Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

Abstract

AbstractBackgroundRecent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance ofKlebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.ResultsThrough a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence ofK. pneumoniaeis beneficial to many other microorganisms it coexists with, notably those from thePantoeagenus. Species belonging to theEnterobacteriaceaefamily were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However,K. pneumoniaewas found to exhibit parasitic and amensalistic interactions withAspergillusandPenicilliumspecies, respectively. To prove this metabolic prediction,K. pneumoniaeandAspergillus fumigatuswere co-cultured under normal and simulated microgravity, whereK. pneumoniaecells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence ofK. pneumoniaecompromised the morphology of fungal conidia and degenerated its biofilm-forming structures.ConclusionOur study underscores the importance ofK. pneumoniaein the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies.