Biology subjects

Hamilton, J. J.

Publications and source records attributed to Hamilton, J. J..

5 recordsLinked to original sources

bioRxiv2018

Metatranscriptomic and thermodynamic insights into medium-chain fatty acid production using an anaerobic microbiome

Biomanufacturing from renewable feedstocks represents a sustainable strategy to offset fossil fuel-based chemical production. One potential biomanufacturing strategy is production of medium chain fatty acids (MCFA) from organic feedstocks using either pure cultures or microbiomes. While the set of microbes in a microbiome can often metabolize more diverse organic materials than a single species, and the role of specific species may be known, knowledge of the carbon and energy flow within and between organisms in MCFA producing microbiomes is only starting to emerge. Here, we integrate metagenomic, metatranscriptomic, and thermodynamic analyses to predict and characterize the metabolic network of an anaerobic microbiome producing MCFA from organic matter derived from lignocellulosic ethanol fermentation conversion residue. A total of 37 high quality (>80% complete, < 10% contamination) metagenome-assembled genomes (MAGs) were recovered from the microbiome and metabolic reconstruction of the 10 most abundant MAGs was performed. Metabolic reconstruction combined with metatranscriptomic analysis predicted that organisms affiliated with Lactobacillus and Coriobacteriaceae degraded carbohydrates and fermented sugars to lactate and acetate. Lachnospiraceae and Eubacteriaceae affiliated organisms were predicted to transform these fermentation products to MCFA. Thermodynamic analyses identified conditions in which H2 is expected to be either produced or consumed, suggesting a potential role of H2 partial pressure on MCFA production. From an integrated systems analysis perspective, we propose that MCFA production could be improved if microbiomes are engineered to use homofermentative instead of heterofermentative Lactobacillus, and if MCFA-producing organisms are engineered to preferentially use a thioesterase instead of a CoA transferase as the terminal enzyme in reverse {beta}-oxidation.\n\nImportanceMixed communities of microbes play important roles in health, the environment, agriculture, and biotechnology. While tapping the combined activity of organisms within microbiomes may allow the utilization of a wider range of substrate over the use of pure cultures for biomanufacturing, harnessing the metabolism of these mixed cultures remains a major challenge. Here, we predict metabolic functions of bacteria in a microbiome that produces medium-chain fatty acids from a renewable feedstock. Our findings lay the foundation to begin addressing how to engineer and control microbiomes for improved biomanufacturing; to build synthetic mixtures of microbes that produce valuable chemicals from renewable resources; and to better understand microbial communities that contribute to health, agriculture, and the environment.

systems biology↗

bioRxiv2018

acI Actinobacteria Assemble a Functional Actinorhodopsin with Natively-synthesized Retinal

Freshwater lakes harbor complex microbial communities, but these ecosystems are often dominated by acI Actinobacteria from three clades (acI-A, acI-B, acI-C). Members of this cosmopolitan lineage are proposed to bolster heterotrophic growth using phototrophy because their genomes encode actino-opsins (actR). This model has been difficult to experimentally validate because acI are not consistently culturable. In this study, using genomes from single cells and metagenomes, we provide a detailed biosynthetic route for many acI-A and -B members to synthesize retinal and its carotenoid precursors. Accordingly, these acI should be able to natively assemble light-driven actinorhodopsins (holo-ActR) to pump protons, in contrast to acI-C members and other bacteria that encode opsins but lack retinal-production machinery. Moreover, we show that all acI clades contain genes for a complex carotenoid pathway that starts with retinal precursors. Transcription analysis of acI in a eutrophic lake shows that all retinal and carotenoid pathway operons are transcribed and that actR is among the most highly-transcribed of all acI genes. Furthermore, heterologous expression of retinal pathway genes shows that lycopene, retinal, and ActR can be made. Model cells producing ActR and the key acI retinal-producing {beta}-carotene oxygenase formed acI-holo-ActR and acidified solution during illumination. Our results prove that acI containing both ActR and retinal-production enzymes have the capacity to natively synthesize a green light-dependent outward proton-pumping rhodopsin.\n\nIMPORTANCEMicrobes play critical roles in determining the quality of freshwater ecosystems that are vital to human civilization. Because acI Actinobacteria are ubiquitous and abundant in freshwater lakes, clarifying their ecophysiology is a major step in determining the contributions that they make to nitrogen and carbon cycling. Without accurate knowledge of these cycles, freshwater systems cannot be incorporated into climate change models, ecosystem imbalances cannot be predicted, and policy for service disruption cannot be planned. Our work fills major gaps in microbial light utilization, secondary metabolite production, and energy cycling in freshwater habitats.

microbiology↗

bioRxiv2017

TaxAss: Leveraging Custom Databases Achieves Fine-Scale Taxonomic Resolution

Taxonomy assignment of freshwater microbial communities is limited by the minimally curated phylogenies used for large taxonomy databases. Here we introduce TaxAss, a taxonomy assignment workflow that classifies 16S rRNA gene amplicon data using two taxonomy reference databases: a large comprehensive database and a small ecosystem-specific database rigorously curated by scientists within a field. We applied TaxAss to five different freshwater datasets using the comprehensive Silva database and the freshwater-specific FreshTrain database. TaxAss increased the percent of the dataset classified compared to using only Silva, especially at fine-resolution family-species taxa levels, while across the freshwater test-datasets classifications increased by as much as 11-40 percent of total reads. A similar increase in classifications was not observed in a control mouse gut dataset, which was not expected to contain freshwater bacteria. TaxAss also maintained taxonomic richness compared to using only the FreshTrain across all taxa-levels from phylum to species. Without TaxAss, most organisms not represented in the FreshTrain were unclassified, but at fine taxa levels incorrect classifications became significant. We validated TaxAss using simulated amplicon data with known taxonomy and found that 96-99% of test sequences were correctly classified at fine resolution. TaxAss splits a datasets sequences into two groups based on their percent identity to reference sequences in the ecosystem-specific database. Sequences with high similarity to sequences in the ecosystem-specific database are classified using that database, and the others are classified using the comprehensive database. TaxAss is free and open source, and available at www.github.com/McMahonLab/TaxAss.\n\nIMPORTANCEMicrobial communities drive ecosystem processes, but microbial community composition analyses using 16S rRNA gene amplicon datasets are limited by the lack of fine-resolution taxonomy classifications. Coarse taxonomic groupings at phylum, class, and order level lump ecologically distinct organisms together. To avoid this, many researchers define operational taxonomic units (OTUs) based on clustered sequences, sequence variants, or unique sequences. These fine-resolution groupings are more ecologically relevant, but OTU definitions are dataset-dependent and cannot be compared between datasets. Microbial ecologists studying freshwater have curated a small, ecosystem-specific taxonomy database to provide consistent and up-to-date terminology. We created TaxAss, a workflow that leverages this database to assign taxonomy. We found that TaxAss improves fine-resolution taxonomic classifications (family, genus and species). Fine taxonomic groupings are more ecologically relevant, so they provide an alternative to OTU-based analyses that is consistent and comparable between datasets.

bioinformatics↗

bioRxiv2017

High-Throughput Metabolic Network Analysis and Metatranscriptomics of a Cosmopolitan and Streamlined Freshwater Lineage

An explosion in the number of available genome sequences obtained through metagenomics and single-cell genomics has enabled a new view of the diversity of microbial life, yet we know surprisingly little about how microbes interact with each other or their environment. In fact, the majority of microbial species remain uncultivated, while our perception of their ecological niches is based on reconstruction of their metabolic potential. In this work, we demonstrate how the \"seed set framework\", which computes the set of compounds that an organism must acquire from its environment (1), enables computational analysis of metabolic reconstructions, while providing new insights into a microbes metabolic capabilities, such as nutrient use and auxotrophies. We apply this framework to members of the ubiquitous freshwater Actinobacterial lineage acI, confirming and extending previous experimental and genomic observations implying that acI bacteria are heterotrophs reliant on peptides and saccharides. We also present the first metatranscriptomic study of the acI lineage, revealing high expression of transport proteins and the light-harvesting protein actinorhodopsin. Putative transport proteins complement predictions of nutrients and essential metabolites while providing additional support to the hypothesis that members of the acI are photoheterotrophs.\n\nImportanceThe metabolic activity of uncultivated microorganisms contributes to numerous ecosystem processes, ranging from nutrient cycling in the environment to influencing human health and disease. Advances in sequencing technology have enabled the assembly of genomes for these microorganisms, but our ability to generate reference genomes far outstrips our ability to analyze them. Common approaches to analyzing microbial metabolism require reconstructing the entirety of an organisms metabolic pathways, or performing targeted searches for genes involved in a specific process. This paper presents a third approach, in which draft metabolic reconstructions are used to identify compounds through which an organism may interact with its environment. These compounds can then guide more intensive metabolic reconstruction efforts, and also provide new hypotheses about the specific contributions microbes make to ecosystem-scale metabolic processes.

microbiology↗

bioRxiv2017

Model communities hint to promiscuous metabolic linkages between ubiquitous free-living freshwater bacteria

Free-living microorganisms with streamlined genomes are very abundant in the environment. Genome streamlining results in losses in the cells biosynthetic potential generating physiological dependencies between microorganisms. However, there exists no consensus on the specificity of these microbial associations. To verify specificity and extent of these associations, mixed cultures were established from three different freshwater environments. These cultures contained free-living streamlined organisms lacking multiple biosynthetic pathways. Among the co-occurring members of the mixed cultures, there was no clear recurring pattern of metabolic complementarity and dependencies. This, together with weak temporal co-occurrence patterns observed using time-series metagenomics, suggests that free-living freshwater bacteria form loose and unspecific cooperative loops. Comparative genomics suggests that the proportion of accessory genes in populations of streamlined bacteria allows for flexibility in interaction partners. Altogether this renders these free-living bacterial lineages functionally versatile despite their streamlining tendencies.

microbiology↗