Literature DB >> 15828767

Identification of major histocompatibility complex-regulated body odorants by statistical analysis of a comparative gas chromatography/mass spectrometry experiment.

Alan Willse1, Anne M Belcher, George Preti, Jon H Wahl, Miranda Thresher, Peter Yang, Kunio Yamazaki, Gary K Beauchamp.   

Abstract

This paper examines the application of gas chromatography/mass spectrometry (GC/MS) in a comparative experiment to identify volatile compounds from urine that differ in concentration between two groups of inbred mice. A complex mixture might comprise several hundred or even thousands of volatile compounds. Because their number and location in a chromatogram are generally unknown, and because components overlap in populous chromatograms, the statistical problems offer significant challenges beyond traditional two-group screening procedures. We describe a statistical procedure to compare two-dimensional GC/MS profiles between groups, which entails (1) signal processing, baseline correction, and peak detection in single ion chromatograms; (2) aligning chromatograms in time; (3) normalizing differences in overall signal intensities; and (4) detecting chromatographic regions that differ between groups. In an application to chemosignaling, we detect differences in GC/MS chromatograms of ether-extracted urine collected from two inbred groups of mice that differ only in genes of the major histocompatibility complex (MHC). Several dozen MHC-regulated compounds are found, including two known mouse pheromones, 2,5-dimethylpyrazine and 2-sec-butyl-4,5-dihydrothiazole.

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Mesh:

Year:  2005        PMID: 15828767     DOI: 10.1021/ac048711t

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  23 in total

1.  Chemical identification of MHC-influenced volatile compounds in mouse urine. I: Quantitative Proportions of Major Chemosignals.

Authors:  Milos V Novotny; Helena A Soini; Sachiko Koyama; Donald Wiesler; Kevin E Bruce; Dustin J Penn
Journal:  J Chem Ecol       Date:  2006-12-27       Impact factor: 2.626

2.  Individual odortypes: interaction of MHC and background genes.

Authors:  Alan Willse; Jae Kwak; Kunio Yamazaki; George Preti; Jon H Wahl; Gary K Beauchamp
Journal:  Immunogenetics       Date:  2006-11-07       Impact factor: 2.846

3.  Structural variation governs substrate specificity for organic anion transporter (OAT) homologs. Potential remote sensing by OAT family members.

Authors:  Gregory Kaler; David M Truong; Akash Khandelwal; Megha Nagle; Satish A Eraly; Peter W Swaan; Sanjay K Nigam
Journal:  J Biol Chem       Date:  2007-06-05       Impact factor: 5.157

Review 4.  In search of the chemical basis for MHC odourtypes.

Authors:  Jae Kwak; Alan Willse; George Preti; Kunio Yamazaki; Gary K Beauchamp
Journal:  Proc Biol Sci       Date:  2010-03-31       Impact factor: 5.349

Review 5.  Neural computations with mammalian infochemicals.

Authors:  A Gelperin
Journal:  J Chem Ecol       Date:  2008-06-14       Impact factor: 2.626

6.  Comparative analysis of volatile constituents from mice and their urine.

Authors:  Frank Röck; Sven Mueller; Udo Weimar; Hans-Georg Rammensee; Peter Overath
Journal:  J Chem Ecol       Date:  2006-05-31       Impact factor: 2.626

7.  MHC signaling during social communication.

Authors:  James S Ruff; Adam C Nelson; Jason L Kubinak; Wayne K Potts
Journal:  Adv Exp Med Biol       Date:  2012       Impact factor: 2.622

8.  A novel approach for nontargeted data analysis for metabolomics. Large-scale profiling of tomato fruit volatiles.

Authors:  Yury Tikunov; Arjen Lommen; C H Ric de Vos; Harrie A Verhoeven; Raoul J Bino; Robert D Hall; Arnaud G Bovy
Journal:  Plant Physiol       Date:  2005-11       Impact factor: 8.340

Review 9.  Toward a systems level understanding of organic anion and other multispecific drug transporters: a remote sensing and signaling hypothesis.

Authors:  Sun-Young Ahn; Sanjay K Nigam
Journal:  Mol Pharmacol       Date:  2009-06-10       Impact factor: 4.436

10.  Urinary volatile compounds as biomarkers for lung cancer: a proof of principle study using odor signatures in mouse models of lung cancer.

Authors:  Koichi Matsumura; Maryanne Opiekun; Hiroaki Oka; Anil Vachani; Steven M Albelda; Kunio Yamazaki; Gary K Beauchamp
Journal:  PLoS One       Date:  2010-01-27       Impact factor: 3.240

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