Background: Dissolved oxygen (DO) in the water is a vital abiotic factor in aquatic animal farming. A hypoxic environment affects the growth, metabolism, and immune system of fish. Glycolipid metabolism is a vital energy pathway under acute hypoxic stress, and it plays a significant role in the adaptation of fish to stressful environments. In this study, we used multi-omics integrative analyses to explore the mechanisms of hypoxia adaptation in Genetically Improved Farmed Tilapia (GIFT, Oreochromis niloticus).

Results: The 96 h median lethal hypoxia (96h-LH50) for GIFT was determined by linear interpolation. We established control (DO: 5 mg/L) groups (CG) and hypoxic stress (96h-LH50) groups (HG) and extracted liver tissues for high-throughput transcriptome and metabolome sequencing. A total of 581 differentially expressed (DE) genes and 1250 DE metabolites were detected between CG and HG, and were annotated using tools at the KEGG database. We verified the transcript levels of eight DE genes by quantitative real-time PCR.

Conclusions: Analyses of essential glycolipid metabolism pathways of GIFT under hypoxia stress showed that, after 96 h of hypoxia stress, lipid metabolism became the primary metabolic pathway in GIFT. Our findings reveal the changes in metabolites and gene expression that occur under hypoxia stress, and shed light on the regulatory pathways that function under such conditions. Ultimately, this information will be useful to devise strategies to decrease the damage caused by hypoxia stress in farmed fish.

Sample Collection: A total of 240 experimental fish were used for the acute hypoxic stress experiment. 240 experimental fish were randomly placed in six tanks (40 fish per tank), three treatment groups (HG) and three 5mg/L control groups (CG). The DO of the three treatment groups was rapidly reduced to 96h-LH50 by pumping nitrogen into the water from a nitrogen gas cylinder. Real-time reading with a DO meter was used to control the DO in water by adjusting oxygen intake. The sampling time was 96h. Ten fish were randomly taken from each tank and anesthetized with 200mg/L MS-222. Then we sampled liver tissue for LC-MS metabolome analysis. All the liver tissues were frozen immediately in liquid nitrogen and stored at -80°C.

Extraction: The liver tissues (about 50 mg) were homogenized in 120 μL pre-cooled 50% methanol buffer using a high-throughput tissue laser (Ningbo, China). The mixture was incubated at home temperature for 10 min and stored at -20°C overnight. After centrifugation at 4000 g for 20 min, the supernatants were transferred into new 96-well plates and used for metabolomics analysis.

Chromatography: All chromatographic separations were performed using an ultra-performance liquid chromatography (UPLC) system (SCIEX, UK) with an ACQUITY UPLC BEH Amide column (100mm*2.1mm, 1.7µm, Waters，UK). The column oven was maintained at 35°C. The flow rate was 0.4 mL/min, and the mobile phase consisted of solvent A (25mM ammonium acetate+25 mM NH4H2O) and solvent B (IPA : ACN=9:1 + 0.1% formic acid). Gradient elution conditions were set as follows: 0～0.5 min, 95% B; 0.5-9.5 min, 95% to 65% B; 9.5~10.5 min，65%~40% B; 10.5～12 min, 40% B; 12～12.2 min，40%~95%B;12.2～15 min，95% B. The injection volume for each sample was 4 µl.

Mass Spectrometry: A high-resolution tandem mass spectrometer TripleTOF5600plus (SCIEX, UK) was used to detect metabolites eluted form the column. The Q-TOF was operated in both positive and negative ion modes using the following parameters: The curtain gas was set to 30 PSI, nebulizer pressure was set to 60 PSI, and an interface heater temperature was 650°C. For positive ion mode, the Ionspray voltage floating was set at 5000 V, respectively. For negative ion mode, the Ionspray voltage floating were set at -4500V, respectively. The data were collected in IDA mode, and the mass spectra scan range was set to mz60 ~ 1200. Quality control (QC) samples were used to evaluate the stability of the LC-MS analysis, and these QC samples were prepared from a mixture of supernatants from all 8 individual liver samples.

Data Transformation: LC−MS raw data files were converted into mzXML format and then processed by the XCMS, CAMERA, and metaX toolbox implemented with the R software.

Metabolite Identification: Each ion was identified by combining retention time (RT) and m/z data. In order to explain the physical and chemical properties and biological functions of metabolites, the online HMDB (http://www.hmdb.ca/), in-house (http://spldatabase.saskatoonlibrary.ca/) and KEGG (http://www.kegg.jp/) databases were used to annotate and identify the metabolite.Each ion was identified by combining retention time (RT) and m/z data. In order to explain the physical and chemical properties and biological functions of metabolites, the online HMDB (http://www.hmdb.ca/), in-house (http://spldatabase.saskatoonlibrary.ca/) and KEGG (http://www.kegg.jp/) databases were used to annotate and identify the metabolite.