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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Interrelationships among methods of estimating microbial biomass across multiple soil orders and biomes

Understanding the role of soil microbes is critical to ecosystem processes, and more thorough comparisons of measurement proxies for soil microbial biomass could broaden the inclusion of explicit microbial parameterization in soil carbon cycling and earth system models. We measured physical, chemical, and biological data from eight soil orders representing 11 major biomes and four climate regions. Four prominent methods to measure microbial abundance—chloroform fumigation extraction (CFE), total DNA yield, gene copy number by quantitative polymerase chain reaction (GCN), and phospholipid fatty acids (PLFA)—were compared to assess their relationships with each other and with soil characteristics. Correlations were observed when comparing methods, with CFE correlating strongly with total DNA yield, GCN, and PLFA; CFE with bacterial GCN and bacterial PLFA; and to a lesser extent, total PLFA and total DNA yield. Correlations improved with the removal of organic soils (Histosols, Gelisols). Comparisons involving extracted DNA were improved by correcting for clay content, due to DNA extraction inefficiencies in clay-rich soils. Correlations involving fungi (PLFA or GCN) were always less significant. These methods could serve as reliable, inter-relatable proxies for the estimation of total soil microbial biomass while recognizing that the proxies are less effective at parsing differences between bacteria and fungi. Here, we provide specific equations to relate measures of soil microbial biomass by these four different methods to enable microbial models to utilize a greater diversity of observed data sources in parameterizations and simulations. Caveats for the equations and their values are also discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Structure-Based Analysis of Semisynthetic Anti-TB Rufomycin Analogues

This study employed structural information from cocrystals of rufomycin 4 (1a) and caseinolytic protein C1 (ClpC1)-NTD-wt to guide design and semisynthesis of rufomycin analogues, evaluate their antituberculosis (TB) biological profiles, and establish structure–activity relationships (SAR). Covering three regions of interest (ROIs, A–C) as modification sites, 14 of the 30 semisynthetic analogues (2–31) showed similar or improved MICs relative to the main natural precursors, rufomycins 4/6 (1a/b). Compounds 5 and 27 exhibited up to 10-fold enhanced potency against Mycobacterium tuberculosis (Mtb) in vitro, with MIC values of 1.9 and 1.4 nM, respectively. Evaluation of ClpC1-binding properties used existing ClpC1-NTD complexes with rufomycin 4 (PDB: 6cn8) and ecumicin (PDB: 6pbs) as references. The newly reported X-ray ClpC1-NTD cocrystal structure of 11 (syn. But4-Cl) revealed significant conformational effects involving the side chains of certain amino acids of the heptapeptide and confirmed the importance of ROIs A–C for medicinal chemistry efforts. Observed interactions of the N-terminal tail of ClpC1 with the rufomycin analogues vs ecumicin explains their different modes of inactivating the ClpC1/P1/P2 homeostatic machinery. Collectively, the observations inform further SAR optimization strategies for the rufomycin class of antibiotics and complement our understanding of their mode of action.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Probing the optical properties and toxicological profile of zinc tungstate nanorods

Zinc tungstate is a semiconductor known for its favorable photocatalytic, photoluminescence, and scintillation properties, coupled with its relatively low cost, reduced toxicity, and high stability in biological and catalytic environments. In particular, zinc tungstate evinces scintillation properties, namely the ability to emit visible light upon absorption of energetic radiation such as x rays, which has led to applications not only as radiation detectors but also for biomedical applications involving the delivery of optical light to deep tissue, such as photodynamic therapy and optogenetics. Here, we report on the synthesis of zinc tungstate nanorods generated via an optimized but facile method, which allows for synthetic control over the aspect ratio of the as-synthesized anisotropic motifs via rational variation of the solution pH. Additionally, we investigate the effect of aspect ratio on their resulting photoluminescent and radioluminescent properties. We further demonstrate the potential of these zinc tungstate nanorods for biomedical applications, such as photodynamic therapy for cancer treatment, by analyzing their toxicological profile within cell lines and neurons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Engineered Membrane Vesicle Production via oprF or oprI Deletion Has Distinct Phenotypic Effects in Pseudomonas putida

Membrane vesicle (MV) production is a natural phenomenon in Gram-negative bacteria and represents an emerging synthetic biology tool for the secretion of biomolecules or bioproducts. Manipulation of membrane components has proven successful in enhancing MV production. However, the impact of membrane disruptions on strain fitness and protein composition warrants further investigation for the use of MVs in industrial bioprocesses. Here, we identify and characterize two genetic engineering strategies for inducing hypervesiculation-deletion of genes for the outer membrane porin OprF or the lipoprotein OprI-in the commonly used platform Pseudomonas putida KT2440. Deletion of oprI generated up to a 1.5-fold increase in MVs, larger MVs with a greater proportion of outer membrane proteins, and no significant impact on strain fitness compared to wild type. In contrast, deletion of oprF, relative to wild type, generated up to a 4-fold increase in MVs but diminished growth, permeabilized membranes, and increased cytosolic protein packaging. Both hypervesiculation phenotypes increased nontargeted and MV-targeted mNeonGreen extracellular signal by up to 6-fold, demonstrating vesiculation as a mechanism for protein secretion. Despite increased blebbing of MVs from gene deletions, proteins involved in membrane biosynthesis were not elevated relative to wild type. Overexpression of gpsA, which initiates glycerophospholipid biosynthesis, in the ΔoprF background improved the membrane integrity by 37% and maintained MV formation, highlighting the importance of membrane biosynthesis in restoring the membrane in hypervesiculating strains. Together, this study provides genetic engineering strategies with corresponding phenotypic outcomes toward providing a synthetic biology toolset for MV deployment in P. putida.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

An Integral Activity-Based Protein Profiling Method for Higher Throughput Determination of Protein Target Sensitivity to Small Molecules

Activity-based protein profiling (ABPP) is a chemoproteomic technique that uses small molecule probes to label active enzymes selectively and covalently in complex proteomes. Competitive ABPP, which involves treatment of the active proteome with an analyte of interest, is especially powerful for profiling how small molecules impact specific protein activities. Advances in higher throughput workflows have made it possible to generate extensive competitive ABPP data across diverse biological samples, making this approach highly appealing for characterizing shared and unique proteins affected by perturbations such as drug or chemical exposures. To use the competitive ABPP approach effectively to understand potential adverse effects of chemicals of concern (CoC), a wide range of concentrations may be needed, particularly for chemicals that lack potency or toxicity data. In this work, we present an integral competitive ABPP method that enables target sensitivity determination for different organophosphate (OP) pesticides as model toxicants. Using previously developed OP-ABPs, we optimized conditions for tandem mass tag (TMT) multiplexing of ABPP samples and compared conventional competitive ABPP involving samples at discrete paraoxon concentrations to pooled samples across that same concentration range. We then expanded our approach to compare protein target sensitivities toward two additional OP pesticides, chlorpyrifos oxon and malaoxon. The results showed that differences in integral intensities for the pooled competition sample can be used to evaluate the relative sensitivity of specific proteins without increasing the overall number of samples. For 8 CoC concentrations of interest, this strategy reduced the number of TMT plexes and the corresponding number of LC–MS/MS analyses 3-fold. In conclusion, we envision the integral ABPP (IABPP) method will provide a means to screen diverse chemicals more rapidly to identify both high and low sensitivity protein targets.

activity-based probes↗
DOE OSTI2025

Pine‐fungal co‐invasion alters whole‐ecosystem properties of a native eucalypt forest

Summary Pine‐fungal co‐invasions into native ecosystems are increasingly prevalent across the southern hemisphere. In Australia, invasive pines slowly spread into native eucalypt forests, creating novel mixed forests. We sought to understand how pine‐fungal co‐invasions impact interconnected above‐ and belowground ecosystem characteristics. We sampled beneath maturePinus radiataandEucalyptus racemosain a pine‐invaded eucalypt forest in New South Wales, Australia. We measured microbial community composition via amplicon sequencing of 16S, ITS2, and 18S rDNA regions, microbial metabolic activity via Biolog plate substrate utilization, and soil, leaf litter, and understory plant characteristics. Pines were associated with decreased topsoil moisture, increased pine litter, and decreased eucalypt litter total phosphorus content. Soils and roots beneath pines had distinct microbial community composition and activity relative to eucalypts, including decreased bacterial diversity, decreased microbial utilization of several C‐ and N‐rich substrates, and enrichment of pine‐associated ectomycorrhizae. Introduced suilloid fungi were abundant across both pine and eucalypt soils and roots. Many ecosystem impacts increased with pine size. Invasive pines and their ectomycorrhizae have significant impacts on eucalypt forest properties as they grow. Interconnected impacts at the scale of individual trees should be considered when managing invaded forests and predicting effects of pine invasions.

Plant Sciences↗
DOE OSTI2024

Quantum-enhanced photoprotection in neuroprotein architectures emerges from collective light-matter interactions

Background Superradiance is the phenomenon of many identical quantum systems absorbing and/or emitting photons collectively at a higher rate than any one system can individually. This phenomenon has been studied analytically in idealized distributions of electronic two-level systems (TLSs), each with a ground and excited state, as well as numerically in realistic photosynthetic nanotubes and cytoskeletal architectures. Methods Superradiant effects are studied here in idealized toy model systems and realistic biological mega-networks of tryptophan (Trp) molecules, which are strongly fluorescent amino acids found in many proteins. Each Trp molecule acts as a chromophore absorbing in the ultraviolet spectrum and can be treated approximately as a TLS, with its 1 L a excited singlet state; thus, organized Trp networks can exhibit superradiance. Such networks are found, for example, in microtubules, actin filaments, and amyloid fibrils. Microtubules and actin filaments are spiral-cylindrical protein polymers that play significant biological roles as primary constituents of the eukaryotic cytoskeleton, while amyloid fibrils have been targeted in a variety of neurodegenerative diseases. We treat these proteinaceous Trp networks as open quantum systems, using a non-Hermitian Hamiltonian to describe interactions of the chromophore network with the electromagnetic field. We numerically diagonalize the Hamiltonian to obtain its complex eigenvalues, where the real part is the energy and the imaginary part is its associated enhancement rate. We also consider multiple realizations of increasing static disorder in either the site energies or the decay rates. Results We obtained the energies and enhancement rates for realistic microtubules, actin filament bundles, and amyloid fibrils of differing lengths, and we use these values to calculate the quantum yield, which is the ratio of the number of photons emitted to the number of photons absorbed. We find that all three of these structures exhibit highly superradiant states near the low-energy portion of the spectrum, which enhances the magnitude and robustness of the quantum yield to static disorder and thermal noise. Conclusion The high quantum yield and stable superradiant states in these biological architectures may play a photoprotective rolein vivo, downconverting energetic ultraviolet photons—absorbed from those emitted by reactive free radical species—to longer, safer wavelengths and thereby mitigating biochemical stress and photophysical damage. Contrary to conventional assumptions that quantum effects cannot survive in large biosystems at high temperatures, our results suggest that macropolymeric collectives of TLSs in microtubules, actin filaments, and amyloid fibrils exhibit increasingly observable and robust effects with increasing length, up to the micron scale, due to quantum coherent interactions in the single-photon limit. Superradiant enhancement and high quantum yield exhibited in neuroprotein polymers could thus play a crucial role in information processing in the brain, the development of neurodegenerative diseases such as Alzheimer’s and related dementias, and a wide array of other pathologies characterized by anomalous protein aggregates.

Physics↗
DOE OSTI2026

Causes and consequences of experimental variation in Nicotiana benthamiana transient expression

Infiltration of Agrobacterium tumefaciens into Nicotiana benthamiana has become a foundational technique in plant biology, enabling efficient delivery of transgenes in planta with technical ease, robust signal, and relatively high throughput. Despite transient expression’s prevalence in disciplines such as synthetic biology, little work has been done to describe and address the variability inherent in this system, a concern for experiments that rely on highly quantitative readouts. In a comprehensive analysis of N. benthamiana agroinfiltration experiments, we model sources of variability that affect transient expression. Our findings emphasize the need to validate normalization methods under the specific conditions of each study, as distinct normalization schemes do not always reduce variation either within or between experiments. Using a dataset of 1915 plants collected over three years, we develop a model of variation in N. benthamiana transient expression, using power analysis to determine the number of individual plants required for a given effect size. Drawing on our longitudinal data, these findings inform practical guidelines for minimizing variability through strategic experimental design and power analysis, providing a foundation for more robust and reproducible use of N. benthamiana in quantitative plant biology and synthetic biology applications.

Tang, Sophia N. [Joint BioEnergy Institute (JBEI),↗
DOE OSTI2025

Data for "Evaluating the industrial potential of emerging biomass pretreatment technologies in bioethanol production and lipid recovery from transgenic sugarcane"

The selection of pretreatment methods is critical to achieving high product yields during bioconversion of lignocellulosic biomass. Hydrothermal, soaking-in-aqueous ammonia, and ionic liquid pretreatment methods are viable candidates for minimizing sugar decomposition, permitting the effective hydrolysis of structural carbohydrates, and producing a fermentable substrate suitable for achieving industrial ethanol titers and yields. In this study, the effect of these three pretreatment methods on non-modified sugarcane cultivar CP88-1762 and two transgenic lipid-accumulating sugarcane lines, oilcane 1565 and oilcane 1566, were investigated and compared in terms of lipid recovery, sugar yield, and ethanol yields within the lignocellulosic biomass conversion pipeline. Fed-batch enzymatic hydrolysis at high solid loading yielded hydrolysates capable of supporting industrial bioethanol titers across all conditions. The highest sugar yields were obtained on ammonia-pretreated biomass hydrolysate (253.73 g L−1), followed by hydrothermally pretreated hydrolysate (213.10 g L−1) and ionic liquid-pretreated hydrolysate (154.20 g L−1). Commercially viable ethanol titers of 100.62 g L−1, 64.47 g L−1, and 52.95 g L−1 were achieved from ammonia, hydrothermal, and ionic liquid pretreated hydrolysate with the corresponding ethanol productivities of 2.08 g L−1 h−1, 0.53 g L−1 h−1, and 0.36 g L−1 h−1. The lower acetic acid concentration in ammonia-pretreated hydrolysate may have enhanced its fermentability relative to the hydrothermal pretreatment condition, as indicated by the differences in ethanol titer and productivity. Lower sugar yields and ethanol productivities under the ionic liquid conditions likely resulted from the inhibitory effect of cholinium lysinate. Oilcane 1565 and oilcane 1566 bagasse accumulated over 16- and 3 times higher lipids than the non-modified sugarcane CP88-1762. The total fatty acid content in the oilcane samples was reduced in ammonia and ionic liquid-pretreated bagasse relative to the hydrothermal pretreatment condition. While all pretreatment techniques tested are industrially viable, the observed differences in titer, productivity, and lipid content indicate that careful selection and validation of upstream processing methods can contribute to improved economic and environmental outcomes.

biomass analytics↗
DOE OSTI2026

Artificial intelligence tools for enzyme engineering and metabolic engineering

Enzyme engineering and metabolic engineering drive innovation in energy biotechnology. In recent years, artificial intelligence (AI) has supported successful applications in designing effective enzymes and productive microbial cell factories. This review summarizes recent advances in enzyme redesign using protein language models, de novo enzyme design with generative models, and AI tools for engineering metabolism and related cellular phenotypes. Across these areas, AI models are shifting from single modality inputs to integrated representations of protein function, metabolic pathways, and cell states. We emphasize that unifying the diverse data representations across scales will be necessary for advancements in energy biotechnology.

Volk, Michael [Univ. of Illinois at Urbana-Champai↗
DOE OSTI2025

Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets

Introduction Data normalization is crucial for multi-omics integration, reducing systematic errors and maximizing the likelihood of discovering true biological variation. Most studies assess normalization for a single omics type or use datasets from separate experiments. Few address time-course data, where normalization might bias temporal differentiation. In this study, we compared common normalization methods and a machine learning approach, Systematical Error Removal using Random Forest (SERRF), using multi-omics datasets generated from the same experiment—even from the same cell lysate. Objectives To develop a straightforward process to assess normalization effects and identify the most robust methods across multi-omics datasets. Methods We analyzed metabolomics, lipidomics, and proteomics datasets from primary human cardiomyocytes and motor neurons exposed to acetylcholine-active compounds over time. Normalization effectiveness was evaluated based on improvement in QC features consistency and observing the change in treatment and time-related variance. Results Probabilistic Quotient Normalization (PQN) and Locally Estimated Scatterplot Smoothing (LOESS) QC were identified as optimal for metabolomics and lipidomics, while PQN, Median, and LOESS normalization excelled for proteomics. These methods consistently enhanced QC feature consistency in metabolomics and lipidomics, and preserved time-related variance or treatment-related variance in proteomics, demonstrating their effectiveness and robustness. SERRF normalization, applied only to metabolomics in this study, outperformed other methods in some datasets but inadvertently masked treatment-related variance in others. Conclusion Our evaluation identified PQN and LoessQC as the top methods for metabolomics and lipidomics, and PQN, Median, and Loess normalization for proteomics, in multi-omics integration in a temporal study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Genetic variations and their interaction with thirdhand smoke exposure on anxiety and memory in Collaborative Cross mice

Thirdhand smoke (THS) is linked to adverse health effects, but the effect of genetic variations on behavioral outcomes is poorly understood. To investigate this, we assessed anxiety- and memory-related behaviors in 820 mice from 21 strains of the genetically diverse Collaborative Cross (CC) mouse that were exposed to THS from 4 through 10 weeks of age. Anxiety was evaluated with a light/dark box assay with a previously established risk score system. Females were generally more sensitive: THS reduced anxiety risk in strains CC013, CC019, and CC051, but increased risk in CC036 and CC061, while males showed no significant effects. Memory was tested using passive avoidance: impairments were observed in both sexes in CC016 and CC019, with sex-dependent effects in CC002 and CC051. A genome-wide association study identified 2,347 SNPs associated with anxiety and 1,568 SNPs with memory, with 32 and 85 SNPs, respectively, interacting with THS exposure. Enrichment analyses revealed distinct biological processes underlying susceptibility, including axonogenesis, synapse organization, cognition, and learning and memory. KEGG pathway analysis identified distinct genetic pathways, including GTPase binding and GTPase regulatory activity, that act as critical molecular switches in the brain that regulate synaptic plasticity, dendritic spine structure, and neuronal signaling, directly influencing anxiety-like behaviors and memory formation. These findings show that THS exposure affects neurobehavioral outcomes in a sex- and genotype-dependent manner, highlighting critical gene-environment interactions and providing a foundation for mechanistic insights into THS neurotoxicity

Anxiety↗
DOE OSTI2025

Recent evolution of risk analyses in atomic bomb survivor studies: new methods and applications

Abstract Several decades ago a dramatic leap forward occurred in the development and application of statistical methods for modeling radiation risk at the Radiation Effects Research Foundation (RERF). Poisson regression analysis for grouped person-year cohort data and the linear excess relative risk model were introduced, and subsequently a devoted software system, Epicure® (https://www.hirosoft.com), was developed by researchers at RERF and at the U.S. National Cancer Institute. Numerous advancements in understanding radiation effects on humans were made possible with these methods, which are still the state-of-the-art for risk assessment at RERF and have remained part of the standard toolbox for radiation—and other environmental—epidemiological studies worldwide. Nevertheless, as our understanding of radiation risk has increased, so have the breadth and depth of questions that require answers based on emerging data that are not amenable to these conventional methods. This overview briefly recounts the conventional methods and then describes our recent diversification into the use or development of new statistical approaches to meet the challenges of burgeoning biological data and emerging mechanistic information. We briefly discuss the development and application of new methods, current and planned, that are part of the RERF Statistics Department’s role in supporting institution-wide research, especially in our collaborations involving the Life Span Study, Adult Health Study, and First-generation Offspring Clinical Study. Some approaches to modeling and assessing radiation risk with newer methods mentioned herein have already been published, while some are still in development or are only beginning at the proposal stage.

Oncology↗
DOE OSTI2026

An Activity-Based Sensing Approach to Monitor Nanomaterial-Promoted Changes in Labile Metal Pools in Living Systems

Metal-based nanoparticles are a promising class of materials for diagnosis and treatment of cancer and other diseases. However, mechanisms of action of these nanomedicines remain insufficiently understood due in large part to our limited understanding of the dynamic equilibria between solid metal nanoparticles and labile metal ions generated from these nanoparticles within complex biological milieus. Here, we apply activitybased sensing to directly identify and investigate the fate of labile copper pools with metal and oxidation state-specificity generated by anticancer copper nanomedicines. We found that treatment of cells with copper-releasing nanoparticles alter labile Cu(I)/Cu(II) ratios through an increase in labile Cu(II), while overall labile copper levels decrease. Labile copper release triggers compensatory responses in two major antioxidant pathways, glutathione (GSH) and nuclear factor erythroid 2-related factor 2 (NRF2), as well as in metal homeostasis to limit copper availability via regulation of copper export (ATP7B) and copper import (CTR1) proteins. These findings establish the value of activity-based sensing as a generalizable approach for labile metal imaging to help decipher molecular mechanisms of bioactive metal nanoparticles and guide the development of more effective nanomedicine diagnostics and therapies to target metal-dependent disease vulnerabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A high throughput assay to detect enzymatic polyethylene oxidation

Biological plastics deconstruction and upcycling have emerged as sustainable alternatives to traditional recycling technologies for plastics waste. The discovery and engineering of efficient thermostable poly(ethylene terephthalate) (PET) hydrolases have made biological PET recycling possible at scale; however, enzymes for non-PET plastics, which account for approximately 70% of all plastics produced, remain largely undiscovered. To accelerate the discovery of such enzymes, we develop a high-throughput screen to detect initial polymer oxidation, specifically that of the C-H bond to an aldehyde. We test 4-hydrazino-7-nitro-2,1,3-benxoxadiozole hydrazine (NBD-H), which reacts with generated aldehydes to form a fluorescent hydrazone on plasma oxidized low-density polyethylene (LDPE) films. Hydrazone generation correlated well with the area of aldehyde peaks as measured by Fourier Transform Infrared Spectroscopy (FTIR) (R2 = 0.92). Moreover, we demonstrate that the probe reliably identifies LDPE-active dye decolorizing peroxidases (DyPs) that generate aldehydes on LDPE films (1.7 – 3.0 fold change relative to background), serving as an effective screen as demonstrated by receiver operating characteristic area under the curve of 0.95. Furthermore, this assay offers an LDPE oxidation screening platform that can be readily parallelized and automated for accelerated discovery of enzymes involved in polyolefin deconstruction.

biocatalysis↗
DOE OSTI2025

Experimental electronic structures of the Fe IV =O bond in S=1 heme vs. nonheme sites: Effect of the porphyrin ligand

High-valent Fe IV =O species are common intermediates in biological and artificial catalysts. Heme and nonheme S=1 Fe IV =O sites have been synthesized and studied for decades but little quantitative experimental comparison of their electronic structures has been available, due to the lack of direct methods focused on the iron. This study allows a rigorous determination of the electronic structure of a nonheme Fe IV =O center and its comparison to an Fe IV =O heme site using 1s2p resonant inelastic X-ray scattering (RIXS) and Fe L-edge X-ray absorption spectroscopy (XAS). Further, variable temperature magnetic circular dichroism (VT-MCD) of the ligand field transitions, combined with nuclear resonance vibrational spectroscopy of the two S=1 Fe IV =O systems show that the equatorial ligand field decreases from a nonheme to a heme Fe IV =O site. Alternatively, RIXS and Fe L-edge XAS combined with MCD show that the Fe dπ orbitals are unperturbed in the Fe IV =O heme relative to the nonheme site because the strong axial Fe-O bond uncouples the Fe dπ orbitals from the porphyrin π-system. As a consequence, the thermodynamics and kinetics of the H-atom abstraction reactions are actually very similar for heme compound II and nonheme Fe IV =O active sites.

bioinorganic chemistry↗
DOE OSTI2025

Three‐trophic level food webs support the safety of a biocontrol agent 3 years after release

Biological control (biocontrol) is a powerful tool for managing invasive alien species and assisting the restoration of native ecosystems. Rigorous post‐release monitoring of biocontrol agents is critical to evaluate the success of biocontrol programs; however, this is still rarely implemented. Here, we combined the use of species interaction networks with a Before‐After Control‐Impact design to evaluate the target and non‐target, direct and indirect effects of the Australian gall wasp Trichilogaster acaciaelongifoliae , released to control the invasive plant Acacia longifolia in Portugal. We compared the structure of plant‐galling insect‐parasitoid food webs before and 3 years after the release of the biocontrol agent. Exhaustive sampling did not detect any non‐target effects, either direct (on non‐target plants) or indirect (on other galling insects via shared plants). Additionally, no significant changes were detected in network structure that could be related to the establishment of the biocontrol agent. This study shows that monitoring biocontrol at the community level is possible and that, when carefully planned, biocontrol poses minimal risk of non‐target effects.

López‐Núñez, Francisco A. [Centre for Functional E↗
DOE OSTI2025

Characterization of the procoagulant phenotype of amniotic fluid across gestation in rhesus macaques and humans

Background: Amniotic fluid (AF) plays a key role in fetal development, yet the evolving composition of AF and its effects of hemostasis and thrombosis are poorly understood. Objectives: Here, we aim to determine how the evolving molecular composition of AF relates to its procoagulant properties. Methods: We analyzed the proteomes, lipidomes and procoagulant properties of AF obtained by amniocentesis from rhesus macaque and human pregnancies at gestational-age matched timepoints. Results When added to human plasma, both rhesus and human AF accelerated clotting time and fibrin generation. We identified proteomic modules associated with clotting time and enriched for coagulation-related pathways. Proteins known to be involved in hemostasis were highly correlated with each other and their intensity of expression varied across gestation in both rhesus and humans. Inhibition of contact pathway did not affect the procoagulant effect of AF. Blocking tissue factor pathway inhibitor reversed the ability of AF to block the generation of FXa. The prothrombinase activity of AF was inhibited by phospholipid inhibitors. The levels of phosphatidylserine in AF were inversely correlated with clotting time. AF promoted platelet activation and secretion in plasma. Conclusions: The addition of AF to plasma enhances coagulation in a manner dependent on phospholipids as well as the presence of proteases and other proteins that directly regulate coagulation. We describe a correlation between clotting time and expression of coagulation proteins and phosphatidylserine in both rhesus and human AF, supporting the use rhesus models for future studies of AF biology.

amniotic fluid↗