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raw mass spectrometry data files (.raw) were imported into Compound Discoverer 3 for spectral processing and database matching, providing qualitative and quantitative metabolite data. Rigorous quality control measures were applied to ensure data accuracy and reliability. Metabolomics data were subsequently processed using the metaX software package. Identified metabolites were annotated using the KEGG database (https://www.genome.jp/kegg/pathway.html), the HMDB database (https://hmdb.ca/metabolites), and the LIPIDMaps database (http://www.lipidmaps.org/). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were conducted to reveal metabolic differences among sample groups. Differentially abundance metabolites (DAMs) were identified based on the criteria Variable Importance in Projection(VIP ) > 1, P-value < 0.05, and fold change (FC) 2 or FC 0.5. Finally, To elucidate the biological significance of the identified metabolites, metabolic pathway analysis was carried out.</p>MetaboLightsPublicLiquid Chromatography MS - positive - hilicLiquid Chromatography MS - negative - hilic<p>UHPLC System: Vanquish UHPLC (Thermo Fisher Scientific, Germany)</p><p><br></p><p>Column: Hypersil Gold column (C18)</p><p><br></p><p>Column Temperature: 40 °C</p><p><br></p><p>Flow Rate: 0.2 mL/min</p><p><br></p><p>Mobile Phase A: 0.1% formic acid</p><p><br></p><p>Mobile Phase B: Methanol</p><p><br></p><p>Chromatographic Gradient Elution Program:</p><p><br></p><p>Time (min) A (%) B (%)</p><p>0 98 2</p><p>1.5 98 2</p><p>3 15 85</p><p>10 0 100</p><p>10.1 98 2</p><p>11 98 2</p><p>12 98 2</p>Combined analysis of transcriptome and metabolome of large size triploid rainbow trout in different seasons.Duo KeaiPeking UniversityLivermass spectrometry assay<p>Total RNA was extracted from triploid rainbow trout samples using the TRIzol reagent method, and the RNA concentration and integrity (RIN value) were determined using the Agilent 2100 bioanalyzer. When the purity of the RNA sample met the ratio of OD260/280 of 1.8-2.2, OD260/230 of 1.0-2.5, and a quality concentration of 40 ng/μL, the sample was considered qualified. The qualified total RNA samples were then used to construct cDNA sequencing libraries. The sequencing libraries were constructed using the Hafei NGS Ultima dual-mode mRNA library preparation kit (provided by Yeisen Biotechnology). After library preparation, sequencing was performed on the Illumina NovaSeq 6000 platform. Metabolite extraction. Take 100 mg of tissue samples, grind them with liquid nitrogen, and place them in an EP tube. Add 500 μL of 80% methanol-water solution. After vortexing, place it in an ice bath and let it stand for 5 minutes at 4°C, then centrifuge at 15,000 g for 20 minutes at 4°C. Take the quantified supernatant and dilute it with mass spectrometry water to a 53% methanol concentration. Centrifuge at 5,000 g for 20 minutes at 4°C, collect the supernatant, and inject it into the liquid chromatography-mass spectrometer for analysis.</p>Oncorhynchus mykisshttps://www.ebi.ac.uk/metabolights/MTBLS15014Zhaonan Li. Qinghai University. lizhaonan123456@163.com.Yanxia Chen. Qinghai University. chenyanxia9568@163.com.<p>Raw data were acquired using a Thermo Q Exactive HF series mass spectrometer and exported in .raw format. The .raw files were directly imported into Compound Discoverer 3.3 software for processing, without the need for an independent data format conversion step. Within the software, raw data underwent peak detection, retention time alignment, peak area correction, and background subtraction to generate a three-dimensional data matrix containing m/z, retention time (RT), and normalized peak areas for subsequent statistical analysis. Data processing was performed on a Linux operating system (CentOS version 6.6) using R and Python; detailed information on specific packages and software versions is provided in the readme file accompanying the results.</p>Groupkeaiduoduo998@126.com<p>Based on the study by Meng Yuqiong et al.[14], which measured the annual water temperature in the Longyangxia Reservoir, the lowest temperature was recorded in February, while the highest occurred in August . June and September are relatively suitable for the growth of triploid rainbow trout. Accordingly, large-sized triploid rainbow trout (2–3 kg) were collected from the Longyangxia Reservoir, located in Minze, Qinghai Province, at the end of February (LLF), May (LLM), August (LLA), and November (LLN). Six fish were sampled at each sampling time, and all were anesthetized using MS-222 (1:10,000). After dissection, liver tissues were collected and immediately frozen in liquid nitrogen. Then the samples were brought back to the laboratory and stored at -80°C for subsequent transcriptome and metabolome analyses.</p>Metabolomicstriploid rainbow troutseasonal variationThermo Scientific Vanquish UHPLC Systemheat stressmetabolomicsuntargeted analysisLivertranscriptomicsintegrative analysisThermo Scientific Q Exactive HFOncorhynchus mykisspooled sampleThermo Scientific Vanquish UHPLC Systemtriploid rainbow troutseasonal variationheat stressmetabolomicsuntargeted analysisLivertranscriptomicsintegrative analysisThermo Scientific Q Exactive HFOncorhynchus mykisspooled sample<p>Mass Spectrometer: Q Exactive HF, Q Exactive HF-X, or Orbitrap Exploris 480 (Thermo Fisher Scientific, Germany).</p><p><br></p><p>Scan Range: m/z 100–1500</p><p><br></p><p>Ion Source: Electrospray ionization (ESI)</p><p><br></p><p>Spray Voltage: 3.5 kV</p><p><br></p><p>Sheath Gas Flow Rate: 35 psi</p><p><br></p><p>Aux Gas Flow Rate: 10 L/min</p><p><br></p><p>Capillary Temperature: 320 °C</p><p><br></p><p>S-lens RF Level: 60</p><p><br></p><p>Aux Gas Heater Temperature: 350 °C</p><p><br></p><p>Polarity: Positive and negative ion modes (separate runs)</p><p><br></p><p>MS/MS Acquisition: Data-dependent acquisition (DDA) mode for MS/MS scans</p>falseCombined analysis of transcriptome and metabolome of large size triploid rainbow trout in different seasonsSeasonal temperature variation poses a major physiological challenge for cold-water aquaculture species, yet the molecular mechanisms underlying seasonal adaptation in large-sized triploid rainbow trout (Oncorhynchus mykiss) remain largely unexplored. Here, we integrated liver transcriptomics and LC-MS-based untargeted metabolomics across four seasons (February, May, August, November) to investigate this process. Seasonal comparisons revealed pronounced molecular reprogramming, with February versus August showing the largest transcriptomic shift (13,359 differentially expressed genes) and May versus November the greatest metabolic divergence (1,036 differentially accumulated metabolites). Pathway enrichment analysis identified significant seasonal regulation of oxidative phosphorylation, ribosome, proteasome, and TCA cycle pathways at the transcript level, alongside altered amino acid biosynthesis, fatty acid biosynthesis, arachidonic acid metabolism, and linoleic acid metabolism at the metabolome level. Integrated network analysis further pinpointed key gene–metabolite correlations. In β-alanine metabolism, amb, aco1, aco3 and ahr91a showed strong positive correlations with carnosine, histidine, and spermine, and negative correlations with aspartate and pantothenate (|r| > 0.8). In fatty acid biosynthesis, accta and acs59z correlated positively with palmitic acid and negatively with palmitoleic acid. In the pentose phosphate pathway, g6pdh and 6pgdh correlated positively with ribose-1-phosphate and negatively with erythrose-4-phosphate. These data indicate that large-sized triploid rainbow trout mount a coordinated adaptive response to seasonal temperature changes: during warm months (August), enhanced TCA cycle and oxidative phosphorylation support increased energy demands, accompanied by accumulation of protective antioxidants such as carnosine and spermine; during cold months (February, November), protein synthesis and ribosome pathways predominate. Collectively, this study reveals the molecular architecture of seasonal adaptation in triploid rainbow trout and provides a foundation for improving stress resilience and aquaculture 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