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Soil surface consortia are easily observed and sampled, allowing examination of their interactions with soil microbiomes. Here, we present metatranscriptomic sequences from Dark Green 1 (DG1), a cyanobacterium-based soil surface consortium, in the presence and absence of an underlying soil microbiome and/or urea.
Photosynthetic Cyanobacteria and their descendants are the only known organisms capable of oxygenic photosynthesis. Their metabolism permanently changed the Earth’s surface and the evolutionary trajectory of life, but little is known about their evolutionary history. Genomes of the Gloeobacterales, an order of deeply divergent photosynthetic Cyanobacteria, may hold clues about the evolutionary process. However, there are only three published genomes within this order, and it is difficult to make broad inferences based on such little data. Here, I describe five species within the Gloeobacterales retrieved from publicly available databases and examine their photosynthetic gene content and the environments in which Gloeobacterales genomes and 16S rRNA gene sequences are found. The Gloeobacterales contain reduced photosystems and inhabit cold, wet-rock, and low-light environments. They are likely present in low abundances due to their low growth rate. Future searches for Gloeobacterales should target these environments, and samples should be deeply sequenced to capture the low-abundance taxa. Publicly available databases contain undescribed taxa within the Gloeobacterales. However, searching through all available data with current methods is computationally expensive. Therefore, new methods must be developed to search for these and other evolutionarily important taxa. Once identified, these novel photosynthetic Cyanobacteria will help illuminate the origin and evolution of oxygenic photosynthesis. Early branching photosynthetic Cyanobacteria such as the Gloeobacterales may provide clues into the evolutionary history of oxygenic photosynthesis, but there are few genomes or cultured taxa from this order. Five new metagenome-assembled genomes suggest that members of the Gloeobacterales all contain reduced photosystems and lack genes associated with thylakoids and circadian rhythms. Their distribution suggests that they may thrive in environments that are marginal for other species, including wet-rock and cold environments. These traits may aid in the discovery and cultivation of novel species in this clade.
With a rise in antibiotic resistance and chronic infection, the metabolic response of Salmonella enterica serovar Typhimurium to various dietary conditions over time remains an understudied avenue for novel, targeted therapeutics. Elucidating how enteric pathogens respond to dietary variation not only helps us decipher the metabolic strategies leveraged for expansion but also assists in proposing targets for therapeutic interventions. In this study, we use a multi-omics approach to identify the metabolic response of Salmonella enterica serovar Typhimurium in mice on both a fibrous diet and high-fat diet over time. When comparing Salmonella gene expression between diets, we found a preferential use of respiratory electron acceptors consistent with increased inflammation in high-fat diet mice. Looking at the high-fat diet over the course of infection, we noticed heterogeneity in samples based on Salmonella ribosomal activity, which is separated into three infection phases: early, peak, and late. We identified key respiratory, carbon, and pathogenesis gene expressions descriptive of each phase. Surprisingly, we identified genes associated with host cell entry expressed throughout infection, suggesting subpopulations of Salmonella or stress-induced dysregulation. Collectively, these results highlight not only the sensitivity of Salmonella to its environment but also identify phase-specific genes that may be used as therapeutic targets to reduce infection.
We use metagenome-assembled genomes (MAGs) to understand single-carbon (C1) compound-cycling—particularly methane-cycling—microorganisms in montane riparian floodplain sediments. We generated 1,233 MAGs (>50% completeness and <10% contamination) from 50- to 150-cm depth below the sediment surface capturing the transition between oxic, unsaturated sediments and anoxic, saturated sediments in the Slate River (SR) floodplain (Crested Butte, CO, USA). We recovered genomes of putative methanogens, methanotrophs, and methylotrophs (n = 57). Methanogens, found only in deep, anoxic depths at SR, originate from three different clades (Methanoregulaceae, Methanotrichaceae, and Methanomassiliicoccales), each with a different methanogenesis pathway; putative methanotrophic MAGs originate from within the Archaea (Candidatus Methanoperedens) in anoxic depths and uncultured bacteria (Ca. Binatia) in oxic depths. Genomes for canonical aerobic methanotrophs were not recovered. Ca. Methanoperedens were exceptionally abundant (~1,400× coverage, >50% abundance in the MAG library) in one sample that also contained aceticlastic methanogens, indicating a potential C1/methane-cycling hotspot. Ca. Methylomirabilis MAGs from SR encode pathways for methylotrophy but do not harbor methane monooxygenase or nitrogen reduction genes. Comparative genomic analysis supports that one clade within the Ca. Methylomirabilis genus is not methanotrophic. The genetic potential for methylotrophy was widespread, with over 10% and 19% of SR MAGs encoding a methanol dehydrogenase or substrate-specific methyltransferase, respectively. MAGs from uncultured Thermoplasmata archaea in the Ca. Gimiplasmatales (UBA10834) contain pathways that may allow for anaerobic methylotrophic acetogenesis. Overall, MAGs from SR floodplain sediments reveal a potential for methane production and consumption in the system and a robust potential for methylotrophy.
Iron and nitrogen are the nutrients that most commonly limit phytoplankton growth in the world’s oceans. The utilization of these resources by phytoplankton sets the biomass available to marine systems and is of particular interest in high-nutrient, low-chlorophyll (HNLC) coastal fisheries.
How soil is managed, particularly for agriculture, exerts stresses upon soil microbiomes, resulting in altered community structures and functional states. Understanding how soil microbiomes respond to combined stresses is important for predicting system performance under different land use scenarios, aids in identification of the most environmentally benign managements, and provides insight into how system function can be recovered in degraded soils. We use a long-established field experiment to study the effects of combined chronic (press) disturbance of the magnitude of organic carbon inputs with acute (pulse) effects of physical disturbance by tillage and chemical disturbance due to inorganic fertilization and pesticide application. We show that because of the variety of ways it can be assessed, biodiversity—here based on microbial small subunit rRNA gene phylotypes—does not provide a consistent view of community change. In contrast, aggregated traits associated with soil microbiomes indicate general loss of function, measured as a reduction of average genome lengths, associated with chronic reduction of organic inputs in arable or bare fallow soils and altered growth strategies associated with rRNA operon copy number in prokaryotes, as well as a switch to pathogenicity in fungal communities. In addition, pulse disturbance by soil tillage is associated with an increased influence of stochastic processes upon prokaryote community assembly, but fungicide used in arable soils results in niche assembly of fungal communities compared to untilled grassland. Overall, bacteria, archaea, and fungi do not share a common response to land management change, and estimates of biodiversity do not capture important facets of community adaptation to stresses adequately.
Pseudomonas putida is a gram-negative bacterial species increasingly utilized in biotechnology due to its robust growth, ability to degrade aromatic compounds, solvent tolerance, and genetic tractability. In this study, we report a comprehensive multi-strain analysis of 164 P. putida strains based on the reconstruction of a pan-putida metabolic network and the formulation of strain-specific genome-scale metabolic models (GEMs). We performed whole-genome sequencing and hybrid assembly for 40 strains, contributing a ~8% increase to the available genomic data for P. putida . Furthermore, high-throughput phenotypic profiling using the Biolog phenotype microarray system for 24 strains on 190 unique carbon sources, along with 15 aromatic compounds not present on Biolog plates, yielded 4,920 unique strain-phenotype measurements. These data were leveraged to curate GEMs for 24 representative strains, including a refined model for strain KT2440, which comprised 1,480 genes and 2,191 metabolites, achieving a prediction accuracy of 91.2% in carbon utilization. Systematic comparison of genomes and GEMs revealed both conserved core pathways and significant allelic and functional divergence across strains, highlighting strain-specific variation in aromatic degradation. While pathways for protocatechuate and phenylacetate degradation were widely conserved, metabolic capabilities for compounds such as ferulate, phenol, and cresols varied markedly, suggesting adaptation to distinct ecological niches. Alleleome analysis of enzymes, such as PcaI and PcaJ, revealed distinct, functionally similar clades, indicating possible convergent evolution or horizontal gene transfer. These results provide computable resources and informative models for selecting P. putida strains with desired traits for biomanufacturing and bioremediation and offer insights into the evolution and phylogeny of the P. putida species.
The question of how new enzyme activities evolve is of great biological interest and, in the context of antibiotic resistance, of great medical importance. Here, we have tested the hypothesis that new antibiotic resistance mechanisms may evolve from promiscuous housekeeping enzymes that have antibiotic modification side activities.
Recent observations have shown the atmospheric greenhouse gas methane (CH 4 ) is consumed by microorganisms (methanotrophs) in caves at rates comparable to CH 4 oxidation in surface soils. Caves are abundant in karst landscapes that comprise 14% of Earth’s land surface area, and therefore may represent a potentially important, but overlooked, CH 4 sink.
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Trilinos is a community-developed, open-source software framework that facilitates building large-scale, complex, multiscale, multiphysics simulation code bases for scientific and engineering problems. Since the Trilinos framework has undergone substantial changes to support new applications and new hardware architectures, this document is an update to “An Overview of the Trilinos project” by Heroux et al. (ACM Transactions on Mathematical Software, 31(3):397–423, 2005). It describes the design of Trilinos, introduces its new organization in product areas, and highlights established and new features available in Trilinos. Particular focus is put on the modernized software stack based on the Kokkos ecosystem to deliver performance portability across heterogeneous hardware architectures. This article also outlines the organization of the Trilinos community and the contribution model to help onboard interested users and contributors.
The majority of future projections in the Coupled Model Intercomparison Project (CMIP5) show more frequent exceedances of the 5 mm day -1 rainfall threshold in the eastern equatorial Pacific rainfall during El Niño, previously described in the literature as an increase in “extreme El Niño events”; however, these exceedance frequencies vary widely across models, and in some projections actually decrease. Here we combine single-model large ensemble simulations with phase 5 of the Coupled Model Intercomparison Project (CMIP5) to diagnose the mechanisms for these differences. The sensitivity of precipitation to local SST anomalies increases consistently across CMIP-class models, tending to amplify extreme El Niño occurrence; however, changes to the magnitude of ENSO-related SST variability can drastically influence the results, indicating that understanding changes to SST variability remains imperative. Future El Niño rainfall intensifies most in models with 1) larger historical cold SST biases in the central equatorial Pacific, which inhibit future increases in local convective cloud shading, enabling more local warming; and 2) smaller historical warm SST biases in the far eastern equatorial Pacific, which enhance future reductions in stratus cloud, enabling more local warming. Additionally, these competing mechanisms complicate efforts to determine whether CMIP5 models under- or overestimate the future impacts of climate change on El Niño rainfall and its global impacts. However, the relation between future projections and historical biases suggests the possibility of using observable metrics as “emergent constraints” on future extreme El Niño, and a proof of concept using SSTA variance, precipitation sensitivity to SST, and regional SST trends is presented.
El Niño–Southern Oscillation (ENSO) influences seasonal Atlantic tropical cyclone (TC) activity by impacting environmental conditions important for TC genesis. However, the influence of future climate change on the teleconnection between ENSO and Atlantic TCs is uncertain, as climate change is expected to impact both ENSO and the mean climate state. We used the Weather Research and Forecasting Model on a tropical channel domain to simulate 5-member ensembles of Atlantic TC seasons in historical and future climates under different ENSO conditions. Experiments were forced with idealized sea surface temperature configurations based on the Community Earth System Model (CESM) Large Ensemble representing: a monthly varying climatology, eastern Pacific El Niño, central Pacific El Niño, and La Niña. The historical simulations produced fewer Atlantic TCs during eastern Pacific El Niño compared to central Pacific El Niño, consistent with observations and other modeling studies. For each ENSO state, the future simulations produced a similar teleconnection with Atlantic TCs as in the historical simulations. Specifically, La Niña continues to enhance Atlantic TC activity, and El Niño continues to suppress Atlantic TCs, with greater suppression during eastern Pacific El Niño compared to central Pacific El Niño. In addition, we found a decrease in the Atlantic TC frequency in the future relative to historical regardless of ENSO state, which was associated with a future increase in northern tropical Atlantic vertical wind shear and a future decrease in the zonal tropical Pacific sea surface temperature (SST) gradient, corresponding to a more El Niño–like mean climate state. Our results indicate that ENSO will remain useful for seasonal Atlantic TC prediction in the future.
MAGMA (Matrix Algebra for GPU and Multicore Architectures) is a pivotal open-source library in the landscape of GPU-enabled dense and sparse linear algebra computations. With a repertoire of approximately 750 numerical routines across four precisions, MAGMA is deeply ingrained in the DOE software stack, playing a crucial role in high-performance computing. Notable projects such as ExaConstit, HiOP, MARBL, and STRUMPACK, among others, directly harness the capabilities of MAGMA. In addition, the MAGMA development team has been acknowledged multiple times for contributing to the vendors’ numerical software stacks. Looking back over the time of the Exascale Computing Project (ECP), we highlight how MAGMA has adapted to recent changes in modern HPC systems, especially the growing gap between CPU and GPU compute capabilities, as well as the introduction of low precision arithmetic in modern GPUs. We also describe MAGMA’s direct impact on several ECP projects. Maintaining portable performance across NVIDIA and AMD GPUs, and with current efforts toward supporting Intel GPUs, MAGMA ensures its adaptability and relevance in the ever-evolving landscape of GPU architectures.
Seed oils are widely used in the food, biofuel, and industrial feedstock industries, with their utility and value determined by total oil content and fatty acid composition. Current high throughput seed oil analysis methods either lack accuracy in total fatty acid profiling or require extensive labor for lipid extraction prior to derivatization to fatty acid methyl esters (FAME) and quantification by gas chromatography (GC). Alternatively, direct whole seed FAME production methods have been developed for the very small seeds in the model species Arabidopsis thaliana but these have generally not been adapted to larger seeds of most oilseed crops. High-throughput direct whole seed FAME production methods were optimized for seeds up to 5 mg each utilizing acid-catalyzed esterification. For the oilseed species Camelina sativa, Thlaspi avernse (pennycress), Cuphea viscosissima, and Brassica napus (var. Canola), the total seed fatty acid content and composition from direct seed esterification to FAME matched that of lipid extract derivatization demonstrating the accuracy of the methods. In combination with seed phenotyping using GridFree, this approach enabled the development of a rapid pipeline for simultaneous seed weight, count, size/shape phenotyping, and oil analysis. For the larger and tougher seeds produced by Limnanthes alba (Meadowfoam) and Cannabis sativa L. (hemp) the whole seed acid-based method proved insufficient, and prior laborious homogenization of seeds was required. Therefore, a rapid one-tube bead homogenization and base catalyzed-esterification method was developed. Base-derived fatty acid esterification cannot derivatize free fatty acids leading to slightly lower total seed fatty acid than acid-catalyzed methods, however the seed oil content and fatty acid composition that is valuable for screening large numbers of samples in research populations was accurately measured. New rapid whole seed fatty acid esterification and phenotyping protocols were developed to accurately assess oilseed lipid content. These methods are particularly valuable in oilseed research, breeding, and engineering applications where efficient analysis of large numbers of samples and accurate oil fatty acid profiling is essential. While having been developed for current and emerging oilseed crops, these methods also provide a foundation from which protocols might be established for new and emerging crop species.
Bioenergy sorghum ( Sorghum bicolor L. Moench) is a promising crop for contributing to the United States bioenergy supply. However, the varying limitations of the marginal lands targeted for its cultivation present a management challenge. This two-year study aimed to investigate how the limitations associated with marginal cropland impact the effects of nitrogen fertilization on the yield of bioenergy sorghum and the uptake of 11 macro- (N, P, K, Ca, Mg, and S) and micronutrients (Fe, Mn, Zn, Cu, and B). The study contrasted prime cropland in central Illinois (Urbana) with three marginal cropland sites in southern (Ewing) and central Illinois (Fairbury and Pesotum). These marginal cropland sites are characterized by varying limitations, including low soil fertility (P and K limitations), leaching and erosion, and flooding, respectively. Four nitrogen rates (0, 56, 112, and 168 kg N ha−1) were tested under eight environments. The average yields and ranges of sorghum biomass were 20.2 (17.0–23.2) Mg ha−1 in Urbana, 18.1 (13.1–19.8) Mg ha−1 in Ewing, 13.8 (9.0–17.3) Mg ha−1 in Fairbury, and 23.3 (14.6–33.0) Mg ha−1 in Pesotum. Optimal N rates were 56 N in Pesotum and 112 N in Urbana, Ewing, and Fairbury. Tissue macronutrient contents in Urbana were generally higher than in the marginal croplands, while micronutrient contents did not show discernible trends. Increasing N rate generally correlated with the macronutrient removal except in Ewing. Comparable sorghum biomass yields were observed between prime and marginal croplands (averaging 18.3 Mg ha−1), but optimal N rates varied between 56 N and 112 N. This suggests that yield gaps can be narrowed by applying the optimal N rates for the respective locations. However, increased removals of macronutrients, especially P and K, with increasing yields indicate the need to revise fertilizer recommendations, particularly for soils deficient in these nutrients. Our study suggests that while sorghum production on marginal cropland is feasible, N management needs to be adapted to the unique limitations associated with various types of marginal cropland.
Rubisco activase is an ATP-dependent chaperone that facilitates dissociation of inhibitory sugar phosphates from the catalytic sites of Rubisco during photosynthesis. In Arabidopsis, Rubisco activase is negatively regulated by dark-dependent phosphorylation of Thr78. The prevalence of Thr78 in Rubisco activase was investigated across sequences from 91 plant species, finding that 29 (∼32%) species shared a threonine in the same position. Analysis of seven C3 species with an antibody raised against a Thr78 phospho-peptide demonstrated that this position is phosphorylated in multiple genera. However, light-dependent dephosphorylation of Thr78 was observed only in Arabidopsis. Further, phosphorylation of Thr78 could not be detected in any of the four C4 grass species examined. The results suggest that despite conservation of Thr78 in Rubisco activase from a wide range of species, a regulatory role for phosphorylation at this site is more limited. This provides a case study for how variation in post-translational regulation can amplify functional divergence across the phylogeny of plants beyond what is explained by sequence variation in a metabolically important protein.
Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology.