Supplementary Materials and Methods s10

SUPPLEMENTARY MATERIALS AND METHODS

Blood gases test Arterial blood samples were obtained from the elbow artery of the explorers in Beijing (altitude approximately 50 m) and at Dome A (4,093 m) under a rehearsed condition. Samples were analyzed using the i-STAT® 1 portable blood gas handheld analyzer system (I-STAT Corp, Princeton, NJ, USA) immediately after it was obtained. All of these operations were performed at room temperature to ensure the comfort of the participants and the proper function of the i-STAT equipment. The baseline (departure) blood gas test was performed within three months prior to the departure for the expedition during a normal workday. After adapting to the high altitude environment in Dome A for 12 days, the explorers received a second blood gas test. The i-STAT analyzer was calibrated prior to each test following the procedures provided by the manufacturer, and blood gas was measured using the i-STAT with CG8+ test cartridges (I-STAT Corp, Princeton, NJ, USA) according to the manufacturer’s recommendations 1. A small volume of blood (approximately 70 µl), in which both ends were dispensed, was injected into the cartridge sample chamber up to the full mark, according to the prescribed filling, and the absence of bubbles was subsequently confirmed. Measurements were obtained by inserting the cartridge into the analyzer, after which the results were displayed on a screen and stored in memory. These measurements were based on the ion-selective electrodes and potentiometric measurements for pH and pCO2; a Clark electrode and ampere-metric measurement were used for the pO2 measurements, along with a controlled internal calibrator. + + 2+ Using the i-STAT system, the pH, pO2, Na , K , Ca , hematocrit, glucose and pCO2 were measured at 37°C via selective electrodes in the disposable cartridges. Oxygen saturation (sO2), bicarbonate concentration (HCO3), total CO2 (TCO2), and base excess (BE) were calculated as follows 2:

Oxygen saturation (sO2) was calculated using an algorithm involving the pH, pO2, and pCO2 values that assumed normal hemoglobin affinity for oxygen:

, where

Bicarbonate concentration was calculated as .

Total CO2 (TCO2) was calculated as

Base excess (BE) was calculated as ,

Any abnormal laboratory values were addressed and evaluated by local doctors, and any samples that had been polluted by the venous blood were excluded. Lung function test Lung function was accessed using the Jaeger Master Scope spirometer (Master Lab, Germany) according to the manufacturer’s instructions and established guidelines3. These methods have been previously described in detail . Spirometry has also been previously shown to be reliable for the measurement of lung function at high altitudes6. As shown in Figure 1B, lung function tests were performed in Shanghai (altitude: approximately 10 m) at departure and at Dome A (altitude: 4,093 m). All explorers received training in the performance of spirometry prior to the first lung function test and were familiarized with the protocol. All measurements were performed in a warm room where the temperature remained relatively constant and above 10°C. Barometric pressure was measured using the on-vehicle barometer (MingGao, China). The following values were measured and recorded automatically using the computer in the lung function test: forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), tidal volume (VT), Breathing Frequency (BF), Minute Ventilation (MV), Expiratory Reserve Volume(ERV), Inspiratory Capacity (IC), Vital Capacity (VC), Peak Expiratory Flow (PEF), Maximal Mid-Expiratory Flow (MMEF75/25), Maximal Breathing Capacity (MVV), the ratio of forced expiratory volume in one second to the forced vital capacity (FEV1/FVC), and the forced expiratory volume at 25%, 50% and 75% (FEF25%, FEF50%, FEF75%).

Cardiac function test The BioZ.ComTM digital noninvasive hemodynamic Cardiodynamics Monitor system (Cardiodynamics, San Diego, CA) was used to evaluate the cardiac function at departure and at Dome A. Changes in the electrical impedance were digitally processed and used to calculate the following parameters: heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), cardiac output (CO), cardiac index (CI), stroke volume (SV), stroke index (SV/SI), velocity index (VI), acceleration index (ACI), pre-ejection period (PEP), systolic time ratio (STR), thoracic fluid content (TFC), systemic vascular resistance (SVR), and the systemic vascular resistance index (SVRI).

Electrocardiographic measurements Electrocardiograms (ECGs) of the explorer/participants at departure and at Dome A were acquired using the 12-lead portable ECG acquisition terminal (Nihon Kohden cardiofax C PEA- 1100K, Tokyo, Japan) according to the directions provided by the manufacturer. After the ECG was reviewed by two cardiologists, the numeric parameters, which were automatically calculated using the Nihon Kohden's ECAPS12C interpretation program, were used for the statistical analyses. The following parameters were used for the analysis: heart rate (HR), P-R interval (PR), QRS complex (QRS), Q-T interval (QT), correction of QT interval (TQc), P-axis deg (P axis), QRS-axis deg (QRS), T-axis deg (T axis), electrode V5 lead R wave amplitude (RV5), electrode V1 lead S wave amplitude (SV1), superposition of electrode V5 lead R wave and electrode V1 lead S wave amplitude (RV5+SV1).

Near-infrared spectroscopy The microvascular O2 availability in the participants’ brain at departure and during their stay at Dome A was measured using a continuous wave near-infrared spectroscopy (NIRS) system (Oxymon Mk III, Artinis Medical Systems BV, the Netherlands) according to the manufacturer’s instructions. The theory of NIRS has been extensively described 7, and the instrument could be operated at high altitude up to 5,500 meters. The NIRS system uses two wavelengths (766 and 861 nm) to emit light waves, which are transmitted and collected by fiber optic bundles (optodes). A modified Lambert-Beer law 8 was used to quantify the changes in [O2Hb], [HHb], and [tHb] as applied to the human body as a scattering medium:

. In these equations, the OD is

the optical density, I0 is the incident radiation, I is the transmitted radiation, ε is the extinction coefficient of the chromophore (mM-1·cm-1), c is the concentration (mM-1) of the chromophore, L is the distance (cm) between the light entry and light exit points,  is the wavelength (nm) used, B is the optical differential path length factor (DPF), which takes into account the scattering of light in tissue, and ODR, represents the oxygen independent light losses due to scattering in the tissue. Assuming ODR, is a constant during a measurement, an optical density change could be converted into a concentration change, B, which was calculated using the formula B=4.99+0.067·age0.814. In general, changes in the optical density in the near infrared region at different wavelengths were converted into changes in the concentration of oxy- and deoxy- hemoglobin ([O2Hb] and [HHb], respectively). Changes in the concentration of total hemoglobin were also calculated ( [tHb] =  [O2Hb] +  [HHb]) and used as an index of the regional total blood volume in the tissue. The sampling frequency of the NIRS data was 10 Hz, and the A/D conversion resolution was 12 bit. Data were stored on a disk for off-line smoothing with a running Gaussian average filter and analyzed using Oxysoft software and Matlab. Prior to the start of the study, the explorers received training and were familiar with the protocol. For NIRS measurements, the subjects sat comfortably in a quiet dim room. The NIRS optodes were placed at each subject’s left prefrontal cortex area enclosing Fp1 of the international EEG 10-20 system [American Electroencephalographic Society, 1994]. The optodes were fixed with elastic black bands around the head and had an interoptode distance of 45 mm in a vertical line. The primary optode position was approximately 3 cm left of the sagittal plane. Next, functional brain activation induction was confirmed by a fast alternate-fisting-and- stretching-palm task with the right hand. If no oxygenation change was detected by NIRS in response to the task, then the optodes were moved (several millimeters) until a response was found. The NIRS stimulating task involved five cycles of 20-sec periods of alternate-fisting-and- stretching palm with the right hand at the fastest rate, alternating with a 40-sec period of rest. The subjects were instructed 2 s before the onset and 1 s before the end of each task.

Changes in [O2Hb], [HHb], and [tHb] were calculated from a baseline defined as the average value over the 10-sec period just prior to the start of each motor task. The maximum oxygenation concentration changes during each individual 20-sec task and the changes at the end of each task were compared with the corresponding baseline. In addition, the time corresponding to the maximum concentration change was also confirmed. Next, five cycles of each alternate-fisting- and-stretching palm task were averaged and time-locked for all variables in each record.

Proﬁle of mood states (POMS) test A Proﬁle of Mood States (POMS) (Educational and Industrial Testing Service, San Diego, CA) questionnaire 10 that was translated to fit the local culture was given to each explorer in the expedition at departure and at Dome A. The POMS measurement can assess six different mood states: depression, fatigue, vigor, confusion, tension-anxiety, and anger-hostility. The scores were calculated using the computer with the following algorithm, and changes in the scores directly reflected the mood state variation. Standard methods for evaluation: Overall, there are 65 questions in the questionnaire, and each question has 5 possible responses. Scores from 0 to 4 represented completely disagree, somewhat disagree, agree depending on the situation, somewhat agree, and completely agree. The scores for each mood state were calculated as follows: 1) Tension: No.2+ No.10+ No.16+ No.20+ (4- No.22) + No.26+ No.27+ No.34+ No.41 2) Depression: No.5+ No.9+ No.14+ No.18+ No.21+No.23+ No.32+ No.35+ No.36+ No.44+ No.45+ No.48+ No.58+ No.61+ No.62 3) Anger: No.3+ No.12+ No.17+ No.24+ No.31+ No.33+ No.39+ No.42+ No.47+ No.52+ No.53+ No.57 4) Vigor: No.7+ No.15+ No.19+ No.38+ No.51+ No.56+ No.60+ No.63 5) Fatigue: No.4+ No.11+ No.29+ No.40+ No.46+ No.49+ No.65 6) Confusion: No.8+ No.28+ No.37+ No.50+（4- No.54）+ No.59+ No.64 Total Mood Disturbance (TMD): tension + depression + anger + fatigue + confusion - vigor scores.

Cytokine and chemokine measurement from the explorers’ sera For the cytokine and chemokine measurements, the sera of the explorers before departure and at Dome A were collected. Samples were analyzed using the Bio-Plex Human Cytokine 25- Plex flow cytometry-based assay kit (Bio-Rad Laboratories, USA) with the Bio-Plex Protein Array system (Bio-Rad Laboratories, USA). Next, the cytokine and chemokine levels were measured, including IL-1, IL-2, IL-4, IL-5, IL-6, IL-6R, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, PDGF, eotaxin, FGF basic, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, RANTES, TNF-α, and VEGF.

Endocrine measurements from the explorers’ sera For endocrine measurements, the sera of the explorer before departure and at Dome A were collected. Serum concentrations of thyroid hormones, including the total triiodothyronine (TT3), free triiodothyronine (FT3), total thyroxine (TT4), free thyroxine (FT4) and thyroid- stimulating hormone (TSH), were investigated using the Chemoluminescence Immunoassay (CLIA) in the clinical laboratory of Peking Union Medical College Hospital. Other endocrine factors, such as testosterone, follicle-stimulating hormone (FSH), (luteinizing hormone) LH, atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP), endothelin-1 (ET-1), and angiotensin II (Ang II), were tested with ELISA kits (MI, USA).

Blood cell separation and RNA isolation Blood cells were isolated from 5 milliliters of peripheral blood from the explorers at departure and at Dome A. After removal of the red blood cells using red blood cell lysis buffer (TIAGEN, CHINA) at room temperature, the purified blood cells lacking erythrocytes were preserved with RNAlater (QIAGEN, USA) and then transported back in a -70°C freezer. Total RNA was isolated using TRIzol reagent (Invitrogen, USA) according to the manufacturer's instructions.

Microarray gene expression detection The Human Gene Expression 4x44K v2 Microarray Kit (Agilent, USA) was used to obtain the gene expression profiles of the blood cells isolated from the explorers at departure and during their stay at Dome A according to the manufacturer's instructions. The microarray assay was analyzed by the Shanghai Bio Corporation of China. Briefly, equal amounts of RNA samples from each subject were labeled with Cy3 and hybridized with complementary probes printed on glass slides. After hybridization, the slides were scanned using a high-resolution microarray scanner (Agilent, USA), and the images were analyzed using the Genespring GX software (Agilent Technologies, USA).

Analysis of the gene expression microarray data Step 1. Microarray gene expression data normalization Microarray data analysis was performed using the GeneSpring GX software version 11.5.1 (Agilent Technologies, Foster City, USA). Raw data were loaded into GeneSpring using RMA preprocessing. The array intensities were normalized using data transformation to set the measurements to less than 0.01 to 0.01, the per chip normalization to the 50th percentile, and the per gene normalization to the median. Next, the normalized data were used to analyze the variance of each group and to generate a heat map for each group. From the clustering results, we found that the gene expression of subject No. 9 was significantly different from the other subjects; in addition, he had previously been to the Antarctic Dome A once, while the other studied participants/explorers had never been to Antarctic Dome A prior to this exploration. Thus, the gene expression profile of subject No. 9 was excluded from further analyses. The above normalized data were log-transformed to calculate the expression value.

Step 2. Screening for statistically significant differences in the gene expression and credibility analyses First, we compared the gene expression of each individual at departure and arrival at Dome A. After normalization, we performed differential analysis with a p-value cutoff of 0.05 and 2-fold changes between time points to identify the differentially expressed genes with statistical significance. We calculated the overlap rates of the GO functions between the down- and up-regulated groups at each occurrence level. The GO functions with lower occurrences reflected individual changes under different environments, and the GO functions with higher occurrences revealed common regulations across most individuals.

Step 3. Differentially expressed gene identification and related PPI network construction A pairwise t-test was used to evaluate whether a probe on the microarray was differentially expressed, and the threshold was set to 0.05, resulting in 4,474 probes identified as differentially expressed in our data. The protein–protein interaction (PPI) information was used to construct a basic network. The interaction data were downloaded from STRING (http://string-db.org/), a public database of known and predicted protein-protein interactions. The PPI data related to humans from this database were utilized. Differentially expressed genes were then mapped onto the basic PPI network, and a network filtration was performed. Specifically, for one interaction in the PPI data, if its two related genes were both differentially expressed, then that interaction would be retained; otherwise, it would be removed. Thus, a sub-network whose nodes were experimentally differentially expressed genes and whose edges were annotated interactions in the STRING was constructed. The genes included in this sub-network were used for further analyses.

Step 4. Gene set analysis For the gene set analysis, we focused on the lowest level Gene Ontology (GO) category identifications and specifically focused on the GO terms that had the most detailed functions in the Biological Process (i.e., the GO terms with no other terms belonging to them; in other words, none of the other GO terms' "is_a:" attribute points to these terms). Next, each of these GO terms was considered as an individual gene set. For each gene set, counts of differentially expressed genes in the PPIs were performed.

Step 5. Gene set presentation We used the Cytoscape software (http://www.cytoscape.org/) for our presentation of GO terms 13. The Enrichment Map 10 plug-in was integrated for the presentation of GO terms. The enrichment map function provided a readout wherein each node in the illustrated network represented a GO term and each edge represented a relationship (i.e., common genes) between two GO terms. The displaying principle of the Enrichment Map was slightly adjusted in our analysis: the size of each node was proportional to the number of differentially expressed genes in the PPI that was included in each GO term, and the thickness of the edge denoted the counts of common differentially expressed genes of the PPI in the two GO terms.

Correlation analysis of phenotypes and phenotype-gene expression Step 1. Screening for statistically significant differences in phenotypes using paired sample t- tests The paired sample t-test determines whether differences between the pre- and post- treatment measured values are significant and makes the assumptions that the data are reliable, repeated samples and are identically normally distributed. The formula for the paired t-test is as follows: , where n is the number of paired samples, denotes the mean of sample difference, is the standard deviation of the sample difference, and ν is the degrees of freedom. , , where d denotes the difference between each of the paired samples. We selected the level of significance as P <0.05 (two-tailed tests).

Step 2. Calculating changes in the measurements between the departure and Dome A We used the changes in the values between the departure and Dome A for the PCC analyses. For most of the phenotypic data, the changed values equaled the Dome A-measured value divided by the value at departure. For the ranked data (psychological tests, POMS parameters, and BE), the changed values equaled the Dome A-measured value minus the departure value.

Step3. Using the Pearson coefficient correlation method for correlation analyses and the false discovery rate (FDR) criterion for multiple testing correction The Pearson coefficient correlation, which is typically denoted r, is a linear dependence method between two variables X and Y, , and the

PCC values were between +1 and −1. Consistent with the criterion of Pearson coefficient correlation14, when the absolute PCC value equals 1, it indicates that the two parameters are in perfect correlation; when the absolute PCC value is no less than 0.8, it indicates that the two parameters are strongly correlated; when the absolute PCC value is more than 0.5, it indicates that the two parameters are in moderate correlation; and when the absolute PCC value is less than 0.2, it indicates that the two parameters are weakly correlated or have no association. In our study, the multiple testing correction p-value of Pearson coefficient correlation was <0.05. P-values were calculated using the Software R and the Benjamini & Hochberg method to control for the false discovery rate (FDR) for multiple testing correction.

Step 4. Creating heat map images to visualize the PCC results We used Matlab 7.0 software to generate the heat map images of the Pearson correlation coefficient results. Warm colors represent significant positive correlations, and cold color represent significant negative correlations. White indicates no significant association between two variables.

Searching published articles in relevant fields for genes strongly related to mood We selected genes strongly related to tension, anger, depression, and fatigue. The standard for a high relationship was an absolute PCC value in the phenotype-gene expression analyses of no less than 0.8. There were only three genes that were strongly related to fatigue; thus, we classified the genes strongly related to the other three mood parameters (tension, anger, and depression) into six groups: one group for genes strongly related to all three emotions, three groups for genes strongly related to two of the three emotions, and three groups for genes with a uniquely strong relationship to one of the mood parameters. Next, we searched for genes that were strongly related to the four mood parameters in the NCBI database: #1: gene name #2: depressed OR gloomy OR depression OR tristimania OR autistic disorder #3: loneliness OR solitude OR solitary OR desolateness #4: autism #5: Schizophrenia OR schizophrenics OR split personality OR mental dissociation OR dementia precox #6: athopia OR psychasthenia OR neurasthenia #7: psychological disorder #8: obsession #9: phobia OR philematophobia OR psychosiphobia #10: anxiety neurosis OR inquietude OR dysphoria #11: mania OR frenzy OR vesania #12: #2 OR #3 OR #4 OR #5 OR #6 OR #7 OR #8 OR #9 OR #10 OR #11 #13: #1 AND #12

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