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Kimbrel, Jeffrey

Publications and source records attributed to Kimbrel, Jeffrey.

Toxin-Antitoxin Systems Reflect Community Interactions Through Horizontal Gene Transfer

Bacterial evolution through horizontal gene transfer (HGT) reflects their community interactions. In this way, HGT networks do well at mapping community interactions, but offer little toward controlling them—an important step in the translation of synthetic strains into natural contexts. Toxin–antitoxin (TA) systems serve as ubiquitous and diverse agents of selection; however, their utility is limited by their erratic distribution in hosts. Here we examine the heterogeneous distribution of TAs as a consequence of their mobility. By systematically mapping TA systems across a 10,000 plasmid network, we find HGT communities have unique and predictable TA signatures. We propose these TA signatures arise from plasmid competition and have further potential to signal the degree to which plasmids, hosts, and phage interact. To emphasize these relationships, we construct an HGT network based solely on TA similarity, framing specific selection markers in the broader context of bacterial communities. This work both clarifies the evolution of TA systems and unlocks a common framework for manipulating community interactions through TA compatibility.

59 BASIC BIOLOGICAL SCIENCES↗

Transporter annotations are holding up progress in metabolic modeling

Mechanistic, constraint-based models of microbial isolates or communities are a staple in the metabolic analysis toolbox, but predictions about microbe-microbe and microbe-environment interactions are only as good as the accuracy of transporter annotations. A number of hurdles stand in the way of comprehensive functional assignments for membrane transporters. These include general or non-specific substrate assignments, ambiguity in the localization, directionality and reversibility of a transporter, and the many-to-many mapping of substrates, transporters and genes. In this perspective, we summarize progress in both experimental and computational approaches used to determine the function of transporters and consider paths forward that integrate both. Investment in accurate, high-throughput functional characterization is needed to train the next-generation of predictive tools toward genome-scale metabolic network reconstructions that better predict phenotypes and interactions. More reliable predictions in this domain will benefit fields ranging from personalized medicine to metabolic engineering to microbial ecology.

Casey, John↗

Dynamic Phaeodactylum tricornutum exometabolites shape surrounding bacterial communities

Summary Roles of different ecological classes of algal exometabolites in regulating microbial community composition are not well understood. Here, we identify exometabolites from the model diatom Phaeodactylum tricornutum and demonstrate their potential to influence bacterial abundances. We profiled exometabolites across a time course of axenic algal growth using liquid chromatography–tandem mass spectrometry. We then investigated growth of 12 bacterial isolates on individual‐identified exometabolites. Lastly, we compared responses of a P. tricornutum ‐adapted enrichment community to additions of two contrasting metabolites: selective growth substrate 4‐hydroxybenzoic acid and putative signaling/facilitator molecule lumichrome. We identified 50 P. tricornutum metabolites and found distinct temporal accumulation patterns. Two exometabolites (of 12 tested) supported growth of distinct subsets of bacterial isolates. While algal exudates and algal presence drove similar changes in community composition compared with controls, exogenous 4‐hydroxybenzoic acid addition promoted increased abundances of taxa that utilized it in isolation, and also revealed the importance of factors relating to algal presence in regulating community composition. This work demonstrates that secretion of selective bacterial growth substrates represents one mechanism by which algal exometabolites can influence bacterial community composition and illustrates how the algal exometabolome has the potential to modulate bacterial communities as a function of algal growth.

4-hydroxybenzoic acid↗

Multiple Mutations Associated with Emergent Variants Can Be Detected as Low-Frequency Mutations in Early SARS-CoV-2 Pandemic Clinical Samples

Genetic analysis of intra-host viral populations provides unique insight into pre-emergent mutations that may contribute to the genotype of future variants. Clinical samples positive for SARS-CoV-2 collected in California during the first months of the pandemic were sequenced to define the dynamics of mutation emergence as the virus became established in the state. Deep sequencing of 90 nasopharyngeal samples showed that many mutations associated with the establishment of SARS-CoV-2 globally were present at varying frequencies in a majority of the samples, even those collected as the virus was first detected in the US. A subset of mutations that emerged months later in consensus sequences were detected as subconsensus members of intra-host populations. Spike mutations P681H, H655Y, and V1104L were detected prior to emergence in variant genotypes, mutations were detected at multiple positions within the furin cleavage site, and pre-emergent mutations were identified in the nucleocapsid and the envelope genes. Because many of the samples had a very high depth of coverage, a bioinformatics pipeline, “Mappgene”, was established that uses both iVar and LoFreq variant calling to enable identification of very low-frequency variants. This enabled detection of a spike protein deletion present in many samples at low frequency and associated with a variant of concern.

60 APPLIED LIFE SCIENCES↗

SARS-CoV-2 Monitoring in Wastewater Reveals Novel Variants and Biomarkers of Infection

Wastewater-based epidemiology (WBE) is a popular tool for the early indication of community spread of infectious diseases. WBE emerged as an effective tool during the COVID-19 pandemic and has provided meaningful information to minimize the spread of infection. Here, we present a combination of analyses using the correlation of viral gene copies with clinical cases, sequencing of wastewater-derived RNA for the viral mutants, and correlative analyses of the viral gene copies with the bacterial biomarkers. Our study provides a unique platform for potentially using the WBE-derived results to predict the spread of COVID-19 and the emergence of new variants of concern. Further, we observed a strong correlation between the presence of SARS-CoV-2 and changes in the microbial community of wastewater, particularly the significant changes in bacterial genera belonging to the families of Lachnospiraceae and Actinomycetaceae . Our study shows that microbial biomarkers could be utilized as prediction tools for future infectious disease surveillance and outbreak responses. Overall, our comprehensive analyses of viral spread, variants, and novel bacterial biomarkers will add significantly to the growing body of literature on WBE and COVID-19.

59 BASIC BIOLOGICAL SCIENCES↗

Life and death in the soil microbiome: how ecological processes influence biogeochemistry

We report soil microorganisms shape global element cycles in life and death. Living soil microorganisms are a major engine of terrestrial biogeochemistry, driving the turnover of soil organic matter — Earth’s largest terrestrial carbon pool and the primary source of plant nutrients. Their metabolic functions are influenced by ecological interactions with other soil microbial populations, soil fauna and plants, and the surrounding soil environment. Remnants of dead microbial cells serve as fuel for these biogeochemical engines because their chemical constituents persist as soil organic matter. This non-living microbial biomass accretes over time in soil, forming one of the largest pools of organic matter on the planet. In this Review, we discuss how the biogeochemical cycling of organic matter depends on both living and dead soil microorganisms, their functional traits, and their interactions with the soil matrix and other organisms. With recent omics advances, many of the traits that frame microbial population dynamics and their ecophysiological adaptations can be deciphered directly from assembled genomes or patterns of gene or protein expression. Thus, it is now possible to leverage a trait-based understanding of microbial life and death within improved biogeochemical models and to better predict ecosystem functioning under new climate regimes.

59 BASIC BIOLOGICAL SCIENCES↗