Ruonan Wu

Deciphering viral ecology in soils is challenging due to soil’s high physicochemical and microbial community complexity. To enhance detection of DNA and RNA viruses, we applied different preparation methods to soils collected from a grassland field experiment. Analyses included metagenomics and metatranscriptomics of size-fractionated extracellular viruses, total soil metagenomics and metatranscriptomics, total soil metatranscriptomics with polyadenylation enrichment, and metagenomics of bacteria/archaea as well as eukaryote-enriched samples. DNA viromes outperformed total soil metagenomes in viral detection and quality. Contrastingly, RNA viromes and total soil metatranscriptomes performed similarly for viral recovery, though RNA viromes yielded higher-quality genomes. Together, our results highlight how different preparation methods can influence the recovery and quality of DNA and RNA vOTUs. Further, we demonstrate the power of different methods in identifying distinct viral communities with unique host predictions, which in turn can have significant implications for ecological investigations related to interkingdom interactions.

Spatially distributed environmental sampling generates highly complex and multidimensional datasets illuminating key insights into microbial diversity, evolutionary-coevolutionary processes, and host-pathogen interactions. While these sampling methods generate high value datasets, dataset size, the dataset integration, visualization, analysis, and provenance tracking present significant bottlenecks to scientific discovery. To address this bottleneck, we developed EcoKMER, an R-Shiny front-end application designed to streamline metagenomic data accessibility and provide geospatial context to data in support of hypothesis-driven investigations into environmental sampling, supported by DataFed as its back-end data management platform. EcoKMER enables interactive visualization and filtering of harmonized metagenomic data and metadata using an interoperable approach, allowing users to extract spatially distributed sample-based metadata on top of environmental parameters such as geolocation, temperature, pH, for investigating ecological changes across time and space enhancing sample processing methods. As an example, we deployed this tool to track analysis of metagenomes from the organisms in the Salish Sea Estuary, consistent with existing community-accepted standards. Built on top of DataFed, a flexible and robust scientific data management system built for data lakehouse architectures, EcoKMER is positioned as a powerful tool to improve sampling strategy decision making, accelerate new insights for collaborative biological and environmental research, and fostering AI-ready analyses designed to enhance discovery and guidance for bioeconomic engineering.

Bacteriophage auxiliary metabolic genes (AMGs) alter host metabolism by hijacking reactions, but previous studies mostly inferred their roles from annotations, ignoring system-wide impacts and phage production. Here we integrate AMGs and phage assembly into a genome-scale metabolic model of Prochloroccocus marinus MED4 infected by P-HM2. We show that 17 directly hijacked reactions substantially affect more than 30% of the reactions in MED4 metabolism, including carbon fixation, photosynthesis, and nucleotide synthesis, distinguishing these AMGs as either phage aligned—shifting feasible reaction velocities in accordance with maximal phage production—or phage antialigned. Pareto optimization reveals that phage-aligned reactions alter phage-host growth trade-offs, while phage-antialigned reactions do not. We experimentally validate our predictions of system-level AMG impacts by measuring the N-dependent effect of P-HM2 cp12 expression on growth in a model relative of the genetically intractable MED4, Synechococcus elongatus . We also show that AMGs’ indirect impacts are synergistically and antagonistically coupled, providing systems-level insight into AMG perturbations and highlighting how nontrivial cascading effects shape host metabolism.

Bacteriophages can rewire host chemistry via auxiliary viral genes (AVGs). Using metagenomic and metatranscriptomic data from the native soil microbiome, we identified transcriptionally active AVGs, including a viral catechol 1,2-dioxygenase (V-C12DO). V-C12DO shares ∼40% sequence identity with its nearest bacterial homologs and lacks the helical dimerization domain. Despite truncation, V-C12DO retains more than ∼25% of the global consensus residues compared to C12DOs across domains of life, including the two tyrosines and two histidines that coordinate the non-heme Fe(III) active site. A 1.7 Å crystal structure also showed the conservation of the canonical β-sandwich scaffold for the iron. We next confirmed that V-C12DO cleaves catechol and remains highly active across a broad range of temperatures (30-60 °C), pH (5.5-9), and salinity (up to 2 M), exceeding those of known bacterial CD12Os. This work shows that truncated phage enzymes preserve the core catalytic chemistry and potentially further expand host metabolic versatility across dynamic environmental conditions.

Leaf-cutter ant fungal gardens are a living library of bioeconomy solutions, where the mutualist fungus Leucoagaricus gongylophorus drives lignocellulose degradation and conversion into valuable bioproducts. However, these gardens are vulnerable to infection by the specialized pathogen Escovopsis weberi, which disrupts garden stability. Despite their ecological importance, the metabolic mechanisms underlying fungal–fungal competition and community-level responses to infection remain poorly understood. To address this gap, we experimentally infected six fungal garden consortia with E. weberi, establishing an infection gradient within each consortium, which we then systematically analyzed using a multi-omics approach. Metabolomic profiling revealed infection-induced alterations in metabolite abundance, while metaproteomic analysis identified fungal-specific shifts in protein levels across a spatiotemporal scale. The integration of multi-omics datasets across all fungal garden consortia provided a comprehensive view of the underlying pathways, revealing that the pathogenic fungus exploits nutrient supplies derived from lignocellulose degradation by native fungi. Reconstruction of active pathways enabled us to trace nutrient flow and map taxon-specific metabolic shifts in response to infection. Specifically, a distinct switch from lysine synthesis by native fungi to lysine catabolism orchestrated by pathogenic fungi. These findings offer a system-level perspective of the molecular alterations driving competitive interactions between native and pathogenic species within the fungal garden consortium. This study not only highlights the power of multi-omics approaches in unraveling the complexity of microbial consortia across ecological scales but also provides an integrated road map to efficiently harness microbiome data for deeper insights into microbial interactions and community responses to perturbations.

Spatially distributed environmental sampling generates highly complex and multidimensional datasets illuminating key insights into microbial diversity, evolutionary-coevolutionary processes, and host-pathogen interactions. While these sampling methods generate high value datasets, dataset size, the dataset integration, visualization, analysis, and provenance tracking present significant bottlenecks to scientific discovery. To address this bottleneck, we developed EcoKMER, an R-Shiny front-end application designed to streamline metagenomic data accessibility and provide geospatial context to data in support of hypothesis-driven investigations into environmental sampling, supported by DataFed as its back-end data management platform. EcoKMER enables interactive visualization and filtering of harmonized metagenomic data and metadata using an interoperable approach, allowing users to extract spatially distributed sample-based metadata on top of environmental parameters such as geolocation, temperature, pH, for investigating ecological changes across time and space enhancing sample processing methods. As an example, we deployed this tool to track analysis of metagenomes from the organisms in the Salish Sea Estuary, consistent with existing community-accepted standards. Built on top of DataFed, a flexible and robust scientific data management system built for data lakehouse architectures, EcoKMER is positioned as a powerful tool to improve sampling strategy decision making, accelerate new insights for collaborative biological and environmental research, and fostering AI-ready analyses designed to enhance discovery and guidance for bioeconomic engineering.

Picocyanobacteria from the genera Prochlorococcus and Synechococcus thrive across the globe in aqueous environments, have relatively small genomes, and have growth dynamics regulated by both viral interactions and abiotic conditions, making them excellent model organisms for exploring host-pathogen coevolution. The Salish Sea, located in the Western coastal waters bordering the USA and Canada, is at the current northern boundary (defined by Prochlorococcus versus Synechococcus prevalence ratios) of the range of Prochlorococcus . Predictions suggest that this boundary will shift northward as warmer waters move northward, providing an excellent system to study host-pathogen dynamics and coevolution in a changing environmental context. In preparation for such studies, we developed and refined methods to sample and sequence cyanobacteria, cyanophages, and their abiotic environment. In addition to basic methodological questions focused on the physical sampling, filtering, viral precipitation, DNA extraction, and technical replicability, we explored how well our filtering and extraction protocols enrich for our main target, picocyanobacteria. The protocol described herein can successfully discriminate large-cell eukaryotic organisms, but size fractionation of picocyanobacteria appears to be affected by the presence of free DNA, multicellular structures, and abundant tycheposons. Our preferred final protocol at the conclusion of these experiments based on yield and processing time is presented. We recovered substantial Prochlorococcus, Synechococcus and amoeba-like sequences in most samples, and preliminary exploration of relative taxon sequence read recoveries across locations, over time, and tidal conditions are also discussed. Approaches described here may be useful to other efforts such as harmful algal bloom monitoring, species isolation and enrichment, water quality assessments, anti-viral discovery, and understanding picocyanobacterial population changes over space and time.

Plasmids play significant roles in microbial adaptation to ecosystems, yet their dynamics remain poorly understood due to identification challenges. We present the Global Soil Plasmidome Resource (GSPR), a comprehensive dataset of 98,728 plasmid sequences amassed from 6860 terrestrial microbial communities and isolates. We explore this resource through various computational approaches, including phylogenetic diversity analysis, host prediction, and extensive functional annotation, to understand the contribution of plasmids to the genetic and functional diversity in soil, correlating these findings with sample type, as well as the soil habitat they were retrieved from. Our analysis reveals insights into plasmid-encoded functions such as effector modules, quorum sensing, and stress resistance, which may contribute to their persistence and microbial adaptation in soil. Furthermore, CRISPR analysis suggests a prevalent role of these elements related to intra-plasmid competition. By contrasting plasmids from cultivated and uncultivated organisms, we identify important functions that expand existing knowledge of plasmid roles in these habitats. This study represents a notable step forward in elucidating plasmid diversity and function within soil microbiomes and establishes a foundational framework for exploring their roles in natural environments.

Viruses are abundant in soils and shape microbial communities in ways that can potentially influence ecosystem processes, yet their contributions to carbon cycling and mineral transformations remain poorly understood. Here we present a multi-phase framework that links virus-host interactions to soil biogeochemistry by combining ecological simulations, genome- and community-scale metabolic modeling, and statistical and machine-learning analyses. We first calibrated microbial abundance profiles under explicit infection scenarios to capture how viral pressure alters community structure, then explored alternative interaction strategies, including kill-the-winner, piggyback-the-winner, and mixed lytic-lysogenic modes, through forward simulations. These ecological shifts were translated into metabolic consequences using exchange fluxes summarized into biologically meaningful categories, while integrated statistical and machine-learning screens elevated subtle but consistent signals. Application of this framework revealed that viral infections shift the balance between organic and inorganic fluxes, redirecting metabolism from diffuse organic transformations toward inorganic pools such as protons and CO2, directly linking viral regulation to respiration and soil carbon balance. The roll-up analysis also isolated perturbations in critical mineral ions, including magnesium, manganese, zinc, and copper, which serve as essential enzymatic cofactors. In piggyback-the-winner scenarios, uptake of these ions was strongly suppressed. Contrasting viral strategies produced distinct community structures and metabolic outcomes, from broad suppression under kill-the-winner dynamics to dramatic redistributions under high-lytic and high-gain lysogenic regimes that collapsed vulnerable microbial populations while promoting opportunists. Together, these results provide a tractable path to trace viral perturbations from host abundance shifts to metabolic flux adjustments and ecosystem-scale processes, offering a practical way to include viruses in earth system models.