Dynamic genetic adaptation of Bacteroides thetaiotaomicron during murine gut colonization.

To understand how a bacterium ultimately succeeds or fails in adapting to a new host, it is essential to assess the temporal dynamics of its fitness over the course of colonization. Here, we introduce a human-derived commensal organism, Bacteroides thetaiotaomicron (Bt), into the guts of germ-free mice to determine whether and how the genetic requirements for colonization shift over time. Combining a high-throughput functional genetics assay and transcriptomics, we find that gene usage changes drastically during the first days of colonization, shifting from high expression of amino acid biosynthesis genes to broad upregulation of diverse polysaccharide utilization loci. Within the first week, metabolism becomes centered around utilization of a predominant dietary oligosaccharide, and these changes are largely sustained through 6 weeks of colonization. Spontaneous mutations in wild-type Bt also evolve around this locus. These findings highlight the importance of considering temporal colonization dynamics in developing more effective microbiome-based therapies.

Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning.

The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4-5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.

Metagenome-assembled genome extraction and analysis from microbiomes using KBase.

Uncultivated Bacteria and Archaea account for the vast majority of species on Earth, but obtaining their genomes directly from the environment, using shotgun sequencing, has only become possible recently. To realize the hope of capturing Earth's microbial genetic complement and to facilitate the investigation of the functional roles of specific lineages in a given ecosystem, technologies that accelerate the recovery of high-quality genomes are necessary. We present a series of analysis steps and data products for the extraction of high-quality metagenome-assembled genomes (MAGs) from microbiomes using the U.S. Department of Energy Systems Biology Knowledgebase (KBase) platform ( http://www.kbase.us/ ). Overall, these steps take about a day to obtain extracted genomes when starting from smaller environmental shotgun read libraries, or up to about a week from larger libraries. In KBase, the process is end-to-end, allowing a user to go from the initial sequencing reads all the way through to MAGs, which can then be analyzed with other KBase capabilities such as phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, RNA-Seq and others. While portions of such capabilities are available individually from other resources, the combination of the intuitive usability, data interoperability and integration of tools in a freely available computational resource makes KBase a powerful platform for obtaining MAGs from microbiomes. While this workflow offers tools for each of the key steps in the genome extraction process, it also provides a scaffold that can be easily extended with additional MAG recovery and analysis tools, via the KBase software development kit (SDK).

Microbial and metabolic succession on common building materials under high humidity conditions.

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.

Physiological and Metabolic Responses of Rice to Reduced Soil Moisture: Relationship of Water Stress Tolerance and Grain Production.

Access to adequate irrigation resources is critical for sustained agricultural production, and rice, a staple cereal grain for half of the world population, is one of the biggest users of irrigation. To reduce water use, several water saving irrigation systems have been developed for rice production, but a reliable system to evaluate cultivars for water stress tolerance is still lacking. Here, seven rice cultivars that have diverse yield potential under water stress were evaluated in a field study using four continuous irrigation regimes varying from saturation to wilting point. To understand the relationship between water stress and yield potential, the physiological and leaf metabolic responses were investigated at the critical transition between vegetative and reproductive growth stages. Twenty-nine metabolite markers including carbohydrates, amino acids and organic acids were found to significantly differ among the seven cultivars in response to increasing water stress levels with amino acids increasing but organic acids and carbohydrates showing mixed responses. Overall, our data suggest that, in response to increasing water stress, rice cultivars that do not show a significant yield loss accumulate carbohydrates (fructose, glucose, and myo-inositol), and this is associated with a moderate reduction in stomatal conductance (gs), particularly under milder stress conditions. In contrast, cultivars that had significant yield loss due to water stress had the greatest reduction in gs, relatively lower accumulation of carbohydrates, and relatively high increases in relative chlorophyll content (SPAD) and leaf temperature (Tm). These data demonstrate the existence of genetic variation in yield under different water stress levels which results from a suite of physiological and biochemical responses to water stress. Our study, therefore, suggests that in rice there are different physiological and metabolic strategies that result in tolerance to water stress that should be considered in developing new cultivars for deficit irrigation production systems that use less water.

Metabolic In Silico Network Expansions to Predict and Exploit Enzyme Promiscuity.

There is a growing consensus that enzymes are capable of catalyzing not just one canonical reaction but entire families of related reactions. These capacities often go unnoticed in the enzyme's native context but can become apparent in engineered metabolism when the enzyme is exposed to novel substrates or high concentrations of pathway intermediates. This chapter describes how to use metabolic in silico network expansion (MINE) databases to predict novel biotransformations and their resulting metabolites. In particular, searching MINEs by structural similarity or with metabolomics data allows scientists to detect, exploit, or avoid these predicted transformations.

Environmental Sources of Bacteria Differentially Influence Host-Associated Microbial Dynamics.

Host-associated microbial dynamics are influenced by dietary and immune factors, but how exogenous microbial exposure shapes host-microbe dynamics remains poorly characterized. To investigate this phenomenon, we characterized the skin, rectum, and respiratory tract-associated microbiota in four aquarium-housed dolphins daily over a period of 6 weeks, including administration of a probiotic during weeks 4 to 6. The environmental bacterial sources were also characterized, including the animals' human handlers, the aquarium air and water, and the dolphins' food supply. Continuous microbial exposure occurred between all sites, yet each environment maintained a characteristic microbiota, suggesting that the majority of exposure events do not result in colonization. Small changes in water physicochemistry had a significant but weak correlation with change in dolphin-associated bacterial richness but had no influence on phylogenetic diversity. Food and air microbiota were the richest and had the largest conditional influence on other microbiota in the absence of probiotics, but during probiotic administration, food alone had the largest influence on the stability of the dolphin microbiota. Our results suggest that respiratory tract and gastrointestinal epithelium interactions with air- and food-associated microbes had the biggest influence on host-microbiota dynamics, while other interactions, such as skin transmission, played only a minor role. Finally, direct oral stimulation with a foreign exogenous microbial source can have a profound effect on microbial stability. IMPORTANCE 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.

From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model.

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.

Network-based metabolic analysis and microbial community modeling.

Network inference is being applied to studies of microbial ecology to visualize and characterize microbial communities. Network representations can allow examination of the underlying organizational structure of a microbial community, and identification of key players or environmental conditions that influence community assembly and stability. Microbial co-association networks provide information on the dynamics of community structure as a function of time or other external variables. Community metabolic networks can provide a mechanistic link between species through identification of metabolite exchanges and species specific resource requirements. When used together, co-association networks and metabolic networks can provide a more in-depth view of the hidden rules that govern the stability and dynamics of microbial communities.

The development of industry-specific odor impact criteria for feedlots using models.

Emissions from feedlot operations are known to vary by environmental conditions and few if any techniques or models exist to predict the variability of odor emission rates from feedlots. The purpose of this paper is to outline and summarize unpublished reports that are the result of a collective effort to develop industry-specific odor impact criteria for Australian feedlots. This effort used over 250 olfactometry samples collected with a wind tunnel and past research to develop emission models for pads, sediment basins, holding ponds, and manure storage areas over a range of environmental conditions and tested using dynamic olfactometry. A process was developed to integrate these emission models into odor dispersion modeling for the development of impact criteria. The approach used a feedlot hydrology model to derive daily feedlot pad moisture, temperature, and thickness. A submodel converted these daily data to hourly data. A feedlot pad emissions model was developed that predicts feedlot pad emissions as a function of temperature, moisture content, and pad depth. Emissions from sediment basins and holding ponds were predicted using a basin emissions model as a function of days since rain, inflow volume, inflow ratio (pond volume), and temperature. This is the first attempt to model all odor source emissions from a feedlot as variable hourly emissions on the basis of climate, management, and site-specific conditions. Results from the holding pond, sediment basin, and manure storage emission models performed well, but additional work on the pad emissions model may be warranted. This methodology mimics the variable odor emissions and odor impact expected from feedlots due to climate and management effects. The main outcome of the work is the recognition that an industry-specific odor impact criterion must be expressed in terms of all of the components of the assessment methodology.