Subjects recruited for analysis were male health volunteers living at sea level. Inclusion criteria were as follows: absence of acute infection during the past 6 months, absence of chronic inflammatory diseases, with no organic diseases, with the age >18 years, and >6 months stay at sea level. Volunteers were excluded if they had any health problems, abnormal complete blood count, chemistry panel or liver function results or those who had been to high altitude during the past 6 months. All subjects’ general health status eligibility were assessed and determined prior to participation including medical history, physical check, and standard blood and urine analysis. They all ascended by train to Golmud district (altitude 4300m) in Qinghai, China. All subjects signed informed consent to all the risks involved in this study and they did not receive any preventative drugs before or during ascending to high altitude. Our study has been approved by the Ethics Committee of the Chinese PLA General Hospital.
AMS assessment and physiological examination
To evaluate AMS status, subjects recruited for this clinical study completed the self-reported questionnaire on the 3rd day arriving at high altitude. The diagnosis of AMS was based on the Lake Louise Score (LLS) system3,12. Five symptoms including headache, gastrointestinal symptoms, fatigue/weakness, dizziness/light- headedness, and difficulty in sleeping were assessed. Participants scored the severity of each symptom from 0 (no symptom) to 3 (maximal severity). Individuals whose total score exceeded 3 points and were accompanied with headache were diagnosed as AMS. And subjects whose LLS scores were lower than 3 points were selected as control group. Blood pressure, heart rate and oxygen saturation were also measured both at sea level and on the 3rd morning after ascending to high altitude. Arm blood pressure (BP) monitor (KD-5903; Andon Health Co., Ltd. Tianjin) was used to determine brachial artery BP of each volunteer for three times and average value was acquired. SpO2 of fingertip and pulse rate were measured with pulse oximeter (YX301; Yuwell Medical Equipment & Supply Co., Ltd., Jiangsu), both of which were performed on right index fingers in the morning before breakfast.
Blood sample collection, conservation and preparation
Venous blood (4ml) was collected in EDTA-K2 tubes from each overnight fasting individual in the morning at sea level and on the third day after ascending to Golmud. Plasma was separated through centrifugation at 3000rpm for 5 minutes, stored at -20°C and then transported to Beijing and stored at -80°C until further analysis.
The preprocessing procedures before GC-MS analysis included deproteinization, drying and chemically derivatization. Plasma was thawed at 4°C for 20 min and centrifuged for 15 min (15,800 rpm, 4°C). To the 100 μl supernatant, 400 μl methanol was added for deproteinization and centrifuged for another 15 min. Then ribitol was added as internal standards at the concentration of 1 mg/ml and mixed on a vortex for 15s. The above samples were put on ice in ventilated cabinet. After methanol was volatilized, they were put at -70°C for over 4h until dried. One microliter of each supernatant was then transferred to the sample for the following GC-MS procures. The remaining was combined to generate a pooled quality control (QC) sample. Then dried samples were derivatized using freshly prepared methoxylamine/pyridine (50 mg/ml) and incubated for 90min (30°C). Next, 80 μl of N-methyl-N- (trimethylsilyl) trifluoroacetamide (MSTFA) with 1% trimethylchlorosilane (TMCS) was added to the samples which were then incubated for 2h at 37°C.
Gas chromatography-mass spectrometry conditions
Samples were analyzed on the Agilent 7890A gas chromatograph with a 5975C time-of-flight mass spectrometer (Agilent Technologies, America) that equipped with an auto-sampler 7693 and electron ionization (EI) source. A 30m×0.25mm×0.25 μm deactivated fused silica capillary column (Agilent Technologies) was used for the GC-MS analysis. The primary temperature was maintained at 60°C for 4 min, programmed to 180°C at a rate of 10°C/ min, and slowed down to the rate of 3°C/min to 260°C, then rise the rate again at 10°C/min until 300°C and held for 10 min. The temperatures of the front injection port, interface and ion source were set at 270°C, 280°C and 230°C, respectively. Helium was used as the carrier gas with a flow rate of 1.0 ml/min. One microliter of sample was injected in the 10:1 split mode. The MS quadrupole temperature was 150°C. The mass spectrometer was operated under electron impact (EI) mode at ionization energy of 70 eV, with a scanning extent of 85-500m/z and was set for five scans per second.
A quality control (QC) sample theoretically identical to the biological samples, with a metabolic and sample matrix composition similar to those of the biological samples under study, is used in our study 13. A pooled QC strategy deriving from all the subject population was applied. Equal quantities were taken from every sample and mixed together as the QC sample. The serum samples and the QC samples were labeled. Before the experimental samples were injected, QC samples were used to equilibrate the GC-MS. All the samples were then analyzed in GC-MS at random in order to avoid the run order effect.
Identification and quantification of metabolites
Data from GC-MS was processed by GC/MSD Chem Station Software (Agilent) for auto-acquisition of GC total ion chromatograms (TICs) and fragmentation patterns. Then all transformed data was processed sequentially with the assistance of Qualitative Analysis B.04. Since there was a serious of split molecular ions for each compound fragmentation pattern, the National Institute of Standards and Technology (NIST) mass spectra library by the Chem Station Software was used to make the comparison. The standard mass chromatogram from NIST, which includes the mass charge ratios and the abundance of compound fragmentation, can search the following parameters including the compound’s name, constitutional formula, retention time, relative molecular weight ect. For each peak, the software generated a list of similarities with those within the NIST library. Peaks with matching factor over 85% were assigned compound names, those having < 85% similarity were listed as unknown metabolites. Internal standard was used to obtain the relative quantification of these compounds. The results were exported in .cef files, which included the above parameters.
Multivariate data processing
Feature extraction and pre-procession of the obtained data were performed using R software, and then normalized and edited into two-dimensional data matrix by Excel 2010 software. Parameters including Retention time (RT), Mass-to-charge ratio (MZ), Observations (samples) and peak intensity were collected. Multiple variables statistical procedures of principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied to observe the plasma profiles from different groups using SIMCA-P 13.0 software (Umetrics AB, Umea, Sweden). The quality of the models was evaluated by the relevant R2X (explainable index) and Q2 (predictable index). PCA models were regarded as valid only if R2 (X) > 0.4. PLS-DA models were regarded as valid only if Q2 > 0.4. All of the data from the differentially expressed compounds were used in calculating PCA models (an unsupervised statistical procedure that uses orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components).
Significantly characteristic differential metabolites or metabolic features between sea level and high altitude groups, AMS and non-AMS groups were screened using the OPLS-DA model. The Paired t-test was selected to measure the significance of each metabolite in separating sea level from high altitude group and AMS from non-AMS groups. Fold change is calculated through the Logarithmic value (2 as the base value) of ratio between AMS group and non-AMS group. A positive value indicates a relatively higher concentration present in AMS group, whereas a negative value indicates a relatively higher concentration in non-AMS group. The differences were considered significant when p <0.05.
Commercial databases, including the Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) databases were used to search for metabolite pathways. More detailed analysis of the relevant pathways and networks of AMS is performed by MetaboAnalyst 3.0, which revealed that these differential metabolites are important for the organism response to high altitude or AMS and are responsible for multiple pathways. MetaboAnalyst is a free, web-based tool that combines results from a powerful pathway enrichment analysis concerning the conditions under study 14.