AAVS_13_10_2234-2246

INTRODUCTION

Bali cattle (Bos sondaicus) constitute are indigenous genetic resource in Indonesia. They are derived from domesticated wild bulls and are officially recognized as local livestock by the Ministry of Agriculture of the Republic of Indonesia (Hikmawaty et al., 2020; Kurlyana et al., 2023). These cattle are known for their high adaptability to tropical environments, efficient utilization of fibrous feeds, and superior carcass yield, making them highly relevant for sustainable livestock systems (Jakaria et al., 2017; Purwantara et al., 2012). To improve the productivity of local breeds, the integration of molecular genetics offers a promising strategy to enhance economically important traits such as growth, feed efficiency, and meat quality (Syarifulaya and Sriasih, 2015).

Meat quality is a multifactorial trait that encompasses sensory attributes (e.g., tenderness, marbling, color, flavor, and water binding) and nutritional component such as fatty acid composition (Gunawan et al., 2021; Listyarini et al., 2022). The composition of unsaturated fatty acids, specifically monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs), plays a vital role in enhancing flavor, oxidative stability, and promoting human health (Gunawan et al., 2021; Muroya et al., 2020).

A dual approach incorporating genetic markers alongside optimized nutritional strategies, particularly the supplementation of key amino acids, is essential. Lysine functions as a primary precursor in protein synthesis, promoting the growth of body tissues and enhancing nitrogen retention, thereby directly contributing to metabolism and feed efficiency (Jin et al., 2022; Liao et al., 2015). Methionine plays a crucial role in transmethylation reactions, lipid metabolism, and antioxidant defenses that collectively enhance feed efficiency and growth (Martínez et al., 2017; Zhou et al., 2016). Supplementing these amino acids has been shown to improve growth, feed efficiency, and intramuscular fatty acid composition, characterized by increased MUFAs and PUFAs and reduced saturated fats (Alfonso et al., 2023; Yi et al., 2025). These effects are associated with upregulation of lipid metabolism genes like FADS1 and FADS2, which are also known to modulate leptin expression (Jump, 2002).

Leptin is a protein hormone with a molecular weight of 16 kDa, comprising 167 amino acids (Kurlyana et al., 2023; Nugroho et al., 2022). Leptin, secreted by adipose tissue, regulates homeostatic energy balance, body weight, feed intake, and lipid metabolism (Anugratama and Hartatik, 2019; Avondo et al., 2019; Friedman, 2014; Münzberg and Heymsfield, 2019). Furthermore, leptin is also present in epithelial cells within the stomach, constituting a significant factor associated with growth rate, body mass, and meat quality, such as carcass fat deposition in cattle (Zalewska et al., 2021). Genetic polymorphisms in the LEP gene have been linked to variations in feed efficiency, growth rate, marbling, and fatty acid profiles in several cattle breeds (Kawaguchi et al., 2017; Sedykh et al., 2020; Wang et al., 2022).

The LEP gene is of particular interest due to its pleiotropic effects on growth, fat metabolism, and overall production efficiency (Mukherjee et al., 2023). Certain genotypes, such as GG or AG, are often associated with lower residual feed intake (RFI), a trait reflecting better feed conversion and energy efficiency (Mota et al., 2017; Nugroho et al., 2022). Despite extensive research on LEP polymorphisms in Bos taurus cattle, their relevance and biological impacts in tropical indigenous breeds like Bali cattle remain poorly understood. This gap is particularly evident in the context of essential amino acid supplementation. The absence of integrative studies combining genetic and nutritional approaches in Bali cattle highlights a key limitation in the current literature. This study addresses this gap by investigating the genetic variation and mRNA expression of the LEP gene in Bali cattle and assessing their association with growth, feed efficiency, meat quality, and fatty acid profile under essential amino acid supplementation. This integrated approach provides a novel perspective on how molecular markers and nutritional strategies can be utilized synergistically to enhance performance and meat quality in tropical cattle. The outcomes are expected to contribute to the development of marker-assisted selection (MAS) and nutrigenomic tools suitable for local breeding programs aimed at sustainable and high-nutritional beef production in tropical environments.

Materials and Methods

This study aimed to analyze the polymorphism and mRNA expression of the leptin gene (LEP) and their associations with growth performance, feed efficiency, meat quality, and fatty acid composition in male Bali cattle. As part of a precision nutrition strategy, essential amino acid supplementation specifically lysine and methionine was applied to evaluate its influence on these parameters. The supplementation was administered as a daily premix consisting of lysine and methionine with bran, each package 150 grams per head. It was provided each morning before basal feeding with elephant grass forage (Pennisetum purpureum), which contained 53.1% total digestible nutrients (TDN) and 11.4% crude protein. This approach aimed to support the anabolic phase and enhance protein synthesis and energy utilization efficiency. The commercial amino acid products used were Smartlys 300 (30% rumen-protected lysine) and SmartMet 600 (60% rumen-protected methionine) by Zhejiang Vega Bio-technology Co., Ltd. All experimental protocols were approved by the Animal Ethics Committee of Hasanuddin University (No. UH21070474).

Animal and experimental design

A total of 30 bulls of Bali cattle, with an average age of 27.10 ± 0.69 months and an initial body weight of 196.78 ± 5.12 kg. were housed for 70 days in individual enclosures measuring 1.5 x 2m at the Maiwa Breeding Center, Hasanudddin University, Barru Regency (Longitude 4°11’38.3” S, 119°40’18.2” E). All subjects received the same feed and were maintained under consistent environmental conditions. Their main diet consisted of elephant grass forage (Pennisetum purpureum), provided ad libitum, with free access to water throughout the day. The supplementation included essential amino acids, specifically lysine (13.3 g/head/day) and methionine (6.7 g/head/day), in addition to a mixture of bran (130 g/head/day), totaling 150 g/head/day, administered each morning before the basal feeding.

Dry matter intake (DMI) was calculated based on the average actual consumption, calibrated to the weekly dry matter content. The average DMI from elephant grass alone was 4.71±0.61 kg/head/day. Body weight was measured weekly before feeding, and water intake was monitored during the study period. Metabolic body weight (MBW) was calculated as BW0.75 (Kleiber, 1932). The feed conversion ratio (FCR) was calculated by dividing the total feed consumption (dry matter intake, DMI/kg) by the body weight gain (kg) (National Research Council, 2000). Residual Feed Intake (RFI) was determined as the residual from a linear regression model of dry matter intake (DMI) against average daily gain (ADG) and metabolic body weight (MBW) (Koch et al., 1963). To determine representative samples for advanced analysis, individual weekly data on body weight and feed intake were collected over the 10th week (70th day) period. These data were used to calculate individual RFI values, based on which two groups were established: high RFI (HRFI; n=3) and low RFI (LRFI; n=3). At the end of the study, these six animals were selected for slaughter and further analysis, including meat quality analysis, fatty acid composition analysis using GC-MS, and qRT-PCR for LEP mRNA expression.

The digestibility of dry and organic matter is calculated based on the acid-insoluble ash content method acid-insoluble ash (AIA) (Van Keulen and Young, 1977). All animals received the same supplementation; thus, the study design did not include a control group without supplementation. Additionally, the limited sample size used in the qRT-PCR analysis (n=3 per RFI group) is recognized as a constraint, which may influence the generalizability of the meat quality findings and gene expression.

Meat quality analysis

Out of the total population, 27 animals that met the inclusion criteria were selected for further analysis. The slaughtering process was conducted under the SNI good slaughtering practice standard 01-6159-1999, by halal-certified slaughterers. Samples of the longissimus dorsi muscle were collected and subjected to color analysis based on the CIE-LAB system (L*, a*, b*) after the blooming process, utilizing a colorimeter (Van Laack et al., 2000). Measurements included pH (24 hours postmortem), tenderness (raw meat and cooked meat) assessed by Warner Bratzler Shear Force (WBSF)(Shackelford et al., 1997), cooking loss, and water holding capacity (WHC) (Dagong et al., 2012; Hamm, 1961).

Fatty acid composition analysis

The composition of fatty acids was analyzed utilizing the gas chromatography-mass spectrometry (GC-MS) technique. Lipids were extracted employing the Soxhlet extraction method (Bligh and Dyer, 1959), and subsequently methylated into FAMEs utilizing methanol sulfate (Morrison and Smith, 1964). Compound identification was conducted using the NIST library, and quantification was determined based on the peak area relative to internal standards (Kind and Fiehn, 2010). The fatty acids identified include saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA). The analysis was performed in a laboratory adhering to Good Laboratory Practice standards (OECD, 1997).

Polymorphism determination of leptin gene

This study employed the PCR-RFLP (Polymerase Chain Reaction-Restriction Fragment Length Polymorphism) method to identify polymorphisms in the leptin (LEP) gene with high accuracy. Blood samples were collected from thirty bulls, and genomic DNA was extracted using the Geneaid Genomic DNA MiniKit following the manufacturer’s protocol. A novel polymorphism within the LEP gene was identified through high-throughput sequencing, and specific primers were designed based on the Bos taurus reference sequence (GenBank HE605298), as reported by Choudhary et al. (2005). The complete primer sequences are listed in Table 1. PCR amplification was conducted in a 25 μL reaction volume containing 2 μL of genomic DNA, 2 μL of forward and reverse primers, 12,5 μL of MyTaq Red Mix, and 8,5 μL of nuclease-free water. The thermocycling conditions on the Sensoquest PCR system were as follows: initial denaturation at 94°C for 5 minutes; 35 cycles of 94°C for 45 seconds, 60°C for 45 seconds, and 72°C for 1 minute; and a final extension at 72°C for 5 minutes. The PCR-RFLP approach involves digesting PCR amplicons with appropriate restriction enzymes to produce distinct polymorphic fragments used as species identification markers (Wolf et al., 2000).

Table 1: Genbank accession number and primer sequences of the Leptin gene (PCR-RFLP and qRT-PCR).

Gene name	Accesion number	Primer sequence	Appli-cation	Size (bp)	TA (°C)	Re-striction enzyme	Digested fragments length (bp)
Leptin	HE605298	F:5’-GTC TGG AGG CAA AGG GCA GAG T-3’	PCR-RFLP	522	60	BsaAI	AA: 522; AG: 522, 441 and 81; GG: 441 and 81
		R: 5’-CCA CCA CCT TGG AGT AG-3’
Leptin	NM_ 173928.2	F: 5'- CTACTTGTGCTCAGCCCCAA -3'	qRT- PCR	155	55
		R: 5'- TGCTCAGTTGCCATGGTGAA -3'
GAPDH	NM_ 001034034.2	F: 5'- ACAGTCAAGGCAGAGAACGG -3'	qRT- PCR	235	55
		R: 5'- GGTTCACGCCCATCACAAAC -3'

PCR-RFLP analysis involved digesting 5 μL of PCR product with 0.3 μL of BsaAI restriction enzyme (New England Biolabs, Catalog No. R0109S), 0.7 μL of buffer, and 1 μL of nuclease-free water, in a final volume of 7 μL. The reaction was incubated at 37 °C overnight. Genotyping was based on fragment length: AA genotype yielded a single 522 bp band; AG genotype yielded three bands (522, 441, and 81 bp); and GG genotype yielded two bands (441 and 81 bp).

Leptin gene mRNA expression analysis

Total RNA was extracted from liver tissues of six Bali cattle exhibiting the highest (n=3) and lowest (n=3) residual feed intake (RFI) values. RNA extraction yielded 1 μg of total RNA per sample. First-strand cDNA was synthesized using the First Strand Transcriptor Synthesis Kit (Thermo Fisher Scientific, Vilnius, Lithuania). One microliter (1 μL) of synthesized cDNA was used per 20 μL qRT-PCR reaction. Quantitative real-time PCR (qRT-PCR) was performed using gene-specific primers for LEP (NM_173928.2) and the iTaq SYBR Green Master Mix with Rox PCR core reagents (Bio-Rad). GAPDH was used as the reference gene for normalization. Relative gene expression was calculated using the ΔCt method, which is the difference in threshold cycles (Ct) between the LEP gene and GAPDH (Harahap et al., 2024; Listyarini et al., 2022; Silver et al., 2006).

Statistical analysis

Genotype and allele frequencies were calculated using Nei and Kumar (2000) method, and Hardy-Weinberg equilibrium was assessed using the chi-square test (Hartl and Clark, 1990). Data were tested for normality using the Shapiro–Wilk test and for homogeneity of variance using Levene’s test before analysis of variance (ANOVA). Associations between leptin genotypes and variables related to growth performance, feed efficiency, meat quality, and fatty acid composition were analyzed using the General Linear Model (GLM) procedure in SAS software version 9.2. Post-hoc comparisons among genotypes were performed using Duncan’s multiple range test, which was selected due to its appropriateness for small sample sizes. However, the limitations of Duncan’s test are acknowledged, and alternative post-hoc methods such as Tukey’s HSD are recommended for future studies to improve robustness. mRNA expression levels were compared using a two-tailed independent t-test. Additionally, Pearson’s correlation analysis was conducted to evaluate the relationships between gene expression levels and residual feed intake (RFI) to explore the potential regulatory role of the LEP gene in metabolic efficiency.

Result and Discussion

Polymorphism of the leptin gene with essential amino acid supplementation

PCR-RFLP amplification of the leptin gene using the BsaAI restriction enzyme produced clear fragment patterns: 522 bp for genotype AA; 522, 441, and 81 bp for genotype AG; and 441 and 81 bp for genotype GG (Figure 1). The figure illustrates representative PCR-RFLP results from a subset of the samples. Genotype distribution showed a predominance of AG (57%), followed by GG (33%) and AA (10%). Allele G had a higher frequency (62%) compared to allele A (38%). The chi-square value was 1.193, indicating that the leptin gene in this Bali cattle population is in Hardy-Weinberg equilibrium. Detailed genotype and allele frequencies are presented in Table 2.

Table 2: The number of animals per genotype and allele frequency in the leptin gene of Bali cattle.

Gen	N	Genotype frequency			Allele frequency		Chi-square (χ2)
Gen	N	AA (n)	AG (n)	GG (n)	A	G	Chi-square (χ2)
Leptin	30	0.10 (3)	0.57 (17)	0.33 (10)	0.38	0.62	1,.193

Table 3: Association of LEP gene polymorphism related to growth and feed efficiency with supplementation of essential amino acids in Bali cattle.

	Genotype (µ±STD) (n=30)
Parameter	AA	AG	GG	P-value
	(n = 3)	(n = 17)	(n = 10)
Growth
IBW (kg)	152.74±6.37b	196.54±29.41a	211.46±18.88a	**0.006
FBW (kg)	190.88±1.23b	235.00±29.89a	249.16±21.57a	**0.008
ADG (kg/day)	0.48±0.11	0.55±0.12	0.54±0.07	0.632
MBW (kg0.75)	47.45±0.61b	56.20±5.78a	59.08±3.88a	**0.006
Feed Efficiency
ADFI (kg DM/day)	4.63±0.49b	5.39±0.75a	5.45±0.44a	0.149
FCR (kg DM/kg gain)	9.95±2.67	10.25±2.62	10.24±1.16	0.977
RFI (kg DM/day)	0.06±0.55	0.05±0.46	-0.10±0.40	0.685
AIA (g DM/day)	42.57±2.14	44.04±4.56	44.16±5.95	0.881

**significant at p<0.01; ab Means with the same letter are not significantly different.

Association of the leptin gene-related growth and feed efficiency with essential amino acid supplementation

The results demonstrated significant associations between leptin genotypes and several growth and feed efficiency parameters (Table 3). Initial body weight (IBW) differed significantly among genotypes (p= 0.006), with GG (211.46 ± 18.88 kg) > AG (196.54 ± 29.41 kg) > AA (152.74 ± 6.37 kg). Final body weight (FBW) also showed a significant difference (p = 0.008), with GG (249.16 ± 21.57 kg) higher than AG (235.00 ± 29.89 kg) and AA (190.88 ± 1.23 kg). Metabolic body weight (MBW) was significantly different (p= 0.006), with GG (59.08 ± 3.88 kg) > AG (56.20 ± 5.78 kg) > AA (47.45 ± 0.61 kg).

Although average daily gain (ADG) did not differ significantly among genotypes (p = 0.632), the AG genotype recorded the highest value (0.55 ± 0.12 kg), followed by GG (0.54 ± 0.07 kg) and AA (0.48 ± 0.11 kg). Similarly, average daily feed intake (ADFI) did not differ significantly (p = 0.149), though GG (5.45 ± 0.44 kg) and AG (5.39 ± 0.75 kg) showed numerically higher values than AA (4.63 ± 0.49 kg). Feed conversion ratio (FCR) also showed no significant difference (p = 0.977) across genotypes: GG (10.24 ± 1.16), AG (10.25 ± 2.62), and AA (9.95 ± 2.67). Residual feed intake (RFI) did not differ significantly (p = 0.685), with the lowest (i.e., most efficient) value in GG (-0.10 ± 0.40), followed by AG (0.05 ± 0.46) and AA (0.06 ± 0.55). Dry matter digestibility as estimated by the AIA index also showed no significant difference (p = 0.881) among genotypes: GG (44.16± 5.95), AG (44.04±4.56), and AA (42.57±2.14). Overall, the GG genotype was associated with more favorable values in several key growth and feed efficiency traits, though not all comparisons were statistically significant. Full results, including exact p-values for all parameters, are provided in Tables 3 and 4.

Association of leptin-related meat quality with essential amino acids supplementation

Meat quality analysis revealed a statistically significant difference in the red color component (a*) among genotypes (p = 0.049), with AG (12.87 ± 1.57) showing the highest value, followed by AA (12.07 ± 2.64) and GG (10.95 ± 1.74). No significant differences were observed for lightness (L*, p = 0.665) and yellowness (b*, p = 0.689). The ultimate pH also did not differ significantly among genotypes (p = 0.552). Measurements of tenderness did not show significant differences in either raw (p = 0.975) or cooked meat (p = 0.179) shear force. AG genotypes showed the highest raw shear force (1.03 ± 0.19), while cooked meat shear force was highest in GG (2.05 ± 0.23). Cooking loss values were also statistically non-significant (p = 0.640), with AG (30.73 ± 3.22), GG (32.00 ± 2.62), and AA (31.41 ± 4.50). Water holding capacity (WHC) did not significantly vary (p = 0.244) between genotypes: GG (29.99 ± 2.91), AG (27.46 ± 3.26), and AA (30.48 ± 9.03).

Table 4: Association of LEP gene polymorphism related to meat quality with supplementation of essential amino acids in Bali cattle.

	Genotype (µ±STD) (n=27)
Traits	AA (n=3)	AG (n=15)	GG (n=9)	P-value
Colour
L*	37.38±6.04	39.25±3.95	40.12±5.06	0.665
a*	12.07±2.64a	12.87±1.57a	10.95±1.74a	*0.049
b*	7.78±1.87	6.99±1.11	7.07±1.78	0.689
pH	5.44±0.40	5.37±0.24	5.51±0.34	0.552
Tenderness
Raw meat	1.00±0.07	1.03±0.19	1.02±0.08	0.975
Cooked meat	1.68±0.51	2.05±0.23	1.95±0.36	0.179
Cooking loss (%)	31.41±4.50	30.73±3.22	32.00±2.62	0.640
WHC (%mgH2O)	30.48±9.03	27.46±3.26	29.99±2.91	0.244

*significant at p<0.05; ab Means with the same letter are not significantly different.

Association of leptin-related fatty acid composition with essential amino acids supplementation

Significant differences in fatty acid composition were observed among genotypes. Within the saturated fatty acid (SFA) group, myristic acid (C14:0) differed significantly (p= 0.005), with highest levels in GG (15.29 ± 5.46). Heptadecanoic acid (C17:0) also differed significantly (p= 0.042). Lauric acid (C12:0) and heneicosanoic acid (C21:0) approached significance (p= 0.051). No significant differences were found for palmitic acid (C16:0, p= 0.140) or arachidic acid (C20:0, p= 0.120), though AA had numerically higher levels. In the MUFA group, cis-11-eicosenoic acid (C20:1) showed a significant difference (p= 0.024), while total MUFA content was not significantly different (p= 0.074). For PUFA, linoleic acid (C18:2n6c) (p = 0.002), EPA (C20:5n3, p = 0.045), DHA (C22:6n3, p = 0.006), and cis-13,16-docosadienoic acid (C22:2, p= 0.038) all differed significantly. Total PUFA content also showed a significant difference among genotypes (p= 0.044), with the highest in GG, followed by AG and AA. To further illustrate these findings, a heatmap visualization was generated to represent the distribution of fatty acid concentrations across the three leptin genotypes (Figure 2). The heatmap highlights distinct clustering patterns and fatty acid profiles that vary with genotype.

Messenger RNA (mRNA) expression of the leptin gene by qRT-PCR

Analysis of mRNA expression using qRT-PCR revealed a statistically significant difference between residual feed intake (RFI) groups (p= 0.041). Cattle with high RFI (HRFI), primarily of the AG genotype, exhibited higher leptin mRNA expression levels compared to low RFI (LRFI) cattle, which were predominantly of the GG genotype. The AA genotype was not represented in either group. Although only six animals (n= 3 per group) were analyzed, the observed differences suggest a potential regulatory response related to feed efficiency. Gene expression data corresponding to RFI groupings are visually presented in Figure 3. These results should be interpreted with caution due to the limited sample size, and further studies with larger cohorts are recommended to validate the findings.

Leptin (LEP) gene polymorphism plays a pivotal role in regulating energy balance and lipid metabolism, which directly influences growth performance and meat quality in livestock. The present study identified three genotypes AA, AG, and GG with AG being the most prevalent in Bali cattle. This genetic diversity corresponds with previous findings in tropical cattle populations and reinforces the LEP gene’s utility as a molecular marker for breeding programs (Kurlyana et al., 2023; Putra and Indriastuti, 2018).

Bali cattle with the GG genotype showed significantly higher initial, final, and metabolic body weights compared to other genotypes, suggesting a growth performance advantage. These findings are in line with prior studies linking the GG genotype to improved growth metrics in various beef cattle breeds (Choudhary et al., 2005; Nkrumah et al., 2005). The enhanced growth performance may be attributed to the influence of leptin on energy metabolism and its downstream activation of growth-related signaling pathways, including JAK/STAT and mTOR (Hamrick, 2017; Wen et al., 2021; Zhou et al., 2016).

Despite non-significant differences in average daily feed intake (ADFI) and feed conversion ratio (FCR), the GG genotype consistently displayed the lowest residual feed intake (RFI), indicating greater metabolic efficiency. This aligns with findings by Mota et al. (2017) and Ujan et al. (2024), highlighting RFI as a robust indicator of feed efficiency. The upregulation of metabolic genes induced by lysine–methionine supplementation is thought to support this efficiency by improving nutrient absorption and utilization (Zhao et al., 2019; Mota et al., 2022).

Feed digestibility measured using the acid-insoluble ash (AIA) method did not vary significantly across genotypes, suggesting that post-absorptive metabolic processes are likely responsible for differences in feed efficiency. While the AIA method is commonly employed, its limited sensitivity to post-absorptive variation is recognized, suggesting future studies should consider using multi-marker digestibility systems for improved precision (Cantalapiedra-Hijar et al., 2018; Connor et al., 2013; Kim et al., 2020).

Regarding meat quality, only the a* value (redness) differed significantly among genotypes, with AG showing the highest value. This parameter, associated with myoglobin content and oxidative stability, is a critical attribute influencing consumer preference (Henriott et al., 2020; Yu et al., 2017). Other meat quality traits such as pH, tenderness, cooking loss, and water holding capacity showed no significant differences, indicating similar muscle structure and postmortem metabolism across genotypes (Wu et al., 2020; Zalewska et al., 2021).

Fatty acid profile analysis revealed significant genotype-dependent differences in lipid metabolic profiles. The GG genotype exhibited higher concentrations of saturated fatty acids, particularly myristic (C14:0) and heptadecanoic acids (C17:0), implying increased lipogenesis and intramuscular fat accumulation. This is consistent with leptin’s regulatory role in activating lipogenic enzymes such as acetyl-CoA carboxylase (ACC) and fatty acid synthase (FAS) (He et al., 2020; Park and Ahima, 2014).

Supplementation with lysine and methionine further enhances these pathways, promoting intramuscular fat deposition (Haruna et al., 2021; Otto et al., 2022; Zhao et al., 2019). This observation may reflect a metabolic inclination of GG genotypes toward enhanced fat synthesis under targeted amino acid feeding regimes. These patterns align with the known role of leptin in adipogenesis and energy balance, especially when nutrient input modulates gene expression (Mota et al., 2022). Conversely, Bali cattle with the AA genotype showed higher levels of polyunsaturated fatty acids (PUFAs), including linoleic acid (C18:2n6c), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA). These long-chain PUFAs contribute to improved nutritional value of meat and are associated with cardiovascular and anti-inflammatory benefits in human health (Djuricic and Calder, 2023; Wu et al., 2020).

The enrichment of these PUFAs in AA genotypes suggests more efficient desaturation and elongation processes, which may be regulated through genotype-specific LEP expression pathways interacting with nutritional signals (Vailati-Riboni et al., 2019; Wood and Enser, 2017). These results suggest increased desaturase and elongase activity, potentially regulated by LEP gene expression and influenced by essential amino acid availability (Lee et al., 2017). Detailed findings for 43 fatty acids across genotypes (AA, AG, GG) are presented in Table 5, which reports mean values ± standard deviation (% total fat) alongside ANOVA-derived p-values. Additional, profiles of fatty acid composition by genotype are provided in Table 6, which further corroborates the existence of genotype-specific variations. Fatty acids are grouped by

Table 5: Association of LEP gene polymorphism related to Fatty acid composition with supplementation of essential amino acids in Bali cattle. The table includes 43 identified fatty acids categorized into saturated, monounsaturated, and polyunsaturated groups. Statistical differences were tested using ANOVA with p-values presented for each comparison.

Fatty acid	Genotype (µ ± STD) (n=27)			P-value
Fatty acid	AA (n=3)	AG (n=15)	GG (n=9)	P-value
Fat content	15.76±4.92	26.76±8.44	29.31±9.06	0.132
Saturated fatty acid	4.34±3.77	16.13±31.78	2.87±4.38	0.408
Capric acid, C4:0	0.00±0.00	0.00±0.00	0.00±0.00	0.000
Capric acid, C6:0	0.003±0.005	0.01±0.01	0.01±0.01	0.288
Capric acid, C8:0	0.02±0.01	0.02±0.01	0.02±0.02	0.702
Capric acid, C10:0	0.07±0.03	0.15±0.09	0.16±0.08	0.223
Capric acid, C11:0	0.00±0.00	0.001±0.003	0.002±0.004	0.437
Lauric acid, C12:0	0.09±0.02b	0.18±0.09ab	0.25±0.11a	0.051
Tridecanoic acid, C13:0	0.06±0.05	0.04±0.02	0.09±0.09	0.179
Myristic acid, C14:0	4.56±0.83b	10.93±4.37a	15.29±5.46a	**0.005
Pentadecanoic acid, C15:0	4.03±5.37	1.73±0.86	2.11±1.41	0.172
Palmitic acid, C16:0	30.81±2.87a	16.66±14.35ab	13.33±11.18b	0.140
Heptadecanoic acid, C17:0	1.42±0.63b	3.90±1.58a	4.75±2.42a	*0.042
Stearic acid, C18:0	37.73±4.64	33.63±20.08	28.29±15.85	0.672
Arachidic acid, C20:0	0.33±0.06b	0.81±0.65ab	1.10±0.43a	0.120
Heneicosanoic acid, C21:0	0.03±0.01b	0.05±0.03ab	0.08±0.05a	0.051
Behenic acid, C22:0	0.07±0.02	0.07±0.04	0.11±0.05	0.068
Tricosanoic acid, C23:0	0.03±0.01	0.04±0.02	0.05±0.02	0.129
Lignoceric acid, C24:0	0.04±0.02	0.07±0.16	0.04±0.03	0.824
Unsaturated acid	2.04±1.41a	0.89±0.39b	0.99±0.76b	*0.034
Monounsaturated acid	79.31±4.94a	68.31±8.48b	65.77±9.12b	0.074
Myristic acid, C14:1	0.09±0.02	1.01±2.46	0.43±0.22	0.645
Pentadecanoic acid, C15:1	0.003±0.006	0.01±0.01	0.40±1.16	0.382
Palmitoleic acid, C16:1	1.25±0.46b	2.83±1.31a	3.17±1.38a	0.100
cis-10-Heptadecanoic acid, C17:1	0.22±0.21	0.41±0.38	0.45±0.38	0.635
Elaidic acid, C18:1n9t	0.18±0.31	2.15±4.50	1.38±0.42	0.632
Oleic Acid, C18:1n9c	11.94±3.30	19.34±9.12	22.42±9.11	0.221
cis-11-Eicosenoic Acid, C20:1	0.04±0.01b	0.11±0.05a	0.13±0.04a	*0.024
Erucic acid, C22:1	0.003±0.006	0.01±0.02	0.01±0.01	0.533
Nervonic acid, C24:1	0.003±0.006	0.01±0.03	0.00±0.00	0.723
Polyunsaturated acid	13.73±3.88b	25.87±8.26a	28.32±9.11a	*0.044
Linolelaidic acid, C18:2n9t	0.03±0.06	0.11±0.21	0.13±0.11	0.696
Linoleic acid, C18:2n6c	1.01±0.77a	0.33±0.15b	0.30±0.20b	**0.002
v-Linolenic acid, C18:3n6	0.01±0.01	0.01±0.02	0.01±0.01	0.764
Linolenic acid, C18:3n3	0.00±0.00	0.02±0.02	0.02±0.03	0.024
cis-11,14-Eicosedienoic acid, C20:2	0.00±0.00	0.003±0.011	0.01±0.02	0.437
cis-8,11,14-Eicosetrienoic acid, C20:3n6	0.19±0.13a	0.07±0.04b	0.08±0.10b	0.074
cis-11,14,17-Eicosatrienoic acid, C20:3n9	0.00±0.00	0.001±0.003	0.00±0.00	0.687
Arachidonic acid, C20:4n6	0.37±0.26	0.20±0.12	0.26±0.29	0.369
cis-13,16-Docosadienoic acid, C22:2	0.01±0.02a	0.00±0.00b	0.002±0.007b	*0.038
cis-5,8,11,14,17-Eicosapentaenoic Acid, C20:5n3	0.37±0.23a	0.13±0.09b	0.16±0.18b	*0.045
cis-4,7,10,13,16,19-Docosahexaenoic acid, C22:6n3	0.04±0.03a	0.01±0.01b	0.01±0.02b	**0.006
Total Fat	95.00±0.00	94.68±0.51	94.66±0.49	0.540

*significant at p<0.05; ** significant at p<0.01; ab Means with the same letter are not significantly different.

Table 6: Profile of fatty acid composition (mean ± STD, % total fat) by LEP genotype (AA, AG, GG) in Bali cattle under essential amino acid supplementation.

Fatty acid	AA (n=3)	AG (n=15)	GG (n=9)	P-value
Lauric acid, (C12:0)	0.09±0.02	0.18±0.09	0.25±0.11	0.051
Myristic acid, (C14:0)	4.56±0.83	10.93±4.37	15.29±5.46	0.005
Palmitic acid, (C16:0)	30.81±2.87	16.66±14.35	13.33±11.18	0.140
Heptadecanoic acid, (C17:0)	1.42±0.63	3.90±1.58	4.75±2.42	0.042
Stearic acid, (C18:0)	37.73±4.64	33.63±20.08	28.29±15.85	0.672
Arachidic acid, (C20:0)	0.33±0.06	0.81±0.65	1.10±0.43	0.120
Heneicosanoic acid, (C21:0)	0.03±0.01	0.05±0.03	0.08±0.05	0.051
Palmitoleic acid, (C16:1)	1.25±0.46	2.83±1.31	3.17±1.38	0.100
cis-11-Eicosenoic Acid, (C20:1)	0.04±0.01	0.11±0.05	0.13±0.04	0.024
Total MUFA	79.31±4.94	68.31±8.48	65.77±9.12	0.074
Linoleic acid, (C18:2n6c)	1.01±0.77	0.33±0.15	0.30±0.20	0.002
Eicosetrienoic acid, (C20:3n6)	0.19±0.13	0.07±0.04	0.08±0.10	0.074
EPA, (C20:5n3)	0.37±0.23	0.13±0.09	0.16±0.18	0.045
DHA, (C22:6n3)	0.04±0.03a	0.01±0.01	0.01±0.02	0.006
cis-13,16-Docosadienoic acid, C22:2	0.01±0.02	0.00±0.00	0.002±0.007	0.038
Total PUFA	13.73±3.88	25.87±8.26	28.32±9.11	0.044

class SFAs, MUFAs, and PUFAs enabling structured comparisons and interpretation. Including the full dataset within the main manuscript enhances data accessibility and scientific transparency, in line with publishing best practices (Wadood et al., 2025). The observed genotype-specific FA distribution underscores the influence of genetic variation on nutrient-driven lipid metabolism. Structured reporting of these findings strengthens reproducibility and aligns with evidence from nutrigenomic studies that emphasize the dynamic relationship between gene variants, dietary modulation, and metabolic phenotype (Zhang et al., 2022). This approach supports more targeted investigation into LEP-driven lipid pathways and their broader implications for beef quality in tropical cattle breeds.

LEP gene expression analysis revealed significantly higher mRNA levels in the HRFI group (p= 0.041), which consisted entirely of AG genotype individuals. In contrast, the LRFI group was predominantly GG. The elevated LEP expression in HRFI cattle may indicate a compensatory regulatory response to energy imbalance. This is consistent with studies in cattle and other species showing increased LEP expression in animals with higher adiposity and lower feed efficiency (Liu et al., 2023; Mota et al., 2017; Pérez-Montarelo et al., 2013). Although the sample size for qRT-PCR analysis was small (n= 3 per RFI group), the observed gene expression trends are biologically meaningful. These results suggest leptin expression could serve as a dynamic biomarker of energy status and feed efficiency, though protein-level validation is needed.

Conclusion

This study confirms that leptin (LEP) gene polymorphisms and mRNA expression are significantly associated with key production traits in Bali cattle, including growth, feed efficiency, meat quality, and fatty acid composition. Genotypic differences influenced both energy utilization and lipid metabolism, with the GG genotype favoring improved feed efficiency, and the AA genotype linked to higher levels of beneficial polyunsaturated fatty acids (PUFAs), such as DHA, EPA, and linoleic acid. Elevated LEP expression in high-RFI cattle (primarily AG genotype) suggests a compensatory mechanism associated with reduced metabolic efficiency. These results underscore the relevance of the LEP gene as a candidate marker for precision breeding programs aimed at enhancing both productivity and meat quality in tropical cattle systems. Future work should include functional validation through LEP knockdown or overexpression, protein-level assessment, and expanded sampling to capture broader phenotypic variability.

Acknowledgements

The authors would like to acknowledge the field technicians who assisted in the execution of the research activities. Appreciation is also extended to the staff of the Integrated Biotechnology Laboratory, Hasanuddin University; the Animal Breeding and Molecular Genetics Laboratory, Faculty of Animal Science, Bogor Agricultural University; and the Hasanuddin University Medical Research Center (HUM-RC), Faculty of Medicine, Hasanuddin University, for their technical support and laboratory facilities throughout the study. The authors are also grateful to Zhejiang Vega Bio-technology Co., Ltd for sponsoring the research materials used in this study. Furthermore, financial support provided by the Indonesia Endowment Fund for Education (LPDP) is gratefully acknowledged.

Novelty Statement

This study is the first to integrate leptin (LEP) gene polymorphism and mRNA expression analysis with the supplementation of essential amino acids (lysine–methionine) to evaluate their combined effects on growth traits, feed efficiency, meat quality, and fatty acid composition in Bali cattle. It presents novel insights into the nutrigenomic interactions between genetic markers and amino acid-driven metabolic regulation, especially in an indigenous tropical cattle breed. The findings contribute a unique basis for the development of precision breeding and nutritional strategies aimed at enhancing livestock productivity and meat quality within tropical production systems.

Author’s Contribution

H: Collecting data, animal maintenance, and data analysis.

LR: Assembled research design and review manuscript

MIAD: Assembled research design, data analysis, and manuscript writing

AN: Assembled research design and reviewed the manuscript.

AG: Reviewed and advised on manuscript improvement and data analysis.

SRAB: Advised on fine-tuning the manuscript.

IS: Contributed to manuscript refinement.

MH: Provided suggestions for manuscript enhancement.

Generative AI and AI-assisted technology statement

AI-assisted technologies were used solely for non-scientific purposes, such as grammar correction, reference formatting, and minor figure editing (e.g., improving resolution). No AI tools were used for data analysis, interpreting results, or scientific writing. All findings and conclusions in this manuscript are the authors’ sole responsibility

Conflict of interest

The authors have declared no conflict of interest.

References

Alfonso CJM , Kozole C, Htoo JK , Huber LA (2023). 272 The Effect of Increasing Standardized Ileal Digestible Methionine Intake on Whole-Body Nitrogen Retention and Plasma Amino Acids, Homocysteine, and Glutathione of Gilts in Late Gestation. Journal of Animal Science, Volume 101, Issue Supplement_2, November 2023, Pages 160–162. https://doi.org/10.1093/jas/skad341.179

Anugratama LE, Hartatik T (2019). Short communication: Identification of leptin gene in crossbred beef cattle. Biodiv. J. Biol. Diver., 21(1): 226–230. https://doi.org/10.13057/biodiv/d210129

Avondo M, Di Trana A, Valenti B, Criscione A, Bordonaro S, De Angelis A, Giorgio D, Di Gregorio P (2019). Leptin gene polymorphism in goats fed with diet at different energy level: Effects on feed intake, milk traits, milk fatty acids composition, and metabolic state. Animals, 9(7): 1–9. https://doi.org/10.3390/ani9070424

Bligh EG, Dyer WJ (1959). The national research council of canada a rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol., 37: 911–917. www.nrcresearchpress.com, https://doi.org/10.1139/o59-099

Cantalapiedra-Hijar G, Abo-Ismail M, Carstens GE, Guan LL, Hegarty R, Kenny DA, McGee M, Plastow G, Relling A, Ortigues-Marty I (2018). Review: Biological determinants of between-animal variation in feed efficiency of growing beef cattle. Animal, 12(s2): s321–s335. https://doi.org/10.1017/S1751731118001489

Choudhary V, Kumar P, BhattacharyaTK, Bhushan B, Sharma A (2005). DNA polymorphism of leptin gene in Bos indicus and Bos taurus cattle. Genet. Mol. Biol., 28(4): 740–742. https://doi.org/10.1590/S1415-47572005000500014

Connor EE, Hutchison JL, Norman HD, Olson KM, Van Tassell CP, Leith JM, Baldwin RL (2013). Use of residual feed intake in Holsteins during early lactation shows potential to improve feed efficiency through genetic selection1. J. Anim. Sci., 91(8): 3978–3988. https://doi.org/10.2527/jas.2012-5977

Dagong MIA, Herman R, Sumantri C, Noor RR, Yamin M (2012). Karakteristik karkas dan sifat fisik daging domba ekor tipis (DET) berdasarkan variasi genotip gen kalpastatin (CAST) (Lokus intron 5–ekson 6). J. Ilmu Ternak dan Vet., 17(1): 13–24. https://doi.org/10.14334/jitv.v17i1.708

Djuricic I, Calder PC (2023). Pros and cons of long-chain omega-3 polyunsaturated fatty acids in cardiovascular health. Ann. Rev. Pharmacol. Toxicol., 63(1): 383–406. https://doi.org/10.1146/annurev-pharmtox-051921-090208

Friedman J (2014). 20 years of leptin: Leptin at 20: An overview. J. Endocrinol., 223(1): T1–T8. https://doi.org/10.1530/JOE-14-0405

Gunawan A, Listyarini K, Harahap RS, Jakaria RK, Sumantri C, Inounu I, Akter SH, Aminul IM, Uddin MJ (2021). Hepatic transcriptome analysis identifies genes, polymorphisms and pathways involved in the fatty acids metabolism in sheep. PLoS One, 16(12 December): 1–26. https://doi.org/10.1371/journal.pone.0260514

Hamm R (1961). Biochemistry of meat hydration. pp. 355–463. https://doi.org/10.1016/S0065-2628(08)60141-X

Hamrick MW (2017). Role of the Cytokine-like Hormone Leptin in Muscle-bone Crosstalk with Aging. J. Bone Metab., 24(1): 1. https://doi.org/10.11005/jbm.2017.24.1.1

Harahap, R. S., Gunawan, A., Endrawati, Y. C., Darusman, H. S., Andersson, G., and Noor, R. R. (2024). A comprehensive study of CYP2E1 and its role in carcass characteristics and chemical lamb meat quality in different Indonesian sheep breeds. PLoS One, 19(9), 1–22. https://doi.org/10.1371/journal.pone.0310336

Hartl DL, Clark AG (1990). Principles of population genetics. Biometrics, 46(2): 546. https://doi.org/10.2307/2531471

Haruna IL, Zhou H, Hickford JGH (2021). Variation in bovine leptin gene affects milk fatty acid composition in New Zealand Holstein Friesian × Jersey dairy cows. Arch. Anim. Breed., 64(1): 245–256. https://doi.org/10.5194/aab-64-245-2021

He ML, Stanford K, Dugan MER, Marquess L, McAllister TA (2020). Association of leptin genotype with growth performance, adipocyte cellularity, meat quality, and fatty acid profile in beef steers fed flaxseed or high-oleate sunflower seed diets with or without triticale dried distiller’s grains. J. Anim. Sci., 98(4). https://doi.org/10.1093/jas/skaa104

Henriott ML, Herrera NJ, Ribeiro FA, Hart KB, Bland NA, Calkins CR (2020). Impact of myoglobin oxygenation level on color stability of frozen beef steaks. J. Anim. Sci., 98(7). https://doi.org/10.1093/jas/skaa193

Hikmawaty J, Gunawan A, Dagong MIA, Rahim L (2020). The mitochondrial DNA D-loop diversity of Bali cattle in breeding centers. IOP Conf. Ser. Earth Environ. Sci., 492(1). https://doi.org/10.1088/1755-1315/492/1/012110.

Jakaria KH, Priyanto R, Baihaqi M, Ulum MF (2017). Prediction of meat quality in Bali cattle using ultrasound imaging. J. Indones. Trop. Anim. Agric., 42(2): 59–65. https://doi.org/10.14710/jitaa.42.2.59-65

Jin X, Li Y, Zhang P, Wu S (2022). Lysine–methionine supplementation improves feed efficiency and nitrogen retention in finishing steers. Trans. Anim. Sci., 6(txac118).

Jump DB (2002). The biochemistry of n-3 polyunsaturated fatty acids. J. Biol. Chem., 277(11): 8755–8758. https://doi.org/10.1074/jbc.R100062200

Kawaguchi F, Okura K, Oyama K, Mannen H, Sasazaki S (2017). Identification of leptin gene polymorphisms associated with carcass traits and fatty acid composition in Japanese Black cattle. Anim. Sci. J., 88(3): 433–438. https://doi.org/10.1111/asj.12672

Kim BG, Lee SA, Park KR, Stein HH (2020). At least 3 days of adaptation are required before indigestible markers (chromium, titanium, and acid insoluble ash) are stabilized in the ileal digesta of 60-kg pigs, but values for amino acid digestibility are affected by the marker. J. Anim. Sci., 98(2). https://doi.org/10.1093/jas/skaa027

Kind T, Fiehn O (2010). Advances in structure elucidation of small molecules using mass spectrometry. Bioanal. Rev., 2(1): 23–60. https://doi.org/10.1007/s12566-010-0015-9

Kleiber M (1932). Body size and metabolism. Hilgardia, 6(11): 315–353. https://doi.org/10.3733/hilg.v06n11p315

Koch RM, Swiger LA, Chambers D, Gregory KE (1963). Efficiency of feed use in beef cattle. J. Anim. Sci., 22(2): 486–494. https://doi.org/10.2527/jas1963.222486x

Kurlyana T, Hartatik T, Sumadi (2023). Association between leptin gene polymorphism and growth traits in Bali cattle. J. Indones. Trop. Anim. Agric., 48(1): 1–9. https://doi.org/10.14710/jitaa.48.1.1-9

Lee SH, Gondro C, Quinn K, van der Werf JHJ, Gurman PM (2017). Fatty acid composition of bovine adipose tissues relates to genetic variants in genes involved in fat metabolism. J. Anim. Sci., 95(6): 2473–2482.

Li T, Bai H, Fang H, Yang L, Yan P (2022). Growth hormone inhibits adipogenic differentiation and induces browning in bovine subcutaneous adipocytes. Growth Horm. IGF Res., 66: 101498. https://doi.org/10.1016/j.ghir.2022.101498

Li Y, Yang M, Lou A, Yun J, Ren C, Li X, Xia G, Nam K, Yoon D, Jin H, Seo K, Jin X (2022). Integrated analysis of expression profiles with meat quality traits in cattle. Sci. Rep., 12(1): 5926. https://doi.org/10.1038/s41598-022-09998-w

Liao SF, Wang T, Regmi N (2015). Lysine nutrition in swine and the related monogastric animals: Muscle protein biosynthesis and beyond. Springer Plus, 4(1): 147. https://doi.org/10.1186/s40064-015-0927-5

Listyarini K, Sumantri C, Rahayu S, Uddin MJ, Gunawan A (2022). Association study and expression analysis of olfactomedin like 3 gene related to meat quality, carcass characteristics, retail meat cut, and fatty acid composition in sheep. Anim. Biosci., 35(10): 1489–1498. https://doi.org/10.5713/ab.21.0406

Liu Z, Xiao T, Liu H (2023). Leptin signaling and its central role in energy homeostasis. Front. Neurosci., 17. https://doi.org/10.3389/fnins.2023.1238528

Martínez Y, Li X, Liu G, Bin P, Yan W, Más D, Valdivié M, Hu CAA, Ren W, Yin Y (2017). The role of methionine on metabolism, oxidative stress, and diseases. Amino Acids, 49(12): 2091–2098. https://doi.org/10.1007/s00726-017-2494-2

Morrison WR, Smith LM (1964). Preparation of fatty acid methyl esters and dimethylacetals from lipids with boron fluoride–methanol. J. Lipid Res., 5(4): 600–608. https://doi.org/10.1016/S0022-2275(20)40190-7

Mota LFM, Bonafé CM, Alexandre PA, Santana MH, Novais FJ, Toriyama E, Pires AV, da Luz SS, Leme PR, Ferraz JBS, Fukumasu H (2017). Circulating leptin and its muscle gene expression in Nellore cattle with divergent feed efficiency. J. Anim. Sci. Biotechnol., 8(1): 71. https://doi.org/10.1186/s40104-017-0203-3

Mota LFM, Santos SWB, Júnior GAF, Bresolin T, Mercadante MEZ, Silva JAV, Cyrillo JNSG, Monteiro FM, Carvalheiro R, Albuquerque LG (2022). Meta-analysis across Nellore cattle populations identifies common metabolic mechanisms that regulate feed efficiency-related traits. BMC Genom, 23(1): 424. https://doi.org/10.1186/s12864-022-08671-w

Mukherjee A, Samanta I, Joardar SN (2023). Biology and significance of leptin in animal production: A perspective. Indian J. Anim. Sci., 93(12): 1131–1139. https://doi.org/10.56093/ijans.v93i12.116875

Munoz-Alfonso CJ, Kozole C, Htoo JK, Huber LA (2023). 272 the effect of increasing standardized ileal digestible methionine intake on whole-body nitrogen retention and plasma amino acids, homocysteine, and glutathione of gilts in late gestation. J. Anim. Sci., 101(Suppl. 2): 160–162. https://doi.org/10.1093/jas/skad341.179

Münzberg H, Heymsfield SB (2019). New insights into the regulation of leptin gene expression. Cell Metab., 29(5): 1013–1014. https://doi.org/10.1016/j.cmet.2019.04.005

Muroya S, Ueda S, Komatsu T, Miyakawa T, Ertbjerg P (2020). Meatabolomics: Muscle and meat metabolomics in domestic animals. Metabolites, 10(5). https://doi.org/10.3390/metabo10050188

National Research Council (2000). Nutrient requirements of beef cattle: Seventh revised edition: Update 2000. In: Nutrient requirements of beef cattle (7th ed.). National Academies Press.

Nei M, Kumar S (2000). Molecular evolution and phylogenetics. Oxford University PressNew York, NY. https://doi.org/10.1093/oso/9780195135848.001.0001

Nkrumah JD, Li C, Yu J, Hansen C, Keisler DH, Moore SS (2005). Polymorphisms in the bovine leptin promoter associated with serum leptin concentration, growth, feed intake, feeding behavior, and measures of carcass merit1. J. Anim. Sci., 83(1): 20–28. https://doi.org/10.2527/2005.83120x

Nugroho T, Widi TSM, Maharani D (2022). The potency of leptin gene as a selection marker of economic traits for madura cattle: Preliminary study. Proc. 9th Int. Semin. Trop. Anim. Prod. (ISTAP 2021), 18(Istap 2021): 231–237. https://doi.org/10.2991/absr.k.220207.048

Oecd (1997). Unclassified ENV/MC/CHEM(98)17 OECD series on principles of good laboratory practice and compliance monitoring number 1 OECD principles on good laboratory practice (as revised in 1997) 61011 Document complet disponible sur Olis dans son format d’origine C. 1: 1–41. https://ntp.niehs.nih.gov/sites/default/files/iccvam/suppdocs/feddocs/oecd/oecd_glpcm.pdf

Otto JR, Mwangi FW, Pewan SB, Adegboye OA, Malau-Aduli AEO (2022). Lipogenic gene single nucleotide polymorphic DNA markers associated with intramuscular fat, fat melting point, and health-beneficial omega-3 long-chain polyunsaturated fatty acids in australian pasture-based bowen genetics forest pastoral angus, hereford. Genes, 13(8): 1411. https://doi.org/10.3390/genes13081411

Park HK, Ahima RS (2014). Leptin signaling. F1000Prime Reports, pp. 6. https://doi.org/10.12703/P6-73

Park SJ, Beak SH, Jung DJS, Kim SY, Jeong IH, Piao MY, Kang HJ, Fassah DM, Na SW, Yoo SP, Baik M (2018). Genetic, management, and nutritional factors affecting intramuscular fat deposition in beef cattle. A review. Asian-Australas. J. Anim. Sci., 31(7): 1043–1061. https://doi.org/10.5713/ajas.18.0310

Pérez-Montarelo D, Fernández A, Barragán C, Noguera JL, Folch JM, Rodríguez MC, Óvilo C, Silió L, Fernández AI (2013). Transcriptional characterization of porcine leptin and leptin receptor genes. PLoS One, 8(6): e66398. https://doi.org/10.1371/journal.pone.0066398

Purwantara B, Noor RR, Andersson G, Rodriguez-Martinez H (2012). Banteng and Bali cattle in Indonesia: Status and forecasts. Reprod. Domest. Anim., 47(Suppl. 1): 2–6. https://doi.org/10.1111/j.1439-0531.2011.01956.x

Putra WPB, Indriastuti R (2018). Leptin gene as potential gene for molecular selection on cattle in Indonesia. Indones. Bull. Anim. Vet. Sci., 27(3): 105. https://doi.org/10.14334/wartazoa.v27i3.1579

Sedykh TA, Kalashnikova LA, Gizatullin RS, Kosilov VI (2020). Effects of leptin gene polymorphism on beef cattle performance. Russ. Agric. Sci., 46(6): 614–618. https://doi.org/10.3103/S1068367420060166

Shackelford SD, Wheeler TL, Koohmaraie M (1997). Repeatability of tenderness measurements in beef round muscles. J. Anim. Sci., 75(9): 2411–2416. https://doi.org/10.2527/1997.7592411x

Silver N, Best S, Jiang J, Thein SL (2006). Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol. Biol., 7(1): 33. https://doi.org/10.1186/1471-2199-7-33

Syarifulaya N, Sriasih M (2015). Identifikasi keragaman gen leptin pada sapi bali dan kambing kacang (Polymorphism of leptin gene in bali cattle and kacang goat). J. Ilmu Teknol. Petern. Indones., 1(1): 47–54.

Ujan JA, Habib SS, Fazio F (2024). Feed intake (RFI) for predicting feed. 10: 241–249.

Vailati-Riboni M, Xu T, Qadir B, Bucktrout R, Parys C, Loor JJ (2019). In vitro methionine supplementation during lipopolysaccharide stimulation modulates immunometabolic gene network expression in isolated polymorphonuclear cells from lactating Holstein cows. J. Dairy Sci., 102(9): 8343–8351. https://doi.org/10.3168/jds.2018-15737

Van Keulen J, Young BA (1977). Evaluation of acid-insoluble ash as a natural marker in ruminant digestibility studies. J. Anim. Sci., 44(2): 282–287. https://doi.org/10.2527/jas1977.442282x

Van Laack RLJ, Liu CH, Smith MO, Loveday HD (2000). Characteristics of pale, soft, exudative broiler breast meat. Poult. Sci., 79(7): 1057–1061. https://doi.org/10.1093/ps/79.7.1057

Wadood AA, Bordbar F, Zhang X (2025). Integrating omics approaches in livestock biotechnology: Innovations in production and reproductive efficiency. Front. Anim. Sci., 6. https://doi.org/10.3389/fanim.2025.1551244

Wang L, Raza SHA, Gui L, Li S, Liu X, Yang X, Wang S, Zan L, Zhao C (2022). Associations between UASMS2 polymorphism in leptin gene and growth, carcass and meat quality traits of cattle: A meta-analysis. Anim. Biotechnol., 33(2): 279–288. https://doi.org/10.1080/10495398.2020.1805327

Wen J, Zhou Y, Zhang Y, Chen H, Wang X (2021). Methionine activates mTOR to regulate protein synthesis in bovine satellite cells. J. Anim. Sci., 99(skab095).

Wolf C, Burgener M, Hübner P, Lüthy J (2000). PCR-RFLP analysis of mitochondrial DNA: Differentiation of fish species. LWT Food Sci. Technol., 33(2): 144–150. https://doi.org/10.1006/fstl.2000.0630

Wood JD, Enser M (2017). Manipulating the fatty acid composition of meat to improve nutritional value and meat quality. In: New aspects of meat quality. Elsevier. pp. 501–535. https://doi.org/10.1016/B978-0-08-100593-4.00023-0

Wu H, Xu L, Ballantyne CM (2020). Dietary and pharmacological fatty acids and cardiovascular health. J. Clin. Endocrinol. Metab., 105(4): 1030–1045. https://doi.org/10.1016/j.plefa.2020.102110

Wu S, Han J, Liang R, Dong P, Zhu L, Hopkins DL, Zhang Y, Luo X (2020). Investigation of muscle-specific beef color stability at different ultimate pHs. Asian-Australas. J. Anim. Sci., 33(12): 1999–2007. https://doi.org/10.5713/ajas.19.0943

Yi S, Wang J, Ye B, Yi X, Abudukelimu A, Wu H, Meng Q, Zhou Z (2025). Guanidinoacetic acid and methionine supplementation improve the growth performance of beef cattle via regulating the antioxidant levels and protein and lipid metabolisms in serum and liver. Antioxidants, 14(5): 559. https://doi.org/10.3390/antiox14050559

Yu QP, Feng DY, Xiao J, Wu F, He XJ, Xia MH, Dong T, Liu YH, Tan HZ, Zou SG, Zheng T, Ou XH, Zuo JJ (2017). Studies on meat color, myoglobin content, enzyme activities, and genes associated with oxidative potential of pigs slaughtered at different growth stages. Asian-Australas. J. Anim. Sci., 30(12): 1739–1750. https://doi.org/10.5713/ajas.17.0005

Zalewska M, Puppel K, Sakowski T (2021). Associations between gene polymorphisms and selected meat traits in cattle. A review. Anim. Biosci., 34(9): 425–1438. https://doi.org/10.5713/ab.20.0672

Zhang T, Wang T, Niu Q, Zheng X, Li H, Gao X, Chen Y, Gao H, Zhang L, Liu GE, Li J, Xu L (2022). Comparative transcriptomic analysis reveals region-specific expression patterns in different beef cuts. BMC Genom., 23(1): 1–11. https://doi.org/10.1186/s12864-022-08527-3

Zhao K, Liu W, Lin XY, Hu ZY, Yan ZG, Wang Y, Shi KR, Liu GM, Wang ZH (2019). Effects of rumen-protected methionine and other essential amino acid supplementation on milk and milk component yields in lactating Holstein cows. J. Dairy Sci., 102(9): 7936–7947. https://doi.org/10.3168/jds.2018-15703

Zhou X, He L, Wan D, Yang H, Yao K, Wu G, Wu X, Yin Y (2016). Methionine restriction on lipid metabolism and its possible mechanisms. Amino Acids, 48(7): 1533–1540. https://doi.org/10.1007/s00726-016-2247-7