Proteomics

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Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in the liver of male mouse


ABSTRACT: Estrogens are suggested to lower the risk of developing metabolic syndrome in both sexes. In this study, we investigated how the increased circulating estrogen-to-androgen ratio (E/A) alters liver lipid metabolism in males. Mice expressing human aromatase enzyme (AROM+ mice), and thus having high circulating E/A, were used as a model. Proteomics and gene expression analyses indicated an increase in the peroxisomal ß-oxidation in the liver of AROM+ mice as compared with their wild type littermates. Correspondingly, metabolomic analysis revealed a decrease in the amount of phosphatidylcholines with long-chain fatty acids in the plasma. With interest we note that the expression of Cyp4a12a enzyme, which specifically metabolizes arachidonic acid (AA) to 20-hydroxy AA, was dramatically decreased in the AROM+ liver. As a consequence, increased amounts of phospholipids having AA as a fatty acid tail were detected in the plasma of the AROM+ mice. Overall, these observations demonstrate that high circulating E/A in males is linked to indicators of higher peroxisomal ß -oxidation and lower AA metabolism in the liver. Furthermore, the plasma phospholipid profile reflects the changes in the liver lipid metabolism.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Liver

SUBMITTER: Anni Vehmas  

LAB HEAD: Garry L. Corthals

PROVIDER: PXD002025 | Pride | 2015-12-23

REPOSITORIES: Pride

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Publications

Optimization of Statistical Methods Impact on Quantitative Proteomics Data.

Pursiheimo Anna A   Vehmas Anni P AP   Afzal Saira S   Suomi Tomi T   Chand Thaman T   Strauss Leena L   Poutanen Matti M   Rokka Anne A   Corthals Garry L GL   Elo Laura L LL  

Journal of proteome research 20150908 10


As tools for quantitative label-free mass spectrometry (MS) rapidly develop, a consensus about the best practices is not apparent. In the work described here we compared popular statistical methods for detecting differential protein expression from quantitative MS data using both controlled experiments with known quantitative differences for specific proteins used as standards as well as "real" experiments where differences in protein abundance are not known a priori. Our results suggest that da  ...[more]

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