Geller-McGrath David

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Geller-McGrath
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David
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  • Article
    Diverse secondary metabolites are expressed in particle-associated and free-living microorganisms of the permanently anoxic Cariaco Basin
    (Nature Research, 2023-02-06) Geller-McGrath, David ; Mara, Paraskevi ; Taylor, Gordon T. ; Suter, Elizabeth ; Edgcomb, Virginia ; Pachiadaki, Maria
    Secondary metabolites play essential roles in ecological interactions and nutrient acquisition, and are of interest for their potential uses in medicine and biotechnology. Genome mining for biosynthetic gene clusters (BGCs) can be used for the discovery of new compounds. Here, we use metagenomics and metatranscriptomics to analyze BGCs in free-living and particle-associated microbial communities through the stratified water column of the Cariaco Basin, Venezuela. We recovered 565 bacterial and archaeal metagenome-assembled genomes (MAGs) and identified 1154 diverse BGCs. We show that differences in water redox potential and microbial lifestyle (particle-associated vs. free-living) are associated with variations in the predicted composition and production of secondary metabolites. Our results indicate that microbes, including understudied clades such as Planctomycetota, potentially produce a wide range of secondary metabolites in these anoxic/euxinic waters.
  • Article
    Metagenomic profiles of archaea and bacteria within thermal and geochemical gradients of the Guaymas Basin deep subsurface
    (Nature Research, 2023-11-27) Mara, Paraskevi ; Geller-McGrath, David ; Edgcomb, Virginia P. ; Beaudoin, David J. ; Morono, Yuki ; Teske, Andreas P.
    Previous studies of microbial communities in subseafloor sediments reported that microbial abundance and diversity decrease with sediment depth and age, and microbes dominating at depth tend to be a subset of the local seafloor community. However, the existence of geographically widespread, subsurface-adapted specialists is also possible. Here, we use metagenomic and metatranscriptomic analyses of the hydrothermally heated, sediment layers of Guaymas Basin (Gulf of California, Mexico) to examine the distribution and activity patterns of bacteria and archaea along thermal, geochemical and cell count gradients. We find that the composition and distribution of metagenome-assembled genomes (MAGs), dominated by numerous lineages of Chloroflexota and Thermoproteota, correlate with biogeochemical parameters as long as temperatures remain moderate, but downcore increasing temperatures beyond ca. 45 ºC override other factors. Consistently, MAG size and diversity decrease with increasing temperature, indicating a downcore winnowing of the subsurface biosphere. By contrast, specific archaeal MAGs within the Thermoproteota and Hadarchaeota increase in relative abundance and in recruitment of transcriptome reads towards deeper, hotter sediments, marking the transition towards a specialized deep, hot biosphere.
  • Article
    Plasmid-borne biosynthetic gene clusters within a permanently stratified marine water column
    (MDPI, 2024-05-02) Mara, Paraskevi ; Geller-McGrath, David ; Suter, Elizabeth A. ; Taylor, Gordon T. ; Pachiadaki, Maria G. ; Edgcomb, Virginia P.
    Plasmids are mobile genetic elements known to carry secondary metabolic genes that affect the fitness and survival of microbes in the environment. Well-studied cases of plasmid-encoded secondary metabolic genes in marine habitats include toxin/antitoxin and antibiotic biosynthesis/resistance genes. Here, we examine metagenome-assembled genomes (MAGs) from the permanently-stratified water column of the Cariaco Basin for integrated plasmids that encode biosynthetic gene clusters of secondary metabolites (smBGCs). We identify 16 plasmid-borne smBGCs in MAGs associated primarily with Planctomycetota and Pseudomonadota that encode terpene-synthesizing genes, and genes for production of ribosomal and non-ribosomal peptides. These identified genes encode for secondary metabolites that are mainly antimicrobial agents, and hence, their uptake via plasmids may increase the competitive advantage of those host taxa that acquire them. The ecological and evolutionary significance of smBGCs carried by prokaryotes in oxygen-depleted water columns is yet to be fully elucidated.
  • Article
    Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict
    (eLife Sciences Publications, 2024-05-02) Geller-McGrath, David ; Konwar, Kishori M. ; Edgcomb, Virginia P. ; Pachiadaki, Maria G. ; Roddy, Jack W. ; Wheeler, Travis J. ; McDermott, Jason E.
    The reconstruction of complete microbial metabolic pathways using ‘omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.
  • Article
    Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict
    (eLife Sciences Publications, 2024-05-02) Geller-McGrath, David ; Konwar, Kishori M. ; Edgcomb, Virginia P. ; Pachiadaki, Maria G. ; Roddy, Jack W. ; Wheeler, Travis J. ; McDermott, Jason E.
    The reconstruction of complete microbial metabolic pathways using ‘omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.
  • Article
    Deep subseafloor sediments in Guaymas Basin harbor cosmopolitan microbiota and traces of hydrothermal populations
    (Nature Research, 2024-09-13) Mara, Paraskevi ; Beaudoin, David J. ; Aiello, Ivano ; Morono, Yuki ; David Geller-McGrath ; Edgcomb, Virginia P. ; Teske, Andreas P.
    Environmental factors shape subsurface microbial ecosystems, and well-characterized sites are ideal for determining how environmental parameters shape sediment communities. Sediments from eight geologically and thermally distinct sites were drilled during International Ocean Discovery Program Expedition 385 in Guaymas Basin, an expedition focused on the hydrothermal deep biosphere. Using high-throughput 16S ribosomal nucleic acid sequencing, cell counts, phylogenetics, metatranscriptomics, and mineralogical/elemental X-ray spectroscopy, we examine linkages and feedbacks between mineral composition, temperature, geochemistry, and microbial populations. We show subsurface life is dominated by heterotrophic, cosmopolitan prokaryotes that thrive within a range of sediments and temperature conditions across Guaymas Basin. Hydrothermally-affiliated lineages are detected in low numbers at sites with steepest temperature gradients, within communities of mesophilic taxa that occur throughout Guaymas Basin and in other marine subsurface habitats. Thus, hydrothermal lineages do not replace the cosmopolitan, mesophilic subsurface community, but remain specific to sites where volcanic intrusions drive hydrothermal circulation.
  • Thesis
    Characterization of microbial primary and secondary metabolism in the marine realm
    (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2024-09) Geller-McGrath, David ; Edgcomb, Virginia P.
    This thesis applies meta-omics data analysis to elucidate the ecological roles of marine microorganisms in diverse habitats and includes the development of new bioinformatics tools to enhance these analyses. In my second chapter, I applied genome mining tools to analyze the gene content and expression of biosynthetic gene clusters (BGCs). The analysis of BGCs through largescale genome mining efforts has identified diverse natural products with potential applications in medicine and biotechnology. Many marine environments, particularly oxygen-depleted water columns and sediments, however, remain under-represented in these studies. Analysis of BGCs in free-living and particle-associated microbial communities along the oxycline water column of the Cariaco Basin, Venezuela, revealed that differences in water column redox potential were associated with microbial lifestyle and the predicted composition and production of secondary metabolites. This experience set the stage for my third chapter, in which I developed MetaPathPredict, a machine learning-based tool for predicting the metabolic potential of bacterial genomes. This tool addresses the lack of computational pipelines for pathway reconstruction that predict the presence of KEGG modules in highly incomplete prokaryotic genomes. MetaPathPredict made robust predictions in highly incomplete bacterial genomes, enabling more accurate reconstruction of their metabolic potential. In my fourth chapter, I performed metagenomic analysis of microbial communities in the hydrothermally-influenced sediments of Guaymas Basin (Gulf of California, Mexico). Previous studies indicated a decline in microbial abundance and diversity with increasing sediment depth. Analysis revealed a distribution of MAGs dominated by Chloroflexota and Thermoproteota, with diversity decreasing as temperature increased, consistent with a downcore reduction in subsurface biosphere diversity. Specific archaeal MAGs within the Thermoproteota and Hadarchaeota increased in abundance and recruitment of metatranscriptome reads towards deeper, hotter sediments, marking a transition to a specialized deep biosphere. In my fifth chapter, I developed MetaPathPredict-E, a deep learningpowered extension of MetaPathPredict for eukaryotic metabolism predictions. Eukaryotic metabolism is diverse, reflecting varied lifestyles across eukaryotic kingdoms, but the complexity of eukaryotic genomes presents challenges for assembly and annotation. MetaPathPredict-E was trained on diverse eukaryotic genomes and transcriptomes, demonstrating a robust performance on test datasets, thus advancing the study of eukaryotic metabolic potential from environmental samples.