Resilient microorganisms in dust samples of the International Space Station-survival of the adaptation specialists

Abstract

Background: The International Space Station (ISS) represents a unique biotope for the human crew but also for introduced microorganisms. Microbes experience selective pressures such as microgravity, desiccation, poor nutrient-availability due to cleaning, and an increased radiation level. We hypothesized that the microbial community inside the ISS is modified by adapting to these stresses. For this reason, we analyzed 8-12 years old dust samples from Russian ISS modules with major focus on the long-time surviving portion of the microbial community. We consequently assessed the cultivable microbiota of these samples in order to analyze their extremotolerant potential against desiccation, heat-shock, and clinically relevant antibiotics. In addition, we studied the bacterial and archaeal communities from the stored Russian dust samples via molecular methods (next-generation sequencing, NGS) and compared our new data with previously derived information from the US American ISS dust microbiome.

Results: We cultivated and identified in total 85 bacterial, non-pathogenic isolates (17 different species) and 1 fungal isolate from the 8-12 year old dust samples collected in the Russian segment of the ISS. Most of these isolates exhibited robust resistance against heat-shock and clinically relevant antibiotics. Microbial 16S rRNA gene and archaeal 16S rRNA gene targeting Next Generation Sequencing showed signatures of human-associated microorganisms (Corynebacterium, Staphylococcus, Coprococcus etc.), but also specifically adapted extremotolerant microorganisms. Besides bacteria, the detection of archaeal signatures in higher abundance was striking.

Conclusions: Our findings reveal (i) the occurrence of living, hardy microorganisms in archived Russian ISS dust samples, (ii) a profound resistance capacity of ISS microorganisms against environmental stresses, and (iii) the presence of archaeal signatures on board. In addition, we found indications that the microbial community in the Russian segment dust samples was different to recently reported US American ISS microbiota.

Keywords: Archaea; Confined habitat; Extremotolerant; International Space Station; Microbiome.

Fig. 1

Maximum-likelihood tree based on the unique 16S rRNA gene sequences of the ISS isolates. Circles indicate the medium they were cultivated in. Squares refer to the sample origin. Stars indicate the heat-shock resistance of the isolates (filled star: survived heat-shock at 80 °C for 15 min; empty star: did not survive heat-shock; others were not tested). Tree was constructed using MEGA6 [34] and displayed by iToL [35]

Fig. 2

Archaeal maximum-likelihood tree: detected taxa and their abundance in different samples of ISS

Fig. 3

Taxonomic profiles of the microbial communities from Russian ISS samples at phylum level. The five most abundant phyla are depicted. We discriminate between untreated and incubated samples. Total counts are given in % (“Abundance”)

Fig. 4

Venn diagram depicting common genera in untreated groups and their corresponding incubated counterparts. In total, signatures of 23 genera were common in all six samples. The PCoA plot on the right side is depicting the dissimilarity between incubated and untreated samples using the unweighted Bray-Curtis distance. No clear cluster pattern is visible between the two groups

Fig. 5

Comparison of the number of retrieved isolates with molecular data. Donuts indicate retrieved sequences on molecular level, filled circles are indicative for cultivated isolates. The larger the donuts/circles are, the more counts of sequences/isolates were obtained. Every sampling site has a different color (see legend). If no circle/donut appears, no sequences/isolates were obtained. It has to be noted, that no NGS data for the untreated “dust collector (2004)” could be retrieved; however, isolates from this sample could be cultivated

Fig. 6

Hierarchical clustering of selected functional genes. The profiles clustered based on sampling site. The color scale reflects relative abundance of genes in % (black: low abundance, violet: high abundance)