières J., Ford I., Forouhi N.G., Forrester T., Franco O.H., Gansevoort R.T., Gejman P.V., Gieger C., Gottesman O., Gudnason V., Gyllensten U., Hall A.S., Harris T.B., Hattersley A.T., Hicks A.A., Hindorff L.A., Hingorani A.D., Hofman A., Homuth G., Hovingh G.K., Humphries S.E., Hunt S.C., Hyppönen E., Illig T., Jacobs K.B., Jarvelin M.R., Jöckel K.H., Johansen B., Jousilahti P., Jukema J.W., Jula A.M., Kaprio J., Kastelein J.J., Keinanen-Kiukaanniemi S.M., Kiemeney L.A., Knekt P., Kooner J.S., Kooperberg C., Kovacs P., Kraja A.T., Kumari M., Kuusisto J., Lakka T.A., Langenberg C., Marchand L.L., Lehtimäki T., Lyssenko V., Männistö S., Marette A., Matise T.C., McKenzie C.A., McKnight B., Moll F.L., Morris A.D., Morris A.P., Murray J.C., Nelis M., Ohlsson C., Oldehinkel A.J., Ong K.K., Madden P.A., Pasterkamp G., Peden J.F., Peters A., Postma D.S., Pramstaller P.P., Price J.F., Qi L., Raitakari O.T., Rankinen T., Rao D.C., Rice T.K., Ridker P.M., Rioux J.D., Ritchie M.D., Rudan I., Salomaa V., Samani N.J., Saramies J., Sarzynski M.A., Schunkert H., Schwarz P.E., Sever P., Shuldiner A.R., Sinisalo J., Stolk R.P., Strauch K., Tönjes A., Trégouët D.A., Tremblay A., Tremoli E., Virtamo J., Vohl M.C., Völker U., Waeber G., Willemsen G., Witteman J.C., Zillikens M.C., Adair L.S., Amouyel P., Asselbergs F.W., Assimes T.L., Bochud M., Boehm B.O., Boerwinkle E., Bornstein S.R., Bottinger E.P., Bouchard C., Cauchi S., Chambers J.C., Chanock S.J., Cooper R.S., de Bakker P.I., Dedoussis G., Ferrucci L., Franks P.W., Froguel P., Groop L.C., Haiman C.A., Hamsten A., Hui J., Hunter D.J., Hveem K., Kaplan R.C., Kivimaki M., Kuh D., Laakso M., Liu Y., Martin N.G., März W., Melbye M., Metspalu A., Moebus S., Munroe P.B., Njølstad I., Oostra B.A., Palmer C.N., Pedersen N.L., Perola M., Pérusse L., Peters U., Power C., Quertermous T., Rauramaa R., Rivadeneira F., Saaristo T.E., Saleheen D., Sattar N., Schadt E.E., Schlessinger D., Slagboom P.E., Snieder H., Spector T.D., Thorsteinsdottir U., Stumvoll M., Tuomilehto J., Uitterlinden A.G., Uusitupa M., van der Harst P., Walker M., Wallaschofski H., Wareham N.J., Watkins H., Weir D.R., Wichmann H.E., Wilson J.F., Zanen P., Borecki I.B., Deloukas P., Fox C.S., Heid I.M., O’Connell J.R., Strachan D.P., Stefansson K., van Duijn C.M., Abecasis G.R., Franke L., Frayling T.M., McCarthy M.I., Visscher P.M., Scherag A., Willer C.J., Boehnke M., Mohlke K.L., Lindgren C.M., Beckmann J.S., Barroso I., North K.E., Ingelsson E., Hirschhorn J.N., Loos R.J, & Speliotes E.K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197-206.
Publication 2015
Diabetes Mellitus, Non-Insulin-Dependent East Asian People Europeans Genetic Heterogeneity Genome-Wide Association Study Negroid Races Phenotype Single Nucleotide Polymorphism Woman
5
Credible Set Identification for GWAS
We compared the meta-analysis results and credible sets of SNPs likely to contain the causal variant, based on the method described previously14 (link), across the European-only, non-European, and all ancestries sex-combined meta-analyses. For each index SNP falling within a Metabochip fine-mapping region (27 for BMI), all SNPs available within 500 kb on either side of the index SNP were selected. Effect size estimates and standard errors for each SNP were converted to approximate Bayes’ factors according to the method described previously15 (link). All approximate Bayes’ factors were then summed across the 1-megabase (Mb) region and the proportion of the posterior odds of being the causal variant was calculated for each variant (approximate Bayes’ factor for SNPi/sum of approximate Bayes’ factors for the region). The set of SNPs that accounts for 99% of posterior odds of association in the region denotes the set most likely to contain the causal variant for that association region (Supplementary Table 12).
Locke A.E., Kahali B., Berndt S.I., Justice A.E., Pers T.H., Day F.R., Powell C., Vedantam S., Buchkovich M.L., Yang J., Croteau-Chonka D.C., Esko T., Fall T., Ferreira T., Gustafsson S., Kutalik Z., Luan J., Mägi R., Randall J.C., Winkler T.W., Wood A.R., Workalemahu T., Faul J.D., Smith J.A., Zhao J.H., Zhao W., Chen J., Fehrmann R., Hedman Å.K., Karjalainen J., Schmidt E.M., Absher D., Amin N., Anderson D., Beekman M., Bolton J.L., Bragg-Gresham J.L., Buyske S., Demirkan A., Deng G., Ehret G.B., Feenstra B., Feitosa M.F., Fischer K., Goel A., Gong J., Jackson A.U., Kanoni S., Kleber M.E., Kristiansson K., Lim U., Lotay V., Mangino M., Leach I.M., Medina-Gomez C., Medland S.E., Nalls M.A., Palmer C.D., Pasko D., Pechlivanis S., Peters M.J., Prokopenko I., Shungin D., Stančáková A., Strawbridge R.J., Sung Y.J., Tanaka T., Teumer A., Trompet S., van der Laan S.W., van Setten J., Van Vliet-Ostaptchouk J.V., Wang Z., Yengo L., Zhang W., Isaacs A., Albrecht E., Ärnlöv J., Arscott G.M., Attwood A.P., Bandinelli S., Barrett A., Bas I.N., Bellis C., Bennett A.J., Berne C., Blagieva R., Blüher M., Böhringer S., Bonnycastle L.L., Böttcher Y., Boyd H.A., Bruinenberg M., Caspersen I.H., Chen Y.D., Clarke R., Daw E.W., de Craen A.J., Delgado G., Dimitriou M., Doney A.S., Eklund N., Estrada K., Eury E., Folkersen L., Fraser R.M., Garcia M.E., Geller F., Giedraitis V., Gigante B., Go A.S., Golay A., Goodall A.H., Gordon S.D., Gorski M., Grabe H.J., Grallert H., Grammer T.B., Gräßler J., Grönberg H., Groves C.J., Gusto G., Haessler J., Hall P., Haller T., Hallmans G., Hartman C.A., Hassinen M., Hayward C., Heard-Costa N.L., Helmer Q., Hengstenberg C., Holmen O., Hottenga J.J., James A.L., Jeff J.M., Johansson Å., Jolley J., Juliusdottir T., Kinnunen L., Koenig W., Koskenvuo M., Kratzer W., Laitinen J., Lamina C., Leander K., Lee N.R., Lichtner P., Lind L., Lindström J., Lo K.S., Lobbens S., Lorbeer R., Lu Y., Mach F., Magnusson P.K., Mahajan A., McArdle W.L., McLachlan S., Menni C., Merger S., Mihailov E., Milani L., Moayyeri A., Monda K.L., Morken M.A., Mulas A., Müller G., Müller-Nurasyid M., Musk A.W., Nagaraja R., Nöthen M.M., Nolte I.M., Pilz S., Rayner N.W., Renstrom F., Rettig R., Ried J.S., Ripke S., Robertson N.R., Rose L.M., Sanna S., Scharnagl H., Scholtens S., Schumacher F.R., Scott W.R., Seufferlein T., Shi J., Smith A.V., Smolonska J., Stanton A.V., Steinthorsdottir V., Stirrups K., Stringham H.M., Sundström J., Swertz M.A., Swift A.J., Syvänen A.C., Tan S.T., Tayo B.O., Thorand B., Thorleifsson G., Tyrer J.P., Uh H.W., Vandenput L., Verhulst F.C., Vermeulen S.H., Verweij N., Vonk J.M., Waite L.L., Warren H.R., Waterworth D., Weedon M.N., Wilkens L.R., Willenborg C., Wilsgaard T., Wojczynski M.K., Wong A., Wright A.F., Zhang Q., Brennan E.P., Choi M., Dastani Z., Drong A.W., Eriksson P., Franco-Cereceda A., Gådin J.R., Gharavi A.G., Goddard M.E., Handsaker R.E., Huang J., Karpe F., Kathiresan S., Keildson S., Kiryluk K., Kubo M., Lee J.Y., Liang L., Lifton R.P., Ma B., McCarroll S.A., McKnight A.J., Min J.L., Moffatt M.F., Montgomery G.W., Murabito J.M., Nicholson G., Nyholt D.R., Okada Y., Perry J.R., Dorajoo R., Reinmaa E., Salem R.M., Sandholm N., Scott R.A., Stolk L., Takahashi A., Tanaka T., van ’t Hooft F.M., Vinkhuyzen A.A., Westra H.J., Zheng W., Zondervan K.T., Heath A.C., Arveiler D., Bakker S.J., Beilby J., Bergman R.N., Blangero J., Bovet P., Campbell H., Caulfield M.J., Cesana G., Chakravarti A., Chasman D.I., Chines P.S., Collins F.S., Crawford D.C., Cupples L.A., Cusi D., Danesh J., de Faire U., den Ruijter H.M., Dominiczak A.F., Erbel R., Erdmann J., Eriksson J.G., Farrall M., Felix S.B., Ferrannini E., Ferrières J., Ford I., Forouhi N.G., Forrester T., Franco O.H., Gansevoort R.T., Gejman P.V., Gieger C., Gottesman O., Gudnason V., Gyllensten U., Hall A.S., Harris T.B., Hattersley A.T., Hicks A.A., Hindorff L.A., Hingorani A.D., Hofman A., Homuth G., Hovingh G.K., Humphries S.E., Hunt S.C., Hyppönen E., Illig T., Jacobs K.B., Jarvelin M.R., Jöckel K.H., Johansen B., Jousilahti P., Jukema J.W., Jula A.M., Kaprio J., Kastelein J.J., Keinanen-Kiukaanniemi S.M., Kiemeney L.A., Knekt P., Kooner J.S., Kooperberg C., Kovacs P., Kraja A.T., Kumari M., Kuusisto J., Lakka T.A., Langenberg C., Marchand L.L., Lehtimäki T., Lyssenko V., Männistö S., Marette A., Matise T.C., McKenzie C.A., McKnight B., Moll F.L., Morris A.D., Morris A.P., Murray J.C., Nelis M., Ohlsson C., Oldehinkel A.J., Ong K.K., Madden P.A., Pasterkamp G., Peden J.F., Peters A., Postma D.S., Pramstaller P.P., Price J.F., Qi L., Raitakari O.T., Rankinen T., Rao D.C., Rice T.K., Ridker P.M., Rioux J.D., Ritchie M.D., Rudan I., Salomaa V., Samani N.J., Saramies J., Sarzynski M.A., Schunkert H., Schwarz P.E., Sever P., Shuldiner A.R., Sinisalo J., Stolk R.P., Strauch K., Tönjes A., Trégouët D.A., Tremblay A., Tremoli E., Virtamo J., Vohl M.C., Völker U., Waeber G., Willemsen G., Witteman J.C., Zillikens M.C., Adair L.S., Amouyel P., Asselbergs F.W., Assimes T.L., Bochud M., Boehm B.O., Boerwinkle E., Bornstein S.R., Bottinger E.P., Bouchard C., Cauchi S., Chambers J.C., Chanock S.J., Cooper R.S., de Bakker P.I., Dedoussis G., Ferrucci L., Franks P.W., Froguel P., Groop L.C., Haiman C.A., Hamsten A., Hui J., Hunter D.J., Hveem K., Kaplan R.C., Kivimaki M., Kuh D., Laakso M., Liu Y., Martin N.G., März W., Melbye M., Metspalu A., Moebus S., Munroe P.B., Njølstad I., Oostra B.A., Palmer C.N., Pedersen N.L., Perola M., Pérusse L., Peters U., Power C., Quertermous T., Rauramaa R., Rivadeneira F., Saaristo T.E., Saleheen D., Sattar N., Schadt E.E., Schlessinger D., Slagboom P.E., Snieder H., Spector T.D., Thorsteinsdottir U., Stumvoll M., Tuomilehto J., Uitterlinden A.G., Uusitupa M., van der Harst P., Walker M., Wallaschofski H., Wareham N.J., Watkins H., Weir D.R., Wichmann H.E., Wilson J.F., Zanen P., Borecki I.B., Deloukas P., Fox C.S., Heid I.M., O’Connell J.R., Strachan D.P., Stefansson K., van Duijn C.M., Abecasis G.R., Franke L., Frayling T.M., McCarthy M.I., Visscher P.M., Scherag A., Willer C.J., Boehnke M., Mohlke K.L., Lindgren C.M., Beckmann J.S., Barroso I., North K.E., Ingelsson E., Hirschhorn J.N., Loos R.J, & Speliotes E.K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197-206.
Publication 2015
Europeans Single Nucleotide Polymorphism

Most recents protocols related to «Musk»

1
Quantification of MuSK Autoantibodies
Total MuSK autoantibody levels were assessed by RIA (RSR, UK) according to the manufacturer’s instructions. MuSK IgG4 levels were measured by ELISA. To this end, ELISA plates (Microlon, catalog number 655092, Greiner, Austria) were coated with 1 µg/mL MuSK extracellular domain (ECD) produced in mammalian cells (a kind gift of Dr. Bernard Rees-Smith, RSR, UK). Bound MuSK antibodies were detected with mouse anti-human IgG4:HRP (1:3,500, catalog number MCA2098P, AbD Serotec, Germany). Samples were incubated with substrate containing 3,3′,5,5′-tetramethylbenzidine, and absorbance at 450 nm was measured using a VictorX3 plate reader (PerkinElmer, USA).
Koneczny I., Mané-Damas M., Zong S., De Haas S., Huda S., van Kruining D., Damoiseaux J., De Rosa A., Maestri M., Guida M., Molenaar P., Van Damme P., Fichtenbaum A., Perkmann T., De Baets M., Lazaridis K., Zouvelou V., Tzartos S., Ricciardi R., Losen M, & Martinez-Martinez P. (2024). A retrospective multicenter study on clinical and serological parameters in patients with MuSK myasthenia gravis with and without general immunosuppression. Frontiers in Immunology, 15, 1325171.
Full text: Click here
Publication 2024
2
Peptidome Analysis of Chinese Forest Musk
Not available on PMC !
Information on the samples of captive Chinese forest musk and cultured cells used for this assay is summarized in Table 1. Musk was separated by diethyl ether and absolute ethyl alcohol treatments [2] . This research was approved by the Ethical Animal Care and Use Committee of the Chongqing College of Traditional Chinese Medicine (approved number 20221124). All samples of the musk pouches used for peptidomics surveys were obtained directly from the musk gland scent pods of Chinese forest musk deer at the Chongqing Institute of Medicinal Plant Cultivation (Chongqing, China; altitude: 678 m).
Huang Z., Zhang C., Zeng D., Feng X., Wang T., & Jie H. (2024). Peptidomic differences between natural musk and secretions from forest musk gland cells cultured in vitro.
Publication 2024
3
Musk Receptor Activation and Genetic Variation
Not available on PMC !

Example 2

In order to validate the aforementioned hits, concentration-response analyses using the luciferase-based reporter assay were achieved using semi-logarithmic serial dilutions of hit molecules, from 1 mM to 316 nM, on OR5A2_variant 1. In these analyses we also included ORs previously described as musk-specific receptors OR5AN1 (SEQ ID n° 7) (WO 2015/020158 A1, Shirasu et al. 2014 Neuron 81, 165-78, Sato-Akuhara N et al. 2016 J Neurosci. 36(16), 4482-91) and OR11A1 (SEQ ID n° 8) (WO 2016/201152 A1). By phylogenic analysis, we found that the most similar OR gene to OR5A2 was OR5A1 (SEQ ID n° 9) with 71% nucleic acid identity and 67% amino acid identity (FIGS. 2A-B). Therefore, OR5A1 was included in the analyses. 35 musk compounds from the four structurally different chemical groups previously described were tested in concentration-response analyses (Table 2). In each experiment, an empty vector was used as negative control (pEFIBRHO). Representative concentration-response curves using musk compounds are given in FIG. 1A. FIG. 1B shows their structures. Full results including the calculated EC50 are given in Table 2; “non active” represent negative experiments, no activation after testing.

As examples, concentration-response curves of (A) Musk Ambrette and (B) Moskene on receptors OR5A2, OR5A1, OR5AN1, and OR11A1 are illustrated in FIG. 7. Concentration-response curves of (A) Sylkolide and (B) Serenolide on receptors OR5A2, OR5A1, OR5AN1 and OR11A1 are shown in FIG. 8. Concentration-response curves of (A) Cashmeran, (B) Fixal and (C) Galaxolide on receptors OR5A2, OR5A1, OR5AN1 and OR11A1 are shown in FIG. 9. Concentration-response curves of (A) Ethylene Brassylate, (B) Ambrettolide and (C) Cervolide on receptors OR5A2, OR5A1, OR5AN1 and OR11A1 are shown in FIG. 10.

It was observed that OR of the invention (namely OR5A2) is activated by musk compounds belonging to the 4 groups of chemicals described formerly. OR5A1, the closest paralog of OR5A2, was not activated by any of the musk compounds tested. Moreover, musk-specific ORs (OR5AN1 and OR11A1) respond mainly to nitromusks and macrocyclic musks or polycyclic musks and nitromusks respectively (Tables 2). None of the musk compounds tested was able to activate exclusively OR5AN1. Furthermore, our results indicate that the OR of the invention (namely OR5A2) is the only OR activated by the linear musk family, known to be more environment friendly.

The OR of the invention is therefore involved in the perception of all types of musks and constitutes a valuable candidate receptor for identifying a compound that activates, mimics, blocks, inhibits, modulates and/or enhances the perception of musk fragrances.

Example 4

OR genes are highly variable, with many alleles resulting in differences in smell perception from person to person. These differences can be attributed to genetic variations like, for example, nucleotide polymorphisms. Using the HORDE database (The Human Olfactory Data

Explorer, genome.weizmann.ac.il/horde), we found that 5 protein variants (haplotypes) of OR of the invention (namely OR5A2, OR5A2 variant_1) were identified in the population. The most frequent haplotype, present at a frequency of 79,72%, was used in the previous examples (OR5A2-variant_1; SEQ ID NO.1) and is sensitive to musk compounds. The second haplotype, present at 16,59%, encodes a proline to leucine substitution at the position 172 (P172L, OR5A2_variant 2; SEQ ID NO.3). Together, the 2 haplotypes are expressed in more than 96% of the population. Amino acids alignment of the 2 haplotypes are represented in FIG. 4.

To test if the substitution could impact the musk sensitivity, luciferase assays were performed as described previously after transfection of both OR5A2 variants. Cells were treated with semi-logarithmic serial dilutions of musk compounds from the four structurally different groups described earlier. In each experiment, an empty vector was used as negative control (pEFIBRHO). Representative concentration-response curves are given in FIG. 5.

These experimental results indicate that OR5A2_variant 2 is not activated by musk compounds (musk xylene, serenolide, Galaxolide®, velvione, cashmeran, musk ketone), unlike the other haplotype of the receptor of the invention. In these experiments, OR5A2_variant 1 shows concentration-response curves similar to those obtained in example 2. Overall, these observations clearly suggest that the haplotype with the substitution in position 172 (P172L) loses its ability to be activated by musk compounds. This observation could explain why different studies have, so far, excluded the OR of invention as a musk receptor (Shirasu et al. 2014 Neuron 81, 165-78; FIG. 5F supplemental, Sato-Akuhara 'N et al. 2016 J Neurosci. 36(16), 4482-91).

US11867685B2. Olfactory receptor involved in the perception of musk fragrance and the use thereof (2024-01-09). CHEMCOM S.A. [BE]. Inventors: Sandra Huysseune [BE], Alex Veithen [BE], Yannick Quesnel [BE].
Full text: Click here
Patent 2024
4
Immortalization of Myoblast Cell Lines
WT and ∆Ig3-MuSK mice were crossed with the H-2 Kb-tsA58 transgenic Immortomouse mouse line (JAX cat. no. 032619), and immortalized myoblast cell lines were established as described previously [51 (link), 52 (link)]. Myoblasts were isolated from postnatal day 2–3-old pups, and cells were subcloned on Matrigel for 1–2 passages and then moved to gelatin-coated plates for all future passages [53 (link)]. Only clones that were WT and ∆Ig3-MuSK homozygous and containing the H-2 Kb-tsA58 transgene were maintained. Cells were cultured at 33 °C in 8% CO2 in Dulbecco’s modified Eagle’s medium supplemented with 20% fetal bovine serum, 2% L-glutamine, 1% penicillin–streptomycin, 1% chicken embryo extract, and 1 U of γ-interferon. For primary cultures, myoblasts were isolated as described previously [53 (link)] and maintained in growth media consisting of Iscove’s modified Dulbecco’s medium (IMDM) containing 20% FBS, 1% CEE, and 1% penicillin–streptomycin and differentiated in IMDM supplemented with 2% horse serum and 1% penicillin–streptomycin when myoblasts reached 70% confluency. For BMP4 treatments, cells were serum starved 5 to 6 h (myoblasts) or were maintained in low-serum differentiation conditions for 24–48 h (myotubes) and then treated with 20–25 ng/ml recombinant human BMP4 protein (R&D Systems, cat. no. 314-BP). For acetylcholine receptor clustering experiments, primary myoblasts were isolated and differentiated into myotubes as in [53 (link)]. Myotubes were stimulated with 10 units of agrin (R&D Systems, cat. no. 550-AG) for 16 h and stained with rhodamine-conjugated α-bungarotoxin (Thermo Fisher, cat. no. T1175) at 1:2000. AChR clusters were counted as previously described [54 (link)].
Jaime D., Fish L.A., Madigan L.A., Xi C., Piccoli G., Ewing M.D., Blaauw B, & Fallon J.R. (2024). The MuSK-BMP pathway maintains myofiber size in slow muscle through regulation of Akt-mTOR signaling. Skeletal Muscle, 14, 1.
Full text: Click here
Publication 2024
5
Musk Sample Preparation and GC-MS Analysis
Sample preparation was performed as previously described with minor modifications (Li et al., 2016 (link)). Twenty milligrams of musk sample from each group were pooled, divided into 2 parts and dissolved in 2.5 mL of diethyl ether and ether alcohol, followed by a 2-h extraction by ultrasonication and centrifugation at 13,000 × g for 5 min. Two milliliters of supernatant was placed into a GC–MS instrument (GCMS-QP2010 Plus, Shanghai, China) for chemical composition detection with the chromatographic conditions as follows: column temperature: 40°C, inlet temperature: 290°C, interface temperature: 220°C, split injection with the pressure of 49.5 kPa. The column temperature program was as follows: 40°C (2 min), 200°C (5 min, 10°C/min), 240°C (5 min, 5°C/min), maintained at 290°C for 15 min. The total and column flow rates were 9.0 and 1.0 mL/min, respectively; the linear velocity was 36.1 cm/s; the purge flow rate was 3.0 mL/min; and the mass scanning range was 33–600 m/z. The acquired data in total ionic chromatograms (TICs) were compared with those in the mass spectral library of the National Institute of Standards and Technology (NIST). A confidence coefficient of above 80% was adopted for the data, and peak area % values were used to determine component variations. Finally, to discriminate the chemical compositions among 6 groups, we performed Partial Least Squares Discriminant Analysis (PLS-DA) after Pareto scaling.
Xu Z., Li F., Liu Q., Ma T., Feng X., Zhao G., Zeng D., Li D, & Jie H. (2024). Chemical composition and microbiota changes across musk secretion stages of forest musk deer. Frontiers in Microbiology, 15, 1322316.
Full text: Click here
Publication 2024

Top products related to «Musk»

1
Qiaamp dna stool mini kit by Qiagen
Sourced in Germany, United States, United Kingdom, Spain, France, Netherlands, China, Canada, Japan, Italy, Australia, Switzerland
The QIAamp DNA Stool Mini Kit is a laboratory equipment product designed for the purification of genomic DNA from stool samples. It is a tool for extracting and isolating DNA from biological specimens.
2
Trizol reagent by Thermo Fisher Scientific
Sourced in United States, China, Japan, Germany, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Netherlands, Belgium, Lithuania, Denmark, Singapore, New Zealand, India, Brazil, Argentina, Sweden, Norway, Austria, Poland, Finland, Israel, Hong Kong, Cameroon, Sao Tome and Principe, Macao, Taiwan, Province of China, Thailand
TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
3
Hiseq 2500 platform by Illumina
Sourced in United States, China, Germany, Canada, Australia, Italy, India, Japan, United Kingdom, Cameroon, Brazil, Norway, Switzerland
The HiSeq 2500 platform is a high-throughput DNA sequencing system designed for a wide range of genomic applications. It utilizes sequencing-by-synthesis technology to generate high-quality sequence data. The HiSeq 2500 platform is capable of producing up to 1 billion sequencing reads per run, making it a powerful tool for researchers and clinicians working in the field of genomics.
4
Qubit dsdna hs assay kit by Thermo Fisher Scientific
Sourced in United States, Germany, United Kingdom, Belgium, China, Italy, France, Australia, Canada, Japan, Finland, Switzerland, Austria, India, Singapore, Denmark, New Zealand
The Qubit dsDNA HS Assay Kit is a fluorescence-based method for quantifying double-stranded DNA (dsDNA) in a sample. The kit provides a sensitive and accurate way to measure DNA concentration in a microplate or cuvette format.
5
Trizol by Thermo Fisher Scientific
Sourced in United States, Germany, China, Japan, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Belgium, Denmark, Netherlands, India, Ireland, Lithuania, Singapore, Sweden, Norway, Austria, Brazil, Argentina, Hungary, Sao Tome and Principe, New Zealand, Hong Kong, Cameroon, Philippines
TRIzol is a monophasic solution of phenol and guanidine isothiocyanate that is used for the isolation of total RNA from various biological samples. It is a reagent designed to facilitate the disruption of cells and the subsequent isolation of RNA.
6
Prism 6 by GraphPad
Sourced in United States, United Kingdom, Canada, China, Germany, Japan, Belgium, Israel, Lao People's Democratic Republic, Italy, France, Austria, Sweden, Switzerland, Ireland, Finland
Prism 6 is a data analysis and graphing software developed by GraphPad. It provides tools for curve fitting, statistical analysis, and data visualization.
7
Rneasy mini kit by Qiagen
Sourced in Germany, United States, United Kingdom, Netherlands, Spain, Japan, Canada, France, China, Australia, Italy, Switzerland, Sweden, Belgium, Denmark, India, Jamaica, Singapore, Poland, Lithuania, Brazil, New Zealand, Austria, Hong Kong, Portugal, Romania, Cameroon, Norway
The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.
8
High capacity cdna reverse transcription kit by Thermo Fisher Scientific
Sourced in United States, Germany, United Kingdom, Japan, Lithuania, France, Italy, China, Spain, Canada, Switzerland, Poland, Australia, Belgium, Denmark, Sweden, Hungary, Austria, Ireland, Netherlands, Brazil, Macao, Israel, Singapore, Egypt, Morocco, Palestine, State of, Slovakia
The High-Capacity cDNA Reverse Transcription Kit is a laboratory tool used to convert RNA into complementary DNA (cDNA) molecules. It provides a reliable and efficient method for performing reverse transcription, a fundamental step in various molecular biology applications.
9
Fetal bovine serum (fbs) by Thermo Fisher Scientific
Sourced in United States, China, United Kingdom, Germany, Australia, Japan, Canada, Italy, France, Switzerland, New Zealand, Brazil, Belgium, India, Spain, Israel, Austria, Poland, Ireland, Sweden, Macao, Netherlands, Denmark, Cameroon, Singapore, Portugal, Argentina, Holy See (Vatican City State), Morocco, Uruguay, Mexico, Thailand, Sao Tome and Principe, Hungary, Panama, Hong Kong, Norway, United Arab Emirates, Czechia, Russian Federation, Chile, Moldova, Republic of, Gabon, Palestine, State of, Saudi Arabia, Senegal
Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
10
Dulbecco modified eagle medium (dmem) by Thermo Fisher Scientific
Sourced in United States, China, United Kingdom, Germany, France, Australia, Canada, Japan, Italy, Switzerland, Belgium, Austria, Spain, Israel, New Zealand, Ireland, Denmark, India, Poland, Sweden, Argentina, Netherlands, Brazil, Macao, Singapore, Sao Tome and Principe, Cameroon, Hong Kong, Portugal, Morocco, Hungary, Finland, Puerto Rico, Holy See (Vatican City State), Gabon, Bulgaria, Norway, Jamaica
DMEM (Dulbecco's Modified Eagle's Medium) is a cell culture medium formulated to support the growth and maintenance of a variety of cell types, including mammalian cells. It provides essential nutrients, amino acids, vitamins, and other components necessary for cell proliferation and survival in an in vitro environment.

More about "Musk"

Musk is a natural substance derived from the scent glands of the male musk deer (Moschus spp.).
Historically, it has been used in traditional medicine and perfumery, with its distinctive, earthy aroma.
However, the renowned entrepreneur and innovator Elon Musk is not directly related to this musk substance, despite the similarity in names.
PubCompare.ai, an AI platform, leverages Elon Musk's expertise in artificial intelligence to help researchers optimize their research protocols and enhance reproducibility.
The platform allows users to locate the best protocols from literature, pre-prints, and patents using AI-driven comparisons, improving research efficiency and enabling more informed decisions.
To conduct research, scientists may utilize various tools and techniques, such as the QIAamp DNA Stool Mini Kit for DNA extraction, TRIzol reagent for RNA isolation, the HiSeq 2500 platform for high-throughput sequencing, the Qubit dsDNA HS Assay Kit for DNA quantification, TRIzol for RNA extraction, Prism 6 for data analysis, the RNeasy Mini Kit for RNA purification, the High-Capacity cDNA Reverse Transcription Kit for cDNA synthesis, and FBS and DMEM for cell culture.
By leveraging PubCompare.ai's AI-powered capabilities, researchers can optimize the use of these tools and enhance the reproducibility of their findings.