Microbial Tracking-2, a metagenomics analysis of bacteria and fungi onboard the International Space Station

Abstract

Background: The International Space Station (ISS) is a unique and complex built environment with the ISS surface microbiome originating from crew and cargo or from life support recirculation in an almost entirely closed system. The Microbial Tracking 1 (MT-1) project was the first ISS environmental surface study to report on the metagenome profiles without using whole-genome amplification. The study surveyed the microbial communities from eight surfaces over a 14-month period. The Microbial Tracking 2 (MT-2) project aimed to continue the work of MT-1, sampling an additional four flights from the same locations, over another 14 months.

Methods: Eight surfaces across the ISS were sampled with sterile wipes and processed upon return to Earth. DNA extracted from the processed samples (and controls) were treated with propidium monoazide (PMA) to detect intact/viable cells or left untreated and to detect the total DNA population (free DNA/compromised cells/intact cells/viable cells). DNA extracted from PMA-treated and untreated samples were analyzed using shotgun metagenomics. Samples were cultured for bacteria and fungi to supplement the above results.

Results: Staphylococcus sp. and Malassezia sp. were the most represented bacterial and fungal species, respectively, on the ISS. Overall, the ISS surface microbiome was dominated by organisms associated with the human skin. Multi-dimensional scaling and differential abundance analysis showed significant temporal changes in the microbial population but no spatial differences. The ISS antimicrobial resistance gene profiles were however more stable over time, with no differences over the 5-year span of the MT-1 and MT-2 studies. Twenty-nine antimicrobial resistance genes were detected across all samples, with macrolide/lincosamide/streptogramin resistance being the most widespread. Metagenomic assembled genomes were reconstructed from the dataset, resulting in 82 MAGs. Functional assessment of the collective MAGs showed a propensity for amino acid utilization over carbohydrate metabolism. Co-occurrence analyses showed strong associations between bacterial and fungal genera. Culture analysis showed the microbial load to be on average 3.0 × 10⁵ cfu/m² CONCLUSIONS: Utilizing various metagenomics analyses and culture methods, we provided a comprehensive analysis of the ISS surface microbiome, showing microbial burden, bacterial and fungal species prevalence, changes in the microbiome, and resistome over time and space, as well as the functional capabilities and microbial interactions of this unique built microbiome. Data from this study may help to inform policies for future space missions to ensure an ISS surface microbiome that promotes astronaut health and spacecraft integrity. Video Abstract.

Keywords: Built environment; International Space Station; Metagenomics; Microbial monitoring; Microbial tracking; Microbiome.

Fig. 1

Metagenomic read counts obtained from surface wipes sampled during flights 4–7. Each dot of the graph represents a location sampled on the ISS. The line represents the average read count for a specific flight from all eight locations. PMA-treated samples were analyzed which measures viable/intact cells. A Read counts of the bacterial population. B Read counts of the fungal population. The Kruskal–Wallis one-way analysis of variance test, followed by the post hoc false discovery rate was used to compare the read counts between flights

Fig. 2

Alpha diversity metrics for MT-1 and MT-2 samples. The species richness (top row), exponentiated Shannon index (middle row), and inverse Simpson index (bottom row) are shown for each sample. Samples are grouped by flight group and colored by surface location

Fig. 3

PCoA plot of MT-1 and MT-2 surface samples. Samples were colored by surface location (A) and flight group (B) to visualize the sample clustering. The distance between the samples was determined using the Euclidean distance

Fig. 4

Top 12 most abundant bacterial species across all surface samples. The percent of mapped reads for each species in each sample. Samples were grouped in columns by surface location and arranged in order by flight group. “Other” refers to those bacterial species detected that were not in the top 12

Fig. 5

Top 12 most abundant fungal species across all surface samples. The percent of mapped reads for each species in each sample. Samples were grouped in columns by surface location and arranged in order by flight group. “Other” refers to those fungal species detected that were not in the top 12

Fig. 6

Co-occurrence analysis. The co-occurrence analysis was performed using the 50 most abundant genera to determine the associations among organisms. The matrix shows which genera co-occurred with each other. Blue shows the positive co-occurrences (i.e., if you find one in the community, it is likely you will find another—for example, Aspergillus and Fusarium), yellow shows the negative co-occurrences (i.e., if you find one, it is likely the other will be absent), and gray shows the random occurrences that are most likely due to random chance alone—for example, Cladosporium and Fusarium

Fig. 7

Heat map of the ISS viable resistome. The metagenomic dataset was analyzed with DeepARG to look for anti-microbial resistance genes. The genes were then grouped into classes of resistance (i.e., “beta-lactam”), which is displayed in this heatmap (y-axis). The heatmap shows the relative abundance of each resistance class in each sample collected, with red being the most abundant and green the least. Gray indicates zero counts detected in that sample. The samples analyzed (x-axis) are from the PMA-treated samples which detect viable/intact cells. x-axis naming: Fx_xS_P, with F referring to the flight (i.e., F4) and S referring to the location of the surface sampled (i.e., 1S is location 1, the port panel next to the cupola). The AMR counts were normalized based on the 16S rRNA counts

Fig. 8

Comparison of ISS viable resistome among flights. NMDS plot comparing the antimicrobial resistance profile of the ISS community. Metagenomic data collected from the PMA-treated samples (viable/intact cells) during each of the flight sampling sessions for both the current MT-2 study (F4–F7) and the previous MT-1 study (F1–F3) was analyzed by DeepARG. Each dot represents a sample and takes into account the presence of an antimicrobial resistance gene and its abundance. Locations are depicted by colors and MT-1 vs MT-2 study, by symbols

Fig. 9

MAG abundance heatmap. Genomes were assembled from the metagenomics reads and placed into “bins,” with each “bin” representing one metagenomic assembled genome (“MAG”). These bins were generated from the metagenomic data consisting of the 32 samples collected during the course of the study. Each bin is shown on the y-axis. The abundances are expressed as genome copies per million reads and presented, with the log10 values plotted. Red represents a high relative abundance and blue a low relative abundance. The white color represents the absence of that bin in the sample. The bin numbers were highlighted based on the phylum it was assigned to, with yellow = Actinobacteria, green = Firmicutes, blue = Bacteroidetes, and purple = Proteobacteria. Full taxonomic info for each bin can be found in Dataset S2. The x-axis shows the sample that was analyzed and the relative abundance of that bin (i.e., MAG) in that sample. x-axis naming: Fx_xS, with F referring to the flight (i.e., F4) and S referring to the location of the surface sampled (i.e., 1S is location 1, the port panel next to the cupola)

Fig. 10

Pie chart of the cluster of orthologous groups. Functional assessment was performed by comparing the annotated MAGs against the COG database and assigning each COG ID to a category. The counts from each category are displayed as a percentage of the total counts in the pie chart