Pamela Weisenhorn

Microbial carbon utilisation shapes ecological niches, community assembly, and nutrient cycling, yet remains difficult to predict from genomes despite well-characterised pathways. Genome-based machine-learning models often generalise poorly across datasets, a limitation commonly attributed to small or taxonomically biased training sets. We integrated binary growth phenotypes for 819 strains across 240 carbon sources from four datasets, focusing on 15 shared sources. Cross-dataset evaluation reduced balanced accuracy from 0.86 (within datasets) to 0.62, and restricting evaluation to in-clade relatives recovered only 0.03 of this loss. Because phylogeny alone could not explain the transfer gap, we restricted training to samples with coherent genotype-phenotype relationships, defined by agreement between experimental labels and annotated pathways. Concordance filtering doubled the number of carbon sources with reproducible genomic signals across datasets (from 6 to 12 of 15), often recovering known pathway genes, while matched random subsets remained at the unfiltered level. The smaller concordant set also approached the cross-dataset performance of models augmented with up to 1800 BacDive genomes per phenotype, demonstrating greater efficiency per sample. Concordance-trained models remained bounded specialists, but separated growers from non-growers among genomes the pathway rules called negative, mostly generalists with broad-specificity transporters. Model confidence made these bounds actionable, since abstention improved accuracy where models transferred and retraining on newly labelled low-confidence genomes gave the largest gains for weaker phenotypes. Collectively, these findings recast generalisation as a problem of label-mechanism agreement and demonstrate how genome-based predictions can be deployed within explicit applicability domains and improved through targeted phenotyping.

Coastal soils are dynamic systems where unique microbial niches are shaped by the intensity and duration of flooding between the terrestrial and aquatic boundaries of the terrestrial-aquatic interface (TAI). We aimed to understand the soil microbial community (16S rRNA gene) along the TAIs of a freshwater versus estuarine region and how it relates to organic matter (OM, via Fourier Transform Ion Cyclotron Resonance Mass Spectrometry). We studied the TAI gradients along a transect from upland (forested), transition (stressed forest), to wetland at three sites in each of the Lake Erie (freshwater) and Chesapeake Bay (estuarine) regions. Microbial communities differed significantly by region, transect position, and site. Contrary to expectations, given their dynamic hydrologies, transitions represented midpoints in microbial richness and diversity. We identified a core microbiome conserved across all transect positions within a region, highlighting potential microbial functions most resilient to environmental change. Indicator taxa unique to each transect position defined specific niches shaped by soil biogeochemistry. Co-expression networks of feature-level β-nearest-taxon indices revealed positive relationships in bacterial and OM feature contributions to community assembly. Our study provides critical insights into microbial communities at the forefront of hydrological changes in coastal areas that connect the land to lakes and oceans and remain vulnerable to changing weather patterns.

We present a KBase tutorial narrative demonstrating construction of stoichiometric metabolic models of iron-oxidizing bacteria. The tutorial describes an extended ModelSEED2 framework, which includes support for iron oxidation. These capabilities complement omics analyses by enabling predictions of physiological dynamics, metabolic fluxes, and the role of iron oxidation in environmental systems.

Leptothrix ochracea creates distinctive iron-mineralized mats that carpet streams and wetlands. Easily recognized by its iron-mineralized sheaths, L. ochracea was one of the first microorganisms described in the 1800s. Yet it has never been isolated and does not have a complete genome sequence available, so key questions about its physiology remain unresolved. It is debated whether iron oxidation can be used for energy or growth and if L. ochracea is an autotroph, heterotroph, or mixotroph. To address these issues, we sampled L. ochracea -rich mats from three of its typical environments (a stream, wetlands, and a drainage channel) and reconstructed nine high-quality genomes of L. ochracea from metagenomes. These genomes contain iron oxidase genes cyc2 and mtoA, showing that L. ochracea has the potential to conserve energy from iron oxidation. Sox genes confer potential to oxidize sulfur for energy. There are genes for both carbon fixation (RuBisCO) and utilization of sugars and organic acids (acetate, lactate, and formate). In silico stoichiometric metabolic models further demonstrated the potential for growth using sugars and organic acids. Metatranscriptomes showed a high expression of genes for iron oxidation; aerobic respiration; and utilization of lactate, acetate, and sugars, as well as RuBisCO, supporting mixotrophic growth in the environment. In summary, our results suggest that L. ochracea has substantial metabolic flexibility. It is adapted to iron-rich, organic carbon-containing wetland niches, where it can thrive as a mixotrophic iron oxidizer by utilizing both iron oxidation and organics for energy generation and both inorganic and organic carbon for cell and sheath production. IMPORTANCE: Winogradsky's observations of L. ochracea led him to propose autotrophic iron oxidation as a new microbial metabolism, following his work on autotrophic sulfur-oxidizers. While much culture-based research has ensued, isolation proved elusive, so most work on L. ochracea has been based in the environment and in microcosms. Meanwhile, the autotrophic Gallionella became the model for freshwater microbial iron oxidation, while heterotrophic and mixotrophic iron oxidation is not well-studied. Ecological studies have shown that Leptothrix overtakes Gallionella when dissolved organic carbon content increases, demonstrating distinct niches. This study presents the first near-complete genomes of L. ochracea , which share some features with autotrophic iron oxidizers, while also incorporating heterotrophic metabolisms. These genome, metabolic modeling, and transcriptome results give us a detailed metabolic picture of how the organism may combine lithoautotrophy with organoheterotrophy to promote Fe oxidation and C cycling and drive many biogeochemical processes resulting from microbial growth and iron oxyhydroxide formation in wetlands.

Since the release of ModelSEED in 2010, the systems biology research community has used the ModelSEED genome-scale metabolic model reconstruction pipeline to build over 200,000 draft metabolic reconstructions that support hundreds of publications. Here we describe the first comprehensive update to this reconstruction tool, with new features such as (i) a dramatically improved representation of energy metabolism, which ensures that models produce accurate amounts of ATP per mol of nutrient consumed; (ii) a new template for Archaea model reconstruction; and (iii) a significantly improved curation of all metabolic pathways with mappings to RAST subsystems annotations. We applied the improved pipeline to build and analyze core and genome-scale models for Archaea and Bacteria genomes in KEGG. The new ModelSEED pipeline generates larger models that possess more reactions and genes and require fewer gap-filled reactions. In addition, we see conserved patterns in the ATP biosynthesis mechanism across phylogeny, and identify clades where our understanding of energy biosynthesis is still poor. The ModelSEED v2 pipeline is currently available only as new reconstruction and gap-filling Apps in the KBase platform.

Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.

Data standardization combined with descriptive metadata facilitate data reuse, which is the ultimate goal of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. Community data or metadata standards are increasingly created through an approach that emphasizes collaboration between various stakeholders. Such an approach requires platforms for collaboration on the development process that centers on sharing information and receiving feedback. Our objective in this study was to conduct a systematic review to identify data standards and reporting formats that use version control for developing data standards and to summarize common practices, particularly in earth and environmental sciences. Out of 108 data standards and reporting formats identified in our review, 32 used GitHub as the version control platform, and no other platforms were used. We found no universally accepted methodology for developing and publishing data standards. Many GitHub repositories did not use key features that could help developers to gather user feedback, or to create and revise standards that build on previous work. We provide guidance for community‐driven standard development and associated documentation on GitHub based on a systematic review of existing practices.

Microorganisms that degrade cellulose utilize extracellular reactions that yield free by-products which can promote interactions with noncellulolytic organisms. We hypothesized that these interactions determine the ecological and physiological traits governing the fate of cellulosic carbon (C) in soil. We performed comparative genomics with genome bins from a shotgun metagenomic-stable isotope probing experiment to characterize the attributes of cellulolytic and noncellulolytic taxa accessing ¹³C from cellulose. We hypothesized that cellulolytic taxa would exhibit competitive traits that limit access, while noncellulolytic taxa would display greater metabolic dependency, such as signatures of adaptive gene loss. We tested our hypotheses by evaluating genomic traits indicative of competitive exclusion or metabolic dependency, such as antibiotic production, growth rate, surface attachment, biomass degrading potential, and auxotrophy. The most ¹³C-enriched taxa were cellulolytic Cellvibrio (Gammaproteobacteria) and Chaetomium (Ascomycota), which exhibited a strategy of self-sufficiency (prototrophy), rapid growth, and competitive exclusion via antibiotic production. Auxotrophy was more prevalent in cellulolytic Actinobacteria than in cellulolytic Proteobacteria, demonstrating differences in dependency among cellulose degraders. Noncellulolytic taxa that accessed ¹³C from cellulose (Planctomycetales, Verrucomicrobia, and Vampirovibrionales) were also more dependent, as indicated by patterns of auxotrophy and ¹³C labeling (i.e., partial labeling or labeling at later stages). Major ¹³C-labeled cellulolytic microbes (e.g., Sorangium, Actinomycetales, Rhizobiales, and Caulobacteraceae) possessed adaptations for surface colonization (e.g., gliding motility, hyphae, attachment structures) signifying the importance of surface ecology in decomposing particulate organic matter. Our results demonstrated that access to cellulosic C was accompanied by ecological trade-offs characterized by differing degrees of metabolic dependency and competitive exclusion. IMPORTANCE Our study reveals the ecogenomic traits of microorganisms participating in the cellulose economy of soil. We identified three major categories of participants in this economy: (i) independent primary degraders, (ii) interdependent primary degraders, and (iii) secondary consumers (mutualists, opportunists, and parasites). Trade-offs between independent primary degraders, whose adaptations favor antagonism and competitive exclusion, and interdependent and secondary degraders, whose adaptations favor complex interspecies interactions, are expected to affect the fate of microbially processed carbon in soil. Our findings provide useful insights into the ecological relationships that govern one of the planet’s most abundant resources of organic carbon. Furthermore, we demonstrate a novel gradient-resolved approach for stable isotope probing, which provides a cultivation-independent, genome-centric perspective into soil microbial processes.