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At least 253 records · Page 14

Vertical Hydrologic Exchange Flows Control Methane Emissions from Riverbed Sediments

CH 4 emissions from inland waters are highly uncertain in the current global CH 4 budget, especially for streams, rivers, and other lotic systems. Previous studies have attributed the strong spatiotemporal heterogeneity of riverine CH 4 to environmental factors such as sediment type, water level, temperature, or particulate organic carbon abundance through correlation analysis. However, a mechanistic understanding of the basis for such heterogeneity is lacking. Here, in this study, we combine sediment CH 4 data from the Hanford reach of the Columbia River with a biogeochemical-transport model to show that vertical hydrologic exchange flows (VHEFs), driven by the difference between river stage and groundwater level, determine CH 4 flux at the sediment–water interface. CH 4 fluxes show a nonlinear relationship with the magnitude of VHEFs, where high VHEFs introduce O 2 into riverbed sediments, which inhibit CH 4 production and induce CH 4 oxidation, and low VHEFs cause transient reduction in CH 4 flux (relative to production) due to reduced advective CH 4 transport. In addition, VHEFs lead to the hysteresis of temperature rise and CH 4 emissions because high river discharge caused by snowmelt in spring leads to strong downwelling flow that offsets increasing CH 4 production with temperature rise. Our findings reveal how the interplay between in-stream hydrologic flux besides fluvial-wetland connectivity and microbial metabolic pathways that compete with methanogenic pathways can produce complex patterns in CH 4 production and emission in riverbed alluvial sediments.

54 ENVIRONMENTAL SCIENCES↗

Leveraging High-resolution Molecular Composition of Soil Organic Matter to Enhance Carbon Cycling Modeling

Soils store more carbon than the atmosphere and vegetation combined, yet Earth system models still struggle to predict how this vast reservoir will respond to environmental change. A central limitation is that most soil biogeochemical models represent organic matter using bulk conceptual pools or chemically homogeneous fractions, preventing direct use of rapidly expanding molecular-scale datasets. Here we develop and test a new soil decomposition framework that explicitly integrates high-resolution information on organic matter composition. First, we construct a molecularly informed litter decomposition module in which plant inputs are partitioned into five functional compound classes—carbohydrates, proteins, lignin-like aromatics, lipids, and carbonyls—using a molecular mixing model calibrated to solid-state 13 C Nuclear Magnetic Resonance (NMR) spectra. Class-specific kinetics, lignin-dependent physical protection, and substrate-driven microbial carbon use efficiency allow the module to capture metabolic tradeoffs associated with enzyme production and nutrient limitation. We then embed this litter module within a microbially explicit whole-soil model that tracks the transformation of these compound classes through particulate organic matter, dissolved organic matter, mineral-associated organic matter, and microbial biomass. High-resolution Fourier Transform Ion Cyclotron Resonance mass spectrometry (FTICR-MS) data are used to link internal pools to measurable soil organic matter fractions and to constrain key process parameters. Applications at soil-core and ecosystem scales demonstrate that the new model reproduces observed soil respiration dynamics while providing mechanistic attribution of CO 2 fluxes to specific chemical classes and pools. Compared to existing frameworks such as the Community Land Model soil biogeochemistry module and the Millennial model, our approach maintains competitive predictive skill while substantially improving interpretability and opportunities for data–model integration. This work illustrates a viable pathway for leveraging molecular-scale observations to reduce structural uncertainty in soil carbon–climate feedback projections.

54 ENVIRONMENTAL SCIENCES↗

Recent Advances in Domesticating Non-model Microorganisms

Non-model microorganisms have been increasingly explored as microbial cell factories for production of chemicals, fuels, and materials owing to their unique physiology and metabolic capabilities. Yet, these microorganisms often lack facile genetic tools for strain development, which hinders their adoption as production hosts. In this review, we describe recent advances in domestication of non-model microorganisms, including bacteria, actinobacteria, cyanobacteria, yeast, and fungi, with a focus on the development of genetic tools. Furthermore, we highlight some successful applications of non-model microorganisms as microbial cell factories.

synthetic biology↗

Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae

Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. Furthermore, this study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.

59 BASIC BIOLOGICAL SCIENCES↗

Whole metagenome sequencing and 16S rRNA gene amplicon analyses reveal the complex microbiome responsible for the success of enhanced in-situ reductive dechlorination (ERD) of a tetrachloroethene-contaminated Superfund site

The North Railroad Avenue Plume (NRAP) Superfund site in New Mexico, USA exemplifies successful chlorinated solvent bioremediation. NRAP was the result of leakage from a dry-cleaning that operated for 37 years. The presence of tetrachloroethene biodegradation byproducts, organohalide respiring genera (OHRG), and reductive dehalogenase (rdh) genes detected in groundwater samples indicated that enhanced reductive dechlorination (ERD) was the remedy of choice. This was achieved through biostimulation by mixing emulsified vegetable oil into the contaminated aquifer. This report combines metagenomic techniques with site monitoring metadata to reveal new details of ERD. DNA extracts from groundwater samples collected prior to and at four, 23 and 39 months after remedy implementation were subjected to whole metagenome sequencing (WMS) and 16S rRNA gene amplicon (16S) analyses. The response of the indigenous NRAP microbiome to ERD protocols is consistent with results obtained from microcosms, dechlorinating consortia, and observations at other contaminated sites. WMS detects three times as many phyla and six times as many genera as 16S. Both techniques reveal abundance changes in Dehalococcoides and Dehalobacter that reflect organohalide form and availability. Methane was not detected before biostimulation but appeared afterwards, corresponding to an increase in methanogenic Archaea. Assembly of WMS reads produced scaffolds containing rdh genes from Dehalococcoides, Dehalobacter, Dehalogenimonas, Desulfocarbo, and Desulfobacula. Anaerobic and aerobic cometabolic organohalide degrading microbes that increase in abundance include methanogenic Archaea, methanotrophs, Dechloromonas, and Xanthobacter, some of which contain hydrolytic dehalogenase genes. Aerobic cometabolism may be supported by oxygen gradients existing in aquifer microenvironments or by microbes that produce O 2 via microbial dismutation. The NRAP model for successful ERD is consistent with the established pathway and identifies new taxa and processes that support this syntrophic process. This project explores the potential of metagenomic tools (MGT) as the next advancement in bioremediation.

59 BASIC BIOLOGICAL SCIENCES↗

Development and Validation of a Process Model and Open-Source Process Simulator for Microalgae-Based Tertiary Phosphorus Recovery

Microalgae-based tertiary wastewater treatment has the potential to meet stringent effluent phosphorus limits, with the added benefit of producing a marketable feedstock. However, the lack of validated mechanistic models and their implementation in process simulators have limited the adoption of this technology. In this study, an updated lumped pathway metabolic model (Phototrophic-Mixotrophic Process Model, PM 2 ), including both photoautotrophic and heterotrophic metabolisms of microalgae, was developed to predict effluent phosphorus concentration and biomass yield in response to dynamic influent and varying environmental conditions. The model was implemented in QSDsan – an open-source, Python-based design and simulation platform – for robust simulation under uncertainty. A global sensitivity analysis was performed to prioritize model parameters for calibration. The model was then calibrated and validated using batch experimental data and 45 days of continuous online monitoring data from a full-scale (568 m 3 ·d -1 ) microalgae-based tertiary wastewater treatment plant (EcoRecover process). In particular, along with dynamic influent composition, temperature and light intensity data with diel variation were provided as model inputs to reflect the microalgal behavior under day-night cycling. Overall, the QSDsan-based microalgae process simulator was able to predict effluent phosphorus within 0.02–0.04 mg-P·L -1 , while also capturing the general trends of state variables according to nutrient availability.

Lumped pathway metabolic model↗

Leveraging the signature of heterotrophic respiration on atmospheric CO 2 for model benchmarking

Spatial and temporal variations in atmospheric carbon dioxide (CO 2 ) reflect large-scale net carbon exchange between the atmosphere and terrestrial ecosystems. Soil heterotrophic respiration (HR) is one of the component fluxes that drive this net exchange, but, given observational limitations, it is difficult to quantify this flux or to evaluate global-scale model simulations thereof. Here, we show that atmospheric CO 2 can provide a useful constraint on large-scale patterns of soil heterotrophic respiration. We analyze three soil model configurations (CASA-CNP, MIMICS, and CORPSE) that simulate HR fluxes within a biogeochemical test bed that provides each model with identical net primary productivity (NPP) and climate forcings. We subsequently quantify the effects of variation in simulated terrestrial carbon fluxes (NPP and HR from the three soil test-bed models) on atmospheric CO 2 distributions using a three-dimensional atmospheric tracer transport model. Our results show that atmospheric CO 2 observations can be used to identify deficiencies in model simulations of the seasonal cycle and interannual variability in HR relative to NPP. In particular, the two models that explicitly simulated microbial processes (MIMICS and CORPSE) were more variable than observations at interannual timescales and showed a stronger-than-observed temperature sensitivity. Our results prompt future research directions to use atmospheric CO 2 , in combination with additional constraints on terrestrial productivity or soil carbon stocks, for evaluating HR fluxes.

54 ENVIRONMENTAL SCIENCES↗

Polar Lakes and Springs as Analogs for Mars

If we accept the fluvial features as evidence of liquid water on Mars we conclude that early in Martian history there was an extensive period during which liquid water was prevalent and stable. The duration of this epoch and the environmental conditions that allowed for water flow are uncertain but because liquid water is the most critical environmental requirement for life on Earth, the observation that there was liquid water on Mars during on early epoch is a strong motivation for considering the possibility of past life. Microbial ecosystems can thrive in polar lakes and springs on Earth, such as those in the Antarctic dry valleys, even when the mean temperatures are well below freezing. Mars need not have ever been above freezing for life to persist. These microbial ecosystems provide specific models for possible past martian ecosystems and can be used in the development of methods to locate and identify analogous Martian fossil forms, if they exist.

McKay, C. P.↗

Effects of Spaceflight Relevant Carbon Dioxide Levels on Pathogenesis Related Microbial Characteristics

Background - Spaceflight can alter microbial pathogenesis - Microgravity (Nickerson et al. 2000 Infect Immun 68(6):3147-52; Crabbe 2011 Appl Environ Mirobiol 77(4):1221-30; Gilbert 2020 NPJ Micrograv 6:4) - Radiation (Dornmayr-Pfaffenhuemer 2011 Astrobiol 11(3):199-205; Romsdahl 2020 Front Microbiol 11:931; Perrone 2020 Microorganisms 8(10):1496) - Elevated CO2 (0.4%, compared to earth at 0.04%) - The effect of slightly elevated CO2 concentrations on microbial pathogenesis has not been studied.

carbon dioxide↗

Bacterial and fungal composition and exometabolites control the development and persistence of soil water repellency

Soil water repellency (SWR), the reduced affinity of soil for water, is a phenomenon that affects soils globally. With worsening climate change, SWR is expected to increase emphasizing the need to understand the mechanisms driving SWR development and persistence. The importance of the soil microbes in SWR has been postulated for decades, but limited research has been conducted into whole-community interactions and the role of community metabolic activity. To address this gap in knowledge, we investigated the direct effect of microbial community composition, activity, and diversity, as well as their associated metabolites on the development and persistence of SWR by inoculating microcosms containing model soils with 15 different microbial communities and quantified respiration and SWR over time. Six communities that consistently produced either a hydrophobic or hydrophilic phenotype were characterized using metagenomics and metabolomics to determine the impact of microbial and metabolite composition and diversity on SWR. We identified several bacterial genera with significant changes in abundance between SWR phenotypes including Nocardiopsis and Kocuria in hydrophilic and Streptomyces and Cutibacterium in hydrophobic. We discovered that hydrophilic communities were more positively connected when compared to hydrophobic communities, which could be due to an increase in defense mechanism genes. Additionally, we identified specific metabolites associated with hydrophilic and hydrophobic phenotypes including an increase in the osmolyte ectoine in hydrophilic and an increase in plant-derived decomposition products in hydrophobic communities. Finally, our research suggests that fungi, previously thought to cause hydrophobicity, may actually contribute to hydrophilicity through their preferential consumption of hydrophobic compounds.

54 ENVIRONMENTAL SCIENCES↗

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↗

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↗

BRCore: an R package implementing flexible selection of core taxa using contribution to Bray-Curtis dissimilarity and neutral model fitting

Identifying core taxa in microbial ecology highlights groups likely to participate in a broad range of potential ecological interactions. Here, we present BRCore, an R package to identify core taxa using abundance-occupancy distributions and beta-diversity contributions across ecological niches, and predict stochastic and deterministic taxa.

59 BASIC BIOLOGICAL SCIENCES↗

A natural view of microbial biodiversity within hot spring cyanobacterial mat communities

This review summarizes a decade of research in which we have used molecular methods, in conjunction with more traditional approaches, to study hot spring cyanobacterial mats as models for understanding principles of microbial community ecology. Molecular methods reveal that the composition of these communities is grossly oversimplified by microscopic and cultivation methods. For example, none of 31 unique 16S rRNA sequences detected in the Octopus Spring mat, Yellowstone National Park, matches that of any prokaryote previously cultivated from geothermal systems; 11 are contributed by genetically diverse cyanobacteria, even though a single cyanobacterial species was suspected based on morphologic and culture analysis. By studying the basis for the incongruity between culture and molecular samplings of community composition, we are beginning to cultivate isolates whose 16S rRNA sequences are readily detected. By placing the genetic diversity detected in context with the well-defined natural environmental gradients typical of hot spring mat systems, the relationship between gene and species diversity is clarified and ecological patterns of species occurrence emerge. By combining these ecological patterns with the evolutionary patterns inherently revealed by phylogenetic analysis of gene sequence data, we find that it may be possible to understand microbial biodiversity within these systems by using principles similar to those developed by evolutionary ecologists to understand biodiversity of larger species. We hope that such an approach guides microbial ecologists to a more realistic and predictive understanding of microbial species occurrence and responsiveness in both natural and disturbed habitats.

Review↗

A Systems Approach to Increasing Carbon Flux to Seed Oil for Biofuels and Bioproducts Production in Camelina sativa (Final Report)

To combat climate change and alleviate the dependency of the United States on fossil fuels, the transition to biofuel crops has long been proposed as a crucial part of the long-term solution. Camelina sativa has emerged as one of the leading commercially viable options for biofuel and bioproduct production for the U.S. Camelina has the advantages of low agronomic inputs and natural resistance to diverse biotic and abiotic stresses relative to other oilseed crops, and Camelina oil-based blends have been tested and approved as liquid transportation fuels. A major limitation in the widespread adoption of Camelina as a viable industrial oilseed crop is its modest oil yields. This project directly investigated possible paths towards increasing oil, by employing tissue-specific and whole plant systems approaches to identify major regulatory bottlenecks. We developed a new high-throughput method for the identification of multi-gene transformants in polyploid species like Camelina sativa and used this to determine that, contrary to what mathematical models suggested, introduction of the microbial Entner-Doudoroff (ED) pathway into Camelina sativa did not result in significant seed oil increases. We developed flux maps providing accurate and statistically robust models of central carbon metabolism in Camelina, including of cultured Camelina embryos. This knowledge was used in the capacitation of a number of junior researchers in gaining knowledge on approaches used for quantitative flux map analyses. We identified several novel Camelina transcription factor genes regulating fatty acid biosynthesis that showed variable effects on seed oil accumulation in transgenic plants. Among a number of community resources, we also developed a knowledge base web resource (CamRegBase) to integrate Camelina gene regulatory information. Together, research outcomes from this project have contributed to a much better understanding of the regulation and bottlenecks to engineer seed oil production in Camelina.

09 BIOMASS FUELS↗