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DOE OSTI2024

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μ ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Active populations and growth of soil microorganisms are framed by mean annual precipitation in three California annual grasslands

Climate influences soil microbial composition and function, but the relative importance of a site's historic climate versus its more immediate environmental conditions is unclear. Using quantitative stable isotope probing (qSIP), we characterized actively growing soil microbial communities and soil properties in three California annual grasslands that span a rainfall gradient and have developed on similar parent material. The soils were assayed in the wet winter season, when environmental conditions are most similar across sites. Since growing populations might be expected to be most responsive to contemporary environmental conditions, we hypothesized that the structure of growing microbial communities would be more similar across the gradient than that of total communities (i.e., including non-growing populations). In addition, we hypothesized that population growth rates would be slowest in the driest site, reflecting a legacy effect of low soil moisture on microbial growth. Soils along the rainfall gradient differed in pH, texture, and cation exchange capacity, but not in total C, C:N or dominant minerals. The radiocarbon (14C) age of soil C (reflecting turnover time) increased with mean annual precipitation but soil respiration was uniformly modern, reflecting microbial reliance on recent C inputs across the sites. The structure of both total and growing microbial communities differed across sites. Across major microbial phyla, including the Actinobacteria, Acidobacteria, Bacteroidetes, Gemmatimonadetes and Proteobacteria, bacterial growth rates were consistently lower in the site with the lowest mean annual precipitation. Taxa that were growing at the dry site alone grew more slowly than taxa that grew at multiple sites. These results reflect the influence of climate history and point to the role of environmental filtering at the driest site in shaping its slower growing microbial community, possibly reflecting adaptation to repeated exposure to water stress. Lastly, across taxa, the growth rate of a taxon at one site was correlated with its growth rate in the other sites. Furthermore, this growth rate coherence is likely a consequence of genetically determined physiological traits and is consistent with the idea that evolutionary history constrains growth rate.

Environmental filtering↗

DOE OSTI2024

Development and cross‐validation of a circumference‐based predictive equation to estimate body fat in an active population

Abstract Objective The U.S. Army uses sex‐specific circumference‐based prediction equations to estimate percent body fat (%BF) to evaluate adherence to body composition standards. The equations are periodically evaluated to ensure that they continue to accurately assess %BF in a diverse population. The objective of this study was to develop and validate alternative field expedient equations that may improve upon the current Army Regulation (AR) body fat (%BF) equations. Methods Body size and composition were evaluated in a representatively sampled cohort of 1904 active‐duty Soldiers (1261 Males, 643 Females), using dual‐energy X‐ray absorptiometry (%BF DXA ), and circumferences obtained with 3D imaging and manual measurements. Sex stratified linear prediction equations for %BF were constructed using internal cross validation with %BF DXA as the criterion measure. Prediction equations were evaluated for accuracy and precision using root mean squared error, bias, and intraclass correlations. Equations were externally validated in a convenient sample of 1073 Soldiers. Results Three new equations were developed using one to three circumference sites. The predictive values of waist, abdomen, hip circumference, weight and height were evaluated. Changing from a 3‐site model to a 1‐site model had minimal impact on measurements of model accuracy and performance. Male‐specific equations demonstrated larger gains in accuracy, whereas female‐specific equations resulted in minor improvements in accuracy compared to existing AR equations. Equations performed similarly in the second external validation cohort. Conclusions The equations developed improved upon the current AR equation while demonstrating robust and consistent results within an external population. The 1‐site waist circumference‐based equation utilized the abdominal measurement, which aligns with associated obesity related health outcomes. This could be used to identify individuals at risk for negative health outcomes for earlier intervention.

Taylor, Kathryn M.↗

DOE OSTI2022

Subsurface hydrocarbon degradation strategies in low- and high-sulfate coal seam communities identified with activity-based metagenomics

Environmentally relevant metagenomes and BONCAT-FACS derived translationally active metagenomes from Powder River Basin coal seams were investigated to elucidate potential genes and functional groups involved in hydrocarbon degradation to methane in coal seams with high- and low-sulfate levels. An advanced subsurface environmental sampler allowed the establishment of coal-associated microbial communities under in situ conditions for metagenomic analyses from environmental and translationally active populations. Metagenomic sequencing demonstrated that biosurfactants, aerobic dioxygenases, and anaerobic phenol degradation pathways were present in active populations across the sampled coal seams. In particular, results suggested the importance of anaerobic degradation pathways under high-sulfate conditions with an emphasis on fumarate addition. Under low-sulfate conditions, a mixture of both aerobic and anaerobic pathways was observed but with a predominance of aerobic dioxygenases. The putative low-molecular-weight biosurfactant, lichysein, appeared to play a more important role compared to rhamnolipids. The methods used in this study—subsurface environmental samplers in combination with metagenomic sequencing of both total and translationally active metagenomes—offer a deeper and environmentally relevant perspective on community genetic potential from coal seams poised at different redox conditions broadening the understanding of degradation strategies for subsurface carbon.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Species-specific ribosomal RNA-FISH identifies interspecies cellular-material exchange, active-cell population dynamics and cellular localization of translation machinery in clostridial cultures and co-cultures

ABSTRACT The development of synthetic microbial consortia in recent years has revealed that complex interspecies interactions, notably the exchange of cytoplasmic material, exist even among organisms that originate from different ecological niches. Although morphogenetic characteristics, viable RNA and protein dyes, and fluorescent reporter proteins have played an essential role in exploring such interactions, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to further investigate interactions in synthetic or semisynthetic consortia. Despite its maturity, several challenges exist in using rRNA-FISH as a tool to quantify individual species population dynamics and interspecies interactions using high-throughput instrumentation such as flow cytometry. In this work, we resolve such challenges and apply rRNA-FISH to double and triple co-cultures of Clostridium acetobutylicum, Clostridium ljungdahlii, and Clostridium kluyveri . In pursuing our goal to capture each organism’s population dynamics, we demonstrate dynamic rRNA, and thus ribosome, exchange between the three species leading to the formation of hybrid cells. We also characterize the localization patterns of the translation machinery in the three species, identifying distinct, dynamic localization patterns among them. Our data also support the use of rRNA-FISH to assess the culture’s health and expansion potential, and, here again, our data find surprising differences among the three species examined. Taken together, our study argues for rRNA-FISH as a valuable and accessible tool for quantitative exploration of interspecies interactions, especially in organisms which cannot be genetically engineered or in consortia where selective pressures to maintain recombinant species cannot be used. IMPORTANCE Though dyes and fluorescent reporter proteins have played an essential role in identifying microbial species in co-cultures, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to quantitatively probe complex interactions between organisms in synthetic consortia. Despite its maturity, several challenges existed before rRNA-FISH could be used to study Clostridium co-cultures of interest. First, species-specific probes for Clostridium acetobutylicum and Clostridium ljungdahlii had not been developed. Second, “state-of-the-art” labeling protocols were tedious and often resulted in sample loss. Third, it was unclear if FISH was compatible with existing fluorescent reporter proteins. We resolved these key challenges and applied the technique to co-cultures of C. acetobutylicum , C. ljungdahlii , and Clostridium kluyveri . We demonstrate that rRNA-FISH is capable of identifying rRNA/ribosome exchange between the three organisms and characterized rRNA localization patterns in each. In combination with flow cytometry, rRNA-FISH can capture sub-population dynamics in co-cultures.

Hill, John D.↗

DOE OSTI2021

Active virus-host interactions at sub-freezing temperatures in Arctic peat soil

Abstract Background Winter carbon loss in northern ecosystems is estimated to be greater than the average growing season carbon uptake and is primarily driven by microbial decomposers. Viruses modulate microbial carbon cycling via induced mortality and metabolic controls, but it is unknown whether viruses are active under winter conditions (anoxic and sub-freezing temperatures). Results We used stable isotope probing (SIP) targeted metagenomics to reveal the genomic potential of active soil microbial populations under simulated winter conditions, with an emphasis on viruses and virus-host dynamics. Arctic peat soils from the Bonanza Creek Long-Term Ecological Research site in Alaska were incubated under sub-freezing anoxic conditions with H 2 18 O or natural abundance water for 184 and 370 days. We sequenced 23 SIP-metagenomes and measured carbon dioxide (CO 2 ) efflux throughout the experiment. We identified 46 bacterial populations (spanning 9 phyla) and 243 viral populations that actively took up 18 O in soil and respired CO 2 throughout the incubation. Active bacterial populations represented only a small portion of the detected microbial community and were capable of fermentation and organic matter degradation. In contrast, active viral populations represented a large portion of the detected viral community and one third were linked to active bacterial populations. We identified 86 auxiliary metabolic genes and other environmentally relevant genes. The majority of these genes were carried by active viral populations and had diverse functions such as carbon utilization and scavenging that could provide their host with a fitness advantage for utilizing much-needed carbon sources or acquiring essential nutrients. Conclusions Overall, there was a stark difference in the identity and function of the active bacterial and viral community compared to the unlabeled community that would have been overlooked with a non-targeted standard metagenomic analysis. Our results illustrate that substantial active virus-host interactions occur in sub-freezing anoxic conditions and highlight viruses as a major community-structuring agent that likely modulates carbon loss in peat soils during winter, which may be pivotal for understanding the future fate of arctic soils' vast carbon stocks.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Ice Nucleating Particle Connections to Regional Argentinian Land Surface Emissions and Weather During the Cloud, Aerosol, and Complex Terrain Interactions Experiment

Abstract Here, we present a multi‐season study of ice‐nucleating particles (INPs) active via the immersion freezing mechanism, which took place in north‐central Argentina, a worldwide hotspot for mesoscale convective storms. INPs were measured untreated, after heating to 95°C, and after hydrogen peroxide digestion. No seasonal cycle of INP concentrations was observed. Heat labile INPs, which we define as “biological” herein, dominated the population active at −5 to −20°C, while non‐heat‐labile organic INPs (decomposed by peroxide) dominated at lower temperatures, from −20 to −28°C. Inorganic INPs (remaining after peroxide digestion), were minor contributors to the overall INP activity. Biological INP concentration active around −12°C peaked during rain events and under high relative humidity, reflecting emission mechanisms independent of the background aerosol concentration. The ratio of non‐heat‐labile organic and inorganic INPs was generally constant, suggesting they originated from the same source, presumably from regional arable topsoil based on air mass histories. Single particle mass spectrometry showed that soil particles aerosolized from a regionally common agricultural topsoil contained known mineral INP sources (K‐feldspar and illite) as well as a significant organic component. The INP activity observed in this study correlates well with agricultural soil INP activities from this and other regions of the world, suggesting that the observed INP spectra might be typical of many arable landscapes. These results demonstrate the strong influence of regional continental landscapes, emitting INPs of types that are not yet well represented in global models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Ageing and quenching through the Ageing Diagram – II. Physical characterization of galaxies

The connection between quenching mechanisms, which rapidly turn star-forming systems into quiescent, and the properties of the galaxy population remains difficult to discern. In this work we investigate the physical properties of MaNGA and SAMI galaxies at different stages of their star formation history. Specifically, we compare galaxies with signatures of recent quenching (Quenched) – Hα in absorption and low D n (4000) – with the rest of the low star-forming and active population (Retired and Ageing, respectively). The analysis is performed in terms of characteristics such as the total stellar mass, half-light radius, velocity-to-dispersion ratio, metallicity, and environment. We find that the Ageing population comprises a heterogeneous mixture of galaxies, preferentially late-type systems, with diverse physical properties. Retired galaxies, formerly Ageing or Quenched systems, are dominated by early-type high-mass galaxies found both at low and dense environments. Most importantly, we find that recently quenched galaxies are consistent with a population of compact low-mass satellite systems, with higher metallicities than their Ageing analogues. We argue that this is compatible with being quenched after undergoing a star-burst phase induced by environmental processes (e.g. ram pressure). However, we also detect a non-negligible fraction of field central galaxies likely quenched by internal processes. This study highlights that, in order to constrain the mechanisms driving galaxy evolution, it is crucial to distinguish between old (Retired) and recently quenched galaxies, thus requiring at least two estimates of the specific star formation rate over different time-scales.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Propagation of viral genomes by replicating ammonia-oxidising archaea during soil nitrification

Ammonia-oxidising archaea (AOA) are a ubiquitous component of microbial communities and dominate the first stage of nitrification in some soils. While we are beginning to understand soil virus dynamics, we have no knowledge of the composition or activity of those infecting nitrifiers or their potential to influence processes. This study aimed to characterise viruses having infected autotrophic AOA in two nitrifying soils of contrasting pH by following transfer of assimilated CO 2 -derived 13 C from host to virus via DNA stable-isotope probing and metagenomic analysis. Incorporation of 13 C into low GC mol% AOA and virus genomes increased DNA buoyant density in CsCl gradients but resulted in co-migration with dominant non-enriched high GC mol% genomes, reducing sequencing depth and contig assembly. We therefore developed a hybrid approach where AOA and virus genomes were assembled from low buoyant density DNA with subsequent mapping of 13 C isotopically enriched high buoyant density DNA reads to identify activity of AOA. Metagenome-assembled genomes were different between the two soils and represented a broad diversity of active populations. Sixty-four AOA-infecting viral operational taxonomic units (vOTUs) were identified with no clear relatedness to previously characterised prokaryote viruses. These vOTUs were also distinct between soils, with 42% enriched in 13 C derived from hosts. The majority were predicted as capable of lysogeny and auxiliary metabolic genes included an AOA-specific multicopper oxidase suggesting infection may augment copper uptake essential for central metabolic functioning. These findings indicate virus infection of AOA may be a frequent process during nitrification with potential to influence host physiology and activity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Towards a more general understanding of the algorithmic utility of recurrent connections

Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Foundational studies by Minsky and Roelfsema argued that computations that require propagation of global information for local computation to take place would particularly benefit from the sequential, parallel nature of processing in recurrent networks. Such “tag propagation” algorithms perform repeated, local propagation of information and were originally introduced in the context of detecting connectedness, a task that is challenging for feedforward networks. Here, we advance the understanding of the utility of lateral and recurrent computation by first performing a large-scale empirical study of neural architectures for the computation of connectedness to explore feedforward solutions more fully and establish robustly the importance of recurrent architectures. In addition, we highlight a tradeoff between computation time and performance and construct hybrid feedforward/recurrent models that perform well even in the presence of varying computational time limitations. We then generalize tag propagation architectures to propagating multiple interacting tags and demonstrate that these are efficient computational substrates for more general computations of connectedness by introducing and solving an abstracted biologically inspired decision-making task. Our work thus clarifies and expands the set of computational tasks that can be solved efficiently by recurrent computation, yielding hypotheses for structure in population activity that may be present in such tasks.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

$\scriptsize\mathrm{{SQuIGG\overrightarrow{L}E}}$: Studying Quenching in Intermediate- z Galaxies—Gas, $\scriptsize\mathrm{{Angu\overrightarrow{L}ar}}$ Momentum, and Evolution

We describe the Studying Quenching in Intermediate-z Galaxies: Gas, $\scriptsize\mathrm{\rm{angu}}\overrightarrow{L}{\rm{ar}}$ momentum, and Evolution ($\scriptsize\mathrm{{SQuIGG\overrightarrow{L}E}}$) survey of intermediate-redshift post-starburst galaxies. We leverage the large sky coverage of the Sloan Digital Sky Survey to select ~1300 recently quenched galaxies at 0.5 < z ≤ 0.9 based on their unique spectral shapes. These bright, intermediate-redshift galaxies are ideal laboratories to study the physics responsible for the rapid quenching of star formation: they are distant enough to be useful analogs for high-redshift quenching galaxies, but low enough redshift that multiwavelength follow-up observations are feasible with modest telescope investments. We use the Prospector code to infer the stellar population properties and nonparametric star formation histories (SFHs) of all galaxies in the sample. We find that $\scriptsize\mathrm{{SQuIGG\overrightarrow{L}E}}$ galaxies are both very massive (M * ~ 10 11.25 M ⊙ ) and quenched, with inferred star formation rates ≲1 M ⊙ yr -1 , more than an order of magnitude below the star-forming main sequence. The best-fit SFHs confirm that these galaxies recently quenched a major burst of star formation: >75% of $\scriptsize\mathrm{{SQuIGG\overrightarrow{L}E}}$ galaxies formed at least a quarter of their total stellar mass in the recent burst, which ended just ~200 Myr before observation. We find that $\scriptsize\mathrm{{SQuIGG\overrightarrow{L}E}}$ galaxies are on average younger and more burst-dominated than most other z ≲ 1 post-starburst galaxy samples. This large sample of bright post-starburst galaxies at intermediate redshift opens a wide range of studies into the quenching process. In particular, the full $\scriptsize\mathrm{{SQuIGG\overrightarrow{L}E}}$ survey will investigate the molecular gas reservoirs, morphologies, kinematics, resolved stellar populations, active galactic nucleus incidence, and infrared properties of this unique sample of galaxies in order to place definitive constraints on the quenching process.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Factorized visual representations in the primate visual system and deep neural networks

Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether (‘invariance’), represented in non-interfering subspaces of population activity (‘factorization’) or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters – lighting, background, camera viewpoint, and object pose – in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Metatranscriptomic analysis reveals dissimilarity in viral community activity between an ice-free and ice-covered winter in Lake Erie

Winter is a relatively under-studied season in freshwater ecology. The paucity of wintertime surveys has led to a lack of knowledge regarding microbial community activity during the winter in Lake Erie, a North American Great Lake. Viruses shape microbial communities and regulate biogeochemical cycles by acting as top-down controls, yet very few efforts have been made to examine active virus populations during the winter in Lake Erie. Furthermore, climate change-driven declines in seasonal ice cover have been shown to influence microbial community structure, but no studies have compared viral community activity between different ice cover conditions. We surveyed surface water metatranscriptomes for viral hallmark genes as a proxy for active virus populations and compared activity metrics between ice-covered and ice-free conditions from two sampled winters. Transcriptionally active viral communities were detected in both winters, spanning diverse phylogenetic clades of putative bacteriophage (Caudoviricetes), giant viruses (Nucleocytoviricota, or NCLDV), and RNA viruses (Orthornavirae). However, viral community activity metrics revealed pronounced differences between the ice-covered and ice-free winters. Viral community composition was distinct between winters and viral hallmark gene richness was reduced in the ice-covered relative to the ice-free conditions. In addition, the observed differences in viral communities correlated with microbial community activity metrics. Overall, these findings contribute to our understanding of the viral populations that are active during the winter in Lake Erie and suggest that viral community activity may be associated with ice cover extent.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

ShadeLab/PAPER_Howe_2023_switchgrass_MetaT

The raw data (metagenomes and metatranscriptomes) for this study are available in the Joint Genomes Institute Genome Portal (https://genome.jgi.doe.gov/portal/ Project ID 503249) with projects designated by year and product type. The MAG genomes analyzed in this paper are available on NCBI, as bioproject PRJNA800073. Plants and microorganisms form beneficial associations. Understanding plant-microbe interactions will inform microbiome management to enhance crop productivity and resilience to stress. Here, we apply a genome-centric approach to identify ecologically important leaf microbiome members on field-grown switchgrass and miscanthus and to quantify their activities for switchgrass over two growing seasons. We integrate metagenome and metatranscriptome sequencing from 192 leaf samples collected over representative time points in crop phenology. We curated 40 medium- and high-quality metagenome-assembled-genomes (MAGs) and focused analysis on seasonal transcript recruitment to them. Classes represented by these focal MAGs (Actinomycetia, Alpha- and Gamma- Proteobacteria, and Bacteroidota) were active and had increases in transcripts for short-chain dehydrogenase, molybdopterin oxidoreductase, and polyketide cyclase in the late season. The majority of MAGs had activated stress-associated pathways, including trehalose metabolism, indole acetic acid degradation, betaine biosynthesis, and reactive oxygen species degradation, suggesting direct engagement with the host environment. We also detected seasonally activated biosynthetic pathways for terpenes (carotenoids and isoprenoids) and for various non-ribosomal peptide pathways that were poorly annotated. Overall, this study overcame laboratory and bioinformatic challenges associated with field-based leaf metatranscriptome analysis to inform both general and likely specialized activities of these phyllosphere populations. These activities collectively support that leaf-associated bacterial populations are seasonally dynamic, responsive to host cues, and interactively engage in feedback with the plant. This analysis represented quality filtering of metagenomes and metatranscriptomes (data-preparation folder), metagenome assemblies (metagenome-assembly folder) and metagenome-assembled genome binning, curation, refinement, annotation (mag-evaluation folder). Abundances of sequencing libraries were calculated based on reads mapped (mapping folder). Additionallly, analysis of our annotated results are also included (analysis folder).

Howe, Adina↗

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 OSTI2020

How directed evolution reshapes the energy landscape in an enzyme to boost catalysis

Two steps forward—now look back Whether designed computationally or uncovered in activity screening, enzymes repurposed for biocatalysis rarely start at the peak of proficiency. However, directed evolution can in some cases increase catalytic efficiency of a poor enzyme by many orders of magnitude. Otten et al. used a suite of biochemical techniques to investigate the origins of rate enhancement in a previously evolved model enzyme. Two conformational states are present in the initial, computationally designed enzyme, but only one is active. Shifting the population toward the active state is one factor in increasing catalytic efficiency during evolution. Single mutations do not greatly increase activity, but the synergistic combination of just two out of 17 substitutions can provide most of the rate enhancement seen in the final, evolved enzyme. Science , this issue p. 1442

Science & Technology - Other Topics↗

DOE OSTI2025

Identifying Hot Spots and Hot Moments of Metabolic Activity in Salt Marsh Sediments through BONCAT-FISH Microscale Mapping (Final Technical Report)

Understanding the biogeography and timing of microbial metabolic activity is a key priority for microbial ecologists. With such knowledge, the cumulative biogeochemical contributions of microbial communities become more predictable, and our ability to both understand the effects of environmental change on microbial activity and build synthetic communities with desired functions will increase substantially. In this project, we advanced this broad, ambitious goal in two substantial ways: we developed a multiplexed Fluorescence In Situ Hybridization (FISH) approach that links microbial identity with metabolic function, and we established a novel Bio-Orthogonal Non-Canonical Amino acid Tagging (BONCAT) technique that can resolve the timing of anabolic activity, providing new resolution of when microbial constituents are growing. We deployed both of these techniques in multiple environmental settings to demonstrate their versatility. At the Little Sippewissett Salt Marsh, we combined a novel dual-BONCAT technique with fluorescence activated cell sorting to determine which population of cells was metabolically active during the day and which was active during the night. We found that Methylobacterium was active during daylight hours, potentially feeding on carbon-rich molecules released from plant roots. Sulfur-cycling microbes dominate the population active during the dark night-time hours. Overall, the work conducted under the auspices of this project developed two promising new techniques for identifying the “hot spots” and “hot moments” of microbial activity in complex communities with taxonomic and functional resolution. We focused largely on a salt marsh sediment context, but are confident that microbial ecologists seeking to understand the microbial role in biogeochemical cycles in a wide range of settings will find our newly developed techniques useful in future work.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A standardized quantitative analysis strategy for stable isotope probing metagenomics

ABSTRACT Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many DNA-SIP studies rely on 16S rRNA gene sequences to identify active taxa, but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA gene sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes and their level of isotopic enrichment were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytical models for identifying active taxa and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg , an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. IMPORTANCE Answering the questions, “who is eating what?” and “who is active?” within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. These questions can be pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganism’s taxonomic identity and genome composition while providing quantitative estimates of the microorganism’s isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.

54 ENVIRONMENTAL SCIENCES↗