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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 OSTI2021

Simultaneous Discovery of Positive and Negative Interactions Among Rhizosphere Bacteria Using Microwell Recovery Arrays

Understanding microbe-microbe interactions is critical to predict microbiome function and to construct communities for desired outcomes. Investigation of these interactions poses a significant challenge due to the lack of suitable experimental tools available. Here we present the microwell recovery array (MRA), a new technology platform that screens interactions across a microbiome to uncover higher-order strain combinations that inhibit or promote the function of a focal species. One experimental trial generates 10 4 microbial communities that contain the focal species and a distinct random sample of uncharacterized cells from plant rhizosphere. Cells are sequentially recovered from individual wells that display highest or lowest levels of focal species growth using a high-resolution photopolymer extraction system. Interacting species are then identified and putative interactions are validated. Using this approach, we screen the poplar rhizosphere for strains affecting the growth of Pantoea sp. YR343, a plant growth promoting bacteria isolated from Populus deltoides rhizosphere. In one screen, we montiored 3,600 microwells within the array to uncover multiple antagonistic Stenotrophomonas strains and a set of Enterobacter strains that promoted YR343 growth. The later demonstrates the unique ability of the platform to discover multi-membered consortia that generate emergent outcomes, thereby expanding the range of phenotypes that can be characterized from microbiomes. This knowledge will aid in the development of consortia for Populus production, while the platform offers a new approach for screening and discovery of microbial interactions, applicable to any microbiome.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗

DOE OSTI2023

Horizontal Gene Transfer and CRISPR Targeting Drive Phage-Bacterial Host Interactions and Coevolution in “Pink Berry” Marine Microbial Aggregates

Phages, which are viruses that infect bacteria, are important components of all microbial systems, in which they drive the turnover of organic matter by lysing host cells, facilitate horizontal gene transfer (HGT), and coevolve with their bacterial hosts. Bacteria resist phage infection, which is often costly or lethal, through a diversity of mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Identifying microbial functional guilds performing cryptic organotrophic and lithotrophic redox cycles in anaerobic granular biofilms

Granular biofilms used in anaerobic digester systems contain diverse microbial populations that interact to hydrolyze organic matter and produce methane within controlled environments. Prior research investigated the feasibility of utilizing granular biofilms obtained from an anaerobic digester to remove nitrate without the addition of exogenous electron donors. These granules possessed a unique structure of alternating light and dark iron sulfide and pyrite rich layers that potentially served as both an electron source and sink, linking carbon, nitrogen, sulfur, and iron cycles. To characterize the functional roles of diverse microbial populations enriched within these layered biofilms, we analyzed metagenomes obtained from three different granules. Comparisons between the functional gene content of forty metagenome assembled genomes (MAGs) identified phylogenetically cohesive functional guilds. Each of these functional MAG clusters was assigned to specific steps in anaerobic digestion (hydrolysis, acidogenesis, acetogenesis, and methanogenesis) and anaerobic respiration (denitrification and sulfate reduction). Comparisons with metagenomes derived from a variety of natural and engineered ecosystems confirmed that the enriched denitrifying bacteria were similar to populations typically found in wetlands and biological nitrogen removal systems. Analysis of read alignments to individual genes within the forty MAGs identified conserved genomic features that were representative of the functions that distinguished functional guilds. Overall, this research illustrates the utility of functional based classification of microorganisms for characterizing ecosystem functions and highlights the potential application of engineered ecosystems to serve as experimental models for complex natural ecosystems.

Ecosystem engineering↗

DOE OSTI2020

Dynamic microbial assembly processes correspond to soil fertility in sustainable paddy agroecosystems

1. Microorganisms underpin the sustainability of agroecosystems, but we have a poor understanding of how the assembly processes that govern microbial community structure interact through time to maintain soil fertility. 2. Ancient rice paddies have a history of cultivation spanning hundreds or thousands of years and provide an ideal opportunity to explore relationships between community assembly processes and long-term agroecosystem sustainability. We used statistical null models to evaluate temporal patterns of bacterial community assembly processes across rice cultivation years and their associations with increases in soil fertility in paddy agroecosystems. 3. We found that stochasticity was a persistent force structuring paddy soil microbial communities, putatively due to frequent flooding that facilities dispersal. However, determinism gradually increased over centuries of agriculture and corresponded to increases in soil fertility. Accordingly, multifunctionality, calculated using enzymes catalyzing key soil functions in carbon, nitrogen, phosphorous and sulfur cycling significantly increased along rice cultivation years. We therefore propose that balanced assembly processes preserve a diverse array of traits that can dominate under various circumstances (stochasticity) while allowing for traits relevant to soil fertility to be selected for by soil fertility (determinism), thus supporting multifunctionality that underlies sustainable agroecosystems. 4. In total, our results highlight that knowledge on temporal microbial assembly processes, community composition, and ecological function in context of soil fertility is key to understanding ecological mechanisms maintaining agroecosystems sustainability.

Liu, Wenjing↗

DOE OSTI2026

Quantitative stable isotope probing (qSIP)-informed metagenomics identifies viruses infecting chemoautotrophs

Aquatic environments absorb ~2.5 gigatonnes of atmospheric carbon each year1, more than the carbon stored in the atmosphere, soils, and all biomass combined. Primary producers transform this dissolved inorganic carbon into biomass that can subsequently flow into other trophic levels, or be released back into the environment through viral lysis. While there is substantial knowledge about the diversity and activity of viruses infecting photoautotrophic primary producers and the ecosystem impact, little is known about viruses infecting chemoautotrophs, representing a gap in our understanding of key processes driving microbial carbon cycling. Here, we combine metagenomics with quantitative 12/13C stable isotopic probing (qSIP) mesocosm experiments in a marine-derived meromictic pond to quantify population-specific isotopic enrichment, identify key chemoautotrophic primary producers, and virus-host dynamics. Isotopically enriched carbon is tracked from the genomes of chemoautotrophs to putative viruses, showing that active populations of hydrogen/sulfur-oxidizing chemoautotrophs (Thiomicrorhabdus, Hydrogenovibrio, Sulfurimonas, Sulfurovum) are targeted by viruses. This work provides the foundation for revealing the diversity and activity of viruses infecting globally-widespread chemoautotrophs. Our study sheds light on trophic interactions that impact microbial carbon cycling in aphotic environments and builds toward biogeochemical models that incorporate viral impacts on chemoautotrophic microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Activity-based, genome-resolved metagenomics uncovers key populations and pathways involved in subsurface conversions of coal to methane

Microbial metabolisms and interactions that facilitate subsurface conversions of recalcitrant carbon to methane are poorly understood. We deployed an in situ enrichment device in a subsurface coal seam in the Powder River Basin (PRB), USA, and used BONCAT-FACS-Metagenomics to identify translationally active populations involved in methane generation from a variety of coal-derived aromatic hydrocarbons. From the active fraction, high-quality metagenome-assembled genomes (MAGs) were recovered for the acetoclastic methanogen, Methanothrix paradoxum, and a novel member of the Chlorobi with the potential to generate acetate via the Pta-Ack pathway. Members of the Bacteroides and Geobacter also encoded Pta-Ack and together, all four populations had the putative ability to degrade ethylbenzene, phenylphosphate, phenylethanol, toluene, xylene, and phenol. Metabolic reconstructions, gene analyses, and environmental parameters also indicated that redox fluctuations likely promote facultative energy metabolisms in the coal seam. The active "Chlorobi PRB" MAG encoded enzymes for fermentation, nitrate reduction, and multiple oxygenases with varying binding affinities for oxygen. "M. paradoxum PRB" encoded an extradiol dioxygenase for aerobic phenylacetate degradation, which was also present in previously published Methanothrix genomes. Finaly, these observations outline underlying processes for bio-methane from subbituminous coal by translationally active populations and demonstrate activity-based metagenomics as a powerful strategy in next generation physiology to understand ecologically relevant microbial populations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Measurements of soil protist richness and community composition are influenced by primer pair, annealing temperature, and bioinformatics choices

ABSTRACT Protists are a diverse and understudied group of microbial eukaryotic organisms especially in terrestrial environments. Advances in molecular methods are increasing our understanding of the distribution and functions of these creatures; however, there is a vast array of choices researchers make including barcoding genes, primer pairs, PCR settings, and bioinformatic options that can impact the outcome of protist community surveys. Here, we tested four commonly used primer pairs targeting the V4 and V9 regions of the 18S rRNA gene using different PCR annealing temperatures and processed the sequences with different bioinformatic parameters in 10 diverse soils to evaluate how primer pair, amplification parameters, and bioinformatic choices influence the composition and richness of protist and non-protist taxa using Illumina sequencing. Our results showed that annealing temperature influenced sequencing depth and protist taxon richness for most primer pairs, and that merging forward and reverse sequencing reads for the V4 primer pairs dramatically reduced the number of sequences and taxon richness of protists. The data sets of primers that targeted the same 18S rRNA gene region (e.g., V4 or V9) had similar protist community compositions; however, data sets from primers targeting the V4 18S rRNA gene region detected a greater number of protist taxa compared to those prepared with primers targeting the V9 18S rRNA region. There was limited overlap of protist taxa between data sets targeting the two different gene regions (80/549 taxa). Together, we show that laboratory and bioinformatic choices can substantially affect the results and conclusions about protist diversity and community composition using metabarcoding. IMPORTANCE Ecosystem functioning is driven by the activity and interactions of the microbial community, in both aquatic and terrestrial environments. Protists are a group of highly diverse, mostly unicellular microbes whose identity and roles in terrestrial ecosystem ecology have been largely ignored until recently. This study highlights the importance of choices researchers make, such as primer pair, on the results and conclusions about protist diversity and community composition in soils. In order to better understand the roles protist taxa play in terrestrial ecosystems, biases in methodological and analytical choices should be understood and acknowledged.

Biotechnology & Applied Microbiology↗

DOE OSTI2022

Spectroscopy-based isotopic (δ 13 C) analysis for high spatial resolution of carbon exchange in the rhizosphere

The rhizosphere is a highly dynamic zone bridging plant roots with needed nutrient resources in soil. While the rhizosphere may be small, it has a disproportionally large impact on plant success and biomass production. A suite of rhizosphere-hosted microbial and geochemical interactions facilitate nutrient acquisition by plant roots, and, in turn, the roots stimulate these processes by supplying organic carbon into the rhizosphere. The small physical dimensions of the rhizosphere, however, can constrain efforts to elucidate key carbon exchange processes and their spatial extent and localization. We present a method for spatially resolved δ 13 C analysis of rhizosphere samples by coupling laser ablation (LA) sampling with isotopic analysis using capillary absorption spectroscopy (CAS) which differs from conventional mass spectrometer (MS) approaches. The CAS system has high sensitivity (requires fewer nanomoles of CO 2 per analysis) than comparable MS systems, which enables reduced sample size requirements to thereby improve spatial resolution (from 25 μm to as low as a projected 5 μm spatial resolution). We demonstrate the utility of CAS using rhizosphere samples from switchgrass plants exposed to 13 CO 2 . As a result, this technique will provide a capability for tracking the extent and spatial distribution of root exudate into the rhizosphere at highly detailed spatial scales.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

SynMADE: synthetic microbiota across diverse ecosystems

The last two decades have witnessed rapid advances in engineering individual microbial strains to produce biochemicals and biomaterials. Furthermore, engineering microbial consortia has been relatively slow. Using systems and synthetic biology approaches, researchers have been developing tools for engineering complex microbiota. In this article, I discuss future directions and visions regarding developing microbiota as a biomanufacturing host. Specifically, I propose that we can develop the soil microbial community itself as a huge bioreactor. Ultimately, researchers will provide a generalizable system that enables us to understand microbial consortium’s interaction and metabolism at diverse temporal and spatial scales to address global problems, including the climate crisis, food inequality, waste issue, and sustainable bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Community standards and future opportunities for synthetic communities in plant–microbiota research

Harnessing beneficial microorganisms is seen as a promising approach to enhance sustainable agriculture production. Synthetic communities (SynComs) are increasingly being used to study relevant microbial activities and interactions with the plant host. Yet, the lack of community standards limits the efficiency and progress in this important area of research. Here, to address this gap, we recommend three actions: (1) defining reference SynComs; (2) establishing community standards, protocols and benchmark data for constructing and using SynComs; and (3) creating an infrastructure for sharing strains and data. We also outline opportunities to develop SynCom research through technical advances, linking to field studies, and filling taxonomic blind spots to move towards fully representative SynComs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Plant immunity: Rice XA21-mediated resistance to bacterial infection

Significance The mechanisms plants employ to resist infection were unknown until just a few decades ago. We now understand that plants utilize diverse classes of immune receptors to recognize and respond to pathogenic microbes and pests. This paper describes the development of the plant immunity field, from early studies on the genetics of disease resistance to our increasing knowledge of how plant receptors interact with their microbial ligands, with an emphasis on the rice immune receptor XA21 and its bacterial ligand.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

The Impact of Stand Age and Fertilization on the Soil Microbiome of Miscanthus × giganteus

Yield of the perennial grass Miscanthus × giganteus has shown an inconsistent and unpredictable response to nitrogen (N) fertilizer, yet fertilization underpins the crop’s environmental and economic sustainability. The interactions among soil microbial communities, N availability, and Miscanthus × giganteus and management may explain changes in plant productivity. In this study, soil samples from different stand ages of Miscanthus × giganteus in a replicated chronosequence field trial were used to investigate the effects of stand age and N fertilizer rates on microbial community structure. We hypothesized that there is a definable Miscanthus × giganteus soil microbiome and that this community varies significantly with stand age and fertilization. Our results showed that the main phyla in soil microbial communities, regardless of plant age, are similar but microbial community structures are significantly different. The variation in observed microbial communities generally decreases with older stand ages. The amount of N fertilizer applied also affected the microbial community structure associated with different aged Miscanthus × giganteus. Specifically, the relative abundance of Proteobacteria (Alphaproteobacteria and Gammaproteobacteria) and Acidobacteria (subgroup Gp1) increased shortly after fertilization and was more associated with younger Miscanthus × giganteus. Furthermore, our results show a significant relationship between bacterial α diversity and fertilization rates and that this response is also affected by stand age. Overall, our results emphasize linkages between microbial community structure, plant age, and fertilization in Miscanthus × giganteus.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Nitrogen acquisition and retention pathways in sustainable perennial bioenergy grass cropping systems

Abstract Perennial tall grasses show promise as bioenergy crops due to high productivity and efficient nutrient use. Ongoing research on bioenergy grasses seeks to reduce their reliance on synthetic nitrogen (N) fertilizer, the manufacture of which relies on fossil fuel combustion. Excessive use of fertilizers also causes adverse environmental consequences and leads to the evolutionary loss of plant traits beneficial to sustainable N cycle. Notably, perennial tall grasses have exhibited the potential to maintain high biomass yield without the need for N fertilizer or causing soil N depletion. Perennial grasses can be adept at interacting with their microbial partners to facilitate N acquisition and retention via mechanisms such as biological N fixation and nitrification inhibition. These inherent N management traits should be preserved and optimized at the this early stage of bioenergy grass breeding programs. This review examines the impact of external N on bioenergy grass production and explores the potential of leveraging advantageous N‐cycling attributes of perennial tall grasses, laying groundwork for future management and research efforts. With minimized dependency on external N input, the cultivation of perennial energy grasses will pave the way toward more resilient agricultural systems and play a significant role in addressing key global energy and environmental challenges.

Agriculture↗

DOE OSTI2021

Hidden Processes During Seasonal Isolation of a High-Altitude Watershed

Biogeochemical processes capable of altering global carbon systems occur frequently in Earth’s Critical Zone–the area spanning from vegetation canopy to saturated bedrock–yet many of these phenomena are difficult to detect. Observation of these processes is limited by the seasonal inaccessibility of remote ecosystems, such as those in mountainous, snow- and ice-dominated areas. This isolation leads to a distinct gap in biogeochemical knowledge that ultimately affects the accuracy and confidence with which these ecosystems can be computationally modeled for the purpose of projecting change under different climate scenarios. To examine a high-altitude, headwater ecosystem’s role in methanogenesis, sulfate reduction, and groundwater-surface water exchange, water samples were continuously collected from the river and hyporheic zones (HZ) during winter isolation in the East River (ER), CO watershed. Measurements of continuously collected ER surface water revealed up to 50 μM levels of dissolved methane in July through September, while samples from 12 cm deep in the hyporheic zone at the same location showed a spring to early summer peak in methane with a strong biogenic signature (<65 μM, δ13C-CH4, -60.76‰) before declining. Continuously collected δ 18 O-H 2 O and δ 2 H-H 2 O isotopes from the water column exhibited similar patterns to discrete measurements, while samples 12 cm deep in the hyporheic zone experienced distinct fluctuations in δ 18 O-H 2 O, alluding to significant groundwater interactions. Continuously collected microbial communities in the river in the late fall and early winter revealed diverse populations that reflect the taxonomic composition of ecologically similar river systems, including taxa indicative of methane cycling in this system. These measurements captured several biogeochemical components of the high-altitude watershed in response to seasonality, strengthening our understanding of these systems during the winter months.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Quantitative genetic-by-soil microbiome interactions in a perennial grass affect functional traits

Plants interact with diverse microbiomes that can impact plant growth and performance. Recent studies highlight the potential beneficial aspects of plant microbiomes, including the possibility that microbes facilitate the process of local adaptation in their host plants. Microbially mediated local adaptation in plants occurs when local host genotypes have higher fitness than foreign genotypes because of their affiliation with locally beneficial microbes. Here, plant adaptation results from genetic interactions of the host with locally beneficial microbes (e.g. host genotype-by-microbiome interactions). We used a recombinant inbred line (RIL) mapping population derived from upland and lowland ecotypes of the diploid C4 perennial bunch grass Panicum hallii to explore quantitative genetic responses to soil microbiomes focusing on functional root and shoot traits involved in ecotypic divergence. We show that the growth and development of ecotypes and their trait divergence depends on soil microbiomes. Moreover, we find that the genetic architecture is modified by soil microbiomes, revealing important plant genotype-by-microbiome interactions for quantitative traits. We detected a number of quantitative trait loci (QTL) that interact with the soil microbiome. Our results highlight the importance of microbial interactions in ecotypic divergence and trait genetic architecture in C4 perennial grasses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Synthetic communities as a model for determining interactions between a biofertilizer chassis organism and native microbial consortia

Biofertilizers are critical for sustainable agriculture because they can replace ecologically disruptive chemical fertilizers while improving the trajectory of soil and plant health. However, for improving deployment, the persistence of biofertilizers within native soil consortia must be elucidated and enhanced. In this study we characterized a high-throughput, modular, and automation-friendly in vitro approach to screen for biofertilizer persistence within soil-derived consortia after co-cultivation with stable synthetic soil microbial communities (SynComs) obtained through a top-down cultivation process. Here, we profiled ~1200 SynComs isolated from various soil sources and cultivated in divergent media types, and we detected significant phylogenetic diversity (e.g. Shannon index >4) and richness (observed richness >400) across these communities. We observed high reproducibility in SynCom community structure from common soil and media types, which provided a testbed for assessing biofertilizer persistence within representative native consortia. Furthermore, we demonstrated that the screening method described herein can be coupled with microbial engineering to efficiently identify soil-derived SynComs in which an engineered biofertilizer organism (i.e. Bacillus subtilis) persists. Accordingly, we discovered that B. subtilis persisted in ~10% of SynComs that generally followed the diversity–invasion principle. Additionally, our approach enabled analysis of the ecological impact of B. subtilis inoculation on SynCom structure and profile alterations in community diversity and richness associated with the presence of a genetically modified model bacterium. Ultimately, this work has established a modular pipeline that could be integrated into a variety of microbiology/microbiome-relevant workflows or related applications that would benefit from assessment of the persistence of a specific organism of interest and its interaction with native consortia.

biofertilizers↗