Rabbit Anti-Human ELOVL5 (internal) Polyclonal antibody

Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p=0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.

The health benefits of the (n-3) PUFA, EPA, and DHA have created a demand for fish and fish oil, the main sources of these PUFA. Production animals, such as poultry, are potential alternate and sustainable sources of EPA and DHA, provided these fatty acids can be synthesized from plant-derived alpha-linolenic acid (ALA, 18:3(n-3)]. Because elongases are potential control points in the conversion of ALA to DHA in rats, we examined the chicken elongases, ELOVL2 and ELOVL5, which had not been characterized. ELOVL2 activity was limited to C20-22 PUFA substrates and the major product of ELOVL2 metabolism of EPA was 24:5(n-3). This indicates that ELOVL2 can sequentially elongate EPA to docosapentaenoic acid [DPA, 22:5{n-3)] and then onto 24:5(n-3). ELOVL5 selectivity was broader with elongation of C18-22 PUFA substrates. The ability of chicken ELOVL5 to efficiently synthesize 24:5(n-3) is unique compared with ELOVL5 enzymes from other species. The expression of ELOVL5 was higher than ELOVL2 in livers of broiler chickens and their expression did not change when dietary ALA was increased from 0.6 to 1.3% of dietary energy for 42 d. The expression of both genes was higher than previously seen in rats. The chicken elongase enzymes are unlike those of any species studied to date, because both ELOVL2 and ELOVL5 have the ability to efficiently elongate DPA. In addition, the relative abundance of ELOVL2 and ELOVL5 in the liver suggests that chickens may be able to metabolize more DPA through to 24:5(n-3), the precursor of DHA, compared with other species such as rats. J. Nutr. 143: 12-16, 2013.