Search Results (3,567)

The longissimus dorsi (LD) and biceps femoris (BF) muscles are important meat-producing regions in camels. Investigating differences in meat quality and proteomic profiles between the LD and BF muscles in Junggar Bactrian camels can provide a molecular basis for regulating camel meat quality and genetic improvement. In this study, 20 healthy adult male Junggar Bactrian camels were selected. Following slaughter, muscle samples were collected from the splenius (SP), triceps brachii (TB), LD, external oblique (EO), gluteus medius (GM), and BF. Meat quality parameters (pH, meat color, shear force, drip loss, and cooking loss) were measured. The LD exhibited the highest meat quality among the six cuts, in contrast to the BF, which showed the lowest. Proteomic analysis of LD and BF from 6 Junggar Bactrian camels was conducted to identify proteins associated with meat quality, yielding 81 differentially expressed proteins (DEPs). Gene Ontology (GO) enrichment analysis highlighted several significantly enriched terms among the DEPs (p < 0.05), including calcium-dependent phospholipid binding, zinc ion binding, and metal ion binding. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis (p < 0.05) further indicated notable enrichment in cytoskeletal organization, 2-oxocarboxylate metabolism, and the citric acid cycle. DEPs associated with meat quality were identified, including tubulin α-chain-like 3 and synaptic function regulator FMR1 isoform X15, which can serve as candidate DEPs for shear force. Protein phosphatase 1 regulatory subunit 14C isoform X1 can serve as a candidate differentially expressed protein for pH. Protein phosphatase 1 regulatory subunit 14C isoform X and anchoring protein repeat domain 1 can serve as candidate DEPs for cooking loss. Membrane-associated protein A4 and membrane-associated protein A7 isoform X1, as well as the transcriptional activator of cytochrome c oxidase 1, can serve as candidate DEPs for color a*. These data may serve as a reference for further studies on how different cuts affect meat quality and for practical efforts to improve camel meat quality. Full article

The outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as one of the most significant global health crises in recent history. Coronaviruses are a diverse group of RNA viruses classified into alpha, beta, gamma, and delta genera, with SARS-CoV-2 belonging to the beta-coronavirus family. The virus exhibits high transmissibility and causes a wide spectrum of clinical manifestations ranging from mild respiratory symptoms to severe complications such as acute respiratory distress syndrome, multi-organ failure, and death, particularly among elderly and immunocompromised individuals. Structurally, SARS-CoV-2 possesses a large single-stranded RNA genome encoding major structural proteins, including spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins, which play critical roles in host-cell recognition and viral infection. Understanding the molecular mechanisms of virus–host interactions, especially protein–protein interactions (PPIs), is essential for uncovering viral pathogenesis and identifying potential therapeutic targets. Traditional experimental techniques for PPI detection, such as yeast two-hybrid and affinity purification methods, are often expensive, labor-intensive, and prone to inaccuracies. Consequently, computational approaches based on machine learning (ML) and deep learning (DL) have gained significant attention for efficient and scalable PPI prediction. These methods use diverse biological information, including protein sequences, structural features, genomic data, Gene Ontology annotations, and interaction networks, to model complex biological relationships. This survey reviews computational approaches to PPI prediction, highlighting ML- and DL-based techniques, methodological advances, performance evaluation practices, and limitations that affect benchmark comparability. It also discusses biological databases and data sources commonly used in PPI studies and explicitly considers how models trained in coronavirus-centered settings may generalize to other viral families with different mechanisms of host interaction. Full article

Gustatory perception in honeybees is a key determinant of foraging decisions and pollen source selection. However, the molecular mechanisms underlying this sensory discrimination remain poorly understood. To investigate these mechanisms during the collection of pollen from different floral sources, this study utilized antennae from worker bees foraging on pear and rapeseed pollen, and non-pollen-foraging workers as controls. Illumina high-throughput transcriptome sequencing was employed to identify differentially expressed genes (DEGs), perform functional annotation, and characterize gustatory receptor (GR) genes. Compared with the control group, 583 DEGs and 516 DEGs were identified in pear-pollen and rapeseed-pollen foragers, respectively, whereas only 73 DEGs were detected between the two pollen-foraging groups. Several DEGs were associated with chemosensory perception, signal transduction, energy metabolism, and immune responses. Notably, genes involved in membrane-associated signaling and stimulus response exhibited differential expression patterns among foraging groups, suggesting adaptive molecular responses to distinct floral resources. Gene Ontology (GO) analysis indicated that DEGs were primarily associated with cellular processes, membrane components, and binding functions. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment highlighted significant involvement in phagosome, phosphatidylinositol signaling system, oxidative phosphorylation, and extracellular matrix–receptor interaction. Notably, seven GR-related genes were identified in the antennal transcriptome, including five known GR genes and two novel candidates, all with complete open reading frames. Four of these genes featured the canonical seven-transmembrane domain structure of insect GRs. Phylogenetic analysis, in addition to the known sugar receptors AmelGR43a, AmelGR64f, and AmelGR64f-X1, based on GRs from Apis mellifera and Drosophila melanogaster suggested that AmelGR28b, AmelGR10, AmelGR12, and AmelGR13 may belong to the bitter taste receptor family. Quantitative real-time PCR (qRT-PCR) validation demonstrated that the expression patterns of the selected seven DEGs were consistent with the RNA-seq results. This study reveals differential expression patterns and potential functional divergence of gustatory receptor genes in Apis mellifera during pollen collection from different floral sources. It provides important molecular evidence for understanding how honeybees accurately recognize and preferentially forage specific pollen sources via gustatory perception, and offers valuable theoretical and practical insights for honeybee behavioral ecology and crop pollination management. Full article

This systematic review assessed transcriptomic evidence on the molecular mechanisms underlying spontaneous preterm birth (sPTB). Major electronic databases were searched from inception to October 2025. Eligible studies examined RNA transcriptomic profiles from maternal pregnancy-related tissues or biofluids in spontaneous preterm labor (sPTL) or preterm prelabor rupture of membranes (PPROM), while indicated or iatrogenic preterm births were excluded. Two reviewers independently screened studies, extracted differentially expressed genes (DEGs), and assessed study quality. DEGs were summarized by tissue type, and recurrent concordant genes were analyzed using Gene Ontology, Reactome, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, with false discovery rate < 0.05 considered significant. Twenty studies were included. Transcriptomic data were derived from placental villi, maternal peripheral blood, decidua, fetal membranes, myometrium, amniotic fluid, and vaginal secretions. Placental villi findings suggested proliferative-metabolic reprogramming and impaired maternal–fetal immune–structural homeostasis, whereas maternal blood profiles reflected systemic immune–inflammatory activation and dysregulated lipid-metabolic pathways. sPTL and PPROM showed potentially distinct signatures involving extracellular matrix disruption, collagen remodeling, matrix degradation, and myeloid/neutrophil-associated inflammation. Transcriptomic profiling may support non-invasive sPTB risk assessment, but standardized, phenotype-specific longitudinal studies are needed to confirm predictive value and clinical utility. Full article

Background: Non-small cell lung cancer (NSCLC) remains one of the leading causes of cancer-related mortality worldwide over the past decade. Single-cell sequencing loses spatial location information and potential cell–cell interactions, making it difficult to interpret molecular features or biological phenomena. Tertiary lymphoid structures (TLSs) inherently require such spatial immune cell distribution information. Although associations between TLS and response to immune checkpoint inhibitors (ICIs) or chemotherapy have been reported, the relationship between TLS and neoadjuvant therapy (ICI combined with chemotherapy) remains unclear. Methods: We performed spatial transcriptomics on NSCLC samples (including one lung squamous cell carcinoma (LUSC) and one lung adenocarcinoma (LUAD)). Multiplex immunohistochemistry (mIHC) was used to identify the TLS, while immunohistochemistry staining (IHC) was used to identify the TLS status and cell characteristics. We evaluated the associations between (mature) TLS density, area proportion and patients’ responses in 66 patients. Results: Heterogeneity of immune cells in NSCLC was found. Gene ontology analysis and cell score comparison identified TLS with activated B and T cells inside, while plasma cells and macrophages were mainly distributed outside TLS. Four genes from antigen-presenting machinery (TAP1, TAP2, B2M, TAPBP) were more highly expressed inside TLS than outside them. Also, TLS exhibited heterogeneity, with both mature and immature TLS. Mature TLS showed an average area of 62,387.43 μm², while the immature TLS showed 51,189.90 μm². The Spearman correlation coefficient of B-cell number and mTLS area showed r = 0.900. TLS density and mature TLS (mTLS) density in the tumor bed were 1.95 ± 0.95 TLS/10 mm² (mean ± SD, n = 34) and 1.13 ± 0.77 mTLS/10 mm², significantly higher than that in the non-responder group (1.18 ± 1.15 TLS/10 mm², 0.70 ± 0.90 mTLS/10 mm², mean ± SD, n = 32) separately. B cells belonging to TLS had a significantly higher density (71.32 ± 55.71 cells/mm², mean ± SD, n = 34) in the responder group than the non-responder group (61.33 ± 111.95 cells/mm², mean ± SD, n = 32) normalized to the tumor bed area. Conclusions: Spatial transcriptomics reveals immune cell heterogeneity and distribution patterns in the NSCLC tumor bed, with activated B and T cells localized inside and plasma cells/macrophages outside. Antigen-presenting machinery (APM)-related genes were highly expressed in TLS accompanied by a high expression of upstream and downstream genes of MHC class I. mTLS have a larger area by mainly containing more B cells. The responder group had a significantly higher (mature) TLS density and larger (mature) TLS area proportion compared with the non-responder group, suggesting their potential function in anti-tumor effect in neoadjuvant treatment. Full article

Total RNA was isolated from sections of healthy testes and Leydig cell tumors of mixed-breed dogs using TMA Master II device. The RNA-seq libraries were sequenced on the Illumina platform. Following differential expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were applied with quality control obtained using FastQC and Trimmomatic. This analysis revealed 1500 transcripts, including 982 upregulated and 168 downregulated genes. The results demonstrated that a significant proportion of these differentially expressed genes are directly involved in the control of sex steroid production (CYP11A1, STAR, and 3β-HSD3B1) or tube formation, angiogenesis, and extracellular matrix remodeling in interstitial cells (ESM1, FGG, and VEGFA). Moreover, we identified the upregulation of transcripts responsible for neurotransmitter or neuroendocrine signaling (SLC6A4, GRIN2C, GABRB3) and cholesterol metabolism and its regulation (GPX3, MSMO1, DHCR24). These genes were strongly associated with the phosphatidylinositol-3-kinase (PI3K)-Protein Kinase B (Akt) cascade and extracellular matrix interactions, features shared with various malignancies. Alterations in estrogen and relaxin signaling appear to be distinctive, understudied mechanisms specific to canine Leydig cell tumors. Concurrently, downregulated genes (e.g., DMRTC2, SEMA3C, ALOX12) were linked with cell differentiation, signaling and immunoregulatory pathway suppression involved in tumorigenesis. A complex transcriptomic profile of canine Leydig cell tumors was developed, revealing a conserved oncogenic core shared in some aspects with human malignancies alongside unique species-specific alterations. Findings seem to be useful for identifying novel diagnostic biomarkers and targeted therapies in veterinary oncology, establishing canine reproductive tissues as a valuable comparative biomedical model for research in human. Full article

Strictosidine synthases (STRs) are catalytic enzymes involved in terpenoid indole alkaloid biosynthesis, whereas STR-like (STRL) genes in cereal crops remain poorly understood. Previous studies of the maize STR-like (STRL) gene family have mainly provided genome-wide identification, phylogenetic classification, structural annotation and expression profiling, but the evolutionary constraints and molecular mechanisms underlying STRL diversification remain insufficiently resolved. In this study, we investigated the maize STRL gene family from an evolutionary and structural perspective by integrating sequence divergence, codon usage bias, selection pressure, protein structural modelling, Gene Ontology (GO) enrichment and tissue-specific expression analysis. A total of 21 ZmSTRL genes were analyzed and their comparative and phylogenetic analyses revealed conserved lineages together with maize-associated expansion patterns. Codon usage and neutrality analyses indicated heterogeneous evolutionary constraints among ZmSTRL genes, suggesting that mutational pressure alone does not explain their sequence divergence. Protein conservation and three-dimensional structural modelling showed a generally conserved STR-related catalytic framework, while member-specific variation in terminal and loop regions suggested localized structural divergence. GO enrichment supported conserved catalytic and metabolic signatures, but these associations were interpreted as putative functional evidence rather than direct functional confirmation. Tissue-specific qRT-PCR analysis revealed divergent expression patterns among selected ZmSTRL genes in root, stem, leaf, and anther tissues, indicating possible regulatory specialization. Overall, this study provides an evolutionary-constraint-based framework for understanding STRL diversification in maize and identifies candidate genes and structural features for future functional validation. Full article

This study aims to determine the prevalence of infection by larvae from the Anisakidae family in fish commonly consumed in the north of Chile. Then, 2968 specimens belonging to 22 different fish genera were studied. Anisakis spp. third-stage larvae were collected and used for PCR and proteomics studies. Trachurus murphyi was the most parasitized species (51.6%), whereas Scomber japonicus (21.3%) and Isacia conceptions (9.5%) were also found parasitized. PCR studies showed that the only species detected was Anisakis simplex. By LC-MS/MS, we identified a total of 8119 peptide precursors, which correspond to 1919 proteins. Gene Ontology analysis indicated that, among molecular functions, catalytic and binding activities were the most highly expressed. Among biological processes, cellular and metabolic processes were the most highly expressed, while among cellular components, cellular anatomical entities and complex-containing proteins were the most highly detected. By in silico analyses, novel putative allergens were detected through comparative analyses with related genera. Among them, apolipophorin is proposed as a potential new allergen. These findings are of relevance for advancing the understanding of allergen–host immune system interactions. Proteomics and bioinformatics studies strongly suggest that A. simplex is a mosaic of allergens whose implications for public health must be properly evaluated. Finally, a One Health approach is proposed to mitigate Anisakidae infections by integrating multisectoral prevention across human and animal interfaces while concurrently preserving aquatic ecosystem integrity. Full article

Background: Discoidin domain receptor 1 (DDR1) is a collagen-activated receptor tyrosine kinase that plays an important role in epithelial cell regulation; its function in cancer appears to be dependent on tumour type. Methods: This study investigated DDR1 expression in large numbers of breast (n = 1416) and ovarian (n = 450) tumours using immunohistochemistry. In addition, RNA sequencing was conducted on DDR1 knockdown breast and ovarian cancer cell lines. Results: In breast cancer, high DDR1 expression was significantly associated with poor patient survival in ER-positive disease and low expression was associated with poor patient survival in ER-negative disease. In ovarian cancer, high DDR1 expression was associated with improved patient survival. In DDR1 knockdown IGROV1 ovarian cancer cells, 770 transcripts were differentially expressed, whilst in DDR1 knockdown T47D breast cancer cells, 3647 transcripts were differentially expressed. Only 149 genes were shared, suggesting that DDR1 drives distinct transcriptional programmes across cancer types. Shared genes between T47D and IGROV1 DDR1 knockdown cells include key regulators of signalling, metabolism, and cytoskeletal organisation such as YWHAE, NCK2, FN1, and ITGB4. Gene Ontology analysis revealed significant enrichment of epithelial cell migration pathways in both cell lines. Conclusions: Current protein expression and transcriptomic data highlight the important prognostic role of DDR1 expression in breast and ovarian cancer and provide hypothesis-generating insights into the contextual and transcriptomic differences between the two cancer types. Full article

Background/Objectives: Patient registries are essential for rare disease research, yet the extensive genetic and phenotypic heterogeneity of primary mitochondrial diseases (PMDs) makes traditional registry development slow and resource-intensive. We established the MSeqDR PMD virtual registry (PMD-VR) to address this gap through systematic literature mining and semi-automated data harmonization. Methods: The PMD-VR captures, standardizes, and harmonizes published case-level PMD data using a semi-automated curation pipeline. A data transformation framework maps heterogeneous raw data terms to standardized common data elements (CDEs). A generative AI (GenAI) platform leveraging large language models (LLMs), augmented by Human Phenotype Ontology (HPO) and external biomedical knowledge sources, accelerates data transformation and generates simulated clinical reports. Results: Currently, PMD-VR contains approximately 11,000 de-identified literature-derived cases, including over 2300 Leigh syndrome spectrum (LSS), 278 MELAS, and 300 CPEO cases. The pipeline mapped 872 heterogeneous terms to 102 standardized CDEs. Pathogenicity assessments were captured for variants in over 7900 cases, including 3800 with mtDNA pathogenic or likely pathogenic variants. Modes of inheritance were inferred for 5212 cases. PMD-VR has supported ClinGen Mitochondrial Diseases Gene Curation Expert Panel (Mito-GCEP) efforts, providing phenotyped evidence for 440 curated LSS cases across 113 PMD genes. Conclusions: PMD-VR is among the largest single PMD registries, offering a scalable, web-accessible platform for generating analysis-ready cohorts from the published literature. It represents a rich resource enabling comprehensive PMD characterization with unprecedented breadth of genetic and phenotypic knowledge. Full article

Soybean (Glycine max) is an important model crop for studying plant functional genes, such as the NAC transcription factor (TF) gene family. The NAC transcription factor (TF) family is one of the largest plant-specific TF families and plays critical roles in plant growth, development, and stress responses. In this study, we performed a pan-genome-wide analysis of NAC genes using 29 soybean genomes. A total of 5051 NAC genes were identified and clustered into 245 orthologous gene groups (OGGs), including 58 core, 88 soft-core, 32 shell, and 67 cloud groups. Based on phylogenetic relationships, the representative NAC OGGs were assigned to 18 subfamilies, 17 of which contained soybean NAC genes. Gene duplication analysis indicated that whole-genome duplication (WGD)/segmental duplication was the predominant driver of NAC family expansion, accounting for 90.88% of duplication events. Approximately 39.30% of NAC genes carried at least one intact transposable element (TE) within 2 kb upstream or downstream regions. NAC genes with copy number variation (CNV) harbored more nearby TEs than non-CNV genes (1.54 vs. 1.31 TEs per gene), and dispensable NAC genes contained more nearby TEs than core NAC genes (1.59 vs. 1.33 TEs per gene). These results indicate a significant association between local TE abundance and NAC gene CNV or dispensability. Selection pressure analysis showed that dispensable NAC genes had higher Ka, Ks, and Ka/Ks values than core genes, suggesting relatively relaxed evolutionary constraints. Expression profiling across six tissues revealed distinct transcriptional patterns among NAC subfamilies. Structurally conserved subfamilies generally showed broader expression, whereas structurally divergent subfamilies displayed greater expression variability. Regulatory network and Gene Ontology (GO) enrichment analyses suggested that conserved subfamilies were mainly associated with stress responses, while divergent subfamilies were related to cell wall regulation, signal transduction, and ion homeostasis. Further analysis of Wm82 drought RNA-seq data prioritized several putative drought-responsive NAC candidates, including Glyma.16G043200, Glyma.06G248900, Glyma.07G050600, Glyma.12G206900, and Glyma.18G261300. Overall, these findings elucidate the mechanisms of expansion and the functional divergence of the NAC gene family at the soybean pan-genome level, providing a theoretical basis for understanding NAC gene evolution and facilitating future crop improvement. Full article

MYB transcription factors (TFs) play essential roles in diverse biological processes, including anther and pollen development, vegetative growth, seed development and germination, and stress responses. However, functional characterization of MYB TFs in maize (Zea mays) lags far behind that in Arabidopsis thaliana and Oryza sativa. In this study, we performed a genome-wide identification of 196 maize MYB TFs, along with phylogenetic analysis and Gene Ontology (GO) annotation. To bridge the knowledge gap, we established an integrated cross-species comparative workflow that systematically maps functionally characterized MYB TFs from Arabidopsis and rice to their maize orthologs. By coupling this homology-based approach with spatiotemporal expression profiling of developing anthers across multiple inbred lines, we prioritized candidate MYB TFs likely involved in anther and pollen development. This integrated strategy provides a useful reference for translating the rich functional knowledge accumulated in model plants to crops with less-characterized genomes. Our study not only establishes a solid foundation for the functional investigation of maize MYB TFs, but also offers promising targets for the mechanistic dissection and molecular breeding application of male sterility in maize. Full article

Tropical maize often exhibits photoperiod sensitivity, which limits its adaptation to temperate regions. Understanding its proteomic dynamics under long-day conditions is therefore crucial for germplasm improvement. This study employed a Tandem Mass Tag (TMT)-based proteomic approach to investigate stage-specific protein expression patterns in the tropical maize inbred line QR273 under long-day conditions (16 h light/8 h dark). Seeds were cultivated in climate chambers, and leaves were collected at the four-leaf (P4) and nine-leaf (P9) stages. A total of 2881 differentially expressed proteins (DEPs) were quantified between the P4 and P9 stages, among which only 7 were upregulated and 2874 were downregulated at the P9 stage. Gene Ontology (GO) enrichment analysis revealed that these DEPs were significantly enriched in processes related to proteolysis, membrane components, and ATP binding. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed the enrichment of DEPs in amino acid biosynthesis, secondary metabolite biosynthesis, and aminoacyl-tRNA biosynthesis pathways. Protein–protein interaction (PPI) network analysis identified 60S ribosomal protein L12, adenosine 5′-phosphosulfate reductase, and RuvB helicase as core hub proteins. Based on functional annotation of representative DEPs, the DEPs were classified into four categories: 9 proteins related to storage material protection, 14 proteins related to protein modification, 12 proteins related to photosynthesis, and 25 proteins with other biological functions. Comparative analysis demonstrated a decrease in storage material protection, protein modification, and photosynthetic capacity at the P9 stage relative to the P4 stage. These findings provide insights into the proteomic dynamics underlying tropical maize development under long-day conditions and offer a theoretical basis for genetic improvement of tropical maize germplasm. Notably, inferences regarding nutrient reallocation based on DEP downregulation are derived solely from proteomic data and require further experimental validation. Full article

Background/Objectives: Inheritance classification in rare diseases remains challenging because curated knowledge is incomplete, heterogeneous, and imbalanced across inheritance categories. Disease-level inheritance modeling can support knowledge organization, annotation review, and hypothesis generation in rare-disease resources. This paper introduces RareFusion-Net, a multimodal benchmark framework for disease-level inheritance classification, and evaluates whether integrating ontology-enriched disease text, structured epidemiological metadata, and gene-association information improves prediction in curated rare-disease knowledge bases. RareFusion-Net is intended for knowledge modeling, not individual patient diagnosis. Methods: We developed RareFusionBalanced, a gated multimodal fusion model that combines biomedical disease descriptions, structured metadata, and gene-related information using auxiliary supervision. Ontology-enriched disease text was treated as the dominant semantic modality, while tabular and gene modalities were incorporated as complementary evidence when available. Robustness was improved using balanced regularization, selective transformer fine-tuning, dropout, weight decay, label smoothing, early stopping, and prediction aggregation across random seeds. Evaluation included accuracy, macro-F1, micro-F1, macro-AUC, mean average precision, calibration metrics, class-wise analysis, statistical testing, and ablation experiments. Results: RareFusionBalanced achieved 0.7382 test accuracy, 0.6284 macro-F1, 0.7382 micro-F1, 0.9183 macro-AUC, and 0.6686 mean average precision. Calibration was favorable, with an expected calibration error of 0.0395 and a Brier-OVR of 0.0528. The multimodal model slightly outperformed TextOnly-TransformerBalanced, but improvement over the best TF-IDF baseline was not statistically significant. Ablation showed ontology-enriched text as the strongest modality, with gene associations adding complementary value. Conclusions: RareFusion-Net provides a practical benchmark for ontology-aware rare-disease inheritance modeling. Results suggest selective multimodal benefit while highlighting minority-class difficulty, limited statistical superiority, need for external validation, and improved biological interpretability. Full article

In addition to its role in energy storage, adipose tissue contributes substantially to energy metabolism, endocrine regulation, and inflammatory processes. Sujiang pigs, a hybrid breed approved by the National Livestock and Poultry Genetic Resources Committee of China as a new national breed in 2013, possess a genetic predisposition for substantial fat deposition, making them an ideal model for investigating the mechanisms underlying adipose tissue accumulation. In this study, back fat (BF; subcutaneous adipose tissue), greater omentum (GOM; visceral adipose tissue), and mesenteric adipose tissue (MAD; visceral adipose tissue) were collected from three 6-month-old male Sujiang pigs for RNA-seq analysis. Comparative analyses identified 3005 differentially expressed genes (DEGs) between BF and GOM, 975 DEGs between BF and MAD, and 892 DEGs between GOM and MAD. To validate the reliability of the sequencing data, five DEGs were randomly selected for RT-qPCR verification. The DEGs were further subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. By integrating protein–protein interaction (PPI) networks with bioinformatics analyses, we identified candidate genes potentially associated with lipid metabolism (e.g., WNT9A, WNT5A, and PDGFRA) and inflammatory responses in adipose tissue (e.g., CSF1R, C1QB, and CD4). These findings indicate potential molecular differences between porcine visceral and subcutaneous adipose tissues and may serve as a reference for further studies on the molecular regulation of adipose tissue metabolism. Full article