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Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe’s entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Python-based open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe’s metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models.

These results provide valuable insights into the ecological influence of exogenous microbial exposure, as well as laying the foundation for improving aquarium management practices. By comparing data for dolphins from aquaria that use natural versus artificial seawater, we demonstrate the potential influence of aquarium water disinfection procedures on dolphin microbial dynamics.

Despite considerable efforts to characterize the microbial ecology of the built environment, the metabolic mechanisms underpinning microbial colonization and successional dynamics remain unclear, particularly at high moisture conditions. Here, we applied bacterial/viral particle counting, qPCR, amplicon sequencing of the genes encoding 16S and ITS rRNA, and metabolomics to longitudinally characterize the ecological dynamics of four common building materials maintained at high humidity. We varied the natural inoculum provided to each material and wet half of the samples to simulate a potable water leak. Wetted materials had higher growth rates and lower alpha diversity compared to non-wetted materials, and wetting described the majority of the variance in bacterial, fungal, and metabolite structure. Inoculation location was weakly associated with bacterial and fungal beta diversity. Material type influenced bacterial and viral particle abundance and bacterial and metabolic (but not fungal) diversity. Metabolites indicative of microbial activity were identified, and they too differed by material.

1Despite considerable efforts to characterize the ecology of bacteria and fungi in the built 2 environment (BE), the metabolic mechanisms underpinning their colonization and successional 3 dynamics remain unclear. Here, we applied bacterial/viral particle counting, qPCR, 16S and ITS 4 rRNA amplicon sequencing, and metabolomics to longitudinally characterize the ecological 5 dynamics of four commonly used building materials maintained at high humidity conditions 6 (~94% RH). We varied the natural inoculum provided to each material by placing them in different 7 occupied spaces, and we wet the surface of half of the samples of each material to simulate a 8 flooding event. As expected, different materials showed different bacterial and viral particle 9 abundance, with wet materials having higher growth rates and lower alpha diversity compared to 10 non-wetted materials. Wetting described the majority of the variance in bacterial, fungal and 11 metabolite structure, and material type only influenced bacterial and metabolic diversity, while 12 location of inoculation was only weakly associated with bacterial and fungal beta diversity. 13 Metabolites indicative of microbial activity were identified, as were those that were native to the 14 surface material. Glucose-phosphate was abundant on all materials (except mold-free gypsum) and 15 was correlated with Enterobacteriaceae, which could indicate a potential bacterial nutrient source. 16 A compound consistent with scopoletin, a plant metabolite with antimicrobial activity, was 17 significantly negatively correlated with Bacillus and positively correlated with Pseudomonas and 18 enriched in medium density fiberboard (MDF) materials. In wet samples, the alkaloids nigragillin 19 and fumigaclavine C, both with antimicrobial properties, were significantly positively correlated 20 with the fungal phylum Ascomycota. Nigragillin, was also negatively correlated with Bacillus and 21 Pseudomonas abundance. Thiabendazole and azoxystrobin (anti-fungal compounds) were highly 22 abundant on mold-resistant gypsum wallboard and likely directly influenced the decreased fungal 23 growth observed on this material. The mold-resistant gypsum material also showed a significant 24 increase in bacterial alpha diversity, and bacterial and viral particle abundance, as well as a 25 decrease in metabolite diversity, likely a result of reduced fungal growth. Penicillium taxa were 26 positively correlated with thiabendazole, which suggested the persistence of resistant strains. Also, 27 2 specific to the wet samples, Bacillus abundance was positively correlated with the azoxystrobin, 28 suggesting bi-directional competitive adaptation, and positively correlated with metabolites known 29 to interfere with Pseudomonas biofilm formation, which could explain the anti-correlation 30 between these taxa. As expected, high moisture conditions enabled faster growth of inoculating 31 microorganisms, whose composition, chemistry, and competition was shaped by surface material, 32 suggesting that...