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DOE OSTI2026

Finding the missing pieces: filling gaps that impede the translation of omics data into models

High-throughput omics technologies such as DNA sequencing have made the sequencing and computational assembly of microbial genomes recovered from the environment relatively routine. Computational inference of the protein products encoded by these genomes, and the associated biochemical functions, should enable the accurate prediction and modeling of microbial metabolism, organismal interactions, and ecosystem processes. However, a lack of scalable, probabilistic protein annotation tools limits the full potential of modeling for understanding the metabolism and biogeochemical cycles of microbial communities. Our approach to improve inference of protein annotations and metabolic models relied on learning from and emulating expert manual curation, leveraging software engineering and data science best practices to scale up the throughput and accuracy of annotations and metabolic model construction, building software to objectively evaluate different annotation strategies, and more closely linking the protein annotation and metabolic model inference process. Outcomes of this research include several improved or new computational tools, including DRAM (Distilled and Refined Annotation of Metabolism) for annotating microbial genomes with protein function and metabolic traits, CAMPER (Curated Annotations for Microbial Polyphenol Enzymes and Reactions) for annotating key polyphenol metabolisms, EC-Bench for comprehensive and unbiased benchmarking of annotation tools, and several apps available via the DOE Systems Biology Knowledgebase (KBase) for building genome-scale metabolic models. We demonstrate that these tools allow us to scalably annotate and understand thousands of genomes for microbial communities from a variety of systems and test cases, including rivers, thawing permafrost, and gut microbiomes. All of these computational tools are available as open-source software, with most broadly and easily accessible to the scientific community via KBase apps.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

RCSB protein data Bank: Next‐generation advanced search for exploration of experimental structures and computed structure models

Abstract The Protein Data Bank (PDB), established in 1971, is the primary global, open‐access archive for experimentally determined 3D macromolecular structures (proteins, RNA, DNA). The research‐focused RCSB.org web‐portal provides access to these data alongside more than one million machine‐learning‐predicted structure models, greatly expanding the available structural landscape. Rapid growth of both experimental and computational structures has increased the need for powerful yet accessible search tools that serve a broad and diverse scientific community. Herein, we describe a redesigned RCSB Protein Data Bank RCSB.org Advanced Search capability that supports intuitive discovery of 3D structures through a unified interface. This interface integrates annotation‐, sequence‐, and 3D structure‐based searches, embeds an interactive 3D viewer, and incorporates curated biological knowledge, such as catalytic site definitions from Mechanism and Catalytic Site Atlas and ligand‐guided structural motifs, for constructing geometry‐driven queries. A new Chemical Search tool allows definition of chemical queries via an integrated drawing tool or standard identifiers, seamlessly combining them with annotation filters. By allowing query definition directly within spatial and chemical contexts, these search interfaces reduce the need for detailed knowledge of residue numbering, chain identifiers, or external cheminformatics software. This capability enables efficient exploration of structures, chemical diversity, and structure–function relationships across all life domains. The redesigned interfaces can be accessed directly at rcsb.org/search/advanced for Advanced Search and rcsb.org/search/chemical for Chemical Search.

Rose, Yana [Research Collaboratory for Structural ↗

DOE OSTI2026

Genome collection processing for “Conserved upper thermal limits and small safety margins in soil copiotrophic bacteria”

We extracted the genomic DNA of 400 randomly selected isolates using a Quick-DNA Microprep Kit (Zymo Research D3020) according to the manufacturer’s protocol. We then submitted the extracted gDNA samples for short-read Illumina sequencing (200 Mbp) at SeqCoast Genomics (Portsmouth, NH, USA). After preprocessing the sequences using Trimmommatic (Bolger et al. 2014), we assembled the genomes using SPADES (Bankevich et al. 2012) and checked the quality of each assembly using QUAST (Gurevich et al. 2013). We processed the genome assemblies using a KBase (v1.4.0) pipeline (Allen et al. 2017; Arkin et al. 2018). Briefly, we used DRAM (v0.1.2) with default settings to annotate the genome assemblies. We then evaluated genome quality and possible contamination levels using CheckM (v1.0.18) (Parks et al. 2015) and retained genomes with completeness above 98% and contamination below 5% (n = 354), following the authors' guidelines. We then obtained taxonomic assignments for all remaining isolates using the Genome Taxonomy Database tool GTDB-Tk (v2.3.2, database version r214) (Chaumeil et al. 2019). We constructed a phylogenetic tree using the tool SpeciesTree (v2.2.0). We then trimmed the tree (using Trim SpeciesTree to GenomeSet- v1.4.0), retaining only tips within our collection with measured thermal performance.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Separate, separated, and together: the transcriptional program of the Clostridium acetobutylicum-Clostridium ljungdahlii syntrophy leading to interspecies cell fusion

ABSTRACT Syntrophic cocultures (hitherto assumed to be commensalistic) of Clostridium acetobutylicum and Clostridium ljungdahlii , whereby CO 2 and H 2 produced by the former feed the latter, result in interspecies cell fusion involving large-scale exchange of protein, RNA, and DNA between the two organisms. Although mammalian cell fusion is mechanistically dissected, the mechanism for such microbial-cell fusions is unknown. To start exploring this mechanism, we used RNA sequencing to identify genes differentially expressed in this coculture using two types of comparisons. One type compared coculture to the two monocultures, capturing the combined impact of interactions through soluble signals in the medium and through direct cell-to-cell interactions. The second type compared membrane-separated versus -unseparated cocultures, isolating the impact of interspecies physical contact. While we could not firmly identify specific genes that might drive cell fusion, consistent with our hypothesized model for this interspecies microbial cell fusion, we observed differential regulation of genes involved in C. ljungdahlii’s autotrophic Wood-Ljungdahl pathway metabolism and genes of the motility machinery. Unexpectedly, we also identified differential regulation of biosynthetic genes of several amino acids, and notably of arginine and histidine. We verified that they are produced by C. acetobutylicum and are metabolized by C. ljungdahlii to its growth advantage. These and other findings, and notably upregulation of C. acetobutylicum ribosomal-protein genes, paint a more complex syntrophic picture and suggest a mutualistic relationship, whereby beyond CO 2 and H 2 , C. acetobutylicum feeds C. ljungdahlii with growth-boosting amino acids, while benefiting from the H 2 utilization by C. ljungdahlii . IMPORTANCE The construction and study of synthetic microbial cocultures is a growing research area due to the untapped potential of defined multi-species industrial bioprocesses and the utility of defined cocultures for generating insight into complex, undefined, natural microbial consortia. Our previous work showed that coculturing C. acetobutylicum and C. ljungdahlii leads to a unique metabolic phenotype (production of isopropanol) and heterologous cell fusion events. Here, we used RNAseq to explore genes involved in and impacted by these fusions. First, we compared gene expression in coculture to each monoculture. Second, we utilized a transwell system to compare gene expression in mixed cocultures to cocultures with both species physically separated by a permeable membrane, isolating the impact of interspecies “touching” on the transcriptome. This study deepens our mechanistic understanding of the C. acetobutylicum-C. ljungdahlii coculture phenotype, laying the groundwork for reverse genetic studies of heterologous cell fusion in Clostridium cocultures.

Willis, Noah B. (ORCID:0009000689365955)↗