Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “biochemicals”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An efficient cre‐based workflow for genomic integration and expression of large biosynthetic pathways in Eubacterium limosum

Abstract Acetogenic Clostridia are obligate anaerobes that have emerged as promising microbes for the renewable production of biochemicals owing to their ability to efficiently metabolize sustainable single‐carbon feedstocks. Additionally, Clostridia are increasingly recognized for their biosynthetic potential, with recent discoveries of diverse secondary metabolites ranging from antibiotics to pigments to modulators of the human gut microbiota. Lack of efficient methods for genomic integration and expression of large heterologous DNA constructs remains a major challenge in studying biosynthesis in Clostridia and using them for metabolic engineering applications. To overcome this problem, we harnessed chassis‐independent recombinase‐assisted genome engineering (CRAGE) to develop a workflow for facile integration of large gene clusters (>10 kb) into the human gut acetogen Eubacterium limosum . We then integrated a non‐ribosomal peptide synthetase gene cluster from the gut anaerobe Clostridium leptum , which previously produced no detectable product in traditional heterologous hosts. Chromosomal expression in E. limosum without further optimization led to production of phevalin at 2.4 mg/L. These results further expand the molecular toolkit for a highly tractable member of the Clostridia, paving the way for sophisticated pathway engineering efforts, and highlighting the potential of E. limosum as a Clostridial chassis for exploration of anaerobic natural product biosynthesis.

Sanford, Patrick A.↗
DOE OSTI2021

Students authoring molecular case studies as a partial course-based undergraduate research experience (CURE) for lab instruction

Understanding the relationship between protein structure and function is a core-learning goal in biochemistry. Students often struggle to visualize proteins as three-dimensional objects that interact with other molecules to affect its biochemical consequences. We describe here a partial course-based undergraduate research experiences that has students exploring protein structure and function hands-on while authoring a molecular case study intended for others to use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Integrative teaching of metabolic modeling and flux analysis with interactive python modules

Abstract The modeling of rates of biochemical reactions—fluxes—in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint‐based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to‐date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C‐metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands‐on computer laboratory component of a four‐day metabolic modeling workshop and participant survey results showed improvements in learners' self‐assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.

Kaste, Joshua A. M.↗
DOE OSTI2023

Model validation and selection in metabolic flux analysis and flux balance analysis

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ 2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Characterization of a Mo-Nitrogenase Variant Containing a Citrate-Substituted Cofactor

Nitrogenase converts N 2 to NH 3 , and CO to hydrocarbons, at its cofactor site. In this work, we report a biochemical and spectroscopic characterization of a Mo-nitrogenase variant expressed in an Azotobacter vinelandii strain containing a deletion of nifV, the gene encoding the homocitrate synthase. Designated NifDK Cit , the catalytic component of this Mo-nitrogenase variant contains a citrate-substituted cofactor analogue. Activity analysis of NifDK Cit reveals a shift of CO reduction from H 2 evolution toward hydrocarbon formation and an opposite shift of N 2 reduction from NH 3 formation toward H 2 evolution. Consistent with a shift in the Mo K-edge energy of NifDK Cit relative to that of its wild-type counterpart, EPR analysis demonstrates a broadening of the line-shape and a decrease in the intensity of the cofactor-originated S=3/2 signal, suggesting a change in the spin properties of the cofactor upon citrate substitution. These observations point to a crucial role of homocitrate in substrate reduction by nitrogenase and the possibility to tune product profiles of nitrogenase reactions via organic ligand substitution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Chemo-Enzymatic Synthesis of Long-Chain Oligosaccharides for Studying Xylan-Modifying Enzymes

Plant research is hampered in several aspects by a lack of pure oligosaccharide samples that closely represent structural features of cell wall glycans. An alternative to purely chemical synthesis to access these oligosaccharides is chemo-enzymatic synthesis using glycosynthases. These enzymes enable the ligation of oligosaccharide donors, when activated for example as α-glycosyl fluorides, with suitable acceptor oligosaccharides. Herein, the synthesis of xylan oligosaccharides up to dodecasaccharides is reported, with glycosynthase-mediated coupling reactions as key steps. The xylo-oligosaccharide donors were protected at the non-reducing end with a 4-O-tetrahydropyranyl (THP) group to prevent polymerization. Installation of an unnatural 3-O-methylether substituent at the reducing end xylose of the oligosaccharides ensured good water solubility. Biochemical assays demonstrated enzymatic activity for the xylan acetyltransferase XOAT1 from Arabidopsis thaliana, xylan arabinofuranosyl-transferase XAT3 enzymes from rice and switchgrass, and the xylan glucuronosyltransferase GUX3 from Arabidopsis thaliana. In case of the glucuronosyltransferase GUX3, MALDI-MS/MS analysis of the reaction product suggested that a single glucuronosyl substituent was installed primarily at the central xylose residues of the dodecasaccharide acceptor, demonstrating the value of long-chain acceptors for assaying biosynthetic glycosyltransferases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Recent Drug Development and Medicinal Chemistry Approaches for the Treatment of SARS‐CoV‐2 Infection and COVID‐19

Abstract COVID‐19, caused by SARS‐CoV‐2 infection, continues to be a major public health crisis around the globe. Development of vaccines and the first cluster of antiviral drugs has brought promise and hope for prevention and treatment of severe coronavirus disease. However, continued development of newer, safer, and more effective antiviral drugs are critically important to combat COVID‐19 and counter the looming pathogenic variants. Studies of the coronavirus life cycle revealed several important biochemical targets for drug development. In the present review, we focus on recent drug design and medicinal chemistry efforts in small molecule drug discovery, including the development of nirmatrelvir that targets viral protein synthesis and remdesivir and molnupiravir that target viral RdRp. These are recent FDA approved drugs for the treatment of COVID‐19.

Pharmacology & Pharmacy↗
DOE OSTI2021

Advances in versatile nanoscale catalyst for the reductive catalytic fractionation of lignin

In the past five years, biomass derived-biofuel and biochemicals were widely studied both in academia and industry as a promising alternative to petroleum. In this review article, the latest progress of the synthesis and fabrication of porous nano-catalysts that are used in catalytic transformations involving hydrogenolysis of lignin is reviewed in terms of their textural property, catalytic activity, and stability. A particular emphasis is dedicated to the catalyst design for the hydrogenolysis of lignin and/or lignin model compounds. Furthermore, the effects of different supports on the lignin hydrogenolysis/hydrogenation are discussed in detail. Finally, the challenges and future opportunities of lignin hydrogenolysis over nanomaterials supported catalysts are also presented.

09 BIOMASS FUELS↗
DOE OSTI2020

Does the leaf economic spectrum hold within plant functional types? A Bayesian multivariate trait meta-analysis

The leaf economic spectrum is a widely-studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale, where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of PFTs. We investigated whether global leaf economic relationships are also present within the scale of plant functional types (PFTs) commonly used by Earth System models, and the extent to which this global-PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFTs and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (SLA, N, P) were the most robust within and across PFTs, suggesting that covariation in these traits is driven by universal leaf construction trade-offs and stoichiometry. Relationships among metabolic traits (R_d, V_{c,max}, J_max) were slightly less consistent, reflecting in part their much sparser sampling (especially for high-latitude PFTs), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across-PFT differences and that within-PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, these covariance were an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships.

biodiversity, ecological modeling, ecology, leaf b↗
DOE OSTI2021

Finding Liebig’s law of the minimum

Abstract Liebig’s law of the minimum (LLM) is often used to interpret empirical biological growth data and model multiple substrates co‐limited growth. However, its mechanistic foundation is rarely discussed, even though its validity has been questioned since its introduction in the 1820s. Here we first show that LLM is a crude approximation of the law of mass action, the state of art theory of biochemical reactions, and the LLM model is less accurate than two other approximations of the law of mass action: the synthesizing unit model and the additive model. We corroborate this conclusion using empirical data sets of algae and plants grown under two co‐limiting substrates. Based on our analysis, we show that when growth is modeled directly as a function of substrate uptake, the LLM model improperly restricts the organism to be of fixed elemental stoichiometry, making it incapable of consistently resolving biological adaptation, ecological evolution, and community assembly. When growth is modeled as a function of the cellular nutrient quota, the LLM model may obtain good results at the risk of incorrect model parameters as compared to those inferred from the more accurate synthesizing unit model. However, biogeochemical models that implement these three formulations are needed to evaluate which formulation is acceptably accurate and their impacts on predicted long‐term ecosystem dynamics. In particular, studies are needed that explore the extent to which parameter calibration can rescue model performance when the mechanistic representation of a biogeochemical process is known to be deficient.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

High foliar K and P resorption efficiencies in old-growth tropical forests growing on nutrient-poor soils

Resorption is the active withdrawal of nutrients before leaf abscission. This mechanism represents an important strategy to maintain efficient nutrient cycling; however, resorption is poorly characterized in old-growth tropical forests growing in nutrientpoor soils. We investigated nutrient resorption from leaves in 39 tree species in two tropical forests on the Guiana Shield, French Guiana, to investigate whether resorption efficiencies varied with soil nutrient, seasonality, and species traits. The stocks of P in leaves, litter, and soil were low at both sites, indicating potential P limitation of the forests. Accordingly, mean resorption efficiencies were higher for P (35.9%) and potassium (K; 44.6%) than for nitrogen (N; 10.3%). K resorption was higher in the wet (70.2%) than in the dry (41.7%) season. P resorption increased slightly with decreasing total soil P; and N and P resorptions were positively related to their foliar concentrations. We conclude that nutrient resorption is a key plant nutrition strategy in these old-growth tropical forests, that trees with high foliar nutrient concentration reabsorb more nutrient, and that nutrients resorption in leaves, except P, are quite decoupled from nutrients in the soil. Seasonality and biochemical limitation played a role in the resorption of nutrients in leaves, but species-specific requirements obscured general tendencies at stand and ecosystem level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site

Abstract To predict ecological responses at broad environmental scales, grass species are commonly grouped into two broad functional types based on photosynthetic pathway. However, closely related species may have distinctive anatomical and physiological attributes that influence ecological responses, beyond those related to photosynthetic pathway alone. Hyperspectral leaf reflectance can provide an integrated measure of covarying leaf traits that may result from phylogenetic trait conservatism and/or environmental conditions. Understanding whether spectra‐trait relationships are lineage specific or reflect environmental variation across sites is necessary for using hyperspectral reflectance to predict plant responses to environmental changes across spatial scales. We measured hyperspectral leaf reflectance (400–2400 nm) and 12 structural, biochemical, and physiological leaf traits from five grass‐dominated sites spanning the Great Plains of North America. We assessed if variation in leaf reflectance spectra among grass species is explained more by evolutionary lineage (as captured by tribes or subfamilies), photosynthetic pathway (C 3 or C 4 ), or site differences. We then determined whether leaf spectra can be used to predict leaf traits within and across lineages. Our results using redundancy analysis ordination (RDA) show that grass tribe identity explained more variation in leaf spectra (adjusted R 2 = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjusted R 2 = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjusted R 2 = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies ( R 2 = 0.65 to <0.01), but R 2 was >0.80 for certain lineages and sites. The relationship between Vc max , a measure of photosynthetic capacity, and spectra was particularly promising. Chloridoideae, a lineage more common at drier sites, appears to have distinct spectra‐trait relationships compared with other lineages. Overall, our results show that evolutionary relatedness explains more variation in grass leaf spectra than photosynthetic pathway or site, but consideration of lineage‐ and site‐specific trait relationships is needed to interpret spectral variation across large environmental gradients.

Pau, Stephanie [Department of Geography University↗
DOE OSTI2025

All the light we cannot see: Climate manipulations leave short and long‐term imprints in spectral reflectance of trees

Abstract Anthropogenic climate change, particularly changes in temperature and precipitation, affects plants in multiple ways. Because plants respond dynamically to stress and acclimate to changes in growing conditions, diagnosing quantitative plant‐environment relationships is a major challenge. One approach to this problem is to quantify leaf responses using spectral reflectance, which provides rapid, inexpensive, and nondestructive measurements that capture a wealth of information about genotype as well as phenotypic responses to the environment. However, it is unclear how warming and drought affect spectra. To address this gap, we used an open‐air field experiment that manipulates temperature and rainfall in 36 plots at two sites in the boreal‐temperate ecotone of northern Minnesota, USA. We collected leaf spectral reflectance (400–2400 nm) at the peak of the growing season for three consecutive years on juveniles (two to six years old) of five tree species planted within the experiment. We hypothesized that these mid‐season measurements of spectral reflectance capture a snapshot of the leaf phenotype encompassing a suite of physiological, structural, and biochemical responses to both long‐ and short‐time scale environmental conditions. We show that the imprint of environmental conditions experienced by plants hours to weeks before spectral measurements is linked to regions in the spectrum associated with stress, namely the water absorption regions of the near‐infrared and short‐wave infrared. In contrast, the environmental conditions plants experience during leaf development leave lasting imprints on the spectral profiles of leaves, attributable to leaf structure and chemistry (e.g., pigment content and associated ratios). Our analyses show that after accounting for baseline species spectral differences, spectral responses to the environment do not differ among the species. This suggests that building a general framework for understanding forest responses to climate change through spectral metrics may be possible, likely having broader implications if the common responses among species detected here represent a widespread phenomenon. Consequently, these results demonstrate that examining the entire spectrum of leaf reflectance for environmental imprints in contrast to single features (e.g., indices and traits) improves inferences about plant‐environment relationships, which is particularly important in times of unprecedented climate change.

Stefanski, Artur [Department of Forest Resources U↗
DOE OSTI2025

When can we detect lianas from space? Toward a mechanistic understanding of liana‐infested forest optics

Abstract Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals—reflectance across wavelengths—from liana‐infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive database, including leaf reflectance spectra from 5424 leaves, fine‐scale airborne reflectance data from 999 liana‐infested canopies, and coarse‐scale satellite reflectance data covering 775 ha of liana‐infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we corroborate with field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and short‐wave infrared regions, linked to lianas' less costly leaf construction compared with trees on average. The existence of a consistent global spectral signal for lianas suggests that large‐scale quantification of liana infestation is feasible. However, because the traits responsible for the liana canopy‐reflectance signal are not exclusive to lianas, accurate large‐scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross‐site patterns also prompt ecological questions about lianas' adaptive similarities in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits.

Environmental Sciences & Ecology↗
DOE OSTI2022

D 3 -creatine dilution for skeletal muscle mass measurement: historical development and current status

The French chemist Michel Eugène Chevreul discovered creatine in meat two centuries ago. Extensive biochemical and physiological studies of this organic molecule followed with confirmation that creatine is found within the cytoplasm and mitochondria of human skeletal muscles. Two groups of investigators exploited these relationships five decades ago by first estimating the creatine pool size in vivo with 14 C and 15 N labelled isotopes. Skeletal muscle mass (kg) was then calculated by dividing the creatine pool size (g) by muscle creatine concentration (g/kg) measured on a single muscle biopsy or estimated from the literature. This approach for quantifying skeletal muscle mass is generating renewed interest with the recent introduction of a practical stable isotope (creatine-(methyl-d 3 )) dilution method for estimating the creatine pool size across the full human lifespan. The need for a muscle biopsy has been eliminated by assuming a constant value for whole-body skeletal muscle creatine concentration of 4.3 g/kg wet weight. The current single compartment model of estimating creatine pool size and skeletal muscle mass rests on four main assumptions: tracer absorption is complete; tracer is all retained; tracer is distributed solely in skeletal muscle; and skeletal muscle creatine concentration is known and constant. Three of these assumptions are false to varying degrees. Not all tracer is retained with urinary isotope losses ranging from 0% to 9%; an empirical equation requiring further validation is used to correct for spillage. Not all tracer is distributed in skeletal muscle with non-muscle creatine sources ranging from 2% to 10% with a definitive value lacking. Lastly, skeletal muscle creatine concentration is not constant and varies between muscles (e.g. 3.89–4.62 g/kg), with diets (e.g. vegetarian and omnivore), across age groups (e.g. middle-age, ~4.5 g/kg; old-age, 4.0 g/kg), activity levels (e.g. athletes, ~5 g/kg) and in disease states (e.g. muscular dystrophies, <3 g/kg). Some of the variability in skeletal muscle creatine concentrations can be attributed to heterogeneity in the proportions of wet skeletal muscle as myofibres, connective tissues, and fat. These observations raise serious concerns regarding the accuracy of the deuterated-creatine dilution method for estimating total body skeletal muscle mass as now defined by cadaver analyses of whole wet tissues and in vivo approaches such as magnetic resonance imaging. A new framework is needed in thinking about how this potentially valuable method for measuring the creatine pool size in vivo can be used in the future to study skeletal muscle biology in health and disease.

body composition↗
DOE OSTI2025

The Future of a Myriad of Accelerated Biodiscoveries Lies in AI‐Powered Mass Spectrometry and Multiomics Integration

The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS‐based “omics” research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range of domains, from health to environment and industrial biotechnology. Beginning with an overview of key challenges inherent in MS software pipelines, this personal perspective explores how AI‐driven solutions can address them to enhance data processing, integration and interpretation. It proposes a paradigm shift in molecular identification and quantitation algorithms, leveraging AI to enable holistic interpretation of MS‐based multiomics data. While centered on MS‐based omics, this holistic AI‐driven paradigm is also critical for connecting dynamic biochemical changes to genomics and transcriptomics contexts, reinforcing the integrative value of MS in multiomics research. Ultimately, this AI‐driven approach could enhance efficiency, accuracy, and molecular breadth of coverage, deepening our systems‐level understanding of biological processes and accelerating a myriad of biodiscoveries.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Incorporation of Membrane Proteins Into Bicontinuous Microemulsions Through Winsor‐III System‐Based Extraction

Abstract The membrane proteins (MP) α‐synuclein (ASYN) and bacteriorhodopsin (BR) were readily incorporated into bicontinuous microemulsions (BμEs) formed by two microemulsion systems: water/heptane/Aerosol‐OT (AOT)/CK‐2,13 and water/dodecane/sodium dodecyl sulfate (SDS)/1‐pentanol. (CK‐2,13 is an alkyl ethoxylate possessing two alkyl tail groups of carbon chain length 2 and 13 and an average degree of ethoxylation of 5.6.) MP were encapsulated in BμEs through preparation of Winsor‐III systems at optimal salinity, with the anionic surfactants AOT and SDS providing the driving force for extraction. Dissolution of ASYN in BμEs greatly increased the former's α‐helicity, similar to ASYN's behavior in the presence of biomembranes, while BμE‐ and vesicle‐encapsulated BR possessed similar secondary structure. Small‐angle neutron scattering (SANS) results clearly demonstrated the direct interaction of MP with the surfactants, resulting in a decrease of surface area per volume for surfactant monolayers due to decreased surfactant efficiency. The SANS signal for ASYN was isolated through the use of neutron contrast matching for the surfactants through partial deuteration of water and oil, one of the first reports of contrast matching for BμEs in the literature. The SANS results of the contrast‐matched sample reflected similar aggregation for ASYN in BμEs as was reported previously for vesicles and SDS solution. This study demonstrates the potential use of BμEs as MP host systems for conducting biochemical reactions such as the conversion of sunlight into adenosine triphosphate by BR and studying the fundamental behavior of MP, such as the role of ASYN dysfunction in Parkinson's disease, as well as for isolation and purification of MP via Winsor‐III‐based extraction.

Hayes, Douglas G.↗
DOE OSTI2021

Subacute safety assessment of recombinant Lactococcus lactis on the gut microbiota of male Sprague–Dawley rats

Abstract BACKGROUND Lactococcus lactis strain pGSMT/MG1363 is a genetically modified microorganism (GMM) that constitutively expresses human metallothionein‐I fusion protein to combine with intracellular lead. Unlike traditional probiotics, pGSMT/MG1363 lacks a history of safe use in food. Administration of microorganism could influence the gut microbial community and consequently confer health benefits or cause disadvantages to the host. To date, little has been done to assess the influence of recombinant strain pGSMT/MG1363 on the stability of gut microbiota. RESULTS Liver, testis and kidney sections of male Sprague–Dawley rats orally administered pGSMT/MG1363 for 6 weeks showed normal structure and no pathological damage. There were no adverse effects on the analyzed serum biochemical parameters between the pGSMT/MG1363 group and the MG1363 group. Principal coordinate analysis showed that, compared with the MG1363 group, the 6‐week‐old fecal gut microbiota of rats fed with pGSMT/MG1363 was not significantly different (Adonis, P = 0.802). pGSMT/MG1363 treatment for 6 weeks did not significantly change the relative abundance of gut microbiota at the phylum and genus levels in comparison with MG1363 treatment. CONCLUSION Compared to the non‐GM strain MG1363 group, administration of the recombinant strain pGSMT/MG1363 for 6 weeks showed no adverse effects on the analyzed physiological parameters and gut microbial compositions of male Sprague–Dawley rats. The results suggested that, in terms of gut microbiota stability, pGSMT/MG1363 could be considered as safe as MG1363, at least for short‐term intake. Further toxicological evaluations still need to be considered before drawing a definite conclusion concerning the safe use of pGSMT/MG1363. © 2021 Society of Chemical Industry.

Chen, Guowei↗