This study was conducted in accordance with animal ethics guidelines and was approved by the Institutional Animal Care and Use Committee (IACUC) of Universiti Putra Malaysia (Ethics Approval/Registration Number: UPM/IACUC/AUP-R064/2016).

A total of twenty healthy female Boer goats, aged between 2 and 3 years (multiparous), with an average body weight of 27.0 ± 0.9 kg and body condition score (BCS) of 2.3 ± 0.1, were used in this study. The animals were housed individually in pens measuring 1.5 m × 0.5 m and were randomly allocated into two dietary treatment groups: (1) a Control group (n=10) receiving a basal maintenance diet, and (2) a Supplemented group (n=10) receiving the same basal diet with additional commercial goat pellet concentrate to provide twice the maintenance energy requirement.

The synchronization and feeding protocols were performed as previously described by (Mohammed et al., 2025). All animals underwent oestrus synchronization using a controlled internal drug release device (CIDR; Eazi-Breed® CIDR®, Pfizer Australia) containing 0.3 g progesterone, which was inserted intravaginally for 18 consecutive days (Days -18 to 0). The feeding intervention for the Supplemented group began on Day -5 and continued for a total of 25 days (Figure 1). Natural mating was conducted from Days 2 to 4 by introducing fertile bucks to all does. Ovulation rates were assessed on Day 19 using trans-rectal ultrasound, pregnancy and embryo loss were monitored using P4 concentration and trans-rectal ultrasonography on Day 27.

Blood samples for plasma progesterone analysis were collected at two-day intervals from Day -5 to Day 27. Samples were centrifuged post-collection immediately, and the plasma was stored at −20 °C until analysis. On Day 27, all does were humanely sacrificed. Ovaries and brains were harvested immediately; CL tissues were collected from both ovaries, while the anterior pituitary was isolated from the brain. All tissue samples were preserved in RNALater® (Qiagen, USA) and stored at −20 °C for subsequent RNA extraction and quantitative RT-PCR analysis.

All goats had ad libitum access to clean drinking water throughout the experimental period. The animals were assigned to one of two dietary treatments: the Control group (n = 10), which received a maintenance-level diet supplying 4.45 MJ/day of metabolizable energy (ME), and the Supplemented group (n = 10), which was fed the same basal maintenance diet with additional concentrate supplementation to provide a total of 8.90 MJ/day of ME, equivalent to twice the maintenance energy requirement. The basal diet, offered to both groups, comprised 70% Napier grass (Pennisetum purpureum) and 30% commercial goat pellet concentrate, formulated on a dry matter basis. Feed was provided twice daily at fixed times (morning and afternoon) to ensure consistency in feeding management and intake patterns. Feed refusals were collected and weighed daily to monitor actual feed intake and ensure accurate estimation of nutrient consumption. All feed offered was generally consumed, with minimal refusals observed throughout the experimental period. Out of the 20 animals initially included in the experiment, data from three does (1 Control and 2 Supplemented) were excluded from statistical analysis due to abnormal feeding behavior, resulting in a final dataset used for analysis. The nutritional composition of the feed, including metabolizable energy, crude protein, and dry matter content of Napier grass and the commercial concentrate, is presented in Table 1. The nutrient requirement was calculated according to the nutrient requirements for small ruminants described by the National Research Council (Council, 2007).

Ovulation rate was evaluated on Day 19 post-breeding by counting the number of corpora lutea (CL) on both ovaries using a 7.5 MHz linear transducer (SSD-900; Aloka, Tokyo, Japan). Pregnancy rate was calculated as the proportion of does confirmed pregnant via transrectal ultrasound on Day 27 and supported by serial plasma progesterone measurements, with levels >2 ng/mL on Day 21 post-breeding indicative of pregnancy. Embryo count and viability were assessed using ultrasound on Day 27, and embryo loss was determined by comparing the number of detected embryos to the expected number based on ovulation data. Additionally, any sudden decline in progesterone concentrations to <2 ng/mL during the experimental period was interpreted as indicative of embryo loss.

Plasma concentrations of leptin and NPY were quantified using commercial kits from QayeeBio (Shanghai, China), while progesterone was measured using an ELISA kit from ENZO Life Sciences Inc. (USA), according to the manufacturer’s protocol. The sensitivity of the assays was 15.6 pg/mL for leptin, 6.25 pg/mL for NPY, and 8.57 pg/mL for progesterone. The intra- and inter-assay variations were 5.7% and 6.0% (Leptin), 8.1% and 11.8% (NPY), and 7.9% and 6.2% (progesterone).

RNA extraction and cDNA synthesis. Total RNA was extracted from each sample using the RNeasy Mini Kit (Qiagen, USA) following the manufacturer’s instructions. Genomic DNA was removed by on-column DNase digestion using RQ1 RNase-Free DNase (Promega, USA). RNA integrity was confirmed by the presence of clear 28S and 18S rRNA bands on a 1% agarose gel. RNA concentration and purity were measured using a NanoVue™ Plus Spectrophotometer, with all samples showing OD 260/280 ratios between 1.8 and 2.0, indicating good quality. For each sample, 1 µg of total RNA was reverse-transcribed into cDNA using the Transcriptor First Strand cDNA Synthesis Kit (Roche, USA), and the cDNA was stored at -20 °C until qPCR analysis.

Primers. The design of primers for amplifying the goat LEPR gene was based on gene sequences from multiple species retrieved from the National Center for Biotechnology Information (NCBI) GenBank database. Multiple sequence alignment was performed using ClustalW2 with selected sequences and the full-length LEPR gene to identify conserved exon regions. Primers were designed within these regions using Primer3 software. Primers for PGRMC1 were adopted from (Mohammed et al., 2025), while those for the reference genes GAPDH and β-actin were obtained from (Celestino et al., 2010); the ubiquitin primer was sourced from (Frota et al., 2011). All primer sequences and their corresponding accession numbers are presented in Table 2.

Quantitative RT-PCR. The RT-qPCR master mix was prepared using the KAPA SYBR FAST qPCR Kit Master Mix (2X) Universal (KAPA Biosystems, USA), and the reactions were run on a LightCycler 480 system (Roche, USA). Each 20 µL reaction contained 10 µL SYBR master mix, 0.4 µL each of forward and reverse primers (200 nM), 2 µL cDNA (100 ng), and 7.2 µL of nuclease-free water. The cycling program included enzyme activation at 95 °C for 5 min, followed by 40 cycles of denaturation at 94 °C for 15 s, annealing at 60 °C for 30 s, and extension at 72 °C for 45 s. Negative controls, using water instead of cDNA, were included to check for any contamination. Melting curve analysis confirmed specific amplification, and the PCR product size was verified on a 2% agarose gel.

Relative gene expression was calculated using the ^{2^−ΔΔ}Ct method (Lixiang et al., 2025), with the Control group serving as the calibrator. All RT-qPCR reactions were performed in technical triplicate, and mean Ct values were used for downstream analysis to minimize intra-assay variation. Amplification efficiencies of the reference genes were 98.3% for β-actin, 96.9% for GAPDH, and 97.9% for ubiquitin, all within the acceptable MIQE range (90%–110%), supporting their suitability for relative quantification. Normalization was performed using the geometric mean of the three reference genes (GAPDH, β-actin, and ubiquitin) to reduce potential bias associated with single-gene normalization. The expression stability of these genes was assessed across all samples based on Ct consistency, and no significant variation was observed among experimental groups, confirming their stability and appropriateness for normalization.

Statistical analyses were performed using SPSS version 26. The effects of the nutritional supplement on PGRMC1 and LEPR mRNA levels were evaluated using an independent t-test, while plasma progesterone concentrations were analyzed with a repeated-measures multivariate test. Spearman’s rank correlation was used to assess relationships between variables. Ovulation rate, pregnancy rate, and embryo loss rate were compared using the Chi-square test. Quantitative RT-PCR results are presented as fold change ± SD relative to the Control group (set to 1), and plasma progesterone concentrations are shown as mean ± SEM. Statistical significance was set at p≤0.05.

Nutritional supplementation for 25 days did not significantly affect ovulation rate (1.00 ± 0.21 versus 1.25 ± 0.24 in the Control), indicating that short-term supplementation did not influence follicular recruitment or ovulatory response. However, despite similar ovulation rates, the pregnancy rate was markedly greater in the supplemented group (87.5 ± 0.88%) compared with the Control group (55.5 ± 0.82; p<0.05%). This divergence between ovulation and pregnancy outcomes indicates that the beneficial effects of supplementation were mediated primarily through post-ovulatory mechanisms rather than increased ovulatory output. The improved pregnancy rate was reflected in embryo recovery data. Supplemented does produced significantly more embryos (9.6 ± 0.71) than Control does (5.7 ± 1.42), with embryo loss substantially reduced by supplementation, declining from 44% in Controls to only 10% in supplemented does (Table 3). These findings strongly suggest that nutritional supplementation enhanced embryo survival during early gestation rather than increasing fertilization per se.

Plasma concentrations of leptin, NPY, and progesterone for the Supplemented (flushing) and Control groups are presented in Figure 2. Overall, does receiving supplementation exhibited higher circulating leptin and progesterone concentrations but lower NPY concentrations compared with Controls, reflecting an improved metabolic and endocrine milieu.

From Days -5 to 11, leptin concentrations were similar between groups (Figure 2A). However, from Day 13 to Day 19, leptin levels were significantly greater in supplemented does than in Controls (p<0.05). Although leptin concentrations declined in both groups following cessation of supplementation on Day 19, values remained significantly elevated in the supplemented group through Day 27 (p<0.05). This sustained elevation indicates that the metabolic effects of supplementation persisted beyond the feeding period. The pattern of NPY concentration (Figure 2B) was inverse to that of leptin. Supplemented does consistently exhibited lower NPY concentrations than Controls, although differences were not significant from Days -5 to 9. From Day 11 to Day 19, NPY concentrations were significantly lower in supplemented does, with the greatest divergence observed on Day 19, after which concentrations declined in both groups. Reduced NPY suggests attenuation of the negative energy balance signal and reduced inhibitory input to the reproductive axis. Plasma progesterone concentrations were low and similar between groups until approximately Day 9, after which concentrations gradually increased (Figure 2C). From Day 13 until the end of the sampling period on Day 27, progesterone concentrations were significantly greater in supplemented does than in Controls (p<0.05). Moreover, the result shows that the concentrations of NPY and leptin were significantly negatively correlated (Figure 3A; r = –0.51, p<0.05) and the concentrations of progesterone and leptin were significantly positively correlated (Figure 3B; r = 0.63, p<0.05). The apparent negative relationship between the concentrations of NPY and progesterone was not significant (Figure 3C). The association between elevated leptin, suppressed NPY, and increased progesterone strongly supports the role of improved metabolic status in enhancing luteal function.

Gene expression analysis revealed that the effects of nutritional supplementation extended beyond systemic endocrine changes to include tissue-specific molecular adaptations. Compared with Control does, LEPR expression was significantly upregulated in pituitary (5.03-fold) and luteal (3.32-fold) tissues in supplemented does (Figure 4; p<0.05), with no significant change observed in ovarian follicles. Increased pituitary LEPR expression suggests enhanced central sensitivity to leptin, potentially amplifying its stimulatory effects on gonadotropin secretion. Concurrent upregulation in luteal tissue indicates a direct ovarian action of leptin during the luteal phase. In contrast, PGRMC1 expression in pituitary tissue was unaffected by supplementation (Figure 5), but was significantly increased in follicular (2.31-fold) and luteal (3.75-fold) tissues (p<0.05).

vance of these molecular adaptations. A moderate positive correlation was observed between PGRMC1 expression and plasma progesterone concentration (r = 0.65, p<0.05), indicating that increased PGRMC1 expression is associated with enhanced luteal progesterone output. LEPR expression also showed a moderate positive correlation with progesterone (r = 0.61, p<0.05), suggesting that leptin signaling contributes to luteal steroidogenesis.

Linear regression analysis (Figure 6) confirmed that expression of both genes significantly predicted progesterone concentration. PGRMC1 expression accounted for approximately 75% of the variation in progesterone levels (R² = 0.75), while LEPR expression explained about 57% (R² = 0.57). These findings underscore the importance of both leptin signaling and progesterone receptor-associated pathways as key mediators linking nutritional status to luteal function.