Different expression of lipid metabolism-related genes in Shandong black cattle and Luxi cattle based on transcriptome analysis

The study investigates molecular mechanisms underlying fat deposition differences between Shandong black cattle, a newly bred composite population (derived from Japanese Black, Luxi, and Bohai Black cattle), and Luxi cattle, a notable local Chinese beef breed. Leveraging RNA-Seq, the authors aimed to characterize transcriptome differences in the longissimus dorsi muscle of 18-month-old individuals, identify differentially expressed genes and pathways related to lipid metabolism, and pinpoint candidate markers for improving beef quality traits such as intramuscular fat composition and flavor. The work seeks to provide a genetic basis for character improvement and the breeding of new cattle varieties with superior meat quality.

High-throughput RNA-Seq has become central for functional gene identification and regulatory network analysis, especially in non-model organisms where it outperforms traditional microarrays. Prior studies identified genes associated with beef quality and fat metabolism (e.g., COL1A2, COL1A1, SPP1, MMP2, MYH3, MYH8, CFD). RNA-Seq analyses in dairy cows linked liver expression changes to fat metabolism during negative energy balance. Other works used digital expression profiling to detect DEGs in adipose tissues linked to subcutaneous fat thickness, and muscle gene expression has been associated with marbling and intramuscular fat deposition in cattle. Collectively, these studies underscore the relevance of lipid metabolism pathways (PPAR, AMPK) and genes such as FABP4 and ADIPOQ in determining meat quality traits.

Experimental animals and management: Six 18‑month‑old healthy beef cattle were used: Shandong black cattle (n=3) and Luxi cattle (n=3), sourced from Shandong Black Cattle Technology Co., Ltd. and Dadi Luxi Cattle, respectively. Animals were managed under NY5127-2002 standards with green coarse feed plus concentrate, thrice-daily feeding, ad libitum water, regular brushing, ventilation, sanitation, and monthly disinfection. Phenotypes (body weight, size measures, backfat thickness, carcass weight) were recorded. Sample collection: Animals were slaughtered per GBT19477-2004 procedures. Longissimus dorsi muscle (between thoracic ribs 12–13) was excised, cut into 2–3 cm³ blocks, snap-frozen in liquid nitrogen (for RNA/protein) or fixed in 4% paraformaldehyde. Aseptic techniques were used during sampling. Fatty acid determination: Fatty acids were methylated following GB/T 9695.2-2008: saponification in NaOH/methanol, methylation with 12–15% BF3/methanol, extraction with saturated NaCl and isooctane, and GC analysis on the isooctane phase. Data were analyzed by one-way ANOVA in SPSS (means ± SD; P<0.05 significant). RNA extraction and sequencing: Total RNA from longissimus dorsi was extracted with TRIzol and treated with DNase I. Quality control used Agilent 2100 (RIN≥6.5) and NanoDrop (OD260/280=1.8–2.2; OD260/230≥2.0). Libraries were sequenced on Illumina HiSeq X Ten. GEO accessions: GSM4904154–GSM4904159. Read processing and mapping: Fastp (-Q 20 -P 90) removed adapters, poly-N, and low-quality reads. Clean reads were aligned to the Bos taurus ARS-UCD1.2 genome using HISAT2 (v2.1.0); Bowtie2 was referenced for indexing/mapping. StringTie (v2.1.3) assembled mapped reads. Differential expression: Expression was quantified as FPKM. Cuffdiff (Cufflinks v2.2.1) computed gene-level FPKM and tested for differential expression with FDR correction; DEGs were defined as FPKM≥1, |fold change|≥2, q<0.05. Functional enrichment and PPI: GO term assignment via Blast2GO; GO and KEGG enrichment with goatools and KOBAS v3.0; clusterProfiler (R) performed GO enrichment (hypergeometric test, q<0.05). PPI networks were inferred using STRING v11 and visualized in Cytoscape v3.7.1. qRT-PCR validation: Reactions (20 µL) included 1 µL cDNA, primers, and RNase-free water; cycling: 94°C 10 min; 40 cycles of 94°C 30 s, 60°C 30 s, 72°C 40 s. GAPDH served as reference; 2^-ΔΔCt quantified relative expression. Primers were listed for genes including FABP7, PCK1, PLIN1, LIPE, ADIPOQ, FABP4, PPARGC1A, COL6A2, COL4A5, MYL genes, ADCY1, ATGL, IPMK, AMPK, AQP7. Western blot: Tissue powders were lysed in RIPA, clarified by centrifugation, denatured, and resolved by SDS-PAGE, transferred, probed with primary antibodies (e.g., anti-beta-actin) and secondary antibodies, and detected by chemiluminescence. Densitometry was performed with ImageJ. Ethics approval was obtained from Qingdao Agricultural University IACUC, following NIH guidelines.

- Phenotypes: Shandong black cattle showed higher average carcass weight (409.67 kg vs 277.00 kg) and greater backfat thickness (1.73 cm vs 1.23 cm) than Luxi cattle. - Fatty acids: Shandong black cattle had a higher unsaturated:saturated fatty acid ratio (1.37:1) vs Luxi (1.24:1). Polyunsaturated fatty acids (PUFAs) were significantly higher in Shandong black cattle (3.97±0.55%) than Luxi (2.36±0.33%, P<0.05). Monounsaturated fatty acids were higher (54.28±3.77% vs 52.11±3.06%). Stearic acid was lower (9.72±0.915% vs 15.43±4.64%, P<0.05), linoleic acid was higher (2.52±0.47% vs 1.06±0.31%, P<0.05) in Shandong black cattle. - RNA-Seq metrics: 223.6 million clean reads total (B: 122.46M; L: 101.15M); mapping rate ~96.6–98.1%; Q30 base rate ~93.6–94.5%. - DEGs: 1,320 DEGs between groups (867 upregulated, 453 downregulated). GO terms enriched included regulation of Wnt signaling, cAMP metabolic process, fat cell differentiation. - Pathways: Regulation of lipolysis in adipocytes was significantly enriched among both up- and downregulated genes. Key lipid metabolism pathways included PPAR signaling and AMPK signaling, along with focal adhesion, ECM-receptor interaction, and adipocytokine signaling. - Representative genes: Network analyses highlighted PPARGC1A, ADCY4, ANKRD6, COL1A1, FABP4, ADIPOQ, PLIN1, PLIN2, and LIPE as central nodes. FABP4 and ADIPOQ shared seven common regulators (PLIN1, PLIN2, PPARGC1A, RXRA, PCK1, LEPR, LEP) and participated in lipolysis regulation, adipocytokine, and PPAR pathways. - Validation: qRT-PCR of selected DEGs showed strong concordance with RNA-Seq (R²=0.973). Western blots confirmed decreased ADIPOQ and FABP4 protein levels and increased MYLPF and MYL3 in agreement with transcript data. - Candidate markers: FABP4 and ADIPOQ were proposed as key candidate marker genes for fat deposition and beef quality improvement.

The study demonstrates that Shandong black cattle have a more favorable intramuscular fatty acid profile (higher UFA/SFA ratio and PUFA levels) than Luxi cattle, consistent with superior meat flavor traits. Transcriptomic comparisons revealed extensive differential expression linked to lipid metabolism, highlighting pathways central to adipogenesis and lipolysis (PPAR, AMPK, adipocytokine, and regulation of lipolysis in adipocytes). Network analyses centered on PPARGC1A and classic lipid metabolism genes (FABP4, ADIPOQ, PLIN1/2, LIPE), suggesting coordinated regulation of fatty acid transport, storage, and mobilization. The convergence of RNA-Seq, qRT-PCR, and protein-level data supports the robustness of these findings. These results directly address the research goal by identifying molecular drivers of fat deposition differences between breeds and offer actionable targets (e.g., FABP4, ADIPOQ) for marker-assisted selection. The insights further underscore breed-dependent genetic bases of intramuscular fat deposition and provide a framework for improving beef quality through genomic selection and breeding strategies.

Through RNA-Seq and integrative validation, this work identified 1,320 DEGs and pinpointed lipid metabolism pathways (PPAR, AMPK, adipocytokine, regulation of lipolysis in adipocytes) differentiating Shandong black cattle from Luxi cattle. FABP4 and ADIPOQ emerged as key candidate marker genes associated with intramuscular fat deposition and meat quality. Findings lay the groundwork for building molecular marker databases to support high-efficiency breeding and conservation of local germplasm. Future work should expand sample sizes, conduct independent biological validations, and identify functional SNPs in candidate genes to enable marker-assisted selection and deeper mechanistic understanding.

The study used a small cohort (n=6 total, three per breed), limiting statistical power and generalizability. RNA-Seq findings were validated by qRT-PCR and Western blot only within these same animals, lacking independent sample validation. Phenotype–gene expression associations were explored in this limited dataset, necessitating larger, independent cohorts and biological replication to confirm associations and refine markers. Further functional studies and SNP discovery in candidate genes are needed to establish causality and utility in breeding programs.