Materials
HFD (18.1% protein, 61.6% fat and 20.3% carbohydrates; D12492) was purchased from Research Diets, Inc. Normal chow diet (NCD) (54% carbohydrate, 32% protein, 14% fat; #8604) was purchased from Teklad Rodent diet , Envigo Inc.
Animals and groups
Male 5-week-old C57BL/6 J mice were sourced from the SLAC laboratory in Shanghai, China. Male mice, aged 4 to 6 weeks and weighing 14-16g, were housed under standardized conditions maintained at a temperature range of 22 to 24°C, with a regulated 12-hour light and 12-hour dark cycle. These animals had free access to food and water throughout the experimental period.
Twelve-eight mice were selected and randomly divided into four groups (n=3): control, HFD (high-fat diet), CIH (chronic intermittent hypoxia), and HFDCIH. Mice in control were cultured under normal oxygen condition. HFG were feed with high-fat food under normal oxygen condition. CHI were feed with normal food and were under normal oxygen condition. HFDCIH were feed with high-fat food under CIH condition. Mice in CIH were treated with intermittent hypoxia in the daytime and with normal oxygen in the night. The whole treatment lasted 24weeks.
All experimental protocols involving animals were conducted in accordance with the ethical principles outlined in the Guide for the Care and Use of Laboratory Animals. Prior to the commencement of the study, these protocols were reviewed and approved by the Bioethics Committee of the Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, ensuring compliance with the highest standards of animal welfare and ethical conduct in scientific research.
Sample collection
After intervention, all the mice were humanely sacrificed using established protocols to minimize suffering and distress, ensuring compliance with ethical guidelines for the humane treatment of laboratory animals. Liver tissues were collected and stored at -80 °C until use.
RNA extraction and transcriptome analysis
RNA were extracted using TRIzol® Reagent Invetrogen (Takara, Japan). Transcription and library construction were conducted in Shanghai Zhongke New Life Biotechnology Co. LTD (Shanghai, China). Gene expression were calculated using FPKM by Counts software.
Metabolome analysis
Collected samples were involved in Ultra-performance liquid chromatography-tandem time-of-flight mass spectrometry (UHPLC-Q-TOF MS). After the sample was thawed slowly at 4°C, an appropriate amount of sample was added to the precooled methanol/acetonitrile/water solution (2:2: 1, v/v), vortexed and mixed, sonicated at low temperature for 30 min, stood at -20 °C for 10 min, centrifuged at 14000 g at 4 °C for 20 min, took the supernatant and dried under vacuum, and added 100 μL aqueous acetonitrile solution for mass spectrometry analysis (acetonitrile: Water =1:1, v/v), vortexed, centrifuged at 14000 g at 4 ° C for 15 min, and the supernatant was injected for analysis.
Hromatographic condition was set as follows. Chromatographic column was gilent 1290 Infinity LC UHPLC HILIC and column tempreture was 25°C. Flow rate 0.5mL /min; the sample size was 2 μL. Mobile phases A was water + 5 mM ammonium acetate + 25 mM ammonia; mobile phases B was acetonitrile. Solvent gradient was set as follows: 95 % B (0-0.5 min), 65 % B (0.5-7 min), 40% B (7-8 min), 40 % B (8-9 min), 95 % B (9-9.1 min), and 95 % B (9.1-12min).
Q-TOF mass spectrum conditions was set as follows. Electrospray ionization (ESI) conditions of AB Triple TOF 6600 was set as follows: Ion Source Gas1 (Gas1): 60, Ion Source Gas2 (Gas2): 60, Curtain gas (CUR): 30, source temperature: 600°C, IonSapary Voltage Floating (ISVF) ± 5500 V (Positive and negative modes); TOF MS scan m/z range: 60-1000 Da, product ion scan m/z range: 25-1000 Da, TOF MS scan accumulation time 0.20 s/spectra, product ion scan accumulation time 0.05 s/spectra. Secondary mass spectra were obtained with information dependent acquisition (IDA) and in high sensitivity mode with Declustering potential (DP): ± 60 V (positive and negative modes), Collision Energy: 35 ± 15 eV, IDA Settings as follows Exclude isotopes within 4 Da, Candidate ions to monitor per cycle: 10.
Differential expressed genes selection
DESeq package [7] in R 4.1.1 was used to perform the differential expression analysis. Differential expressed genes between control and HFD, CIH and HFDCIH were identified with threshold as P.adj < 0.05 and |log2foldchange| > 1. Following, VennDiagram package [8] in R was conducted in the above two groups to obtain the key genes that affect the hepar in obese mice under hypoxia condition. For investigating the biological functions of these key genes, they were finally involved into function analysis in Metascape database [9]. Furthermore, protein-protein interaction (PPI) network and MCODE analysis were conducted.
Differential metabolites selection
The Variable Importance for the Projection (VIP) obtained from the PLS-DA model [10] model is usually used to measure the intensity of influence and the explanatory ability of the expression patterns of metabolites on the classification and discrimination of samples of each group of samples, and to explore the differential lipid molecules with biological significance. The metabolites with VIP > 1 are considered to have a significant contribution in model. Metabolomics is usually considered OPLS-DA VIP > 1 and P value < 0.05 as the screening criterion for significant difference metabolites. In this study, differential metabolites in control vs. HFD and CIH vs. HFDCIH were screened, and the differential metabolites screened by positive and negative ion modes were combined. The metabolomics enrichment analysis in significant differential metabolites were performed in mtaboanalyst database [11, 12].
Correlation analysis among differential key genes and metabolites
For investigating the potential mechanism of obesity under hypoxia condition, person correlation was used to explore the correlation among key genes and metabolites. Pheatmap package in R was used to visualized the results. Combined with differential gene and metabolite enrichment results, the combined pathway analysis module in metaboanalyst database was used to conduct a comprehensive analysis of transcriptomic and metabolomic data at the level of metabolic pathways, revealing metabolic pathways and key genes affected by obesity under hypoxia conditions.
Statistic analysis
All the above analyses were conducted in R software (version 4.1.1, R Foundation for Statistical Computing, Vienna, Austria).
The study design was showed in Figure 1.