Adiponectin plays a role in energy metabolism for musk secretion in scent glands of muskrats (Ondatra zibethicus)

Regulatory Roles of Peroxisomal Metabolic Pathways Involved in Musk Secretion in Muskrats

In this study, we analyzed the main components of muskrat musk by gas chromatography-mass spectrometry, the results showed that muskrat musk contained fatty acids (29.32%), esters (31.89%), cholesterol (4.38%), cyclic ketones (16.31%), alcohols (6.42%) and other compounds, among which 9-octadecenoic acid accounted for 4.89%. We also analyzed the genes of the metabolic pathway in the scent gland at the transcriptomic level during musk-secreting and non-secreting seasons by RNA-seq (RNA sequencing). We detected 21 genes in the peroxisomal metabolic pathways, including PEX14(peroxin-14) and ACOX3(acyl-CoA oxidase), which exhibited significant differential expression between the musk-secreting season and the non-secreting season (p < 0.05). The RNA-seq results for these genes were validated by reverse transcription PCR(RT-PCR) for both seasons. In addition, we examined changes in the composition of muskrat musk from the glandular cells of scent glands cultured in vitro after RNA interference-mediated silencing of 2 differentially expressed genes, ACOX3 and HSD17B4(D-bifunctional protein, DBP). The 9-Octadecenoic acid content in muskrat musk decreased significantly following the silencing of ACOX3 and HSD17B4(D-bifunctional protein, DBP). These results suggest that peroxisomal metabolic pathways play important roles in the regulation of musk secretion in scent glands in the muskrat.

Quality markers of animal medicinal materials: Correlative analysis of musk reveals distinct metabolic changes induced by multiple factors

BACKGROUND

Common farming environmental elements, such as longitude, latitude, and altitude, and physiological conditions, such as age and body weight, are thought to influence medicinal animal homeostasis and material quality by altering endocrine functions for primary and secondary metabolite formation. However, the currently available methods for evaluating complex components of traditional Chinese animal medicines have insufficient sensitivity and specificity.

PURPOSE

Characterizing the primary/secondary metabolomes of medicinal animals is essential for understanding their material basis, controlling product quality, and reflecting on distribution interactions. Therefore, this study aimed to screen ecological- and physiological-related metabolites in captive Moschus berezovskii throughout the collection period based on the quality marker (Q-marker) concept.

STUDY DESIGN AND METHODS

Fifty-one musk deer samples from 12 different distribution farms ranging in age from 2 to 11 years were enrolled. Differentially expressed musk metabolites were assessed via chromatography-tandem mass spectrometry technologies. A metabolome that mapped connections among these factors was established using chemometric and topological calculations.

RESULTS

Statistical analysis revealed that muscone, cis-9-hexadecenal, antioxidant 2264, prasterone-3-sulfate, androstan-17-one, and 1,2-benzenedicarboxylic acid showed significantly altered expression. Partial least squares (PLS) regression analysis of qualified data for these 6 secondary metabolites (active components) demonstrated that age is the most important factor underlying the varying levels of muscone, androstan-17-one and 1,2-benzenedicarboxylic acid. Furthermore, weight was the most important factor for cis-9-hexadecenal, longitude was important for antioxidant 2264, latitude was important for prasterone-3-sulfate, and altitude was important for antioxidant 2264, androstan-17-one and 1,2-benzenedicarboxylic. Metabolite analysis within the MetaboAnalyst (MetPA) suite showed that 18 candidate biomarker metabolites were screened, including allantoin, glycine, serine, creatine, alanine, taurine, lactate, 2-oxoglutarate (2-OG), fumarate, proline, xanthine, cytosine, carnitine, arginine, threonine, aspartate, and urea. Metabolic network analysis showed 4 important pathways that were involved: arginine and proline metabolism, the urea cycle, aspartate metabolism, and glycine, serine and threonine metabolism.

CONCLUSION

Using this combined metabolomic and chemometric approach, this study was successful in screening Q-markers for musk quality control and provided new insights into correlations among "ecological & physiological factors→Q-markers→metabolites", which potentially provides crucial information for musk breeding and material quality control.