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On-orbit microbial succession patterns of the China Space Station during the construction period

Microbiome volume 13, Article number: 73 (2025) Cite this article

Abstract

Background

The China Space Station (CSS) modules feature many areas that are difficult to clean and thus susceptible to microbial outbreaks. A new sampling method utilizing an equivalent material sheet was applied to characterize the diversity of microbes that accumulated in inaccessible areas in orbit on the CSS. Equivalent material sheet is a membrane made of the same material as the wall of the module.

Results

Fifty samples were collected from interior surfaces (work, sleeping, and sanitary areas) of the Tianhe core module and the Wentian and Mengtian experimental modules, covering three flights by the Shenzhou (SZ)-12 to SZ-14 astronaut crews from 2021 to 2022. The numbers of culturable bacteria and fungi that accumulated during the on-orbit periods of each flight ranged from 0 to 2.83 × 109 colony-forming units/100 cm2. The number of bacteria detected by quantitative PCR (qPCR) ranged from 1.24 × 105 to 2.59 × 109 rRNA gene copies/100 cm2, with an average viability of 65.08%. A total of 103 bacterial strains and 27 fungal strains were cultured and isolated. The dominant culturable microorganisms were mainly from the genera Bacillus, Staphylococcus, Aspergillus, Cladosporium, and Penicillium. High-throughput sequencing results showed that the predominant bacteria were Pseudomonas, Stenotrophomonas, Methylobacterium-Methylorubrum, Sphingomonas, Bacillus, Staphylococcus, and Nocardiopsis. The microbial diversity in each module varied significantly with sampling time and sampling area. In the early stage of CSS construction with the SZ-12 crew, the microbial species evenness in the modules was high; later, with the SZ-13 crew, Pseudomonas began to appear as the dominant microorganism. More than half (58.80%) of the bacteria on module surfaces originated from the human skin and oral environments. Lactobacillus was present in all areas of the three modules at all sampling times. The biomarker bacteria Stenotrophomonas sp., isolated from the work area in the Tianhe core module, are typically derived from plants. SourceTracker analysis indicated that most of the microbes in the orbiting CSS came from human bodies, and that microbial diversity was significantly altered with each crew change.

Conclusion

Future efforts at microbial prevention and control on orbit should emphasize the human and plant origins of microbes. Information on the microbial diversity in the condensate zone could be useful to guide the development of new strategies to prevent and control microbes during space flight.

Video Abstract

Introduction

The China Space Station (CSS) is a state-level manned space laboratory built by the People’s Republic of China. The core module of the CSS was completed in 2021, and in 2022, the Wentian and Mengtian experimental modules were successively docked with the core module. As of 2024, the construction status of the CSS is a “T” configuration of the three modules. The CSS operates at an orbital altitude of 350–450 km and an inclination of 42–43°, and its designed lifespan is 10 years. The internal environment of the CSS is a typical “extreme special environment” [1,2,3], characterized by low radiation dose, microgravity, long-term sealing, a 24–25 ℃ temperature, and 30–70% humidity. This special environment supports the long-term residence of humans while providing favorable conditions for microbial reproduction.

There are three main sources of microorganisms in space stations: modules, cargo, and humans. Module-source microorganisms are attached to the interior spaces of the modules and enter orbit during module launch. During the early stages of CSS construction, each module was strictly sterilized before launch; however, it was difficult to avoid small amounts of microbial residue [4]. Cargo-source microorganisms, which enter the modules with the cargo or packaging materials, are similarly few in number but difficult to avoid. Human-source microorganisms are brought into the modules by astronauts. For reference, a normal, healthy adult harbors trillions of microorganisms [5].

Early reports on the microbial ecology of the on-orbit Mir space station (MIR) and International Space Station (ISS) indicated the presence of large numbers of microorganisms in long-term on-orbit space stations. Furthermore, these microorganisms were shown to adversely affect the health of astronauts and the safe operation of on-orbit instruments and equipment. The space environment can negatively affect astronaut immune function [6] and may support increases in the pathogenicity and drug resistance of certain microorganisms [7,8,9,10,11]. Pathogenic microorganisms may also contaminate water or food [12]. Therefore, microorganisms in the space environment pose a greater threat to astronaut health than those in the terrestrial environment [13]. Astronauts aboard MIR have experienced microbial infections including conjunctivitis, acute respiratory events, and dental infections [14]. Additionally, microorganisms can corrode spacecraft materials, including metals and a variety of polymeric materials [15,16,17,18], posing significant potential hazards to space flight safety.

The MIR was in orbit from 1986 to 2001. During this period, several different forms of microbial sampling were conducted. A 2004 paper reported on the continuous sampling of modules during the 15 years of MIR operation [19]. An article published in 2001 showed that Kawamura et al. conducted a microbiological study on the air, water, and inner walls of the MIR in 1997 [20]. Sampling methods included air vacuum sampling, collection of condensate, and swabbing of the inner walls. In 2004, Ott et al. described their collection of three free condensate samples on the MIR in 1998 [21]. In 2005, Song et al. [22] reported a culture method that was used to study microorganisms in the MIR water system. Because these studies were conducted early on, the detection of microorganisms relied on cultivation methods. However, research has shown that cultivation methods only detect about 1% of microorganisms in typical natural environments [23], with the vast majority of microorganisms being unculturable and thus going undetected. Furthermore, traditional 16S rRNA gene amplification and Sanger sequencing methods are unable to detect all microorganisms from environments with low microbial populations, capturing only a fraction of the more abundant microorganisms [24]. Therefore, the data on microorganisms on the MIR are limited.

The ISS has been in operation from 1998 to the present. Early ISS microbial detection methods were similarly culture based. For example, an article published in 2005 showed that Novikova et al. conducted a 6-year microbiological study on the air and surfaces of ISS starting in 1998 [25]. They used two sampling tools — the SAS air sampler (PBI International, Italy) and surface pipette kits with swabs — for culture and analysis of the numbers and types of bacteria and fungi.

With the rapid development of high-throughput sequencing technology in the twenty-first century [26], microbial analysis methods have gradually been applied in research on the ISS. In 2012, Venkateswaran et al. conducted the first culture-independent 16S rRNA gene detection of microorganisms in the ISS [27], while Lang et al. [28], Sieraf et al. [3], and Madrid et al. [29] reported the results of spatial microbiome research in 2017, 2019, and 2022, respectively. While the findings of these studies suggest that on-orbit microbial diversity changes over time, the microbial ecology data of the ISS were collected after 14 years of operation; thus, the pattern of microbial change in the early days of the station is unknown. Furthermore, some studies have shown that microbial community structure changes with astronaut activities and time [19, 30], while others found that microbial community structure does not change significantly over time [3, 31]. This paradox of microbial patterns in the space environment requires further elucidation.

This study describes microbial research conducted on the interior surfaces of CSS modules from 2021/06/17 to 2022/12/05. During this period, the Shenzhou (SZ)−12 to SZ-14 crew members successively carried out scientific research missions in orbit. We present here the results of culture and quantitative real-time PCR (qPCR) and microbiomics, revealing for the first time the comprehensive changes in microbial communities within the CSS at the construction stage. Applying state-of-the-art microbial molecular ecology tools, we were able to analyze microbial community succession on the CSS during the construction period, enabling speculation on microbial sources as well as potential hazards. Finally, we offer a theoretical foundation for microbial safety control on the CSS and other manned spacecraft in the future.

Results

Counting of cultivable microorganisms

The number of colony-forming units (CFU) per 100 cm2 was calculated from colony counts on spread plates. The SZ-12, SZ-13, and SZ-14 flight samples contained 0–8.53 × 106 CFU/100 cm2, 6.67 × 102 to 2.75 × 105 CFU/100 cm2, and 0–2.83 × 109 CFU/100 cm2 of total cultivable bacteria, respectively, and 0–1.62 × 105 CFU/100 cm2, 0–1.53 × 104 CFU/100 cm2, and 0–1.01 × 108 CFU/100 cm2 of total cultivable fungi, respectively. Spread plate counts were used to determine the numbers of microorganisms in the work, sleeping, and sanitary areas obtained using Luria–Bertani (LB) and potato dextrose agar (PDA) during three flight missions (Fig. 1a). We also calculated the average number of cultivable microorganisms in each flight mission in the three regions (Fig. 1b). Contact plate experiments were conducted on SZ-13 and SZ-14 flight samples. The results showed that the numbers of cultivable bacteria in SZ-13 and SZ-14 samples were 0–20 CFU/100 cm2 and 0–2.35 × 102 CFU/100 cm2, respectively, and the numbers of cultivable fungi were 0–2.52 × 102 CFU/100 cm2 and 0–28 CFU/100 cm2, respectively. All culture count results are shown in Supplementary Table 1.

Fig. 1
figure 1

Numbersof microorganisms obtained using spread plate cultivation. The scatter in the graph represents the number of microorganisms belonging to each of the samples in this grouping. Some samples with a microbial count of 0 cannot be shown (refer to supplementary Table 1) and were counted in the calculation of the mean value. a Numbers of microorganisms in the work, sleeping, and sanitary areas of the three flight missions obtained using LB and PDA plates. The green columns represent the mean microbial counts in the sleeping area group, the red columns represent the mean microbial counts in the work area group, and the purple columns represent the mean microbial counts in the sanitary area group. b Average level of cultivable microorganisms in the three regions during each flight mission. The columns drawn with a slash represent the average number of bacteria in the group, and the white columns represent the average number of fungi in the group

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Identification of cultivable microorganisms

The 130 microorganisms isolated from all samples comprised 103 bacteria and 27 fungi, with 25 bacterial and 18 fungal genera. The phylogenetic trees of cultivable bacteria and fungi are shown in Figs. 2 and 3, respectively. The dominant bacterial genera were Bacillus and Staphylococcus, accounting for 45.63% and 14.56% of the total bacterial species, respectively. The dominant fungal genera were Aspergillus, Cladosporium, and Penicillium, accounting for 18.52%, 14.81%, and 7.41% of the total fungal species. The accession numbers for the 16S rRNA and internal transcribed spacer (ITS) sequences are shown in Supplementary Table 2. Three genera of bacteria — Bacillus, Pantoea, and Pseudomonas — were obtained from condensate samples using culture-based methods.

Fig. 2
figure 2

Phylogenetic tree of culturable bacteria. Individual microorganisms are color-coded by taxonomic family, with 18 families in total. The end of each strain name is labeled with the region of the space station module from which the strain was isolated. There were 11 subgroups: SZ-12 Work area, 12 Sleeping area, 13 Work area, 13 Sleeping area, 13 Sanitary area, 14-TH Work area, 14-TH Sleeping area, 14-TH Sanitary area, 14-WT Work area, 14-WT Sleeping area, and 14-MT Work area. Phylogenetic tree constructed using MEGA software (Version 11.0.13), with the bootstrap replication number set to 1000

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Fig. 3
figure 3

Phylogenetic tree of culturable fungi. Individual microorganisms are color-coded by taxonomic family level, with 10 families in total. The end of each strain name is labeled with the region of the space station module from which the strain was isolated. There were 11 subgroups: SZ-12 Work area, 12 Sleeping area, 13 Work area, 13 Sleeping area, 13 Sanitary area, 14-TH Work area, 14-TH Sleeping area, 14-TH Sanitary area, 14-WT Work area, 14-WT Sleeping area, and 14-MT Work area. Phylogenetic tree constructed using MEGA software (Version 11.0.13), with the bootstrap replication number set to 1000

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Total and viable bacterial counts

Culturable microorganism counting is a simple, easy method that all space stations use to rapidly assess microbial contamination levels on orbit. However, because culturable microorganisms represent a very small percentage of all microorganisms, we also used qPCR to obtain complete information on the total numbers of microorganisms in the samples. For all collected samples, the ITS region was amplified by PCR to detect fungi. But the result showed that no fungi can be detected by PCR. Figure 4 shows the total and viable bacterial counts in each sample. The average numbers of microorganisms (given in terms of the copy number of rRNA genes) in SZ-12, SZ-13, and SZ-14 samples were 1.11 × 106 copies/100 cm2, 2.15 × 107 copies/100 cm2, and 3.26 × 108 copies/100 cm2, respectively. The average number of bacteria in the three flights showed an upward trend. The average number of microorganisms contaminating the sleeping area was low (9.23 × 106 copies/100 cm2) compared with that in the sanitary area (6.45 × 108 copies/100 cm2). Fungi were not detected by qPCR.

Fig. 4
figure 4

PCR-based detection ofbacteria in all samples. a Total bacterial numbers of the three flight missions (SZ-12, SZ-13, and SZ-14). b Total bacterial counts at three sampling sites (Work area, Sleeping area, and Sanitary area). c Numbers of total viable bacteria on two flight missions (SZ-13 and SZ-14). d Numbers of viable bacteria at three sampling sites (Work area, Sleeping area, and Sanitary area) on SZ-13 and SZ-14

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The mean numbers of viable bacteria in the SZ-13 and SZ-14 flight samples were 4.99 × 106 copies/100 cm2 and 1.76 × 108 copies/100 cm2, respectively. The calculated percentages of viable bacteria in the SZ-13 and SZ-14 flight samples were 60.14% and 69.07%, respectively, revealing that the percentage of surviving bacteria was higher in the SZ-14 flight than in the SZ-13 flight. The average percentage of viable bacteria in all samples was 65.08%, while the mean numbers of viable bacteria in the sleeping, work, and sanitary areas were 7.82 × 106 copies/100 cm2, 5.94 × 107 copies/100 cm2, and 3.64 × 108 copies/100 cm2, respectively. The mean percentages of viable bacteria in these three areas were 68.94%, 59.91%, and 72.49%, respectively. Bacterial survival was found to be lowest in the work area and highest in the sanitary area.

Bacterial sequencing using the Illumina MiSeq system

Bacterial community

The V3–V4 region of the 16S rRNA gene was amplified from the total DNA of all non-propidium monoazide (PMA)-treated bacteria. Twenty-two samples were successfully amplified and sequenced (Table 1). A total of 1,000,027 clean tags with sequence lengths mainly distributed between 400 and 440 bp were obtained, generating 2525 amplicon sequence variants (ASVs). In addition, the sample from the condensate area of SZ-12 flight was successfully amplified to yield 125 ASVs. The ASV and average ASV data within the group corresponding to all samples are shown in Supplementary Table 3. A box plot of the UniFrac distance (Supplementary Fig. 1), a rarefaction curve of 23 samples (Supplementary Fig. 2), a Shannon–Wiener curve of 23 samples (Supplementary Fig. 3), specifications accumulation curves (Supplementary Fig. 4), and rank ambiguity curves (Supplementary Fig. 5) are also provided. The 22 samples were manually divided into seven groups on the basis of the astronauts’ activity patterns in different module regions: SZ-12 work area (W_12), SZ-13 sleeping area (S_13), SZ-13 work area (W_13), SZ-14 sanitary area (C_14), SZ-14-TH work area (W_TH_14), SZ-14-WT work area (W_WT_14), and SZ-14-MT work area (W_MT_14).

Table 1 Sample information
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The bacterial genera and families detected in all of the surface samples are shown in Fig. 5a, b, and c. At the genus level, Pseudomonas had the highest proportion, followed by Stenotrophomonas, Methylobacterium-Methylorubrum, Sphingomonas, Bacillus, Staphylococcus, and Nocardiopsis. The most abundant bacteria in the various samples were as follows: SZ-12 work area, Nocardiopsis (14.39%), Staphylococcus (10.15%), Lactobacillus (9.10%), Paenibacillus (8.94%), Muribaculaceae (8.51%), and Pantoea (7.46%); SZ-13 sleeping area, Pseudomonas (79.20%), Ralstonia (4.27%), Staphylococcus (2.35%), Cutibacterium (1.33%), Sphingomonas (0.90%), and Streptococcus (0.76%); SZ-13 work area, Pseudomonas (69.06%), Acinetobacter (7.89%), Lysinibacillus (7.18%), Ralstonia (2.11%), Staphylococcus (1.70%), and Lactobacillus (0.79%); SZ-14 sanitary area, Sphingomonas (45.36%), Bacillus (39.71%), Pseudomonas (1.51%), Stenotrophomonas (0.62%), Blautia (0.56%), and Rhodococcus (0.56%); SZ-14 Tianhe module work area, Pseudomonas (40.89%), Stenotrophomonas (35.04%), Curtobacterium (5.43%), Brevundimonas (5.36%), Bacillus (1.89%), and Bacteroides (1.74%); SZ-14 Wentian module work area, Methylobacterium-Methylorubrum (51.77%), Pseudomonas (29.41%), Stenotrophomonas (8.12%), Staphylococcus (2.03%); and SZ-14 Mengtian module work area, Pseudomonas (54.13%), Stenotrophomonas (17.90%), Chloroplast (4.84%), Acinetobacter (2.66%), Bacillus (1.24%), and Brevundimonas (1.23%). Figure 5c illustrates the diversity characteristics of each location.

Fig. 5
figure 5

Bacterial community composition analysis results. a, b Histograms of each group of samples of genus level (a) and family level (b) analysis. c Histograms of each sample of genus level. d Heat map analysis of the top 20 most abundant ASVs. e Venn diagram showing the number of unique and shared ASVs for each group

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The heatmap in Fig. 5d shows the top 20 most abundant microbial genera across all samples. Pseudomonas had high abundance and was detectable in samples from all three flight missions. It had the highest proportions of any microbe in SZ-13 and SZ-14 flight samples, averaging 74.13% and 31.49%, respectively. ASVs specific to each group of samples accounted for 79.80% of the total ASVs (Fig. 5e). Only one ASV, belonging to the genus Lactobacillus, was common to all seven groups, suggesting that this bacterial genus was present over time and across different regions of the modules. For the analysis of the single condensate sample collected from SZ-12, we constructed a genus-level evolutionary tree (Fig. 6a) and a Krona species display diagram (Fig. 6b). A total of 41 bacterial genera were detected, with the vast majority belonging to the three phyla: Actinobacteriota, Firmicutes, and Proteobacteria. The dominant genera were Pantoea, Nocardiopsis, and Paenibacillus.

Fig. 6
figure 6

β diversity analyseis and bacterial community composition analysis of condensate water. a Genus-level phylogenetic tree of microorganisms within the condensate samples. b Schematic representation of microbial clonal species in the condensate samples. The circles represent the different taxonomic levels from the inside out. The size of each sector represents the relative abundance of the different species. c PCA of the seven sample groups (999 permutations; significance threshold, P < 0.05). The red, yellow, and green circles represent the SZ-12, SZ-13, and SZ-14 sample clusters, respectively. The blue circle represents the SZ-14 sample cluster collected from the sanitary area. d Comparison of PCA of the microbial compositions of CSS and ISS samples (999 permutations; significance threshold, P < 0.05). The yellow circle represents SZ-12 to SZ-14 sample clusters collected from the CSS, and the blue circle represents sample clusters collected from the ISS

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Beta diversity

Figure 6c shows the results of Principal Component Analysis (PCA) of CSS samples from the three flights. Clustering was observed in all three batches of samples, with the SZ-12 sample clusters circled in red (P < 0.05), the SZ-13 sample clusters (which were the most concentrated) in yellow (P < 0.05), and the SZ-14 sample clusters in green (P < 0.05) (Fig. 6c). This indicated that the three flight batches showed significant differences in community structure. The differences among SZ-12 samples were relatively small. Similarly, the clustering relationship of SZ-13 samples was close to that of SZ-12, and the microbial compositions of samples collected from the work and sleeping areas were very closely clustered. The clustering forms of each sample in SZ-14 were relatively loose compared with those in SZ-12 and SZ-13. It is worth noting that the samples in the SZ-14 group showed obvious regional specificity, which was reflected by an individual cluster of the three sanitary area samples (blue circle in Fig. 6c; P < 0.05). PCA comparing the CSS and ISS revealed clusters of CSS samples (yellow circle; P < 0.05) and ISS samples (blue circle; P < 0.05) (Fig. 6d). Data from an analysis of similarities (ANOSIM) test between groups using the Bray–Curtis distances were supportive of the aforementioned results, illustrating that microbial diversity was very different between the two on-orbit space station modules (P < 0.05) (Supplementary Table 4).

Alpha diversity

We used the observed species, phylogenetic diversity (PD)_whole_tree, Shannon, and Simpson indexes to describe the alpha diversity in each group of samples from 3 flights. From the perspective of location, the observed species index of the SZ-12 work area was very low (Fig. 7a). In contrast, while the Simpson index of the SZ-12 workspace was very high (Fig. 7d), indicating a low number of species, the distribution was even, with no dominant population. The remaining six groups of samples exhibited dominant microbial populations.

Fig. 7
figure 7

Bacterial alpha diversity. a Observed_species; b PD whole tree; c Shannon index; d Simpson index

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SourceTracker analysis

Analysis using the SourceTracker algorithm revealed that most of the bacteria in SZ-14 (44.74%) were derived from SZ-13, with a smaller amount (12.67%) from SZ-12, and the remainder (42.59%) coming later, as shown in Fig. 8a. SZ-14 microorganisms from SZ-13 mainly originated from the oral cavity (47.21%) and skin (12.69%), with the remaining 40.10% from unknown sources. Those from SZ-12 included mostly oral (47.05%) and skin (12.65%) microorganisms, with the remaining 40.30% from unknown sources. Microorganisms not originating from SZ-12 or SZ-13 were of three main environmental types — oral (0.27%), skin (57.11%), and soil (0.09%) — with 42.54% from unknown sources. No microorganisms originating from the gut were found. Overall, up to 58.80% of module microorganisms were from the human mouth and skin.

Fig. 8
figure 8

Bacterial sources, interrelationships, and biomarker bacteria in the different module areas. a Results of SourceTracker analysis. The inner circle shows the proportion of microorganisms in SZ-14 that came from SZ-12, SZ-13, and other sources, while the outer circle shows the proportions of microorganisms originating from skin, oral, soil, and unknown environments in each area. b LefSe analysis, showing the differences in bacteria among the seven sample groups, using a non-parametric Kruskal–Wallis test threshold of 0.05, a Wilcoxon rank sum test threshold of 0.05, and an LDA score threshold set to 3. c Cladogram illustrating the number and phylogenetic status of individual biomarker bacteria. d Co-occurrence network diagram. The red line in the graph represents a positive correlation and the green line represents a negative correlation

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Linear discriminant analysis (LDA) effect size (LEfSe)

The results of LEfSe performed on the seven groups of samples are shown in Fig. 8b and c. This figure illustrates the genus-level biomarker bacteria identified in the following groups: W_TH_14, Stenotrophomonas; W_WT_14, Methylobacterium and Methylobacterium-Methylorubrum; S_13, Pseudomonas; C_14, Sphingomonas and Sphingomonas paucimobilis. All of these bacteria had LDA scores > 5, indicating that they were all significant biomarkers of the respective source areas. No biomarkers with LDA scores > 5 were identified in the W_12, W_13, and W_MT_14 groups.

Co-occurrence of microbial taxa

A co-occurrence network graph based on the relationships among the top 20 most abundant ASVs was drawn, with the red line representing a positive correlation and the green line representing a negative correlation (Fig. 8d). This figure illustrates that ASV_1 had the highest abundance and belonged to the Pseudomonas genus. It was only positively correlated with ASV_11, ASV_4, ASV_9, and ASV_10, all of which belonged to the genus Stenotrophomonas and exhibited additional positive correlations with other ASVs. The network P and r values can be found in Supplementary Tables 5 and 6, respectively.

Discussion

Our cultivation results showed that the number of culturable microorganisms was very unevenly distributed in the CSS. Furthermore, the number of microorganisms obtained by the spread plate method was higher than that obtained by the contact plate method, as evidenced by the respective orders of magnitude of the results: specifically, ranges of 0–252 CFU/100 cm2 for contact plates and 0–2.83 × 109 CFU/100 cm2 for spread plates. The average numbers of bacteria and fungi obtained from contact plate culture were 16.52 CFU/100 cm2 and 10.67 CFU/100 cm2, respectively, compared with 5.82 × 107 CFU/100 cm2 and 2.08 × 106 CFU/100 cm2, respectively, from spread plate culture. Because of its ease of operation, the contact plate method has been a commonly used tool for the detection of on-orbit microbial counts at the MIR and ISS. However, the microbial counts obtained from the contact analysis are much smaller than the true values, possibly because of the gentle external force used to press on the contact plates, resulting in the collection of a small number of microorganisms from each sample. In addition, because the contact area is small and the colonies may not be uniformly distributed on the culture medium, the colonies are prone to grow into a lawn, resulting in observational errors and failing to reflect the true level of contamination. For the spread plate method in this study, we used a cotton swab to rub the surface of each equivalent material sheet sample, enabling the collection of more microorganisms; after 20 min of full elution and shaking, the collected microorganisms were sufficiently released from the swab. The results obtained from spread plate culture of microbial samples collected from the ISS showed an average of 7 × 105 bacterial CFU/m2 and a maximum of 7 × 105 CFU/m2 of fungi [31]. It was reported that the numbers of culturable bacteria and fungi inside the MIR exhibit fluctuations depending on the mission, with bacterial and fungal counts ranging from approximately 10–107 CFU/m3 and 102–107 CFU/m3, respectively [19].

It is worth mentioning that the sampling method we used for the analysis of microorganism contamination in the CSS is very different from that used in the MIR and the ISS. The sampling method used in previous studies of microbial contamination in the ISS and MIR was to stick or wipe the module surface directly with contact plates, cotton swabs, or wet wipes, thus focusing on detecting real-time microbial levels within the module. This study represents the first utilization of equivalent material sheets as samples for the collection and study of microorganisms in orbit. Each equivalent material sheet was adhered to the respective module surface for the duration of each flight, and was not subjected to any cleaning operation. Thus, the equivalent material sheets reflected the accumulated levels of microbial contamination of inaccessible areas throughout the flight for each crew.

The dominant bacterial genera obtained by culture-based methods in this study were Bacillus and Staphylococcus, and the dominant fungal genera were Aspergillus, Cladosporium, and Penicillium. The most abundant culturable bacteria in the ISS were reportedly Bacillus and Staphylococcus, and the dominant fungi were Aspergillus niger, Penicillium, and Malassezia spp. [2, 32]. In the MIR module, the dominant genera of culturable bacteria were Staphylococcus, Corynebacterium, Bacillus, and Micrococcus [19], and the dominant fungus was Penicillium chrysogenum [33]. By comparison, the dominant culturable bacteria and fungi in the CSS were very similar to those in the ISS and MIR: namely, Bacillus, Staphylococcus, and Penicillium were culturable in all three space stations, and Aspergillus was the dominant fungus in both the CSS and ISS.

Cultivable microorganisms are considered to account for about 1% of the total number of microorganisms in nature. The dominant bacteria in the orbiting CSS identified through high-throughput sequencing methods comprised seven genera: Pseudomonas, Stenotrophomonas, Methylobacterium-Methylorubrum, Sphingomonas, Bacillus, Staphylococcus, and Nocardiopsis. Of these, only Pseudomonas, Stenotrophomonas, Bacillus, Staphylococcus, and Nocardiopsis were culturable. This suggested that a large number of non-culturable microorganisms also exist in the orbiting space station.

Two research projects, the Microbial Tracking (MT)−1 [3] and MT-2 [31] studies, were conducted in 2015–2016 and 2017–2018, respectively. During this period, seven in-orbit samples were taken from eight fixed points inside the ISS. The results reported for 2019 and 2022 showed no significant differences in the alpha diversity of microorganisms of all samples collected during the seven flights, indicating that microbial alpha diversity data do not vary with space and time. This conclusion is not entirely consistent with our experimental results. In our study, the uniformity of microbial species in the SZ-12 samples was significantly higher than that in the subsequent SZ-13 and SZ-14 samples. This indicated that, in the early stage of the construction of the CSS, the number of microorganisms in the module was low and the degree of uniformity was high, with dominant species gradually appearing over time in SZ-13 and SZ-14.

Regarding the beta diversity of the seven samples collected in the ISS module, no apparent differences at the microbiome level were observed between locations. However, there were significant differences in the microbiome between flight groups [31]. This finding is partially consistent with our experimental results, in which the microbial community structure showed a similarly significant difference at the three sampling time points. Interestingly, our SZ-14 sample analysis demonstrated that there was also significant variability between samples taken from different zones at the same time point, as evidenced by the significant difference in microbial diversity between the sanitary zone samples and the work and sleeping zone samples on the same flight. Therefore, we speculate that the microbial composition characteristics of the different zones in the CSS may be related to the work–life behavior patterns of the astronauts in each zone. Finally, a comparison of the PCAs of the CSS and ISS showed that there were also significant differences in the microbial communities on the different space stations.

Although some scholars studying microbial contamination on the ISS have argued that microorganisms in the space environment are not necessarily more threatening than those on the ground [34, 35], the indirect effects of microorganisms on human health and their ability to corrode materials in the ISS environment are well documented and should not be ignored. The 16S rRNA gene sequencing results in this study were not 100% certain at the species level, but did support our hypothesis that the following strains have a high probability of being present in the CSS: Pseudomonas cedrina, Bacillus cereus, Staphylococcus aureus, Staphylococcus hominis, Aspergillus versicolor, Aspergillus flavus, Cladosporium halotolerans, and Cladosporium anthropophilum.

P. cedrina is easy to grow and forms biofilms on non-biological surfaces [36]. B. cereus can proliferate in pre-packaged refrigerated foods to cause foodborne diseases via the production of enterotoxins. Microbial spores can tolerate extreme environments, and the spores of B. cereus are more hydrophobic than those of any other Bacillus sp., making them easier to adhere to surfaces [37]. S. aureus is a conditionally pathogenic bacterium that normally colonizes the skin surface, oropharynx, and upper respiratory tract of the human body and can cause a wide range of infections. It is resistant to antibiotics and has a strong ability to form biofilms [38]. A. versicolor produces spore proteins that cause allergies in humans [39], and can survive at high temperatures and produce aflatoxins [40]. Cladosporium is a major pathogen of plant and animal diseases that is usually isolated from soil, food, textiles, and other organic matter [41]. It is worthwhile to conduct future research to develop effective methods for eliminating these microorganisms.

The Venn diagram showed that one of the ASVs present in all groups was a bacterium of the genus Lactobacillus, which is found in many parts of the human body, playing important roles in the microbiota of the human digestive system and the female reproductive system [42]. Lactobacillus form biofilms in the vaginal and intestinal microbiota [43] that enable it to maintain population dominance [44], which is crucial for human health [45]. Additionally, Lactobacillus can be sourced from fermented foods, dairy products, fermented vegetables, and meat products [46]. Thus, it can be inferred that the prevalence of Lactobacillus in the CSS is related to the astronauts. So far, there have been no reports of Lactobacillus bacteria posing a threat to space stations or astronauts in orbit. Based on the above considerations, we may consider not completely removing it from the CSS.

The co-occurrence network diagram showed that Stenotrophomonas bacteria were co-cultivated with 24.07% of the microorganisms in the CSS environment, indicating that inhibiting the growth of Stenotrophomonas may also inhibit the growth of the others. Stenotrophomonas bacteria are ubiquitous in the environment, mainly found in the soil and with plants, and some species have the potential to infect humans or contaminate water bodies [47].

The biomarker bacteria of group W_TH_14 was Stenotrophomonas sp. The genus Stenotropiomonas is also mainly present in plants and soil but does not have plant pathogenicity. Stenotropiomonas maltophilia is considered the most important species, benefiting plant growth and health, but may cause human infection and exhibits extensive drug resistance [47]. Plants that have been cultured in the CSS module include Arabidopsis, tomatoes, rice, and cabbage. Microorganisms closely related to plants have become biomarkers of the work area of the Tianhe core module, indicating the need to pay high attention to the prevention and control of microorganisms carried by plant sources in the in-orbit experimental plans. The biomarkers of group W_WT_14 were Methylobacterium sp. and Methylobacterium-Methylorubrum. A notable feature of Methylobacterium, which is commonly found in the atmosphere, soil, and phylloplane, is the ability to utilize a single carbon substrate as the sole source of carbon and energy [48]. The emergence of biomarker bacteria in the Wentian module work area is sufficient to demonstrate the ability of in-orbit microorganisms to tolerate nutrient-poor environments. The biomarker of group S_13 was Pseudomonas sp. Pseudomonas is found in a variety of environments, exhibiting diverse metabolic and environmental adaptability [49]. It was the dominant population in the sleeping area of the Tianhe core module of SZ-13, and was also widely present throughout the entire CSS. The biomarker of group C_14 was Sphingomonas sp. Sphingomonas often coexists with plants and is an opportunistic pathogen that can easily corrode drinking water pipelines made of copper [50]. Sphingomonas appeared in the sanitary area inside the Tianhe core module during SZ-14, and its pathogenicity and corrosion abilities are worthy of attention in future research.

The condensate area, which has high humidity and is thus prone to microbial outbreaks, has been a priority area for microbial prevention and control in the space stations of various countries. In this study, we collected samples from the on-orbit condensate area of the CSS for the first time and provided a comprehensive analysis of the microbial diversity. Microorganisms of the genus Pantoea, as the most abundant microorganism in the condensate samples, are usually derived from soil and water bodies [51]. Microorganisms of the genus Nocardiopsis, as the second most abundant microorganism in the condensate samples, are characterized by tolerance to salt, alkaline, and desiccation conditions [52]. Because Pantoea has been cultured, a method to inhibit the propagation of this microorganism could be a valuable contribution to the microbial prevention and control of condensation water.

During the orbiting period of the SZ-14 flight crew, two experimental modules were successively docked with the core module. These two experimental modules carried a large amount of experimental cargo into orbit, theoretically creating a very high likelihood of introducing microorganisms from the new cargo into the SZ-14 samples. Nevertheless, our SourceTracker analysis showed that up to 57.41% of the bacteria in SZ-14 originated from SZ-13 and SZ-12. In addition, the microorganisms originating from human oral and skin environments accounted for up to 58.80% of the microorganisms in the SZ-14 samples. This is similar to results found in the “Home Microbiome Project” which found that the home microbiome was largely sourced from humans [53]. The proportion of microorganisms originating from soil was much lower, and microorganisms from the gut were completely absent. This finding suggested that the microorganisms in the cabin were closely related to human skin contact and the exchange of saliva droplets during the astronauts’ conversations. Any untraceable microorganisms may be attributable to differences in microorganisms carried by individual astronauts or by bottlenecks in the analytical methods.

The special structures of space station modules feature many inaccessible areas under the module panels, behind the instruments, in the crevices of various circuit structures, and other spaces, where the accumulation of pathogenic and corrosive microorganisms could ultimately lead to the deterioration of the module environment. Contamination by human-source microorganisms is inevitable, but plant-source microorganisms should be targeted for key prevention and control. Meanwhile, future research should be dedicated to developing new microbial detection devices with ultra-high sensitivity and ultra-rapid results in orbit, as well as surface coating materials for the spacecraft cabin structure that offer long-term active sterilization and self-cleaning capabilities. Such work will be of great practical significance for the prevention and control of microbial contamination of the CSS.

Methods

Sample collection and sampling sites

The equivalent material sheet used for sampling comprised a membrane made of the same material as the wall of the module at the sampling site, with a size of 5 cm × 5 cm (25 cm2). Specifically, the material composition of the two types of equivalent material sheets is as follows: composite hard material mainly made of polytetrafluoroethylene, and Metas soft fabric woven with chitosan. For sampling, the crew astronauts pasted the equivalent material sheet on the surface of a designated module area on the day of entry, then removed each sheet to a sterile sealed bag 1 to 2 days before the astronauts were due to return to the Earth’s surface. The equivalent material sheets descended to the ground with the manned spacecraft and were brought back to the laboratory within 24 h after descent. The equivalent material sheets were kept at room temperature during transportation, without any sterilization. Upon arrival at the ground laboratory, sample processing and microbiological analyses were carried out within 12 h.

The microbial samples in this study were collected from the indicated sampling sites in the Tianhe core module (work, sleeping, and sanitary areas), Wentian experimental module (work and sleeping areas), and Mengtian experimental module (work area) of the CSS. The sample collection sites and other information are shown in Fig. 9 and Table 1, and the sample collection times are provided in Fig. 10. All three batches of samples were collected from three separate SZ-12, SZ-13, and SZ-14, and were placed in orbit for three months, six months, and six months, respectively. For Sampling 1, samples were collected from 12 sites in the Tianhe core module (8 from the work area, 3 from the sleeping area, and 1 from the condensate area). The SZ-12 flight crew entered the on-orbit CSS on 2021/06/17 and pasted equivalent material sheets onto the surface of the module walls. After 90 days (2021/09/15), the sheets were removed and placed in sealed bags, which were brought back to the ground by the SZ-12 human-crewed spacecraft. For Sampling 2, samples were collected from 17 sites in the Tianhe core module (9 from the work area, 6 from the sleeping area, and 2 from the sanitary area). The flight crew entered the on-orbit CSS on 2021/10/16 and pasted equivalent material sheets onto the surface of the module walls. After 181 days (2022/04/15), the sheets were removed and placed in sealed bags, which were brought back to the ground by the SZ-13 human-crewed spacecraft. For Sampling 3, samples were collected from 14 sites in the Tianhe core module (5 from the work area, 4 from the sleeping area, and 5 from the sanitary area), five sites in the Wentian experimental module (3 from the work area and 2 from the sleeping area), and two sites in the Mengtian experimental module (all from the work area). The SZ-14 flight crew entered the on-orbit CSS on 2022/06/05, pasted equivalent material sheets onto the surface of the Tianhe core module walls, and removed them after 181 days (2022/12/03). Similarly, equivalent material sheets were pasted on the wall surfaces of the two areas on the respective days of the docking of the Wentian and Mengtian experimental modules, and were left for 131 days and 32 days, respectively, before being removed. All of the above samples were brought back to the ground by the SZ-14 manned spacecraft.

Fig. 9
figure 9

Schematic illustration of the distribution of sampling sites. The sampling sites for the SZ-12, SZ-13, and SZ-14 flights are labeled as follows: red dots for SZ-12 (with red pentagrams indicating the sampling sites in the condensate region), blue dots for SZ-13, and green dots for SZ-14

Full size image
Fig. 10
figure 10

Schematic timeline of sample collectionduring the SZ-12, SZ-13, and SZ-14 flights. Timeline of the placement of equivalent material sheets, recovery of sample sheets, and arrival at the ground laboratory for subsequent experiments

Full size image

Sample processing

The sample processing procedure is illustrated in Fig. 11. Sample processing was carried out at an ultra-clean bench in the ground laboratory. First, the equivalent material sheets were pressed onto a contact plate with agar medium, placed into a tube containing sterile phosphate-buffered saline (PBS) solution, vortexed for 20 min, and spun dry by using a mesh liner tube at low-speed centrifugation (3500 rpm/min for 10 min), and finally removed after centrifugation. This yielded a total of 50 tubes of microbial eluates. A portion of these eluates was immediately used as samples for microbial culture. The remaining samples were placed in a refrigerator at 4 °C for subsequent nucleic acid analysis conducted within 24 h.

Fig. 11
figure 11

Sample processing flowchart

Full size image

Cultivation and counting

For contact plate culture of samples collected and brought back to Earth by the SZ-13 and SZ-14 crews, equivalent material sheets were attached to contact plates containing both LB agar (Cat. No. 22700025, Invitrogen, Carlsbad, CA. USA) and PDA (Cat. No: CM139, Oxford, UK). For spread plate culture, all sample eluents were cultured on LB agar and PDA plates. LB agar plates were cultured at 37 ℃, and colonies were counted at 48 h. PDA plates were cultured at 28 ℃, and colonies were counted at 120 h. The number of microbial CFUs per 100 cm2 was calculated from the plate culture count results. The data were graphically represented using GraphPad Prism (Version 9.5.1).

Gene sequencing and identification

After colony counting, single colonies were re-streaked on fresh plates to ensure the purity of the culture, and incubated at the appropriate temperature for an appropriate amount of time [31]. DNA was extracted from pure cultures of bacteria and fungi. Bacterial DNA extraction was performed using the TIANamp Bacteria DNA Kit (Cat. No. DP302-02, TIANGEN, Tianjin, China), following the manufacturer’s recommended instructions except that 50 µL of molecular grade water (pH 7.0) was used instead of TE buffer in the final wash step for nucleic acids. The extracted bacterial DNA (~ 100 ng/µL; optical density [OD]260/OD280 ratio = 1.80–1.96) samples served as a template for subsequent PCR amplification of the bacterial 16S rRNA gene using the 27F and 1495R primers [54]. For fungi, approximately 5 mg (wet weight) of purified fungal cells were initially frozen in liquid nitrogen, followed by homogenization in a ball mill. Fungal genomic DNA was extracted using the DNAsecure Plant Kit (Cat. No. DP320-02, TIANGEN, Tianjin, China), following the manufacturer’s instructions. In the final step, 50 µL of molecular grade water (pH 7.0) was used to wash the nucleic acids. The purified DNA sample was utilized for subsequent molecular operations, employing the ITS1 and ITS4 primers [55] to amplify the ITS region between the small and large rRNA subunit genes. After successful amplification of the bacterial and fungal rRNA regions, Sanger sequencing was performed using the ABI3730XL instrument. MEGA software (Version 11.0.13) was utilized for multiple data alignments by CLUSTALW [56], followed by the neighbor-joining method to construct a phylogenetic tree, with the number of bootstrap replications set to 1000. The accession numbers of the bacterial 16S rRNA and fungal ITS sequences are shown in Figs. 2 and 3, respectively.

PMA treatment and DNA extraction

The remaining microbial eluate was divided equally into two portions, transferred to two sterile centrifuge tubes, and centrifuged at 12,000 rpm for 5 min. The supernatant was then removed to obtain a bacterial precipitate. One tube was left untreated, and the other was treated with PMA as follows: 200 µL of 50 µM PMA was added to the centrifuge tube, pipetted up and down to mix well, incubated in the dark for 5 min at room temperature, exposed to light for 15 min, and centrifuged at 12,000 rpm for 5 min. The supernatant was discarded, and the precipitate was washed thrice with sterile PBS buffer and thrice with sterile water to obtain the PMA-treated bacterial precipitate [57]. Only the SZ-12 samples here were not subjected to PMA treatment due to low biomass.

Subsequent DNA extractions using both of the eluate samples were performed by using the E.Z.N.A. Soil DNA Kit (Cat. No. D5625-01, Omega Biotek, Doraville, GA, USA). We slightly modified the manufacturer’s protocol as follows: (1) the first step was changed to utilize a 1.5-mL sterile centrifuge tube instead of a Disruptor Tube; (2) the steps involving the addition of 200 µL cHTR Reagent and subsequent centrifugation were omitted; and (3) in the final step, 50 µL of molecular grade water (pH 7.0) was used instead of elution buffer to wash the nucleic acids. The remaining steps were performed in accordance with the standard instructions provided. Extracted DNA (~ 50 ng/µL, OD260/OD280 ratio = 1.80–1.96) was used as template for subsequent qPCR. The DNA extracted from the samples that were not treated with PMA was subjected to MiSeq analysis.

qPCR quantification

The copy number of rRNA genes in all samples was quantified using qPCR on a 7300 ABI real-time PCR system (Applied Biosystems, Foster City, CA, USA). The copy number of rRNA genes in PMA-treated samples represents the number of viable bacteria. Each 25-µL reaction mixture included 1 µL of template DNA, 12.5 µL of 2 × SuperReal PreMix Plus (Cat. No. FP205-02, TIANGEN, Tianjin, China), and primers targeting the V3 region (341F [5′-CCT ACG GGA GGC AGC AG-3′] and 534R [5′-ATT ACC GCG GCT G-3′]) [58], each at a final concentration of 0.2 µM. The melting temperature, specificity, and amplification efficiency of the PCR products were assessed. The optimal qPCR conditions were determined to be an initial denaturation at 95 °C for 3 min, followed by 40 cycles of denaturation at 95 °C for 15 s and annealing/extension at 60 °C for 35 s. Experiments were conducted using the V3 region of the 16S rRNA gene of a standard substance with known dilution, and standard curves were plotted. Three parallel experiments were conducted for each data point. All qPCR reactions included a set of template-free negative controls, and the accuracy of qPCR was ensured through linear regression analysis (r2 > 0.99). The results were visualized using GraphPad Prism (Version 9.5.1). The number of 16S rRNA gene copies per 100 cm2 was calculated according to Formula (1):

$$T=t\:\times\:A/a\:\times\:B/b\:\times\:100/c$$
(1)

T: number of 16S rRNA gene copies per 100 cm.2

t: number of 16S rRNA gene copies calculated from the standard curve.

a: amount of DNA used as template for each reaction.

A: total amount of DNA extracted.

b: amount of eluent used for DNA extraction.

B: total amount of eluent.

c: the square measure of the equivalent material sheet (cm2).

Illumina MiSeq analysis

The V3–V4 region of the 16S rRNA gene in bacterial samples was amplified and sequenced using pair-end (PE) double-ended sequence data. The Illumina MiSeq raw data have been uploaded to the NCBI website, with the accession numbers provided in Supplementary Table 3. The obtained FASTQ data were split into different samples based on the barcode sequence. Pearl software was used to control the quality of the FASTQ data, removing ambiguous bases and primer mismatch sequences. The sequences were trimmed, and bases with quality values lower than Q20 were removed. The paired-end sequences were merged based on the overlap of the reads, setting the minimum overlap to 10 bp and the P value to 0.0001 to obtain the FASTA sequence. VSEARCH software was employed to identify and remove chimeric FASTA sequences using the UCHIME [59] method based on known databases. For unknown databases, the UCHIME de novo method was used to eliminate chimeras while also removing short sequences that did not meet the requirements. The above steps utilized Pearl (v0.9.6) [60], VSEARCH (v2.13.3) [61], and qc_fasta_v2 (v2.0). The FASTQ data were transformed into raw tags by removing barcode and primer sequences, followed by quality control and concatenation. The raw tags were further transformed into clean tags by removing chimeras and short sequences. Using QIIME (v1.8.0) [62] and VSEARCH (v2.13.3) [61], all clean tags were partitioned into ASVs using the UPARSE clustering [60] and UNOISE3 denoising [63] methods. Singletons were removed from ASV data. Subsequently, the ASVs were subjected to bioinformatic statistical analysis [64]. Based on the ASV clustering results, rarefaction analysis was performed using Mothur (v1.48.0) [65]. Rarefaction curves were generated using R (v3.6.0). The reshape2 and ggplot2 packages of R (v3.6.0) were utilized for species composition analysis and creation of bar charts. Distance matrix calculations and clustering analysis were performed using the Bray–Curtis distance metric and the complete linkage clustering method. Heat maps were generated using the vegan, vegadist, and hclust packages in R (v3.6.0). The number of common and unique ASVs across multiple samples was counted, and analyses were conducted using the R (v3.6.0) Venn package to create Venn diagrams. PCA and plotting were executed by analyzing the composition of different sample ASVs (97% similarity) and calculating the Euclidean distance, using the ade4, ggplot2, and ggrepl packages in R (v3.6.0). Raw data collected from ISS samples [3] were downloaded from the NCBI website and analyzed together with CSS samples using PCA. ANOSIM of the Bray–Curtis distances was performed using the vegan package. Alpha diversity analysis was performed on the dataset using QIIME (v1.8.0) [62]. For individual condensate samples, the QIIME (v1.8.0) MAFFT platform was used for sequence alignment, FastTree [66] was used for tree construction, and KronaTools (v2.6) [67] was used to visualize the classification hierarchy and abundance information of the species.

Using SourceTracker (Version: 0.9.1 [Beta]), we filtered out ASVs that satisfied the criteria of P value > 0.05 and correlation value | R |< 0.6. We analyzed the proportion of microorganisms in the SZ-14 samples that originated from previous batches of samples, and the proportion of microorganisms originating from four hypothetical source environments, namely soil [68], skin [69], oral [70], and gut [62], which are considered to be important sources of indoor microorganisms [71]. Traceability analysis results were visualized using GraphPad Prism (Version 9.5.1). LEfSe analysis was conducted using Python (3.8.10), with an LDA score threshold set to > 3. The top 20 ASVs by absolute abundance from all samples were selected for correlation analysis. ASVs that met the criteria of P value > 0.05 and |R|< 0.6 were selected, and a co-occurrence network graph was then drawn using the igraph and psych packages in R (v3.6.0).

Conclusions

This study is the first to research microbial ecology on the on-track CSS. On the three flight missions that were analyzed, the number of microorganisms in the cabin ranged from 0 to 2.83 × 109 CFU/100 cm2 and 1.24 × 105 to 2.59 × 109 rRNA gene copies/100 cm2, with an average viability of 65.08%. The dominant cultivable bacteria included Bacillus and Staphylococcus, while the main fungal genera included Aspergillus, Cladosporium, and Penicillium. The MiSeq results showed that the dominant bacterial genera in the cabin were Pseudomonas, Stenotrophomonas, Methylobacterium-Methylorubrum, Sphingomonas, Bacillus, Staphylococcus, and Nocardiopsis. Suggesting that there was a significant difference in microbial diversity between the CSS and ISS. The microbial composition and diversity varied significantly over time and space. More than half (58.80%) of the microorganisms in the cabin originated from the human skin and oral environments, and Lactobacillus was identified as the only genus of microorganisms that remained constant in various module regions of the CSS during the construction period. Conditional pathogenic microorganisms of plant and human were found in the CSS. We recommend that future experiments designed to prevent and control microbial contamination in the cabin focus on plant-derived microorganisms.

Data availability

The Illumina Miseq raw data have been uploaded to NCBI website, the access numbers are shown in Supplementary Table 1. The 16S rRNA and ITS gene sequences of the isolated strains have been uploaded to NCBI GenBank, with login numbers listed in Supplementary Table 2.

Abbreviations

CSS:

China Space Station

ISS:

International Space Station

MIR:

MIR space station

SZ:

Shenzhou

qPCR:

Quantitative real-time polymerase chain reaction

PBS:

Phosphate-buffered saline

LB:

Luria-Bertani agar medium

PDA:

Potato dextrose agar medium

CFU:

Colony-forming unit

PMA:

Propidium monoazide

ASV:

Amplicon sequence variant

PCR:

Polymerase chain reaction

References

  1. Yamaguchi N, Roberts M, Castro S, Oubre C, Makimura K, Leys N, et al. Microbial monitoring of crewed habitats in space—current status and future perspectives. Microbes Environ. 2014;29:250–60.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Checinska A, Probst AJ, Vaishampayan P, White JR, Kumar D, Stepanov VG, et al. Microbiomes of the dust particles collected from the International Space Station and Spacecraft Assembly Facilities. Microbiome. 2015;3:1–18.

    Article  Google Scholar 

  3. Sielaff AC, Urbaniak C, Mohan GBM, Stepanov VG, Tran Q, Wood JM, et al. Characterization of the total and viable bacterial and fungal communities associated with the International Space Station surfaces. Microbiome. 2019;7:1–21.

    Google Scholar 

  4. Zhang Y, Zhang L-T, Li Z-D, Xin C-X, Li X-Q, Wang X, et al. Microbiomes of China’s space station during assembly, integration, and test operations. Microb Ecol. 2019;78:631–50.

    Article  PubMed  Google Scholar 

  5. Lloyd-Price J, Abu-Ali G, Huttenhower C. The healthy human microbiome. Genome Medicine. 2016;8:1–11.

    Article  Google Scholar 

  6. Gmünder F, Konstantinova I, Cogoli A, Lesnyak A, Bogomolov W, Grachov A. Cellular immunity in cosmonauts during long duration spaceflight on board the orbital MIR station. Aviat Space Environ Med. 1994;65:419–23.

    PubMed  Google Scholar 

  7. Aunins TR, Erickson KE, Prasad N, Levy SE, Jones A, Shrestha S, et al. Spaceflight modifies Escherichia coli gene expression in response to antibiotic exposure and reveals role of oxidative stress response. Front Microbiol. 2018;9:310.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Singh NK, Bezdan D, Checinska Sielaff A, Wheeler K, Mason CE, Venkateswaran K. Multi-drug resistant Enterobacter bugandensis species isolated from the International Space Station and comparative genomic analyses with human pathogenic strains. BMC Microbiol. 2018;18:1–13.

    Article  Google Scholar 

  9. Ilyin VK, Skedina MA, Solovieva ZO, Artamonov AA. Databases of the evolution of the microbiome and its drug susceptibility in astronauts and hermetic facility operators. Journal of Space Safety Engineering. 2023;10:161–5.

    Article  Google Scholar 

  10. Taylor P. Impact of space flight on bacterial virulence and antibiotic susceptibility. Infection and Drug Resistance. 2015;8:249–62.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Bijlani S, Stephens E, Singh NK, Venkateswaran K, Wang CCC. Advances in space microbiology iScience. 2021;24:102395.

    PubMed  CAS  Google Scholar 

  12. La Duc MT, Sumner R, Pierson D, Venkat P, Venkateswaran K. Evidence of pathogenic microbes in the International Space Station drinking water: reason for concern? Habitation. 2004;10:39–48.

    Article  PubMed  Google Scholar 

  13. Pierson D, Bruce R, Ott CM, Castro V, Mehta S. Microbiological lessons learned from the space shuttle. 41st International Conference on Environmental Systems. 2011;5266:1-11.

  14. Evans CH Jr, Ball JR. Safe passage: astronaut care for exploration missions. Washington, DC: National Academies Press; 2001.

    Google Scholar 

  15. Rcheulishvili N, Zhang Y, Papukashvili D, Deng Y-L. Survey and evaluation of spacecraft-associated aluminum-degrading microbes and their rapid identification methods. Astrobiology. 2020;20:925–34.

    Article  PubMed  Google Scholar 

  16. Klintworth R, Reher H, Viktorov A, Bohle D. Biological induced corrosion of materials II: new test methods and experiences from MIR station. Acta Astronaut. 1999;44:569–78.

    Article  PubMed  CAS  Google Scholar 

  17. Ott CM. Microbiology and the International Space Station. COSPAR WORKSHOP ON: Refining Planetary Protection Requirements for Human Missions. 2018;JSC-E-DAA-TN56370.

  18. Alekhova T, Aleksandrova A, Novozhilova TY, Lysak L, Zagustina N, Bezborodov A. Monitoring of microbial degraders in manned space stations. Appl Biochem Microbiol. 2005;41:382–9.

    Article  CAS  Google Scholar 

  19. Novikova ND. Review of the knowledge of microbial contamination of the Russian manned spacecraft. Microb Ecol. 2004;47:127–32.

    Article  PubMed  CAS  Google Scholar 

  20. Kawamura Y, Li Y, Liu H, Huang X, Li Z, Ezaki T. Bacterial population in Russian space station “Mir.” Microbiol Immunol. 2001;45:819–28.

    Article  PubMed  CAS  Google Scholar 

  21. Ott CM, Bruce RJ, Pierson DL. Microbial characterization of free floating condensate aboard the Mir Space Station. Microb Ecol. 2004;47:133–6.

    Article  PubMed  CAS  Google Scholar 

  22. Song B, Leff LG. Identification and characterization of bacterial isolates from the Mir space station. Microbiol Res. 2005;160:111–7.

    Article  PubMed  Google Scholar 

  23. D’Onofrio A, Crawford JM, Stewart EJ, Witt K, Gavrish E, Epstein S, et al. Siderophores from neighboring organisms promote the growth of uncultured bacteria. Chem Biol. 2010;17:254–64.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Venkateswaran K, La Duc M, Vaishampayan P. Genetic inventory task: final report, JPL Publication 12–12. Pasadena, CA: Jet Propulsion Laboratory, California Institute of Technology. 2012;1:117.

    Google Scholar 

  25. Novikova N, De Boever P, Poddubko S, Deshevaya E, Polikarpov N, Rakova N, et al. Survey of environmental biocontamination on board the International Space Station. Res Microbiol. 2006;157:5–12.

    Article  PubMed  Google Scholar 

  26. Chen Y, Wu B, Zhang C, Fan Z, Xie Q. Current progression: application of high-throughput sequencing technique in space microbiology. Biomed Res Int. 2020;2020:1–13.

    CAS  Google Scholar 

  27. Venkateswaran K, Vaishampayan P, Cisneros J, Pierson DL, Rogers SO, Perry J. International Space Station environmental microbiome — microbial inventories of ISS filter debris. Appl Microbiol Biotechnol. 2014;98:6453–66.

    Article  PubMed  CAS  Google Scholar 

  28. Lang JM, Coil DA, Neches RY, Brown WE, Cavalier D, Severance M, et al. A microbial survey of the International Space Station (ISS). PeerJ. 2017;5:e4029.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Madrigal P, Singh NK, Wood JM, Gaudioso E, Hernández-del-Olmo F, Mason CE, et al. Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome. Microbiome. 2022;10:134.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Avila-Herrera A, Thissen J, Urbaniak C, Be NA, Smith DJ, Karouia F, et al. Crewmember microbiome may influence microbial composition of ISS habitable surfaces. PLoS ONE. 2020;15:e0231838.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Urbaniak C, Morrison MD, Thissen JB, Karouia F, Smith DJ, Mehta S, et al. Microbial Tracking-2, a metagenomics analysis of bacteria and fungi onboard the International Space Station. Microbiome. 2022;10:100.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Satoh K, Nishiyama Y, Yamazaki T, Sugita T, Tsukii Y, Takatori K, et al. Microbe-I: fungal biota analyses of the Japanese experimental module KIBO of the International Space Station before launch and after being in orbit for about 460 days. Microbiol Immunol. 2011;55:823–9.

    Article  PubMed  CAS  Google Scholar 

  33. Makimura K, Hanazawa R, Takatori K, Tamura Y, Fujisaki R, Nishiyama Y, et al. Fungal flora on board the Mir-space station, identification by morphological features and ribosomal DNA sequences. Microbiol Immunol. 2001;45:357–63.

    Article  PubMed  CAS  Google Scholar 

  34. Mora M, Wink L, Kögler I, Mahnert A, Rettberg P, Schwendner P, et al. Space Station conditions are selective but do not alter microbial characteristics relevant to human health. Nat Commun. 2019;10:3990.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Blaustein RA, Mcfarland AAG, Maamar ASB, Lopez AA, Castro-Wallace BS. Pangenomic approach to understanding microbial adaptations within a model built environment, the International Space Station, relative to human hosts and soil. mSystems. 2019;4:e00281–00218.

  36. Zabielska J, Kunicka-Styczyńska A, Otlewska A. Adhesive and hydrophobic properties of Pseudomonas aeruginosa and Pseudomonas cedrina associated with cosmetics. Ecol Quest. 2018;28:41–6.

    Google Scholar 

  37. Granum PE, Lindbäck T. Bacillus cereus. In: Doyle MP., Buchanan RL, editors. Food microbiology: fundamentals and frontiers. Washington, DC: ASM Press; 2012.p.491–502.

  38. Khalil MA, Elhariry HM, Alzaidi TM. Disinfectant as removal agent of the pre-formed biofilm by Staphylococcus sp. isolated from dental clinics in Taif, KSA. Periodicum Biologorum. 2021;123:19–27.

  39. Benndorf D, Müller A, Bock K, Manuwald O, Herbarth O, Von Bergen M. Identification of spore allergens from the indoor mould Aspergillus versicolor. Allergy. 2008;63:454–60.

    Article  PubMed  CAS  Google Scholar 

  40. Krishnan S, Manavathu EK, Chandrasekar PH. Aspergillus flavus: an emerging non-fumigatus Aspergillus species of significance. Mycoses. 2009;52:206–22.

    Article  PubMed  CAS  Google Scholar 

  41. Lee DJ, Lee JS, Lee HB, Choi Y-J. Four endophytic ascomycetes new to Korea: Cladosporium anthropophilum, C. pseudocladosporioides, Daldinia eschscholtzii, and Nigrospora chinensis. Korean J Mycol. 2019;47:187–197.

  42. Duar RM, Lin XB, Zheng J, Martino ME, Grenier T, Pérez-Muñoz ME, et al. Lifestyles in transition: evolution and natural history of the genus Lactobacillus. FEMS Microbiol Rev. 2017;41:S27–48.

    Article  PubMed  Google Scholar 

  43. Lin XB, Wang T, Stothard P, Corander J, Wang J, Baines JF, et al. The evolution of ecological facilitation within mixed-species biofilms in the mouse gastrointestinal tract. ISME J. 2018;12:2770–84.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Salas-Jara MJ, Ilabaca A, Vega M, García A. Biofilm forming Lactobacillus: new challenges for the development of probiotics. Microorganisms. 2016;4:35.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Martín R, Miquel S, Ulmer J, Kechaou N, Langella P, Bermúdez-Humarán LG. Role of commensal and probiotic bacteria in human health: a focus on inflammatory bowel disease. Microb Cell Fact. 2013;12:1–11.

    Article  Google Scholar 

  46. O’Callaghan J, O’Toole PW. Lactobacillus: host–microbe relationships. Between Pathogenicity and Commensalism. 2011;119–154.

  47. Ryan RP, Monchy S, Cardinale M, Taghavi S, Crossman L, Avison MB, et al. The versatility and adaptation of bacteria from the genus Stenotrophomonas. Nat Rev Microbiol. 2009;7:514–25.

    Article  PubMed  CAS  Google Scholar 

  48. Green PN, Ardley JK. Review of the genus Methylobacterium and closely related organisms: a proposal that some Methylobacterium species be reclassified into a new genus, Methylorubrum gen. Int J syst Evol Microbiol. 2018;68:2727–48.

    Article  PubMed  CAS  Google Scholar 

  49. Silby MW, Winstanley C, Godfrey SA, Levy SB, Jackson RW. Pseudomonas genomes: diverse and adaptable. FEMS Microbiol Rev. 2011;35:652–80.

    Article  PubMed  CAS  Google Scholar 

  50. White DC, Sutton SD, Ringelberg DB. The genus Sphingomonas: physiology and ecology. Curr Opin Biotechnol. 1996;7:301–6.

    Article  PubMed  CAS  Google Scholar 

  51. Walterson AM, Stavrinides J. Pantoea: insights into a highly versatile and diverse genus within the Enterobacteriaceae. FEMS Microbiol Rev. 2015;39:968–84.

    Article  PubMed  CAS  Google Scholar 

  52. Bennur T, Kumar AR, Zinjarde S, Javdekar V. Nocardiopsis species: incidence, ecological roles and adaptations. Microbiol Res. 2015;174:33–47.

    Article  PubMed  Google Scholar 

  53. Lax S, Smith DP, Hampton-Marcell J, Owens SM, Handley KM, Scott NM, et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science. 2014;345:1048–52.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Weisburg WG, Barns SM, Pelletier DA, Lane DJ. 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol. 1991;173:697–703.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Korabečná M, Liška V, Fajfrlík K. Primers ITS1, ITS2 and ITS4 detect the intraspecies variability in the internal transcribed spacers and 5.8 S rRNA gene region in clinical isolates of fungi. Folia Microbiol. 2003;48:233–238.

  56. Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994;22:4673–80.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Blachowicz A, Mayer T, Bashir M, Pieber T, De León P, Venkateswaran K. Human presence impacts fungal diversity of inflated lunar/Mars analog habitat. Microbiome. 2017;5:1–16.

    Article  Google Scholar 

  58. Rahman MME, Hossain DM, Suzuki K, Shiiya A, Suzuki K, Dey TK, et al. Suppressive effects of Bacillus spp. on mycelia, apothecia and sclerotia formation of Sclerotinia sclerotiorum and potential as biological control of white mold on mustard. Australas Plant Pathol. 2016;45:103–17.

    Article  Google Scholar 

  59. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30:614–20.

    Article  PubMed  CAS  Google Scholar 

  61. Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Miller GE, Engen PA, Gillevet PM, Maliha S, Masoumeh S, Forsyth CB, et al. Lower neighborhood socioeconomic status associated with reduced diversity of the colonic microbiota in healthy adults. PLoS ONE. 2016;11:e0148952.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.

    Article  PubMed  CAS  Google Scholar 

  64. Stackebrandt E, Goebel BM. Taxonomic note: a place for DNA-DNA reassociation and 16S rRNA sequence analysis in the present species definition in bacteriology. Intjsystbacteriol. 1994;44:846–9.

    CAS  Google Scholar 

  65. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Navas-Molina JA, Peralta-Sánchez JM, González A, McMurdie PJ, Vázquez-Baeza Y, Xu Z, et al. Advancing our understanding of the human microbiome using QIIME. Methods Enzymol. 2013;531:371–444.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Keylock C. Simpson diversity and the Shannon-Wiener index as special cases of a generalized entropy. Oikos. 2005;109:203–7.

    Article  Google Scholar 

  68. Zhang N, Juneau P, Huang R, He Z, Sun B, Zhou J, et al. Coexistence between antibiotic resistance genes and metal resistance genes in manure-fertilized soils. Geoderma. 2021;382:114760.

    Article  CAS  Google Scholar 

  69. Wang Y, Yu Q, Zhou R, Feng T, Hilal MG, Li H. Nationality and body location alter human skin microbiome. Appl Microbiol Biotechnol. 2021;105:5241–56.

    Article  PubMed  CAS  Google Scholar 

  70. Hao Z, Zhu Y, Fu Y, Yang J, Meng C, Dong C, et al. Effects of long-term enclosed environment on human health based on the analysis of salivary microbiota and cytokines. Microbiology Spectrum. 2022;10:e00254-e222.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Barberán A, Ladau J, Leff JW, Pollard KS, Menninger HL, Dunn RR, et al. Continental-scale distributions of dust-associated bacteria and fungi. Proc Natl Acad Sci USA. 2015;112:5756–61.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The author expresses gratitude to the astronauts of SZ-12, SZ-13, and SZ-14 for their assistance in collecting samples. We thank Michelle Kahmeyer-Gabbe, PhD, from Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the English text of a draft of this manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC), under grant no. 32270119, and the SpaceStation Engineering and Aerospace Technology test field project under grant no. 2019HjS002. Author Ying Zhang received both research support.

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  1. Ying Zhang and Yuan Peng contributed equally to this work.

Authors and Affiliations

  1. School of Life Science, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Beijing, 100081, China

    Ying Zhang, Yuan Peng, Tao Wei, Hong Wang & Zimu Guo

  2. Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing, 100094, China

    Xi Qu, Lantao Zhang & Xiang Wang

  3. School of Life Science, Jiangsu Normal University, No.101, Shanghai road, Tongshan district, Xuzhou, Jiangsu Province, 221116, China

    Weijie Liu

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  1. Ying Zhang
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Contributions

Y.Z. designed the entire study, guided experimental methods, obtained funding, and revised the manuscript. Y.P.completed the experimental operation, analyzed the experimental data, and wrote the manuscript. X.Q.and L.T. Z. assisted in collecting samples. T.W., H.W. and Z.M.G. helped complete the experiment. W.J. L. Participated in the design of the experiment. X.W. participated in the design of the experiments and gave support on the coordination of all resources of the CSS. All authors reviewed the manuscript.

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Correspondence to Ying Zhang, Weijie Liu or Xiang Wang.

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Zhang, Y., Peng, Y., Qu, X. et al. On-orbit microbial succession patterns of the China Space Station during the construction period. Microbiome 13, 73 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40168-024-02025-2

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Keywords

Microbiome

ISSN: 2049-2618

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