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Sullivan, M. B.

Publications and source records attributed to Sullivan, M. B..

At least 19 recordsLinked to original sources

CheckAMG: Accurate Identification of Auxiliary Viral Genes with Genome-Language Models

Viruses shape microbial metabolism through auxiliary viral genes (AVGs) that reprogram host metabolism, physiology, and gene regulation during infection. Identifying AVGs is complicated by ambiguous viral versus cellular assignment, inconsistent curation, and reliance on sequence homology that misses divergent functions. Here we present CheckAMG, which pairs annotation-based AVG prediction, curated with a per-gene viral scoring system and ontological mapping of metabolic function, with a finetuned genome-language model for annotation-independent discovery. Benchmarked against DRAM-V and VIBRANT across three ecosystems, CheckAMG produced more strongly supported auxiliary metabolic gene (AMG) calls, and uniquely among AMG tools reports auxiliary physiological (APGs) and regulatory (AReGs) genes. Applied to 1,005,980 fragmented or complete soil and human-gut viral genomes, CheckAMG identified 541,199 AVGs tracking biome-specific protein clusters and functions, including 109,107 proteins with no detectable sequence similarity to any reference database. CheckAMG thus provides a reproducible, scalable framework for identifying the genes through which viruses reprogram host biology.

bioinformatics↗

Cross-family and phage-specific gene requirements for Klebsiella infection revealed by scalable RB-TnSeq genetic screens

Bacteriophages are being cataloged at an accelerating pace and are recognized as key players in nutrient and energy cycling across ecosystems. Yet the bacterial genetic determinants that govern phage-host specificity and infection success remain poorly understood, particularly in clinically and ecologically important genera such as Klebsiella where prior receptor characterization has been almost entirely limited to capsulated strains. Here we used a randomly barcoded, genome-wide, loss-of-function transposon mutant library (RB-TnSeq) of Klebsiella sp. M5al, a naturally acapsular, nitrogen-fixing rhizobacterium, to generate the first systematic, cross-family map of phage receptor gene dependencies in Klebsiella. Challenging the library against 25 double-stranded DNA phages spanning five families in 213 parallel assays, we identified 42 bacterial genes associated with phage infection, of which 15 had no prior association with phage infection in any bacterial system. Disruption of surface receptor biosynthesis genes conferred cross-resistance across multiple phage families, while intracellular gene disruptions had predominantly phage-specific effects. Clonal validation of eight genes confirmed LPS outer core biosynthesis genes as primary receptor determinants alongside additional host factors spanning outer membrane transport, cofactor biosynthesis, and two-component signaling. Comparative analysis across all 25 phages revealed that phage genus rather than family is the stronger predictor of host gene dependency profiles, a finding with direct implications for the functional annotation of uncharacterized phage isolates and rational phage cocktail design. Together, these findings provide a community resource for linking phage genomic diversity to functional host interaction space in this ecologically and clinically important genus. Author SummaryBacteriophages, or phages, are viruses that infect bacteria and play central roles in shaping microbial communities across ecosystems. Despite their ecological importance, the bacterial genes that determine infection outcomes remain poorly understood. Using a scalable barcoded transposon sequencing (RB-TnSeq) approach, we mapped host gene requirements for infection by 25 diverse phages of Klebsiella sp. M5al, a soil-associated plant-growth-promoting bacterium. We identified 42 bacterial genes associated with phage infection, spanning surface receptors, transcriptional regulators, and metabolic and protein-folding pathways. Infection strategies broadly clustered by phage genus and family, while phage-specific differences arose primarily in intracellular processes such as transcription and protein folding. These findings reveal both conserved and phage-specific host interactions that define infection strategies and establish a scalable framework for linking phage genomic diversity to function, with practical implications for predicting phage host range, designing phage cocktails for therapy, and understanding phage-driven dynamics in natural microbial communities.

microbiology↗

Virus community response rewires methane-suppressed peat microcosm carbon metabolism

Human activities are accelerating permafrost thaw and subsequent methane emissions from increased microbial activity, prompting microbiome engineering efforts as an emissions mitigation strategy. We recently demonstrated that catechin amendment could drastically reduce methane emissions (>80%) in peat microcosms by enriching catechin-degrading prokaryotes that outcompeted methanogens for hydrogen. However, viral contributions to such microbiome-level responses remain unexplored and we hypothesized that viral dynamics could help shape the microbiome response as nutrient amendments may alter cellular physiology in ways that could induce lytic viral activity. Here, we performed virus ecogenomics analyses of the previously-studied time-resolved multi-omics data collected from catechin-amended peat microcosms. We conservatively identified 900 putatively lytic viral operational taxonomic units (vOTUs), with 41% predicted to infect active host genomes including the most transcriptionally active vOTUs predicted to infect key catechin-degrading genera (Clostridium and undescribed Bacillota JAGFXR01). Notably, a single JAGFXR01-targeting vOTU dominating the viral response (>40% of community viral transcription; 20-156-fold more abundant than its host), which we interpreted as induction resulting in intense lytic activity that could release catechin-degradation intermediates to other community members. Consistent with this, gene expression analysis revealed elevated catechin-intermediate degradation and hydrogenase signals in 34 additional polyphenol-degrading metagenome-assembled genomes. These findings support a model consistent with a viral shunt-like process that extends our previous prokaryote-centric model: viral lysis of fast-growing catechin degraders redistributes phenolic intermediates to diverse phenol-degrading taxa that sustain methane suppression via hydrogen consumption. Beyond carbon-cycling importance in this system, elucidating unintended virus-mediated responses to nutrient and prebiotic interventions will enable more predictable and effective microbiome engineering strategies across soil, ocean, and human ecosystems.

microbiology↗

Scalable and systematic hierarchical virus taxonomy with vConTACT3

Viruses are key players in diverse ecosystems, but studying their impacts is technically and taxonomically challenging. Taxonomic complexities derive from undersampling, diverse DNA and RNA genomes with multiple evolutionary origins, and lack of a universal barcode gene. While virus ecogenomics has expanded access to and understanding of the virosphere, available classification tools poorly scale to modern discovery-based datasets, lack taxonomic resolution, and/or are unable to classify novel sequence space. Here we develop, benchmark, and release vConTACT3, a machine learning-based tool that improves scalability and accuracy, adds extensive user-requested features, expands classification to both eukaryote and prokaryote viruses for 4/6 officially recognized realms, and establishes accurate hierarchical taxonomy from genus to order. Application to 48,069 public virus genomes provided new taxonomy assignments for thousands of taxa, revealed support for fewer taxonomic ranks than currently available, and systematically identified taxonomically problematic areas across the virosphere.

bioinformatics↗

Sub-daily Bermuda Atlantic Time Series virus sampling reveals taxonomy, host, and functional differences at the population, but not community level

Ocean microbes contribute to biogeochemical cycles and ecosystem function, but they do so under top-down pressure imposed by viruses. While viruses are increasingly understood spatially and beginning to be incorporated into predictive modeling, high-frequency ocean virus dynamics remain understudied due to methodological challenges. Here we sampled stratified Bermuda Atlantic Time Series (BATS) waters for 112 hours at sub-daily 4-(surface) or 12-(deep chlorophyll maximum) hour intervals, purified viral particles from these samples, sequenced their metagenomes, and used the resulting data to characterize high-frequency virus community dynamics. Aggregated community diversity metrics changed with depth, but were not statistically significant temporally at a fixed location. However, finer-scale population-level analyses revealed both depth and temporal change, including physicochemical depth-driven differences and, in surface waters, thousands of viral populations that exhibited statistically significant diel rhythms. Statistical analyses revealed three main archetypes of temporal dynamics that themselves differed in abundance patterns, host predictions, viral taxonomy, and gene functions. Among these, highlights include viruses resembling an archetype with a night peaking pattern in activity that include an over-representation of viruses that putatively infect Prochlorococcus, a phototrophic cyanobacteria. Together, these efforts provide baseline community-and population-scale short-time-frame observations relevant to future climate state modeling.

microbiology↗

The cervicovaginal microbiome of pregnant people living with HIV on antiretroviral therapy in the Democratic Republic of Congo: A Pilot Study and Global Meta-analysis.

Recent studies are revealing that a suboptimal cervicovaginal microbiome (CVMB), including enrichment of anaerobic bacteria associated with multiple female genital disorders, and adverse pregnancy and birth outcomes in pregnant people. Problematically, however, the majority of the available data to date are biased towards highly developed, Global North countries, leaving underrepresented populations like the Democratic Republic of Congo (DRC) poorly characterised. Here, we investigate the CVMB from a cohort of 82 pregnant people living with HIV (PLWH) on antiretroviral therapy (ART) from the DRC. Specifically, we explore the associations between the CVMB via 16S rRNA gene sequencing and maternal peripheral immune factors. Additionally, we compare the CVMB of PLWH-ART from DRC to publicly available CVMB data (5 studies, 1861 samples) in a meta-analysis to elucidate the impact of HIV on the CVMB. Combined, these analyses revealed differences in community structure and predicted function of the microbiota between PLWH-ART and pregnant people without HIV (PWoH). Taxonomically, the CVMB of DRC PLWH-ART were enriched for Lactobacillus iners-dominated CVMBs (53%) or a diverse, polymicrobial CVMB, i.e., bacterial vaginosis (BV) (43%). Functional predictions made from these taxa suggested that protein-coupled receptors, amino sugar and nucleotide sugar metabolism, fatty acid metabolism, and polycyclic aromatic hydrocarbon degradation pathways were differentially abundant between communities. Correlation with host plasma immune factors revealed putative links between some CVMB metrics (e.g., alpha diversity and species abundance) that have been linked to adverse pregnancy and birth outcomes. ImportanceHIV remains prevalent in sub-Saharan Africa, where it has been linked to adverse birth outcomes. . Suboptimal CVMBs have shown similar links. This pilot study fills critical gaps in understanding how HIV interacts with the pregnant CVMB in populations underrepresented in microbiome research, like the Democratic Republic of Congo. We identified maternal systemic immune factors associated with suboptimal CVMBs that have been linked to poor birth outcomes. In a global meta-analysis, we found significant taxonomic and functional difference in the CVMBs between pregnant people living with and without HIV, revealing potential biomarkers that for increased risks for adverse birth outcomes. These findings provide crucial insights into CVMB features that may influence pregnancy health in pregnant people living with HIV, guiding future research and tailored interventions to support safer pregnancies in the DRC and similar populations.

microbiology↗

CRESSENT: a Bioinformatic Toolkit to Explore and Improve ssDNA Virus Annotation

Single-stranded DNA (ssDNA) viruses are important components of diverse ecosystems, however, it remains challenging to systematically identify and classify them. This is in part due to their broad host range and resulting genomic diversity, structure, and rapid evolution rates. In addition, distinguishing genuine ssDNA genomes from contaminating sequences in metagenomic datasets (e.g., from commercial kits) has been an unresolved issue for years. Here, we present CRESSENT (CRESS-DNA Extended aNnotation Toolkit), a comprehensive and modular bioinformatic pipeline focused on ssDNA virus genome-to-analysis and annotation. The pipeline integrates multiple functionalities organized into six modules: sequence dereplication, decontamination, phylogenetic analysis, motif discovery, stem-loop structure prediction, and recombination detection. Each module can be used independently or in combination with others, allowing researchers to customize their analysis workflow. With this tool, researchers can comprehensively and systematically include ssDNA viruses in their viromics workflows and facilitate comparative genomic studies, which are often limited to dsDNA viruses, therefore leaving behind a crucial component of the microbiome community under study.

bioinformatics↗

Precision Prediction of Microbial Ecosystem Impact on Host Metabolism Using Genome-Resolved Metagenomics

Microorganisms often drive ecosystem function, yet precision disturbance response and ecosystem impact predictions remain challenging due to poorly captured ecological and metabolic interconnectedness and functional redundancy. For example, while mammalian gut dysbiosis is recognized to influence host metabolism, key microbiota and mechanisms governing their effects remain poorly understood. Here we developed a genome-resolved eco-systems biology workflow to predict how gut microbial metabolism affects mammalian health, and we applied it to a spinal cord-gut axis dataset. By scaling and integrating temporally resolved network analytics and consensus statistical approaches, we identified largely previously uncharacterized microbial species that best predict host physiology following neurological impairment. In silico validation through "complete" pathway-centric and comparative genomic analyses revealed that among these species, the major encoded microbial metabolic changes were in pathways directly linked to host nitrogen balance, and they varied by host sex and microbial ecotype/species. Moreover, we identified the exact bacterial species (and their draft genome sequences) driving urease-dependent versus amino acid-dependent nitrogen gut metabolism - findings that explain previously mechanistically-ambiguous, but clinically relevant, ammonia-driven host nitrogen imbalance. More broadly, these ecology- and community-aware approaches provide a framework to study dynamic, interconnected microbiomes that advances from enrichment-based single-taxon and single-gene correlations towards building microbe(s)-driven mechanistic insights that integrate community context and whole pathways.

microbiology↗

Hidden viral players: Diversity and ecological roles of viruses in groundwater microbiomes

Groundwater ecosystems harbor diverse microbial communities adapted to energy-limited, light-deprived conditions, yet the role of viruses in these environments remains poorly understood. Here, we analyzed 1.26 terabases of metagenomic and metatranscriptomic data from seven wells in the Hainich Critical Zone Exploratory (CZE) to characterize groundwater viromes. We identified 257,252 viral operational taxonomic units (vOTUs) ([≥]5 kb), with 99% classified as novel, highlighting extensive uncharacterized viral diversity. Viruses exhibited a distinct host range, primarily targeting Proteobacteria, Candidate Phyla Radiation (CPR) bacteria, and DPANN archaea. Notably, CPR lineages displayed low virus-host ratios and viral CRISPR targeting multiple hosts, suggesting a "virus decoy" mechanism where they may absorb viral pressure, protecting bacteria hosts. Additionally, 3,378 vOTUs encoded auxiliary metabolic genes (AMGs) linked to carbon, nitrogen, and sulfur cycling, with viruses targeting 31.5% of host metabolic modules. These findings demonstrate viruses influence on microbial metabolic reprogramming and nutrient cycling in groundwater, shaping subsurface biogeochemistry.

microbiology↗

The Spinal Cord-Gut Axis Regulates Gut Microbial Homeostasis: Insights from a New Murine Metagenomic Catalog

The spinal cord, a nexus for brain-body crosstalk, controls gut physiology and microbial homeostasis, but the underlying mechanisms remain unclear. Using genome-resolved longitudinal metagenomics in male and female C57BL/6 mice before and up to 6 months after disrupting the spinal cord-gut axis, we reconstructed over 6,500 microbial draft genomes. This "Mouse B6 Gut Catalog" improved or doubled species- and strain-level representation in other published catalogs. Impaired spinal cord-gut crosstalk induced persistent, sex-, time- and lesion-specific alterations in community composition, marked by a consistent loss of Lactobacillus johnsonii. Feeding this key bacterium to mice with a clinically relevant spinal cord injury improved host health. Genome-resolved, community-contextualized metabolic profiling revealed that shifts in carbohydrate- mediated microbe-microbe interactions explain the reduction of L. johnsonii. These findings identify carbohydrate metabolism as a keystone mechanism shaping gut microbiota and emphasize that mammalian health and gut ecosystem function depend on a functional spinal cord-gut axis. Additionally, these data improve murine microbiome catalogs and demonstrate that metagenome-informed microbial interventions can improve host health and likely mitigate long-term dysbiosis.

microbiology↗

Emergent higher-order interactions enable coexistence in phage-bacteria community dynamics

Diverse phage-bacteria communities coexist at high densities in environmental, agricultural, and human-associated microbiomes. Phage-bacteria coexistence is often attributed to coevolutionary processes mediated by complex, pairwise infection networks. Here, using in vitro experiments and mathematical models, we explore how higher-order interactions function as a complementary, ecological feedback mechanism to stabilize phage-bacteria communities. To do so, we examine an environmentally-derived, synthetic phage-bacteria community comprised of five marine heterotrophic bacteria (Cellulophaga baltica and Pseudoalteromonas strains) and five associated phage. We used Bayesian inference to reconstruct free phage production in one-step growth experiments and then forecasted pairwise phage-bacteria community dynamics over multiple infection cycles. In contrast to model predictions of rapid bacterial population collapse, each bacterial strain persisted in the community. We hypothesized and then experimentally validated the relevance of infection attenuation at relatively high viral densities. We extended models into a community context, corroborating complex coexistence of all phage and bacteria. Life history traits inferred in community fits often differed from those inferred in a pairwise context, implicating higher-order interactions as an additional, ecological stabilization mechanism. Follow-up experiments confirm that phage traits (including burst size) can shift when infecting single vs. multiple strains. More broadly, these findings suggest that complex community coexistence of phage and bacteria may be more common than anticipated when including feedback mechanisms outside of the growth-dominated regimes of fitted pairwise models that do not reflect the full scope of ecologically relevant contexts.

ecology↗

Planetary-scale marine community modeling predicts metabolic synergy and viral impacts

The oceans buffer against climate change via biogeochemical cycles underpinned by microbial metabolic activities. While planetary-scale surveys provide baseline microbiome data, inferring metabolic and biogeochemical impacts remains challenging. Genome-scale modeling has addressed analogous issues at the cellular level, highlighting key metabolic reactions contingent upon specific environmental conditions. Here we adapt this mechanistic modeling framework towards analyzing global ocean microbial communities to reveal metabolic processes predicted to maintain ecosystem functioning. To achieve this, we developed a genome-scale superorganism metabolic model for each TARA Ocean metagenome or metatranscriptome (i.e., limited to reactions known from heterotrophic prokaryotes and viruses), and evaluated these models to establish a community-wide metabolic phenotype for each sample. To validate, we showed that even with reaction-mappable genes only ([~]1/4 of the total genes), model composition revealed metabolism-inferred ecological zones that matched taxonomy-inferred zones. Model inferred metabolic phenotypes revealed reaction cooperation associated with microbial metabolism and organism diversity. These phenotypes also suggest elevated ecological roles for viruses as model predictions suggest they genomically target community-critical metabolic reactions that underpin metabolic phenotype stability, and also demonstrate that, as metabolites are better understood, immediate estimates could be made for where viruses remineralize versus sink carbon. While this new constraints-based, agile, and mechanistic modeling framework is highly upgradable, it already begins to convert molecular-scale environmental omics data to ecological and even planetary-scale biogeochemical features that will better bring microbes and their viruses into Earth system and climate models.

systems biology↗

Mobile genetic elements that shape microbial diversity and functions inthawing permafrost soils

The worlds ecosystems are shaped by microbiota. Their niches and their impacts depend on functional profiles influenced by gene gains and losses. While culture-based experiments demonstrate that mobile genetic elements (MGEs) can mediate gene flux, quantitative field data on the rates and impacts of MGE activity remains scarce. Here we leverage large-scale soil meta-omic data to develop and apply analytics for studying MGEs in complex natural systems. In our model permafrost-thaw ecosystem, Stordalen Mire, we identify [~]2.1 million MGE recombinases across 89 microbial phyla to assess ecological distributions, affected functions, past mobility, and current activity. This revealed MGEs shaping natural genetic diversity via differential impacts on major phyla; affecting a wide range of functions, including diverse regulatory and metabolic genes affecting carbon flux and nutrient cycling; and moving at rates that should significantly influence the realized functional profiles of natural microbiomes. These findings and this systematic meta-omic framework open new avenues to better investigate MGE diversity, activity, mobility, and impacts in nature.

microbiology↗

Synthetic community Hi-C benchmarking provides a baseline for virus-host inferences

Microbiomes influence diverse ecosystems, and viruses increasingly appear to impose key constraints. While viromics has expanded genomic catalogs, host identification for these viruses remains challenging due to the limitations in scaling cultivation-based approaches and the uncertain reliability and relative low resolution of in silico predictions - particularly for understudied viral taxa. Towards this, Hi-C proximity ligation uses sequenced, cross-linked virus and host genomic fragments to infer virus-host linkages and has now been applied in at least ten studies. However, its accuracy remains unknown. Here we assess Hi-C performance in recovering virus-host interactions using synthetic communities (SynComs) composed of four marine bacterial strains and nine phages with known interactions and then apply optimized bioinformatic protocols to natural soil samples. In SynComs, standard Hi-C sample preparations and analyses showed poor normalized contact score performance (26% specificity, 100% sensitivity, incorrect matches up to class level) that could be dramatically improved by Z-score filtering (Z [≥] 0.5, 99% specificity), though at reduced sensitivity (62% down from 100%). Detection limits were established as reproducibility was poor below minimal phage abundances of 105 PFU/mL. Applying optimized bioinformatic protocols to natural soil samples, we compared virus-host linkages inferred from proximity-ligated Hi-C sequencing with predictions generated by in silico homology-based and machine learning-based bioinformatic approaches. Prior to Z-score thresholding, agreement was relatively high at the phylum to family levels (72%), but not at the genus (43%) or species (15%) levels. Z-score thresholding reduced sensitivity (only 34% of predictions were retained), with only modest improvements in congruence with bioinformatic methods (48% or 18% at genus or species levels, respectively). Regardless, this led to 79 genus-level-congruent virus-host linkages and 293 new ones revealed by Hi-C alone - i.e., providing many new virus-host interactions to explore in already well-studied climate-critical soils. Overall, these findings provide empirical benchmarks and methodological guidelines to improve the accuracy and reliability of Hi-C for virus-host linkage studies in complex microbial communities.

microbiology↗

RNA virus ecogenomics along a subarctic permafrost thaw gradient

Climate change thaws permafrost, which releases greenhouse gases partly from dormant microorganisms awakening and metabolizing organic matter. Though DNA viruses that infect these soil microbes have been studied, little is known on soil RNA viruses, which typically infect microeukaryotes. Here we identify and characterize 2,651 RNA viruses from a 4-year time series of bulk soil metatranscriptomes derived from the climatically fragile Stordalen Mire ecosystem --a long studied permafrost peatland. RNA virus diversity was structured by habitat (palsa, bog, and fen), and these patterns correlated with pH and carbon dioxide and methane emissions. Further, host prediction, virus-encoded metabolite-transforming and information-processing functions suggested roles in ecosystem-scale carbon fluxes and contribute to greenhouse gases emissions. Together, these RNA virus ecogenomic data in permafrost provide essential baseline information for integration into predictive models to support hypothesis testing.

microbiology↗

Seasonal Enhancement of the Viral Shunt Catalyzes a Subsurface Oxygen Maximum in the Sargasso Sea

Subsurface oxygen maxima (SOMs) occur directly beneath the mixed layer of stratified water columns across oligotrophic open ocean basins. SOMs occur seasonally and are hypothesized to result from elevated microbial net primary productivity (NPP). Here, we set out to identify mechanistic drivers of the SOM near the Bermuda Atlantic Time Series (BATS) site in the Sargasso Sea in October 2019. Coupled time-series analysis of metatranscriptomics, flow cytometry, and family-specific cyanophage quantification revealed elevated Prochlorococcus abundances, cyanophage abundances, and cyanophage-specific transcriptional activity in the SOM. These findings spurred us to analyze historical oxygen saturation profiles at BATS - identifying a repeated, seasonal cycle in SOM emergence associated with elevated virus-like particles and Prochlorococcus numbers. Returning to the 2019 study site, we found that transcriptional markers for increased dissolved organic matter uptake by copiotrophic bacteria were enriched in the SOM, consistent with enhanced catabolic activity due to the viral shunt. In addition, Prochlorococcus exhibited enrichment in ammonium transport transcripts at the SOM, consistent with increased responsiveness to remineralization activity by heterotrophs. Altogether, these findings suggest that enhanced viral lysis leads to locally elevated nutrient recycling and oxygen production, further reinforcing hypotheses that viruses may play a critical role in the emergence of SOMs in the oligotrophic ocean.

ecology↗

Polyphenol rewiring of the microbiome reduces methane emissions

Methane mitigation is regarded as a critical strategy to combat the scale of global warming. Currently, about 40% of methane emissions originate from microbial sources, which is causing strategies to suppress methanogens--either through direct toxic effects or by diverting their substrates and energy--to gain traction. Problematically, current microbial methane mitigation knowledge derives from rumen studies and lacks detailed microbiome-centered insights, limiting translation across ecosystems. Here we utilize genome-resolved metatranscriptomes and metabolomes to assess the impact of a proposed methane inhibitor, catechin, on greenhouse gas emissions for high-methane- emitting peatlands. In microcosms, catechin drastically reduced methane emissions by 72-84% compared to controls. Longitudinal sampling allowed for reconstruction of a novel catechin degradation pathway involving Actinomycetota and Clostridium, which break down catechin into smaller phenolic compounds within the first 21 days, followed by degradation of phenolic compounds by Pseudomonas_E from days 21 to 35. These genomes also co-expressed hydrogen-uptake genes, suggesting that hydrogenases may act as a hydrogen sink during catechin degradation, depriving methanogens of substrates. This was supported by decreased gene expression in hydrogenotrophic and hydrogen-dependent methylotrophic methanogens under catechin treatment. We also saw reduced gene expression from genomes inferred to be functioning syntrophically with hydrogen-utilizing methanogens. We propose that catechin metabolic redirection effectively starves hydrogen-utilizing methanogens, offering a potent avenue for curbing methane emissions across diverse environments including ruminants, landfills, and constructed or managed wetlands.

microbiology↗

Insights into the role of root exudates in bacteriophage infection dynamics

Bacteriophages impact soil bacteria through lysis, altering the availability of organic carbon and plant nutrients. However, the magnitude of nutrient uptake by plants from lysed bacteria remains unknown, partly because this process is challenging to investigate in the field. In this study, we extend ecosystem fabrication (EcoFAB 2.0) approaches to study plant-bacteria-phage interactions by comparing the impact of phage-lysed and uninfected 15N-labeled bacterial necromass on plant nitrogen acquisition and rhizosphere exometabolites composition. We show that grass Brachypodium distachyon derives some nitrogen from amino acids in uninfected Pseudomonas putida necromass but not from virocell necromass. Additionally, the bacterial necromass elicits the formation of rhizosphere exometabolites, some of which (guanosine), alongside tested aromatic acids (p-coumaric and benzoic acid), show distinct effects on bacteriophage-induced lysis when tested in vitro. The study highlights the dynamic feedback between bacterial necromass and plants and suggests that root exudate metabolites can impact bacteriophage infection dynamics.

plant biology↗