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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.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Proteomic insights into the physiology and metabolism of oleaginous yeasts and filamentous fungi

Fungi are vital to the bioeconomy, serving as key producers of food, beverages, biofuels, and medicines, while also acting as essential resource recyclers in ecosystem management. For nearly a century, oleaginous yeast and filamentous fungi have been explored for their proficiency in oleochemicals production and carbon storage. Lipogenesis is one of the most well-studied fungal processes, with substantial progress having been made through reductionist biochemical approaches; however, the physiology and metabolism of fungal systems operating under different conditions arise from the functions of thousands of proteins, for which very little is known outside of model yeast. In this review, we discuss how proteomics provides a valuable analytical approach to contextualize lipogenesis within a complex biological system, where lipid accumulation is fundamentally governed by changes in proteins of multiple pathways. In the past two decades, proteomics has been applied to study stress response to nutrient limitations, metabolism of various carbon and nitrogen sources, the lipid droplet hub of carbon storage, protein post-translational modifications and signaling pathways, as well as oleochemical biosynthesis, thereby advancing our understanding of the oleaginous phenotype. Over 40 studies are reviewed herein to evaluate the impact, critically assess the utility, and propose future applications of proteomics. In the coming years, large systems-level proteomics studies will lay a foundation for marrying modeling and metabolic engineering strategies to optimize oleochemicals production in oleaginous fungi.

Lipid production↗
DOE OSTI2025

PET-FBA: A lightweight enzyme allocation and thermodynamics-constrained flux analysis approach to explore Escherichia coli metabolic adaptation to intracellular acidification

Escherichia coli employs diverse strategies to adapt to acidic environments that disrupt enzyme activity and the thermodynamic feasibility of essential reactions. To understand the impact of pH stress on cell metabolism, we present the PET-FBA (pH-, Enzyme protein allocation-, and Thermodynamics-constrained Flux Balance Analysis) framework. PET-FBA extends genome-scale modeling by integrating enzyme protein costs and reaction Gibbs free energy changes. Additionally, by incorporating pH-dependent enzyme kinetics in response to intracellular acidification, this framework enables the simulation of E. coli's metabolic adjustments across varying external pH levels. The model's accuracy is validated by comparing in silico growth simulations with experimental measurements under both anaerobic and aerobic conditions, as well as in silico gene knockouts of essential genes. By explicitly incorporating pH effects, our model accurately replicates the metabolic shift towards lactate production as the primary fermentation product at low pH in anaerobic conditions. This shift is only predicted when enzyme kinetics are dynamically adjusted as a function of pH. Further analysis revealed that this shift can be attributed to the reduced protein efficiency of the acetyl-CoA branch compared to lactate dehydrogenase under acidic stress, which then becomes crucial for maintaining NAD regeneration and cell growth at low pH. Furthermore, we identified strategies for enhancing cell growth under acidic anaerobic conditions by improving the enzyme activity of lactate dehydrogenase and pyruvate formate lyase, which increases NAD production efficiency and reduces enzyme protein allocation costs. Designed as a lightweight yet versatile framework, PET-FBA enables efficient genome-scale metabolic analysis. Using E. coli as a model system, our framework provides a systematic approach to understanding metabolic responses to environmental stress, pinpointing key metabolic bottlenecks, and identifying potential targets for strain optimization.

42 ENGINEERING↗
DOE OSTI2021

Translating dosimetry of Dibenzo[ def,p ]chrysene (DBC) and metabolites across dose and species using physiologically based pharmacokinetic (PBPK) modeling

We report that Dibenzo[def,p]chrysene (DBC) is an environmental polycyclic aromatic hydrocarbon (PAH) that causes tumors in mice and has been classified as a probable human carcinogen by the International Agency for Research on Cancer. Animal toxicity studies often utilize higher doses than are found in relevant human exposures. Additionally, like many PAHs, DBC requires metabolic bioactivation to form the ultimate toxicant, and species differences in DBC and DBC metabolite metabolism have been observed. To understand the implications of dose and species differences, a physiologically based pharmacokinetic model (PBPK) for DBC and major metabolites was developed in mice and humans. Metabolism parameters used in the model were obtained from experimental in vitro metabolism assays using mice and human hepatic microsomes. PBPK model simulations were evaluated against mice dosed with 15 mg/kg DBC by oral gavage and human volunteers orally microdosed with 29 ng of DBC. DBC and its primary metabolite DBC-11,12-diol were measured in blood of mice and humans, while in urine, the majority of DBC metabolites were obeserved as conjugated DBC-11,12-diol, conjugated DBC tetrols, and unconjugated DBC tetrols. The PBPK model was able to predict the time course concentrations of DBC, DBC-11,12-diol, and other DBC metabolites in blood and urine of human volunteers and mice with reasonable accuracy. Agreement between model simulations and measured pharmacokinetic data in mice and human studies demonstrate the success and versatility of our model for interspecies extrapolation and applicability for different doses. Furthermore, our simulations show that internal dose metrics used for risk assessment do not necessarily scale allometrically, and that PBPK modeling provides a reliable approach to appropriately account for interspecies differences in metabolism and physiology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Matlab implementation of a novel semi-structured kinetic model for methanotroph-photoautotroph cocultures

This paper presents the MatLab implementation details of a novel semi-structured kinetic model for methanotroph-photoautotroph cocultures. This includes the parameterization of the modeling equations, and the initialization of the simulation based on experimental conditions. More importantly, it provides details on how the differential equations governing mass balances in both gas and liquid phases are integrated together to simulate the system dynamics over time. The semi-structured kinetic model for methanotroph-photoautotroph coculture is validated using a wide range of experimental conditions. The model: Accurate predicts both the coculture growth in liquid phase and the gas composition changes in head space over time; Explicitly models the exchange of in situ produced O 2 and CO 2 within the coculture; Considers the self-shading effect on the growth of photoautotroph.

42 ENGINEERING↗
DOE OSTI2021

RNA-Seq Reveals that Light and Darkness Are Different Stimuli in Freshwater Heterotrophic Actinobacteria

Light is a ubiquitous source of both energy and information in surface environments, and regulates gene expression not only in photosynthetic microorganisms, but in a broad range of photoheterotrophic and heterotrophic microbes as well. Actinobacteria are keystone species in surface freshwater environments, where the ability to sense light could allow them to coordinate periods of nutrient uptake and metabolic activity with primary production. The model freshwater Actinobacteria Rhodoluna (R.) lacicola strain MWH-Ta8 and Aurantimicrobium(A.) photophilum strain MWH-Mo1 grow faster in the light than in the dark, but do not use light energy to support growth. Here, we characterize transcription throughout a light-dark cycle in R. lacicola and A. photophilum. In both species, some genes encoding carbohydrate metabolism and storage are upregulated in the light. However, expression of genes of the TCA cycle is only coordinated with light availability in R. lacicola. In fact, the majority of genes that respond to light and darkness in these two species are different, even though their light-responsive phenotypes are similar. The ability to respond to light and darkness may be widespread in freshwater Actinobacteria, but the genetic networks controlled by these two stimuli may vary significantly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Microbially mediated climate feedbacks from wetland ecosystems

Wetlands are crucial nodes in the carbon cycle, emitting approximately 20% of global CH4 while also sequestering 20%–30% of all soil carbon. Both greenhouse gas fluxes and carbon storage are driven by microbial communities in wetland soils. However, these key players are often overlooked or overly simplified in current global climate models. Here, we first integrate microbial metabolisms with biological, chemical, and physical processes occurring at scales from individual microbial cells to ecosystems. This conceptual scale-bridging framework guides the development of feedback loops describing how wetland-specific climate impacts (i.e., sea level rise in estuarine wetlands, droughts and floods in inland wetlands) will affect future climate trajectories. Furthermore, these feedback loops highlight knowledge gaps that need to be addressed to develop predictive models of future climates capturing microbial contributions. We propose a roadmap connecting environmental scientific disciplines to address these knowledge gaps and improve the representation of microbial processes in climate models. Together, this paves the way to understand how microbially mediated climate feedbacks from wetlands will impact future climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Challenges in integrating dissolved organic matter chemodiversity into kinetic models of soil respiration

The chemodiversity of dissolved organic matter (DOM) in soil has been proposed to influence the microbial metabolism and fate of belowground organic carbon (C). However, integrating DOM chemistry into soil C cycle models to improve predictions of C stocks and fluxes—beyond simply considering DOM pool size—remains a challenge. While recent research suggests that incorporating DOM chemodiversity into models can improve predictions of microbial respiration, there is still a lack of mechanistic understanding describing how DOM chemodiversity affects microbial metabolism and soil respiration. Here, we evaluated whether DOM chemodiversity was a determinant of soil respiration using paired measurements of high-resolution DOM chemistry, obtained from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), and potential soil respiration rates from across the United States (U.S.), all data provided by the Molecular Observation Network. Our objectives were to (1) assess statistical relationships between DOM chemodiversity and microbial respiration, and (2) evaluate the ability of kinetic models to leverage DOM chemistry to explain empirical relationships found in statistical models. Statistical regressions revealed that DOM chemodiversity (alpha diversity) was nonlinearly related to potential soil respiration rates, both independently and through its interactions with DOM and total C concentrations. In soils with relatively high DOM but low total C concentrations, potential soil respiration rates were negatively correlated with DOM alpha diversity, whereas in soils with relatively low DOM and high total C concentrations showed the opposite trend. However, when metabolic transition theory kinetic models were modified to include chemodiversity, their performance was comparable to traditional Monod kinetics approaches, which simulate respiration rates as a function of DOM concentration. The inability to account for nonlinearities in DOM chemodiversity–respiration relationships highlight an opportunity to advance substrate uptake kinetics by establishing causal links between DOM chemodiversity, microbial metabolism trade-offs, and potential interactions under varied environmental conditions.

Bioenergetic model↗
DOE OSTI2023

Diverse electron carriers drive syntrophic interactions in an enriched anaerobic acetate-oxidizing consortium

Abstract In many anoxic environments, syntrophic acetate oxidation (SAO) is a key pathway mediating the conversion of acetate into methane through obligate cross-feeding interactions between SAO bacteria (SAOB) and methanogenic archaea. The SAO pathway is particularly important in engineered environments such as anaerobic digestion (AD) systems operating at thermophilic temperatures and/or with high ammonia. Despite the widespread importance of SAOB to the stability of the AD process, little is known about their in situ physiologies due to typically low biomass yields and resistance to isolation. Here, we performed a long-term (300-day) continuous enrichment of a thermophilic (55 °C) SAO community from a municipal AD system using acetate as the sole carbon source. Over 80% of the enriched bioreactor metagenome belonged to a three-member consortium, including an acetate-oxidizing bacterium affiliated with DTU068 encoding for carbon dioxide, hydrogen, and formate production, along with two methanogenic archaea affiliated with Methanothermobacter_A. Stable isotope probing was coupled with metaproteogenomics to quantify carbon flux into each community member during acetate conversion and inform metabolic reconstruction and genome-scale modeling. This effort revealed that the two Methanothermobacter_A species differed in their preferred electron donors, with one possessing the ability to grow on formate and the other only consuming hydrogen. A thermodynamic analysis suggested that the presence of the formate-consuming methanogen broadened the environmental conditions where ATP production from SAO was favorable. Collectively, these results highlight how flexibility in electron partitioning during SAO likely governs community structure and fitness through thermodynamic-driven mutualism, shedding valuable insights into the metabolic underpinnings of this key functional group within methanogenic ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Novel Microbial Based Enzymatic CO 2 Fixation Mechanisms

The broad, long term goal of the research is to provide insight into the mechanism of novel carboxylation reactions catalyzed by enzymes involved in bacterial alkene, ketone, and epoxide metabolism. Novel convergent pathways in the model microorganism Xanthobacter autotrophicus have been described in which epoxides produced by alkene oxidation and ketones produced by the oxidation of alcohols are further carboxylated and utilized in energy yielding pathways. Both alkene/epoxide and alcohol/ketone pathways involve distinct carboxylases with unique molecular properties and cofactor requirements that represent alternative mechanisms of CO 2 fixation. The main focus and aims of this project is to provide structural basis for the mechanistic understanding of the reactions catalyzed by 2-ketopropyl coenzyme M oxidoreductase / carboxylase and acetone carboxylase, the terminal steps in the convergent pathways of alkene/epoxide and alcohol ketone metabolism in X. autotrophicus strain Py2. Interestingly, coenzyme M (CoM), the simplest known organic coenzyme, has been shown to have a central role in the pathway of alkene/epoxide carboxylation. Prior to this discovery CoM was thought only to be involved in methanogenesis in archaea. In addition to the carboxylation mechanisms, we are examining the enzymology of the biosynthesis of CoM in bacteria and we have already determined that it is biochemically distinct from that of CoM synthesized by methanogens. The proposed studies will reveal new and unique insights into CO 2 fixation pathways and carboxylation chemistry. We are utilizing a multidisciplinary approach involving kinetic studies, site-specific amino acid substitution studies, and the determination of high-resolution structures of enzymes in the presence of substrate analogs and mechanism base inhibitors to ascertain information concerning the biochemical mechanisms of enzyme catalyzed reactions. Within the context of the mission of the Department of Energy and the core activities of the Energy Biosciences, the results obtained in the study will reveal new insights into these novel carboxylation reactions and will provide the basis for the comparison of the mechanism of these interesting enzymes to other well-characterized biological mechanisms for CO 2 fixation and carboxylation. Knowledge concerning the mechanism of these enzymes provides the basis for an increased fundamental understanding of carboxylation chemistry that could contribute to future mechanisms for CO 2 capture and fixation and, in turn, to mitigating the effects of increasing concentrations of CO 2 on global climate change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Reconfiguring Plant Metabolism for Biodegradable Plastic Production

For decades, plants have been the subject of genetic engineering to synthesize novel, value-added compounds. Polyhydroxyalkanoates (PHAs), a large class of biodegradable biopolymers naturally synthesized in eubacteria, are among the novel products that have been introduced to make use of plant acetyl-CoA metabolic pathways. It was hoped that renewable PHA production would help address environmental issues associated with the accumulation of nondegradable plastic wastes. However, after three decades of effort synthesizing PHAs, and in particular the simplest form polyhydroxybutyrate (PHB), and seeking to improve their production in plants, it has proven very difficult to reach a commercially profitable rate in a normally growing plant. This seems to be due to the growth defects associated with PHA production and accumulation in plant cells. Here, we review major breakthroughs that have been made in plant-based PHA synthesis using traditional genetic engineering approaches and discuss challenges that have been encountered. Then, from the point of view of plant synthetic biology, we provide perspectives on reprograming plant acetyl-CoA pathways for PHA production, with the goal of maximizing PHA yield while minimizing growth inhibition. Specifically, we suggest genetic elements that can be considered in genetic circuit design, approaches for nuclear genome and plastome modification, and the use of multiomics and mathematical modeling in understanding and restructuring plant metabolic pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Application of physiologically based pharmacokinetic modeling for sertraline dosing recommendations in pregnancy

Pregnancy is a period of significant change that impacts physiological and metabolic status leading to alterations in the disposition of drugs. Uncertainty in drug dosing in pregnancy can lead to suboptimal therapy, which can contribute to disease exacerbation. A few studies show there are increased dosing requirements for antidepressants in late pregnancy; however, the quantitative data to guide dose adjustments are sparse. We aimed to develop a physiologically based pharmacokinetic (PBPK) model that allows gestational-age dependent prediction of sertraline dosing in pregnancy. A minimal physiological model with defined gut, liver, plasma, and lumped placental-fetal compartments was constructed using the ordinary differential equation solver package, ‘mrgsolve’, in R. We extracted data from the literature to parameterize the model, including sertraline physicochemical properties, in vitro metabolism studies, disposition in nonpregnant women, and physiological changes during pregnancy. The model predicted the pharmacokinetic parameters from a clinical study with eight subjects for the second trimester and six subjects for the third trimester. Based on the model, gestational-dependent changes in physiology and metabolism account for increased clearance of sertraline (up to 143% at 40 weeks gestational age), potentially leading to under-dosing of pregnant women when nonpregnancy doses are used. The PBPK model was converted to a prototype web-based interactive dosing tool to demonstrate how the output of a PBPK model may translate into optimal sertraline dosing in pregnancy. Quantitative prediction of drug exposure using PBPK modeling in pregnancy will support clinically appropriate dosing and increase the therapeutic benefit for pregnant women.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A mouse pancreatic organoid model to compare PD-L1 blocking antibodies

Immune checkpoint inhibitors (ICIs) have changed the therapeutic landscape for cancer patients, but diabetes, a rare, severe immune-related endocrinopathy, is linked to ICI therapy. It is unclear whether glycosylation of ICIs may play a role in the development of this adverse event and how the physiological effects of different ICIs on pancreatic cells should be evaluated. We used a mouse pancreatic organoid model to compare three PD-L1 blocking antibodies in the presence or absence of IFNγ using a metabolic bioanalyzer. Modulation of ICI glycosylation altered its metabolic effects on mouse pancreatic organoids, suggesting that this model could be used to monitor and compare ICIs and to study the mechanisms underlying the development of IC-mediated diabetes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Multimodal imaging of hemorrhagic transformation biomarkers in an ischemic stroke model.

Abstract Hemorrhagic transformation of ischemic stroke has devastating consequences, with high mortality and poor functional outcomes. Animal models of ischemic stroke also demonstrate the potential for hemorrhagic transformation, which complicates biochemical characterization, treatment studies, and hinders poststroke functional outcomes in affected subjects. The incidence of hemorrhagic transformation of ischemic stroke in animal model research is not commonly reported. The postmortem brain of such cases presents a complex milieu of biomarkers due to the presence of healthy cells, regions of varying degrees of ischemia, dead and dying cells, dysregulated metabolites, and blood components (especially reactive Fe species released from lysed erythrocytes). To improve the characterization of hemorrhage biomarkers on an ischemic stroke background, we have employed a combination of histology, X-ray fluorescence imaging (XFI), and Fourier transform infrared (FTIR) spectroscopic imaging to assess 122 photothrombotic (ischemic) stroke brains. Rapid freezing preserves brain biomarkers in situ and minimizes metabolic artifacts due to postmortem ischemia. Analysis revealed that 25% of the photothrombotic models had clear signs of hemorrhagic transformation. The XFI and FTIR metabolites provided a quantitative method to differentiate key metabolic regions in these models. Across all hemorrhage cases, it was possible to consistently differentiate otherwise healthy tissue from other metabolically distinct regions, including the ischemic infarct, the ischemic penumbra, blood vessels, sites of hemorrhage, and a region surrounding the hemorrhage core that contained elevated lipid oxidation. Chemical speciation of deposited Fe demonstrates the presence of heme-Fe and accumulation of ferritin.

Pushie, M. J. (ORCID:0000000174945427)↗
DOE OSTI2023

RootSlice —A novel functional-structural model for root anatomical phenotypes

Root anatomy is an important determinant of root metabolic costs, soil exploration, and soil resource capture. Root anatomy varies substantially within and among plant species. RootSlice is a multicellular functional-structural model of root anatomy developed to facilitate the analysis and understanding of root anatomical phenotypes. RootSlice can capture phenotypically accurate root anatomy in three dimensions of different root classes and developmental zones, of both monocotyledonous and dicotyledonous species. Several case studies are presented illustrating the capabilities of the model. For maize nodal roots, the model illustrated the role of vacuole expansion in cell elongation; and confirmed the individual and synergistic role of increasing root cortical aerenchyma and reducing the number of cortical cell files in reducing root metabolic costs. Integration of RootSlice for different root zones as the temporal properties of the nodal roots in the whole-plant and soil model OpenSimRoot/maize enabled the multiscale evaluation of root anatomical phenotypes, highlighting the role of aerenchyma formation in enhancing the utility of cortical cell files for improving plant performance over varying soil nitrogen supply. Such integrative in silico approaches present avenues for exploring the fitness landscape of root anatomical phenotypes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder

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.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A mixture parameterized biologically based dosimetry model to predict body burdens of polycyclic aromatic hydrocarbons in developmental zebrafish toxicity assays

Polycyclic aromatic hydrocarbons (PAHs) are a group of environmental toxicants found ubiquitously as complex mixtures in human-impacted environments. Developmental zebrafish exposures have been used widely to study PAH toxicity, but most studies report nominal exposure concentrations. Nominal exposure concentrations can be unreliable dose metrics due to differences in toxicant bioavailability resulting from disparate exposure methodologies and chemical properties. Toxicokinetic modeling can predict toxicant tissue doses to facilitate comparison between exposures of different chemicals, methodologies, and biological models. We parameterize a biologically based dosimetry model for developmental zebrafish toxicity assays for 9 PAHs. The model was optimized with measurements from media, tissue, and plastic plate walls throughout a static developmental exposure to a mixture of 10 PAHs of high abundance within the Portland Harbor Superfund Site. Plate binding, volatilization, zebrafish permeability, and tissue—media partitioning coefficients vary widely between PAHs. Model predictions accounted for 83% and 54% of 48 hpf body burdens within a factor of 2 resulting from exposures to mixtures and individual PAHs, respectively. Accounting for solubility significantly improves model performance. Competition for active sites in metabolizing enzymes may change biotransformation kinetics between individual PAH and mixture exposures. Area under the curve estimations of concentrations in zebrafish resulted in altered hazard rankings from nominal exposure concentrations. Future work will be oriented to generalizing the model to other PAHs. This PAH dosimetry model improves the interpretability of developmental zebrafish toxicity assays by providing time-resolved body burdens from nominal exposure concentrations.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Plant terpene specialized metabolism: complex networks or simple linear pathways?

From the perspectives of pathway evolution, discovery and engineering of plant specialized metabolism, the nature of the biosynthetic routes represents a critical aspect. Classical models depict biosynthesis typically from an end-point angle and as linear, for example, connecting central and specialized metabolism. As the number of functionally elucidated routes increased, the enzymatic foundation of complex plant chemistries became increasingly well understood. The perception of linear pathway models has been severely challenged. With a focus on plant terpenoid specialized metabolism, we review here illustrative examples supporting that plants have evolved complex networks driving chemical diversification. The completion of several diterpene, sesquiterpene and monoterpene routes shows complex formation of scaffolds and their subsequent functionalization. These networks show that branch points, including multiple sub-routes, mean that metabolic grids are the rule rather than the exception. This concept presents significant implications for biotechnological production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Discovery, characterization, and application of chromosomal integration sites in the hyperthermophilic archaeon Sulfolobus islandicus

Sulfolobus islandicus , an emerging archaeal model organism, offers unique advantages for metabolic engineering and synthetic biology applications owing to its ability to thrive in extreme environments. Although several genetic tools have been established for this organism, the lack of well-characterized chromosomal integration sites has limited its potential as a cellular factory. Here, in this work, we systematically identified and characterized 13 artificial CRISPR RNAs targeting eight integration sites in S. islandicus using the CRISPR-COPIES pipeline and a multi-omics-informed computational workflow. We leveraged the endogenous CRISPR-Cas system to integrate the reporter gene lacS and validated heterologous expression through a β-galactosidase assay, revealing significant positional effects. As a proof of concept, we utilized these sites to genetically manipulate lipid ether composition by overexpressing glycerol dibiphytanyl glycerol tetraether (GDGT) ring synthase B (GrsB). This study expands the genetic toolbox for S. islandicus and advances its potential as a robust platform for archaeal synthetic biology and industrial biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗