Detection of Anti-Müllerian Hormone (AMH) mRNA in Serum and Ovarian Tissue of Local Indonesian Cattle Using EvaGreen-based RT–qPCR
Dikman, Dicky Mohammad | Puspitasari, Heni | Waluyo, Seagames | Kurniawan, Muhammad ‘Ahdi | Supriyadi, Supriyadi | Srianto, Pudji | Luqman, Epy Muhammad | Suprayogi, Tri Wahyu | Madyawati, Sri Pantja | Safitri, Erma | Lestari, Tita Damayanti
Anti-Müllerian hormone (AMH) has been identified as a potential biomarker for assessing ovarian reserve and reproductive capacity in cattle, with high heritability and relatively stable expression. However, studies detecting AMH mRNA in bovine serum remain scarce due to the matrix’s inherently low RNA yield, susceptibility to degradation, and potential presence of PCR inhibitors. This study provides an exploratory validation of AMH mRNA detection in serum — a challenging matrix compared with ovarian tissue — using EvaGreen-based reverse transcriptase–quantitative PCR (RT–qPCR). Specific primers for the AMH and β-actin genes were designed in silico and validated through melting curve analysis and linearity testing. The results showed high amplification efficiency (AMH: 100.2%, R² = 0.994; β-actin: 109.1%, R² = 0.996), with specific amplification of both targets. AMH detection in serum samples was successful in some samples, while the β-actin gene was consistently amplified as a reference gene. Despite the low RNA quality from serum and the presence of organic contaminants, the method demonstrated its feasibility for detecting AMH transcripts in a minimally invasive manner. Physiologically, AMH levels positively correlate with antral follicle count, superovulation success, and embryo quality, and are sensitive to heat stress and other environmental factors. These findings provide a foundational basis for developing molecular diagnostic approaches based on AMH gene expression in cattle reproductive management programs and support the future development of efficient, accurate, and context-specific biomolecular-assisted selection technologies for tropical livestock systems.
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Эту запись предоставил Airlangga University