TY - JOUR
T1 - Monitoring changes in the healthy female metabolome across the menstrual cycle using GC × GC-TOFMS
AU - Eshima, Jarrett
AU - Ong, Stephanie
AU - Davis, Trenton J.
AU - Miranda, Christopher
AU - Krishnamurthy, Devika
AU - Nachtsheim, Abigael
AU - Stufken, John
AU - Plaisier, Christopher
AU - Fricks, John
AU - Bean, Heather D.
AU - Smith, Barbara S.
N1 - Funding Information:
This work was supported by the Arizona Biomedical Research Commission (Grant No. ADHS16-162403 ). We would like to thank Vi Nguyen for her time collecting subject samples, Joel Lusk for his project input and assistance, and Emily Hanzlick for her assistance in the calculation of metabolite retention indices.
Funding Information:
This work was supported by the Arizona Biomedical Research Commission (Grant No. ADHS16-162403). We would like to thank Vi Nguyen for her time collecting subject samples, Joel Lusk for his project input and assistance, and Emily Hanzlick for her assistance in the calculation of metabolite retention indices.
Publisher Copyright:
© 2019
PY - 2019/7/15
Y1 - 2019/7/15
N2 - Urinary metabolomics offers a non-invasive means of obtaining information about the system-wide biological health of a patient. Untargeted metabolomics approaches using one-dimensional gas chromatography (GC) are limited due to the chemical complexity of urine, which poorly detects co-eluting low-abundance analytes. Metabolite detection and identification can be improved by applying comprehensive two-dimensional GC, allowing for the discovery of additional viable biomarkers of disease. In this work, we applied comprehensive two-dimensional GC coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) to the analysis of urine samples collected daily across 28-days from 10 healthy female subjects for a personalized approach to female reproductive health monitoring. Through this analysis, we identified 935 unique volatile metabolites. Two statistical methods, a modified T-statistic and Wilcoxon Rank Sum, were applied to analyze differences in metabolome abundance on ovulation days as compared to non-ovulation days. Four metabolites (2-pentanone, 3-penten-2-one, carbon disulfide, acetone) were identified as statistically significant by the modified T-statistic but not the Rank Sum, after a false-discovery rate of 0.1 was set using a Benjamini-Hochberg correction. Subsequent analyses by boxplot indicated that the putative volatile metabolic biomarkers for fertility are expressed in increased or decreased abundance in urine on the day of ovulation. Individual analysis of metabolome expression across 28-days revealed some subject-specific features, which suggest a potential for long-term, personalized fertility monitoring using metabolomics.
AB - Urinary metabolomics offers a non-invasive means of obtaining information about the system-wide biological health of a patient. Untargeted metabolomics approaches using one-dimensional gas chromatography (GC) are limited due to the chemical complexity of urine, which poorly detects co-eluting low-abundance analytes. Metabolite detection and identification can be improved by applying comprehensive two-dimensional GC, allowing for the discovery of additional viable biomarkers of disease. In this work, we applied comprehensive two-dimensional GC coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) to the analysis of urine samples collected daily across 28-days from 10 healthy female subjects for a personalized approach to female reproductive health monitoring. Through this analysis, we identified 935 unique volatile metabolites. Two statistical methods, a modified T-statistic and Wilcoxon Rank Sum, were applied to analyze differences in metabolome abundance on ovulation days as compared to non-ovulation days. Four metabolites (2-pentanone, 3-penten-2-one, carbon disulfide, acetone) were identified as statistically significant by the modified T-statistic but not the Rank Sum, after a false-discovery rate of 0.1 was set using a Benjamini-Hochberg correction. Subsequent analyses by boxplot indicated that the putative volatile metabolic biomarkers for fertility are expressed in increased or decreased abundance in urine on the day of ovulation. Individual analysis of metabolome expression across 28-days revealed some subject-specific features, which suggest a potential for long-term, personalized fertility monitoring using metabolomics.
KW - Fertility
KW - GC × GC-TOFMS
KW - Ovulation
KW - Personalized diagnostics
KW - Urine
UR - http://www.scopus.com/inward/record.url?scp=85065744133&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065744133&partnerID=8YFLogxK
U2 - 10.1016/j.jchromb.2019.04.046
DO - 10.1016/j.jchromb.2019.04.046
M3 - Article
C2 - 31108321
AN - SCOPUS:85065744133
SN - 1570-0232
VL - 1121
SP - 48
EP - 57
JO - Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences
JF - Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences
ER -