Alteration of the gut microbiota changes during the one-year Antarctic deployment in a group of Chinese Han population

Abstract

Background

It is common knowledge that people’s intestinal microbiota is significantly influenced by the external environment. Although the Antarctic continent has been discovered for nearly 200 years, it is still unclear how this environment affects the human intestinal microbiota, especially that of the Chinese Han population.

Methods

Twelve explorers underwent a one-year Antarctic deployment from December 2017 to December 2018. The gut microbiota and clinical indexes at five time points, including two months (T1), five months (T2), eight months (T3), 11 months (T4) of residence in Antarctica and 7 months after returning to China (T5), were investigated.

Results

The intestinal microbiota of the participants was changed after one-year Antarctic deployment even after they left Antarctica. For the microbiota tested after returning to China (T5), the amount of Citrobacter, Akkermansia and conditional pathogens such as Escherichia-Shigella increased significantly (P < 0.05). The concentration of the major biochemical indicators in the serum exhibited an increasing trend before T3, and decreased significantly at T4. When tested again at T5, most of the serum concentrations increased, only 5-hydroxytryptamine was significantly decreased. Spearman correlation analysis showed the change in the relative abundance of Anaerotruncus was negatively associated with the changes in the concentration of total thyroxine, alanine transaminase, γ-glutamyl transpeptidase, serum cystatin C and apolipoprotein A1. The relative abundance change in Citrobacter was positively associated with the change in the concentration of uric acid.

Conclusion

By objectively analyzing the influence of the Antarctic environment on the change of intestinal microbiota, we were able to provide theoretical support for subsequent Antarctic related research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-024-03569-x.

Background

The Antarctic continent, one of the five polar regions of the Earth, has been explored for nearly 200 years [1, 2]. Due to its harsh climatic conditions, there is no stable human society settled on Antarctica. The Antarctic continent is also called the “natural laboratory” for its relatively primitive natural environment [3]. Since the 1950s, many countries have established perennial or permanent research stations in Antarctica for long-term comprehensive multidisciplinary research. In addition to physically experiencing the extreme natural environment conditions, such as low temperature, blizzards, dryness, strong ultraviolet rays and photoperiod changes, the participants in the overwintering team also had to face huge psychological challenges such as long-term isolation, lack of privacy, and emotional and interpersonal deprivation [4, 5]. Antarctica is a continent of extremes and contrasts without in-between. With the development of technology, great attention has been paid to the health management of expedition members during their voyages and stays in Antarctica. However, many areas of research, such as how the gut and oral microbiota of individuals would be altered after being residing in the extreme environment, stress and diet remain unexplored.

The human oral and gut microbiota are closely related to the immunological, hormonal, and metabolic homeostasis of human beings [6]. It also has significant impacts on various diseases and specific environmental adaptations [7, 8]. Moreover, the gut microbiota is an important indicator of human adaptation to the environment. Studies have reported that the extreme environment and gut microbiota would collectively affect the physical and psychological status of human beings [4, 9, 10]. Jin et al. found that the tested terminal restriction fragment length polymorphism (T-RFLP) patterns showed inter-individual variability throughout the duration of the Antarctic expedition, and Bifidobacterium spp. exhibited a tendency to decrease during the expedition and was recovered afterwards [11]. The study suggested that the extreme environment significantly influenced the composition of the gut microbiota. However, the changes in the microbiota and the physiological indicators of individuals entering and leaving Antarctica are still unclear.

Here, we aimed to explore the changes in the microbiota of Antarctic explorers at different time points during and after a one-year Antarctic deployment from December 2017 to December 2018. The correlation between the gut microbiota and clinical indexes was also investigated. We selected five representative time points: two months (summer, T1), five months (before-wintering, T2), eight months (mid-wintering, T3), 11 months (the end of wintering, T4) of residence in Antarctica and 7 months after returning to China (T5). The gut microbiota compositions at these five time points were analyzed by using 16 S rRNA gene sequencing. This study objectively examined the influence of Antarctica environment on the change of intestinal microbiota, which might provide theoretical support for subsequent Antarctic-related research.

Methods

Study design

All study protocols were approved by the medical ethics committee of Shanghai East Hospital and were in accordance with Declarations of Helsinki. The study was clearly introduced to all participants. Informed consent forms were signed by all participants before the collection of saliva, throat swabs, fecal samples, blood, and personal information. We carefully designed the experiment and the enrollment criteria to largely eliminate differences due to preexisting factors, including diet and lifestyle, in this microbiome study. Participants in this study were 12 winter-over Han Chinese crew members who flew to Great Wall Station (62°12′59″S, 58°57′52″W) from Shanghai via Chile on December 9, 2017 and flew back to Shanghai on December 10, 2018.

All participants met the inclusion criteria: (1) They were medically and psychologically qualified for duty prior to deployment; (2) They had no active thyroid, adrenal, or infectious disease and no psychiatric disorder; (3) The did not take any medication that might potentially affect the study. The diets and lifestyles of all the participants in Antarctica were highly similar. Their diet in Antarctica was mainly frozen meat and vegetables, and yogurt (Bright Dairy Co. LTD, Shanghai, China) was supplied in stages, from departure to the end of March, 2018 and from July to August 2018, while participants had different diets in China, mainly fresh meat, vegetables and fruits. Accordingly, the design largely eliminated several confounding factors (diet and lifestyles). Metadata statistics are listed in Table S1.

Measurements

The first sample group was collected two months after arriving in Antarctica (February 2018), followed by sampling every month until the month before leaving Antarctica (November 2018). After returning to China in December 2018, the last samples were collected in July 2019, with a total of 11 samplings. We mainly analyzed 5 representative time points. They were labeled as T1 (February 2018, summer), T2 (May 2018, before-wintering), T3 (August 2018, mid-wintering), T4 (November 2018, end-wintering) and T5 (July 2019, 7 month after returning to China).

All participants were weighed, measured, and registered for blood pressure at departure. Subjects rested for 30 min before measuring blood pressure and heart rate. The middle part of the fresh stool (approximately 0.2 g) was obtained with a sterile fecal sampler and aliquoted to a new 1.5 ml Eppendorf tube. Serum was separated from the blood sample after clotting and centrifugation to perform 17 clinical tests using a biochemical automatic analyzer (cobas c 702; Roche, USA). In addition, serum 5-hydroxytryptamine (5-HT) was measured using the Serotonin ELISA kit (DLD Diagnostika GmbH, Hamburg, Germany) following the manufacturer’s instructions. All the samples collected were stored at − 80 °C and transported together to China at − 80 °C, where they were maintained until being tested. All measurements were performed at the end of the study to minimize systematic errors. The blood test items and basic statistical descriptions are presented in Table S2.

DNA extraction, PCR amplification, and 16 S rRNA gene sequencing

Genomic DNA was extracted from the samples by the same laboratory staff using the E.Z.N.A. Stool DNA Kit (Omega Bio-tek, Inc., GA, USA) and stored at -20 °C for further analysis. The extracted DNA from each sample was used as the template to amplify the V3 ~ V4 region of 16 S rRNA genes. The negative control was taken throughout the entire workflow.

The primers F1 and R2 (5’-CCTACGGGNGGCWGCAG-3’ and 5’-GACTACHVGGGTATCTAATCC-3’), corresponding to positions 341 to 805 in the Escherichia coli 16 S rRNA gene, were used to amplify the V3 ~ V4 region of each sample by PCR. PCR reactions were run in an EasyCycler 96 PCR system (Analytik Jena AG, Thuringia, Germany). The products from different samples were indexed and mixed at equal ratios for sequencing by Shanghai Mobio Biomedical Technology Co. Ltd. (Shanghai, China) using the Miseq platform (Illumina Inc., USA) according to the manufacturer’s instructions.

Data analysis

Clean data were extracted using USEARCH 8.0 after removing low-quality bases and adaptors. Quality-filtered sequences were clustered into unique sequences and sorted in order of decreasing abundance. Operational Taxonomic Units (OTUs) were classified based on 97% similarity after removing chimeric sequences with UPARSE (version 7.1 http://drive5.com/uparse/). Subsequently, the phylogenetic affiliation of each 16 S rRNA gene sequence was analyzed by Ribosomal Database Project Classifier (http://rdp.cme.msu.edu/) against the Silva (SSU123) 16SrRNA database using a confidence threshold of 70%.

The α-diversity metrics were assessed based on the non-parametic Shannon-Wiener (SW) diversity, Ace and Chao1 indices. Both weighted and unweighted UniFrac were calculated in QIIME [12]. Principal coordinate analysis (PCoA) plot was generated to visualize the unweighted UniFrac dissimilarity by QIIME. The statistical significances among groups were tested by PERMANOVA in QIIME. Taxa with differential abundance among groups were detected by linear discriminant analysis (LDA) effect size (LEfSe). All bar plots and PCoA plots were generated in R (http://www.R-project.org/).

Statistical analysis

The non-parametric Mann-Whitney U test with multiple-testing correction or non-parametric Kruskal-Wallis test with multiple-testing correction was employed to test for significant differences between two groups or among multiple groups. A false discovery rate (FDR)-corrected P value < 0.05 was considered statistically significant.

Results

This study enrolled 12 male Han individuals, aged 42.91 ± 10.39 years old, from all over China. Four of them (33.3%) had winter-over experience. Feces, saliva, throat swabs and blood of all 12 individuals were collected from T1 to T4. However, only 11 members were included in the T5 group for personal reasons. The detailed schedule is shown in Fig. 1. 16s rRNA sequencing yielded an average of 55,702 reads for each sample. The species accumulation curve analysis indicated that the sample size of feces, saliva, and throat swabs was sufficient (Fig. S1A). The rarefaction curve analysis revealed that all samples reached saturation (Fig. S1B).

Fig. 1.

Overview of the analysis schedule. The fecal, saliva, throat swabs and blood samples were collected and analyzed. The fecal samples were subjected to 16 S rRNA gene sequencing followed by diversity analysis, bacterial composition analysis and differential analysis. The blood samples were analyzed with 18 clinical indexes. Finally, analysis of the association and network between the gut microbiota and clinical indexes were used to illustrate the correlations and group-specific signatures

The distribution of microbiota in feces, saliva, and throat swab samples

The differences of α-diversity in the feces samples versus the other sample types were statistically significant (feces vs. saliva, feces vs. throat swab, both P < 0.0001, Mann-Whitney U test). The Shannon index was highest in throat swab, followed by saliva and feces (Fig. 2A). PCoAs at the genus level showed clear trends of aggregation for all types of samples. The microbial compositions of saliva and throat swabs were more similar compared those of the feces (Fig. 2B). The number of OTUs shared by saliva and throat swabs was 329, which was significantly greater than that shared by saliva and feces (11) as well as that shared by feces and throat swabs (123), respectively (Fig. 2C). Given that the feces samples had the lowest α-diversity, they were chosen for subsequent analysis. The dominant microorganism analysis at the genus level revealed that Faecalibacterium, Bacteroides, Subdoligranulum, Lachnospiraceae incertae sedis, and Bifidobacterium were dominant in feces samples, while the dominant bacteria in saliva and throat swab samples were Streptococcus, Prevotella, Veillonella, Neisseria and Porphyromonas (Fig. 2D). However, saliva and throat swab samples also have certain differences in their microbiomes. These results collectively demonstrated that the individualized differences in microbiota composition in the same part of the human body are smaller than those in different parts.

Fig. 2.

The microbial characteristics of the three types of samples. (A) α-diversity analysis estimated by Shannon index. The boxplot displays five values, which include the minimum observation (lower edge), the 25th percentile (Q1), the median, the 75th percentile (Q3), and the maximum observation (upper edge). The FDR-corrected P values shown were based on the Mann-Whitney U test with multiple-testing correction. (B) Principal coordinate analysis (PCoA) score plots based on Bray-Curtis distance at genus level. (C) The number of microorganisms shared and unique to the three types of samples. (D) The dominant microorganisms of the three types of samples. F, feces; S, saliva; TS, throat swab. *P < 0.05, **P < 0.01

Variations in the gut microbiota diversity during and after the one-year Antarctic deployment

The α-diversity analysis based on the Shannon index, Ace and Chao1 estimators showed no significant changes in community richness among the five groups (P > 0.05, Fig. 3A and S2). This indicated that the complexity of the microbial community composition did not change significantly during the participants’ residence in Antarctica and after returning to China. PCoAs with unweighted UniFrac distances illustrated that most of the T5 group data were clustered near T4 rather than T1 (Fig. 3B). The unweighted pair-group method with arithmetic means (UPGMA) tree analysis showed that the microbial community composition of the T5 group was more similar to that of T4 than to T1, which validated the results of PCoA (Fig. 3C). Furthermore, the unweighted UniFrac distance of each group decreased gradually, indicating that the individual differences of the microbiota composition among the tested population were reduced during their stay in Antarctica. Interestingly, the level of individual differences did not change after returning to China, although their living environment was completely different (Fig. 3D). The above results demonstrated that the intestinal microbiota of individuals changed during the one-year Antarctic deployment, and this change could persist for quite a long time even after they left Antarctica.

Fig. 3.

Diversity and distance of the intestinal microbial communities in each group. (A) α-diversity analysis estimated by Shannon index. The non-parametric Mann-Whitney U test was used to test for significant differences between any two groups. Comparison of multiple groups was done using a non-parametric Kruskal-Wallis test. (B) PCoA based on the unweighted UniFrac distances of the microbial communities among the five groups. P value was calculated by PERMANOVA. (C) UPGMA tree based on the unweighted UniFrac distances between the five groups. (D) Unweighted UniFrac distances between the five groups. The boxplot displays five values, which include the minimum observation (lower edge), the 25th percentile (Q1), the median, the 75th percentile (Q3), and the maximum observation (upper edge). * P < 0.05, ** P < 0.01, *** P < 0.001

Differences in gut microbiota composition in five groups

The most abundant phyla in each group are illustrated in Fig. 4A. At the phylum level, the dominant bacteria in the population were Firmicutes, Bacteroides, Actinobacteria, and Proteobacteria, with average proportions of 0.79, 0.12, 0.06, and 0.02, respectively, suggesting a western-style dietary microbiota composition. Compared with those in the T2, T4 and T5 groups, the relative abundances of Actinobacteria were significantly greater in the T1 and T3 groups, which may be related to the phased supply of yogurt. The relative abundance of Proteobacteria in the T5 group was higher than that of other groups (Fig. S3A).

Fig. 4.

Bacterial composition in each group. (A) Relative abundance of the bacteria at phylum level. (B) Relative abundance of the bacteria at genus level

At the genus level, the abundances of Bacteroides, Escherichia-Shigella, Anaerotruncus and Citrobacter in the Han population changed significantly in response to residence in Antarctica (Fig. 4B and Fig. S3B). In particular, Escherichia-Shigella and Citrobacter were significantly increased in the T5 group, compared with those in T1-T4 groups. We also found a genus of bacteria, Alistipes, which increased in a step-like manner, and decreased significantly after returning to China (Fig. S4).

The differences between groups were determined by LEfSe with a P value < 0.05 and the logarithm of LDA > 2 at the genus level. Compared to the results in Antarctica (T1-T4), there were significant changes in many genera after returning to China. Conditional pathogenic bacteria such as Escherichia coli, Klebsiella and Bilophila increased significantly (Fig. 5A). The genus Akkermansia, which is closely related to human immunity, was significantly enriched in the T5 group (Fig. 5B).

Fig. 5.

The linear discriminant analysis effect size (LEfSe) at the genus level between groups. (A) Genera selected by LEfSe for group T1, T2, T4 and T5. (B) Genera selected by LEfSe between T5 and the other groups. The genera are shown on the y axis and logarithmic LDA scores are shown on the x axis

Variations in the clinical indexes during and after the one-year Antarctic deployment

To test the changes in serum indexes for different durations in Antarctica, clinical indexes, including hypothalamic-pituitary-thyroid axis hormones, liver function indexes, renal function indexes, blood lipid indexes and neurotransmitter, were obtained from blood biochemistry measurements and ELISAs (Table S2). The UPGMA tree analysis showed that the microbial community composition of the T5 group was more similar to that of the T2 and T3 groups than to T1 (Fig. 6A), suggesting that the Han individuals with Antarctic experience maintained a signature of polar altitude even 7 months after returning to China. The hierarchical clustering test using all the serum indexes showed that the T5 group was comparable to T2 and T3, rather than T1 (Fig. 6B). Serum concentrations of most biochemical indexes, especially total thyroxine (TT4), alanine transaminase (ALT), γ-glutamyl transpeptidase (GGT), uric acid (UA), creatinine (Urea), serum creatinine (Scr), serum cystatin C (CysC), triglyceride (TG), total cholesterol (TC), high density lipoprotein (HDL), apolipoprotein A1 (ApoA1) and apolipoprotein B (ApoB), exhibited increasing trends before mid-of-wintering (T3) but decreased significantly by the end of the winter (T4) (P < 0.05). Additionally, most of the clinical indexes increased at seven months after returning to China (T5), while 5-HT was significantly decreased (P < 0.05). However, the changes in other indexes, including TSH, TT3, FT3, and FT4 showed no significant differences among the five groups (Fig. S5A).

Fig. 6.

PCA and heat map of the groups based on the clinical parameters and the trend for clinical parameters. (A) UPGMA tree based on the unweighted UniFrac distances of the clinical parameter data between the five groups. (B) Heat map and hierarchical clustering of the groups based on all the clinical parameter data. Groups are shown on the y axis, while clinical parameters are shown on the x axis. The values of the scale correspond to the values converted by z-score

Correlations between clinical indexes and the gut microbiota

To explore the relationship between clinical serum indicators and the gut microbiota, we performed a Spearman univariate correlation analysis between the clinical indexes and the genera (relative abundance > 0.01) in the gut with a coefficient R of > 0 and a P value of < 0.05 (Fig. 7). Since the abundances of Bacteroides, Escherichia-Shigella, Anaerotruncus and Citrobacter changed significantly during the one-year Antarctic deployment (P < 0.05), we specifically analyzed the correlation between the changes in the genera and the changes in the clinical indexes. Spearman correlation analysis showed that the change in the relative abundance of Anaerotruncus was negatively associated with the changes in the concentration of TT4 (r = -0.27, P = 0.049), ALT (r = -0.51, P < 0.001), GGT (r = -0.35, P < 0.01), CysC (r = -0.33, P = 0.016) and ApoA1 (r = -0.34, P = 0.013). Besides, the relative abundance change in Citrobacter was positively associated with the change in the concentration of UA (r = 0.33, P = 0.014).

Fig. 7.

Correlations between changes in genus abundance and changes in clinical indexes. The heat-map shows the Spearman correlation coefficient between changes in clinical indexes and changes in relative abundance of individual genera. The intensities of the colors represent the degree of association between the concentration changes of clinical indexes and in the relative abundance changes of individual genera as measured by the Spearman’s correlations. * P < 0.05, ** P < 0.01, *** P < 0.001

Discussion

This study systematically and objectively investigated the changes in the intestinal microbiota and the changes in clinical serum indexes of a group of Han people in Antarctica at different residence times. We demonstrated that the complexity and composition of intestinal microbiota changed significantly during and even after the one-year extreme environment expedition. We also showed the correlation between clinical serum indexes and intestinal microbiota changes in the studied population.

The Shannon index was highest in throat swab, followed by saliva and feces. Therefore, the feces samples with the lowest α-diversity were chosen for subsequent analysis. Few study directly compared the alpha diversity among different sample types. The higher Shannon index of the throat swab and saliva in this study might because the oral microbiome is both connected to the gut microbiome through the gastrointestinal tract and is exposed to the external environment [13]. In a meta-analysis conducted by Maki et al. [14], the alpha diversity of microbiome samples was much higher in the oral samples, as compared to the stool samples. They found that stool is ranked high in species richness, but low in Shannon diversity, while saliva is ranked low in specifies richness but high in Shannon diversity.

From the microbiota analysis, including the α-diversity analysis and the UPGMA analysis based on the bacterial composition, we found that the T5 group was more similar to T4 rather than T1. After returning to China from Antarctica (T5), although the living environment of the participants changed, the individualized differences in their intestinal microbiota did not change significantly compared to those found during their stay in Antarctica. It is possible that the long-term polar environment has a continuous influence on the gut microbiota, and the specific intestinal microbiota that has reached a steady state can hardly change within a certain period of time. Our results were in consistent with Jia et al. [15] who demonstrated that plateau-living Han population was closer to native Tibetans than to Han population living on the plain based on the UPGMA and PCoA analyses. In addition, the intestinal microbiota of the Han population that returned to the plain area for 3 months still resembled that of the plateau-living Han population rather than that of the Han population living on the plain [15]. This finding supported that the gut microbiota probably required a long recovery period after experiencing a severe and lasting extreme environment, such as extremely low temperature and hypoxia. Our study also showed the complexity of the microbial community composition did not significantly change during the residence in Antarctica and after returning to China.

The most abundant bacteria in the intestine of normal Han individuals are Firmicutes (0.388), Bacteroides (0.278), Actinobacteria (0.082), and Proteobacteria (0.021) [16]. Due to the polar specific diet (mainly meat dishes such as pork, chicken, seafood) and the cold environment, the Firmicutes in the intestinal microbiome increased significantly (0.79), and the Bacteroides decreased greatly (0.12), showing microbiota composition of a western-style dietary. Our findings were consistent with previous studies [17, 18], which indicated that the overall structure of gut microbiota was principally influenced by long-term dietary intake and extreme environment. But many studies have linked the western-style diet to inflammation, diabetes, cardiovascular risks, obesity, and metabolic syndromes [19, 20]. Compared to the biodiversity on Chinese mainland, Antarctica’s biodiversity is relatively lower [21]. After returning to China, the explorers were in the environment with a more complex microbial community, which caused a significant increase in conditional pathogenic bacteria populations and stimulated the host immune response. The increase in Akkermansia might be a result of induced intestinal adaptive immune response during homeostasis [22]. Interestingly, we found a genus of bacteria, Alistipes, that increased in a step-like manner, and it decreased significantly after returning to the normal environment. The Alistipes spp. is a relatively new genus of bacteria isolated from clinical samples, it has been shown to play a pathogenic role in diseases such as anxiety, myalgic encephalomyelitis/chronic fatigue syndrome and depression [23, 24]. These findings are related to evidence of an increase in Alistipes in people with depression because expeditions in Antarctica often experience fatigue and stress.

The clinical parameters analysis showed that South Pole expeditioners had a significant reduction in most of the indexes at the end of wintering. In terms of thyroid function, our expeditioners only experienced the significant increase of TT4 during wintering and reduction at the end of wintering. Whereas a study on winter-over members at Zhongshan Station found a significant decreasing trend in free thyroxine (FT4) during the winter, and increasing trends in thyrotropin (TSH) and total triiodothyronine (TT3) when returning to China [25]. These discrepancies may also be due to varying degrees of habitability and social support. Compared with the explorers at Zhongshan Station, our participants at Great Wall Station in sub-Antarctic region experienced a much warmer environment and milder photoperiodic alteration since they are at a lower latitude location.

Correlation analysis allowed us to identify several new bacterial genera that are potentially implicated in the host metabolic health. We observed positive correlations between Citrobacter abundance and serum concentration of UA. This possibility is consistent with the fact that Citrobacter spp. used UA as an energy source anaerobically, but not aerobically [26]. We also found negative correlation between the abundance of Anaerotruncus and serum concentration of TT4, ALT, GGT, CysC and ApoA1. As shown in Fig. S2, Anaerotruncus spp. increased significantly at the end of wintering (T4), and decreased significantly after returning to China (T5). It has been reported that the genera Anaerotruncus, isolated by microbial culturomics from fecal specimen of a Frenchman after bariatric surgery, mainly involved in the carbohydrate metabolism pathway, producing beneficial substances acetic acid and butyric acid [27, 28]. As a probiotic, it may play a role in regulating liver and kidney function and lipid metabolism.

There are some limitations in this study which should be declared. First, a more comprehensive investigation in gut microbiota would have been possible with samples during the baseline period. In addition, we were constrained by the small numbers of eligible study participants and the differences among individuals were inevitable. For the correlation analysis between clinical indicators and the gut microbiota, we adopt the Spearman univariate correlation analysis that has been widely utilized in references [15, 29–32]. However, this method doesn’t consider auto-correlation and a linear-mixed model-based approach or other equivalents would have been more appropriate [33]. Besides, the version of Silva (ver. 123) used for taxonomic assignment was outdated. It is a pity that this study lasted from 2017 to 2019, and we are unable to redo the analysis due to time limitation. The strengths of this study included the unique environment, the controlled-diet design, the frequent sampling and the relatively long observation period. In contrast to previous studies, we applied 16 S rRNA amplicon profiling, which allowed us to understand both gut microbiota response and clinical indexes. Therefore, we were able to gain more information about host–gut microbiota interactions in response to environment.

Conclusion

We demonstrated that the gut microbiota of Han individuals was changed after one-year Antarctic deployment even after they left Antarctica. Spearman correlation analysis showed that the change in the relative abundance of Anaerotruncus was negatively associated with the changes in the concentration of TT4, ALT, GGT, CysC and ApoA1. Also, the relative abundance change in Citrobacter was positively associated with the change in the concentration of UA. This study will improve our understanding of the influence of the Antarctic environment on the gut microbiota and blood clinical indexes, as well as the adaptation mechanisms and intervention goals for Antarctic adaptation.

Electronic supplementary material

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Abbreviations

T-RFLP

terminal restriction fragment length polymorphism

OTUs

Operational Taxonomic Units

SW

Shannon-Wiener

PCoA

Principal coordinate analysis

LDA

linear discriminant analysis

UPGMA

unweighted pair-group method with arithmetic means

ALT

alanine transaminase

UA

uric acid

Scr

serum creatinine

CysC

Serum cystatin C

TG

Triglyceride

TC

Total cholesterol

HDL

High density lipoprotein

ApoA1

apolipoprotein A1

ApoB

apolipoprotein B

Author contributions

Conception and design: WW, GC and AZ. Formal analysis: LW and DG. Funding acquisition: WW and GC. Methodology: LW, DG, WG, YZ, and MZ. Supervision: WW, GC and AZ. Writing the original draft: LW and DG. Writing, review, and editing: WW, GC and AZ. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the National Natural Science Foundation of China [grant number 81971990, and 82172326], and the Science and Technology Commission of Shanghai Municipality [Grant number 20dz1207200].

Data availability

Raw sequencing data of the 16 S rRNA gene V3–V4 regions and accompanying information are available in Sequence Read Archive database under accession number PRJNA665845.

Declarations

Ethics approval and consent to participate

All study protocols were approved by the medical ethics committee of Shanghai East Hospital and were in accordance with Declarations of Helsinki. Informed consent forms were signed by all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.