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Environmental metabolomes are fundamentally coupled to microbially-linked biogeochemical processes within ecosystems. However, significant gaps exist in our understanding of their spatiotemporal organization, limiting our ability to uncover transferrable principles and predict ecosystem function. We propose that a theoretical paradigm, which integrates concepts from metacommunity ecology, is necessary to reveal underlying mechanisms governing metabolomes. We call this synthesis between ecology and metabolomics ‘meta-metabolome ecology’ and demonstrate its utility using a mass spectrometry dataset. We developed three relational metabolite dendrograms using molecular properties and putative biochemical transformations and performed ecological null modeling. Based upon null modeling results, we show that stochastic processes drove molecular properties while biochemical transformations were structured deterministically. We further suggest that potentially biochemically active metabolites were more deterministically assembled than less active metabolites. Understanding variation in the influences of stochasticity and determinism provides a way to focus attention on which meta-metabolomes and which parts of meta-metabolomes are most likely to be important to consider in mechanistic models. We propose that this paradigm will allow researchers to study the connections between ecological systems and their molecular processes in previously inaccessible detail. Despite growing interest in environmental metabolomics, we lack conceptual frameworks for considering how metabolites vary across space and time in ecological systems. Here, the authors apply (species) community assembly concepts to metabolomics data, offering a way forward in understanding the assembly of metabolite assemblages.

Plant survival during environmental stress greatly affects ecosystem carbon (C) cycling, and plant–microbe interactions are central to plant stress survival. The release of C-rich root exudates is a key mechanism plants use to manage their microbiome, attracting beneficial microbes and/or suppressing harmful microbes to help plants withstand environmental stress. However, a critical knowledge gap is how plants alter root exudate concentration and composition under varying stress levels. In a greenhouse study, we imposed three drought treatments (control, mild, severe) on blue grama ( Bouteloua gracilis Kunth Lag. Ex Griffiths), and measured plant physiology and root exudate concentration and composition using GC–MS, NMR, and FTICR. With increasing drought severity, root exudate total C and organic C increased concurrently with declining predawn leaf water potential and photosynthesis. Root exudate composition mirrored the physiological gradient of drought severity treatments. Specific compounds that are known to alter plant drought responses and the rhizosphere microbiome mirrored the drought severity-induced root exudate compositional gradient. Despite reducing C uptake, these plants actively invested C to root exudates with increasing drought severity. Patterns of plant physiology and root exudate concentration and composition co-varied along a gradient of drought severity.

The molecular diversity of dissolved organic matter (DOM) in soil depends on the stage of plant litter decomposition and microbial metabolism. Yet the contributions of catabolic and anabolic processes on DOM molecular diversity, and their consequences for organic carbon mineralization, remain unclear. To address this question, we used an 18 O-H 2 O isotope-labelling approach to track microbial transformation of DOM during blue grama grass ( Bouteloua gracilis ) decomposition and determine how these processes alter molecular diversity. Here, we show that 18 O-isotopically labeled compounds indicate that microbially produced DOM increases functional molecular diversity (recognizing compound dissimilarity) during early decomposition (days) but not at later stages (months). Furthermore, carbon mineralization from DOM is most strongly correlated with molecular weight, highlighting the role of chemical properties in regulating microbial decomposition. Our findings suggest that early microbial catabolic and anabolic metabolism enhances DOM molecular diversity, whereas later decomposition favors the accumulation of fewer, recycled microbial compounds. Microbial decomposition of plant litter initially increases the molecular diversity of dissolved organic matter, but prolonged microbial recycling diminishes this diversity, highlighting how microbial metabolism influences soil carbon turnover.

The microbial and molecular characterization of the ectorhizosphere is an important step towards developing a more complete understanding of how the cultivation of biofuel crops can be undertaken in nutrient poor environments. The ectorhizosphere of Setaria is of particular interest because the plant component of this plant-microbe system is an important agricultural grain crop and a model for biofuel grasses. Importantly, Setaria lends itself to high throughput molecular studies. As such, we have identified important intra- and interspecific microbial and molecular differences in the ectorhizospheres of three geographically distant Setaria italica accessions and their wild ancestor S . viridis . All were grown in a nutrient-poor soil with and without nutrient addition. To assess the contrasting impact of nutrient deficiency observed for two S . italica accessions, we quantitatively evaluated differences in soil organic matter, microbial community, and metabolite profiles. Together, these measurements suggest that rhizosphere priming differs with Setaria accession, which comes from alterations in microbial community abundances, specifically Actinobacteria and Proteobacteria populations. When globally comparing the metabolomic response of Setaria to nutrient addition, plants produced distinctly different metabolic profiles in the leaves and roots. With nutrient addition, increases of nitrogen containing metabolites were significantly higher in plant leaves and roots along with significant increases in tyrosine derived alkaloids, serotonin, and synephrine. Glycerol was also found to be significantly increased in the leaves as well as the ectorhizosphere. These differences provide insight into how C 4 grasses adapt to changing nutrient availability in soils or with contrasting fertilization schemas. Gained knowledge could then be utilized in plant enhancement and bioengineering efforts to produce plants with superior traits when grown in nutrient poor soils.

Despite the importance of microbial soil organic matter (SOM) respiration in regulating the flux of carbon between soils and the atmosphere, soil carbon cycling models remain primarily based on climate and soil properties, leading to large uncertainty in predictions. To address this knowledge gap, we analyzed high‐resolution water‐extractable SOM profiles from soil cores collected across the United States by the 1,000 Soils Pilot of the Molecular Observation Network. Our innovation lies in using machine learning to distill thousands of SOM formula into tractable units; and it enables integrating data from molecular measurements into soil respiration models. In surface soils, SOM chemistry provided better estimates of potential soil respiration than soil physicochemistry, and using them combined yielded the best prediction. Overall, we identify specific subsets of organic molecules that may improve predictions of global soil respiration and create a strong basis for developing new representations in process‐based models. Plain Language Summary Soil organic carbon is one of the largest and most active pools in the global carbon cycle. Microbial decomposition of soil organic matter (SOM) releases large amounts of carbon dioxide (CO2) to the atmosphere. This process is soil microbial respiration. To evaluate if SOM composition can improve predictions of soil respiration, we collected soils from across the continental US, and analyzed both soil physicochemistry and molecular SOM composition, as part of the Molecular Observation Network. We developed machine learning based workflow to extract key SOM signatures and used the SOM signatures to predict potential rate of soil respiration, compared to standard soil physicochemistry. The results suggested that SOM composition improved the prediction of potential soil respiration in surface soils, where most soil carbon is actively cycled. In deeper soils, model performance was not improved by SOM chemistry, possibly due to the greater importance of mineral‐associated SOM. Our results identified key SOM molecules in predicting potential soil respiration and supported the significance of SOM dynamics in future development of soil carbon models. Key Points Molecular measurements of dissolved soil organic matter (SOM) are critical to accurately predict soil respiration at the continental scale Machine learning extracts key molecules from complex high‐resolution SOM profiles to explain differences in potential soil respiration Dissolved SOM profiles provide more power in predicting potential respiration in surface soils than subsoils

Dissolved organic matter (DOM) is vital to ecosystem functions, influencing nutrient cycles and water quality. Understanding the processes driving DOM chemistry variation remains a challenge. By examining these processes through a community ecology perspective, we aim to understand the balance between stochastic forces (e.g., random mixing of DOM) and deterministic forces (e.g., systematic loss of certain types of DOM molecules) shaping DOM chemistry. Previous research on stochastic and deterministic influences over DOM chemistry applied null models to aquatic environments and subsurface pore water. Our study extends this to variably inundated riverbed sediments, which are widespread globally. We studied 38 river reaches across biomes, finding that DOM chemistry within most sites was governed by deterministic processes that were highly localized and led to spatial divergence in DOM chemistry within each reach. The degree of determinism varied substantially across reaches and we hypothesized this was related to differences in sediment moisture. Our findings partially supported this, showing that the upper limit of determinism decreased with increasing sediment moisture. We integrated our results with previous studies to develop a post-hoc conceptual model proposing that DOM assemblages become more deterministic along the continuum from river water to saturated sediment pore spaces to drier sediments or soils. This conceptual model aligns with previous work linking DOM chemistry to the Damköhler number and hydrologic connectivity, suggesting generalizable patterns and processes that can be further revealed by quantifying the stochastic-deterministic balance through space, time, and across scales.

Biochemical transformations of organic matter (OM) are a primary driver of river corridor biogeochemistry, thereby modulating ecosystem processes at local to global scales. OM transformations are driven by diverse biotic and abiotic processes, but we lack knowledge of how the diversity of those processes varies across river corridors and across surface and subsurface components of river corridors. To fill this gap we quantified the number of putative biotic and abiotic transformations of organic molecules across diverse river corridors using ultra-high-resolution mass spectrometry. The number of unique transformations is used here as a proxy for the diversity of biochemical processes underlying observed profiles of organic molecules. For this, we use public data spanning the contiguous United States (ConUS) from the Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS) consortium. Our results show that surface water OM had more biotic and abiotic transformations than OM from shallow hyporheic zone sediments (1–3 cm depth). We observed substantially more biotic than abiotic transformations, and the numbers of biotic and abiotic transformations were highly correlated with each other. We found no relationship between the number of transformations in surface water and sediments and no meaningful relationships with latitude, longitude, or climate. We also found that the composition of transformations in sediments was not linked with transformation composition in adjacent surface waters. We infer that OM transformations represented in surface water are an integrated signal of diverse processes occurring throughout the upstream catchment. In contrast, OM transformations in sediments likely reflect a narrower range of processes within the sampled volume. This indicates decoupling between the processes influencing surface water and sediment OM, despite the potential for hydrologic exchange to homogenize OM. We infer that the processes influencing OM transformations and the scales at which they operate diverge between surface water and sediments.

Peatlands store vast amounts of carbon, with deep peat carbon remaining stable due to limited thermodynamic energy and transport. However, climate change-induced increases in labile carbon inputs could destabilize these stores. Here, we combined DNA stable isotope probing with stable isotope-assisted metabolomics employing a multi-platform approach to investigate microbial dynamics driving deep peat carbon degradation upon labile carbon (e.g., glucose) amendment. Our findings highlight the vulnerability of deep peat carbon, as glucose addition triggers the breakdown of older organic matter. By uniquely integrating these techniques, we identified active glucose metabolizers to specific microbial populations and mapped carbon flow through microbial networks, elucidating their role in priming recalcitrant carbon mineralization. This multi-omics approach offers crucial insights into how changing resources reshape the peatland microbiome, enhancing our understanding of deep carbon processing, and refining model parameterization to predict microbial responses and carbon cycle feedbacks under global change pressures. Glucose addition to peatland soils promotes decomposition of older buried carbon through enhanced microbial activity, according to DNA analysis and isotope labelling of peatland soil.

Coastal terrestrial–aquatic interfaces (TAIs) are dynamic zones of biogeochemical cycling influenced by salinity gradients. However, there is significant heterogeneity in salinity influences on TAI soil biogeochemical function. This heterogeneity is perhaps related to unrecognized mechanisms associated with carbon (C) chemistry and microbial communities. To investigate this potential, we evaluated hypotheses associated with salinity-associated shifts in organic C thermodynamics; biochemical transformations; and nitrogen-, phosphorus-, and sulfur-containing heteroatom organic compounds in a first-order coastal watershed on the Olympic Peninsula of Washington, USA. In contrast to our hypotheses, thermodynamic favorability of water-soluble organic compounds in shallow soils decreased with increasing salinity (43–867 µS cm−1), as did the number of inferred biochemical transformations and total heteroatom content. These patterns indicate lower microbial activity at higher salinity that is potentially constrained by accumulation of less-favorable organic C. Furthermore, organic compounds appeared to be primarily marine- or algae-derived in forested floodplain soils with more lipid-like and protein-like compounds, relative to upland soils that had more lignin-, tannin-, and carbohydrate-like compounds. Based on a recent simulation-based study, we further hypothesized a relationship between C chemistry and the ecological assembly processes governing microbial community composition. Null modeling revealed that differences in microbial community composition – assayed using 16S rRNA gene sequencing – were primarily the result of limited exchange of organisms among communities (i.e., dispersal limitation). This results in unstructured demographic events that cause community composition to diverge stochastically, as opposed to divergence in community composition being due to deterministic selection-based processes associated with differences in environmental conditions. The strong influence of stochastic processes was further reflected in there being no statistical relationship between community assembly processes (e.g., the relative influence of stochastic assembly processes) and C chemistry (e.g., heteroatom content). This suggests that microbial community composition does not have a mechanistic or causal linkage to C chemistry. The salinity-associated gradient in C chemistry was, therefore, likely influenced by a combination of spatially structured inputs and salinity-associated metabolic responses of microbial communities that were independent of community composition. We propose that impacts of salinity on coastal soil biogeochemistry need to be understood in the context of C chemistry, hydrologic or depositional dynamics, and microbial physiology, while microbial composition may have less influence.

Exceptionally preserved fossils retain soft tissues and often the biomolecules that were present in an animal during its life. The majority of terrestrial vertebrate fossils are not traditionally considered exceptionally preserved, with fossils falling on a spectrum ranging from very well-preserved to poorly preserved when considering completeness, morphology and the presence of microstructures. Within this variability of anatomical preservation, high-quality macro-scale preservation (e.g., articulated skeletons) may not be reflected in molecular-scale preservation (i.e., biomolecules). Excavation of the Hayden Quarry (HQ; Chinle Formation, Ghost Ranch, NM, USA) has resulted in the recovery of thousands of fossilized vertebrate specimens. This has contributed greatly to our knowledge of early dinosaur evolution and paleoenvironmental conditions during the Late Triassic Period (~212 Ma). The number of specimens, completeness of skeletons and fidelity of osteohistological microstructures preserved in the bone all demonstrate the remarkable quality of the fossils preserved at this locality. Because the Hayden Quarry is an excellent example of good preservation in a fluvial environment, we have tested different fossil types (i.e., bone, tooth, coprolite) to examine the molecular preservation and overall taphonomy of the HQ to determine how different scales of preservation vary within a single locality. We used multiple high-resolution mass spectrometry techniques (TOF-SIMS, GC-MS, FT-ICR MS) to compare the fossils to unaltered bone from extant vertebrates, experimentally matured bone, and younger dinosaurian skeletal material from other fluvial environments. FT-ICR MS provides detailed molecular information about complex mixtures, and TOF-SIMS has high elemental spatial sensitivity. Using these techniques, we did not find convincing evidence of a molecular signal that can be confidently interpreted as endogenous, indicating that very good macro- and microscale preservation are not necessarily good predictors of molecular preservation.