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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

New methods for trace analysis of gamma-irradiated pentaerythritol tetranitrate

High explosives (HEs) are used in a diverse range of applications in which they could be exposed to various radiation levels that may cause potential chemical changes. This study further evaluated pentaerythritol tetranitrate (PETN) that was previously aged with a low-level 2 kGy dose of gamma irradiation in order to understand chemical changes caused by irradiation. Both unirradiated PETN and gamma-irradiated PETN were analyzed using ultra high-pressure liquid chromatography coupled to quadrupole time of flight mass spectrometry (UHPLC-QTOF). The resulting data were processed in a non-targeted manner using Fisher's ratio analysis and multivariate curve resolution-alternating least squares (MCR-ALS) to aid in discovery and identification of the chemical changes brought about by irradiation without a priori knowledge. The application of using UHPLC-QTOF in combination with chemometric techniques for the analysis of irradiated samples has not previously been performed. Here, in this work, we show how to use this method to provide chemical information that would otherwise not be discernible, such as the discovery of degradation of the various homologues of PETN. Major differences identified with radiolytic aging of the PETN sample included decomposition products that resulted from the degradation of the trigger linkage – the O–NO 2 bonds – resulting in the formation of alcohol and aldehyde groups. Similar degradation was also observed in the PETN homologues as well as interconversion from one homologue to another.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Detection of anaerobic and aerobic bacteria from commercial tattoo and permanent makeup inks

ABSTRACT Tattooing and use of permanent makeup (PMU) have dramatically increased over the last decade, with a concomitant increase in ink-related infections. Studies have shown evidence that commercial tattoo and PMU inks are frequently contaminated with pathogenic microorganisms. Considering that tattoo inks are placed into the dermal layer of the skin where anaerobic bacteria can thrive and cause infections in low-oxygen environments, the prevalence of anaerobic and aerobic bacteria should be assessed in tattoo and PMU inks. In this study, we tested 75 tattoo and PMU inks using the analytical methods described in the FDA Bacteriological Analytical Manual Chapter 23 for the detection of both aerobic and anaerobic bacterial contamination, followed by 16S rRNA gene sequencing for microbial identification. Of 75 ink samples, we found 26 contaminated samples with 34 bacterial isolates taxonomically classified into 14 genera and 22 species. Among the 34 bacterial isolates, 19 were identified as possibly pathogenic bacterial strains. Two species, namelyCutibacterium acnes(four strains) andStaphylococcus epidermidis(two strains) were isolated under anaerobic conditions. Two possibly pathogenic bacterial strains,Staphylococcus saprophyticusandC. acnes, were isolated together from the same ink samples (n= 2), indicating that tattoo and PMU inks can contain both aerobic (S. saprophyticus) and anaerobic bacteria (C. acnes). No significant association was found between sterility claims on the ink label and the absence of bacterial contamination. The results indicate that tattoo and PMU inks can also contain anaerobic bacteria. IMPORTANCE The rising popularity of tattooing and permanent makeup (PMU) has led to increased reports of ink-related infections. This study is the first to investigate the presence of both aerobic and anaerobic bacteria in commercial tattoo and PMU inks under aerobic and anaerobic conditions. Our findings reveal that unopened and sealed tattoo inks can harbor anaerobic bacteria, known to thrive in low-oxygen environments, such as the dermal layer of the skin, alongside aerobic bacteria. This suggests that contaminated tattoo inks could be a source of infection from both types of bacteria. The results emphasize the importance of monitoring these products for both aerobic and anaerobic bacteria, including possibly pathogenic microorganisms.

Biotechnology & Applied Microbiology↗
DOE OSTI2022

Identification and genetic characterization of mitochondrial citrate transporters in Aspergillus niger

Aspergillus niger is a major cell factory for citric acid production, and the process of citrate export from mitochondria to cytoplasm is predicted to be one of rate-limiting steps in citric acid accumulation. Currently, the mitochondrial citrate transporters (Ctps) in A. niger are not fully characterized. Here, six putative Ctp encoding genes ( ctpA to ctpF ) were identified based on their homology with a mitochondrial citrate transporter Sc Ctp1 from Saccharomyces cerevisiae . Disruption of individual ctpA to ctpF caused varying degrees of decline in citric acid accumulation at different fermentation stages, whereas a mutant strain S1696 with disruption of all six ctp s showed complete loss of citiric acid production. S1696 also exhibited delayed growth, reduced conidia formation, and decreased pigmentogenesis. Exogenous addition of citrate partially restored the conidia formation and pigmentogenesis in S1696 mutant. Reintroduction of individual ctp s ( ctpA to ctpF ) into S1696 at the amyA locus showed that ctpA , ctpB , and ctpD restored the citric acid titers to 88.5, 93.8, and 94.6% of the parent strain, respectively. Additionally, the formation of conidia and pigment production was partially restored after reintroduction of ctpA , ctpB , or ctpD . Overexpression of respective ctpA , ctpB , and ctpD in the parent strain resulted in increases in citric acid accumulation by 32.8, 19.3, and 24.2%, respectively. These results demonstrate that CtpA, CtpB, and CtpD play important roles in citric acid transport across the mitochondrial membrane and function in a redundant manner. Enhancement of citric acid transport process can serve as a target for boosting citric acid accumulation in A. niger .

Cao, Wei↗
DOE OSTI2026

Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning

Detecting concealed chemicals and explosives remains a critical challenge in global security. Terahertz time-domain spectroscopy (THz-TDS) offers a promising non-invasive and stand-off detection technique owing to its ability to penetrate optically opaque materials without causing ionization damage. While many chemicals exhibit distinct spectral features in the terahertz range, conventional terahertz-based detection methods often struggle in real-world environments, where variations in sample geometry, thickness, and packaging can lead to inconsistent spectral responses. In this study, we present a chemical imaging system that integrates THz-TDS with deep learning to enable accurate pixel-level identification and classification of different explosives. Operating in reflection mode and enhanced with plasmonic nanoantenna arrays, our THz-TDS system achieves a peak dynamic range of 96 dB and a detection bandwidth of 4.5 THz, supporting practical, stand-off operation. By analyzing individual time-domain pulses with deep neural networks, the system exhibits strong resilience to environmental variations and sample inconsistencies. Blind testing across eight chemicals—including pharmaceutical excipients and explosive compounds—resulted in an average classification accuracy of 99.42% at the pixel level. Notably, the system maintained an average accuracy of 88.83% when detecting explosives concealed under opaque paper coverings, demonstrating its robust generalization capability. These results highlight the potential of combining advanced terahertz spectroscopy with neural networks for highly sensitive and specific chemical and explosive detection in diverse and operationally relevant scenarios.

Imaging and sensing↗
DOE OSTI2024

Use of Legacy Maritime Protocols Increases Exploitability of Virtual Aids to Navigation

With increased reliance on Virtual Aid(s) to Navigation (VAtoN) - also known as electronic Aid(s) to Navigation (eAtoN), or virtual buoys - a cyber event is likely to cause disruption to international maritime shipping. VAtoN has no physical hardware for visual reference and displays only on a vessel’s Electronic Chart Display Information System (ECDIS) and Automatic Radar Plotting Aid (ARPA); therefore, mariners must rely on the accuracy of the information provided. As VAtoN uses the National Maritime Electronics Association (NMEA) 0183 protocol for both Global Navigation Satellite System (GNSS) and Automatic Identification System (AIS), an insecure protocol that has been proven susceptible to spoofing, denial, and manipulation, the likelihood of a cyber-related event increases substantially.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

A Model Based Approach to Extract Health Information from Textual Data

In current nuclear power plants (NPPs) a large amount of condition-based data is being generated and stored to assess and monitor component health and performance. The format of this data can be either numeric (e.g., pump vibration data) or textual (e.g., condition report which assess component health). While assessing component health from numeric data can be performed with a large variety of methods, the extraction of information from textual data still remains a challenge. Natural language processing (NLP) methods are starting to be deployed in current NPPs mainly to filter out incident reports (IRs) that are not safety related by employing supervised machine learning methods. However, these methods do not really provide the quantitative information that might be contained in IRs. This paper presents an approach to extract information from textual data (e.g., from IRs, maintenance reports) that is based on NLP data analytics methods coupled with model-based system engineer (MBSE) models. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence; such analysis includes: part of speech (POS) tagging (i.e., identification of grammatic elements of each string - e.g., nouns, verbs), named entity recognition (i.e., identification of text entities - e.g., names, dates, events), and relation extraction (e.g., coreference resolution). On the other hand, semantic analysis is designed to analyze the logic structure of a sentence. Through a specific set of rules, our methods can identify whether a sentence contains health information of a component (e.g., degraded performance, anomaly behavior) or the causal relationship between two events (i.e., a cause-effect pair). An innovative element of our approach is that semantic analysis relies on MBSE models to identify links between textual elements. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. This paper presents in detail how the integration of NLP methods and MBSE models is performed. Few analysis examples focusing on centrifugal pumps are presented.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Spark plasma sintered, MoNbTi-based multi-principal element alloys with Cr, V, and Zr

MoNbTi, MoNbTiZr, CrMoNbTiZr, and MoNbTiVZr multi-principal element alloys (MPEAs) were fabricated via spark plasma sintering (SPS) and investigated for use in high-strength applications. The fabrication method by SPS and powder metallurgy differs from those presented in the prior literature, where most MPEAs are fabricated using arc melting (AM) methods. Here cryogenic milling was used to maximize potential defect sinks (grain boundaries) for radiation resistance and to increase ultimate tensile strength through the Hall-Petch effect. SPS was chosen for consolidation in order to maintain a fine-grained structure during densification. Each alloy was characterized using x-ray diffraction and scanning electron microscopy for phase identification and compositional homogeneity. The base ternary alloy MoNbTi presented a predominantly single BCC system with minor cubic phases. The introduction of additional alloying elements—Zr, V, and Cr—heightened the phase complexity and increased the fractions of a secondary BCC phase and an HCP phase from Zr. The addition of Cr induced a larger fraction of the Laves phase to form. The addition of V caused the precipitation of small Mo inclusions. Thermodynamic analysis was performed to understand the separation of phases in each alloy. Discrepancies among the phase predictions generated by thermodynamic models, phases previously presented in the literature, and the characterization results suggest that MPEA fabrication methods, especially solid-state methods, require significant investigation to ensure that alloys can remain stable throughout their anticipated service lifetimes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Locating the Source of Forced Oscillations in Transmission Power Grids

A forced oscillation event in power grids refers to a state where malfunctioning or abnormally operating equipment causes persisting periodic disturbances in the system. While power grids are designed to damp most perturbations during standard operations, some of them can excite normal modes of the system and cause significant energy transfers across the system, creating large oscillations thousands of miles away from the source. Localization of the source of such disturbances remains an outstanding challenge due to a limited knowledge of the system parameters outside of the zone of responsibility of system operators. Here, we propose a new method for locating the source of forced oscillations that addresses this challenge by performing a simultaneous dynamic model identification using a principled maximum likelihood approach. We illustrate the validity of the algorithm on a variety of examples where forcing leads to resonance conditions in the system dynamics. Our results establish that an accurate knowledge of system parameters is not required for a successful inference of the source and frequency of a forced oscillation. We anticipate that our method will find a broader application in general dynamical systems that can be well described by their linearized dynamics over short periods of time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Driver Identification Dataset

The ORNL Driver Identification Dataset was created to collect and analyze driving behavior data from 50 different drivers. Each driver operated a 2014 Kenworth T270 Class 6 truck around Fort Collins, Colorado while various data sources recorded their driving behavior and vehicle performance. The dataset includes CANbus (Controller Area Network) data, GPS data, inertial measurement data, and biometric data from a heart rate monitor. A cyberattack was executed during each drive, which caused multiple dashboard warning lights to illuminate and set the tachometer and speedometer to zero, regardless of actual speed. The attack was stopped either after one minute or if the driver pulled over. By downloading the dataset, you agree to the following: 1) I will not use or disclose the data for any purpose other than Research as that term is defined in 10 CFR 745.102. 2) I will not, under any circumstances, request or accept private or linking identifiers for the data used. 3) I will not attempt to determine the identity of the individuals associated with the data. 4) I will use appropriate safeguards to prevent the use or disclose of the data for any purpose other than Research.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Ultrapotent influenza hemagglutinin fusion inhibitors developed through SuFEx-enabled high-throughput medicinal chemistry

Seasonal and pandemic-associated influenza strains cause highly contagious viral respiratory infections that can lead to severe illness and excess mortality. Here, we report on the optimization of our small-molecule inhibitor F0045(S) targeting the influenza hemagglutinin (HA) stem with our Sulfur-Fluoride Exchange (SuFEx) click chemistry–based high-throughput medicinal chemistry (HTMC) strategy. A combination of SuFEx- and amide-based lead molecule diversification and structure-guided design led to identification and validation of ultrapotent influenza fusion inhibitors with subnanomolar EC 50 cellular antiviral activity against several influenza A group 1 strains. X-ray structures of six of these compounds with HA indicate that the appended moieties occupy additional pockets on the HA surface and increase the binding interaction, where the accumulation of several polar interactions also contributes to the improved affinity. The compounds here represent the most potent HA small-molecule inhibitors to date. Our divergent HTMC platform is therefore a powerful, rapid, and cost-effective approach to develop bioactive chemical probes and drug-like candidates against viral targets.

Science & Technology - Other Topics↗
DOE OSTI2021

Observables for recoil identification in high-definition Gas Time Projection Chambers

Directional detection of nuclear recoils is broadly desirable in nuclear and particle physics. At low recoil energies, this capability may be used to confirm the cosmological origin of a dark matter signal, to penetrate the so-called neutrino floor, or to distinguish between different neutrino sources. Gas Time Projection Chambers (TPCs) can enable directional recoil detection if the readout granularity is sufficiently high, as is the case when micro-pattern gaseous detectors (MPGDs) are utilized. A key challenge in such detectors is identifying and rejecting background electron recoil events caused by gamma rays from radioactive contaminants in the detector materials and the environment. We define new observables that can distinguish electron and nuclear recoils, even at keV-scale energies, based on the simulated ionization's topology. Here we perform a simulation study that shows these observables outperform the traditionally used discriminant, dE/dx, by up to three orders of magnitude. Furthermore, these new observables work well even at ionization energies well below 10keV and remain robust even in the regime where directionality fails.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Web-based Preprocessing and Visualization of 3D FIB Tomography Data for Nuclear Fuel Characterization

Three-dimensional (3D) focused ion beam (FIB) tomography enables reconstruction of internal nuclear fuel features that can't be fully evaluated through surface imaging alone. This capability supports characterization of fuel constituents and defects under thermal and irradiation conditions relevant to microreactor development. However, large tomography datasets can create data-handling, loading, and visualization challenges, especially when image-stack preparation and file conversion must be completed with separate tools. The Computational Ultraspatial Tomography Toolkit for High-Resolution Object Analysis Tools (CUTTRHOAT) is an open-source web application being developed to display FIB tomography datasets available through the Nuclear Research Data System (NRDS). The current alpha version requires prepared HDF5 datasets and has limited integrated data-preparation capabilities. This project improves CUTTHROAT by adding dataset-folder selection, automatic input detection, dataset scanning, missing-slice identification, blank-slice insertion, and image-stack-to-HDF5 conversion. Two applications will be compared: the baseline CUTTHROAT alpha workflow and the updated application containing the integrated data-handling and preprocessing functions. Evaluation will consider dataset detection accuracy, conversion success, loading time, rendering responsiveness, application stability, and user interaction. Preliminary results demonstrate successful loading of existing HDF5 files and converted image stacks, while testing also identified performance reductions caused by excessive blank-slice generation. The updated workflow reduces reliance on external preparation tools and supports more direct movement from image stacks to color-code 3D visualization. Future work includes refining missing-slice handling, integrating additional preprocessing functions, like a denoising feature, parsing TIFF metadata for automatic voxel scaling, and adding manual X, Y, and Z voxel-spacing inputs for PNG and JPEG.

36 - MATERIALS SCIENCE↗
DOE OSTI2021

Nonlinear elasticity and damping govern ultrafast dynamics in click beetles

Many small animals use springs and latches to overcome the mechanical power output limitations of their muscles. Click beetles use springs and latches to bend their bodies at the thoracic hinge and then unbend extremely quickly, resulting in a clicking motion. When unconstrained, this quick clicking motion results in a jump. While the jumping motion has been studied in depth, the physical mechanisms enabling fast unbending have not. Here, we first identify and quantify the phases of the clicking motion: latching, loading, and energy release. We detail the motion kinematics and investigate the governing dynamics (forces) of the energy release. We use high-speed synchrotron X-ray imaging to observe and analyze the motion of the hinge’s internal structures of four Elater abruptus specimens. We show evidence that soft cuticle in the hinge contributes to the spring mechanism through rapid recoil. Using spectral analysis and nonlinear system identification, we determine the equation of motion and model the beetle as a nonlinear single-degree-of-freedom oscillator. Quadratic damping and snap-through buckling are identified to be the dominant damping and elastic forces, respectively, driving the angular position during the energy release phase. The methods used in this study provide experimental and analytical guidelines for the analysis of extreme motion, starting from motion observation to identifying the forces causing the movement. Here, the tools demonstrated here can be applied to other organisms to enhance our understanding of the energy storage and release strategies small animals use to achieve extreme accelerations repeatedly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

CO2 Valorization via Rewiring Carbon Metabolic Network

Energizing the world sustainably requires revolutionizing the way we harness natural resources. New renewable technologies include the microbiological upgrading of CO2, the primary greenhouse gas that causes climate change. This project aims to develop a new bioeconomy by taking up CO2 and converting it into high-value products using an autotrophic microbe. Specifically, the team constructed an acetogenic bacterium, Clostridium ljungdahlii, and developed gas fermentation processes that convert syngas (CO2/H2/CO) to the platform compound 3-hydroxybutyrate (3HB). The project has achieved several key successes, including the design and construction of 3HB genes in the gas-fermenting bacterium, the identification of a native 3HB pathway, the conversion of syngas to 3HB through strain engineering, and the improvement of the conversion through rational and iterative strain development. The optimized strain has achieved industry-relevant production rates (0.1 g/L/h) and titers (9.2 g/L), and sustained continuous gas fermentation for 3HB production for > 50 days in a bench-top gas bioreactor. The project has both industrial and scientific implications, including the exploitation of host microbes and processes to improve industrial-scale production of value-added compounds from CO2, and the promotion of gas fermentation technology. The team is also strengthening R&D synergies with other teams and industrial partners in the CO2 consortium, developing cutting-edge approaches for rapid and rational engineering of gas-fermenting hosts, and dissecting the thermodynamics and kinetics of processes for biological syngas valorization. So far, one patent application and one research article have resulted from this work, and the team anticipates additional significant outcomes from this project.

3-hydroxybutyrate↗
DOE OSTI2024

Overview of RFID Applications Utilizing Neural Networks

As Radio Frequency Identification (RFID) methods continue to evolve to higher levels of complexity, one form of machine learning is making its appearance. The use of Neural Networks (NN) in the RFID field is steadily increasing, and in the fields of localization and activity recognition, promising results are being shown from a variety of research. RFID applications fall primarily under two types of problems including regression and classification. We analyze RIFD localization techniques which fall under regression, and activity recognition which falls under classification. Many works don’t classify themselves as activity recognition methods, but because they fall under the classification category, we still consider them as activity recognition techniques. This research overviews the Neural Network models in the localization field based on whether they can perform independently of the environment in which they were tested. For activity recognition and accessory fields, the major methods involve tag-based and tag-free approaches. In conclusion, after the models are surveyed, a comparison study is given to examine what may be the cause for increased accuracy between different Neural Network models.

42 ENGINEERING↗
DOE OSTI2024

Power System Frequency Dynamics Modeling, State Estimation, and Control using Neural Ordinary Differential Equations (NODEs) and Soft Actor-Critic (SAC) Machine Learning Approaches

With the global energy transition of the electric power system, grid control, supervision, and protection is becoming more challenging. With the increasing integration of renewable energy sources (RES), the system dynamics are changing, causing traditional power system dynamic modeling with swing equation-based modeling approaches to fail. Additionally, the converter-dominated power grid is decreasing the system inertia, making the power system more fragile to the frequency swings. This paper first investigates and compares the application of a model-based Kalman filter state estimation approach with (i) a model-free machine learning approach --- neural ordinary differential equations (NODEs) --- and (ii) a data-driven system identification (SysId) approach to model and infer critical state values of the power system frequency dynamics. Then a model predictive control (MPC) framework is compared to a model-free Soft Actor-Critic (SAC) reinforcement learning (RL) control algorithm in providing efficient fast frequency response (FFR) to the power system frequency dynamics. The approaches are compared in terms of their performance goals as well as their per-timestep computational efficiency. Furthermore, the comparative study for state estimation shows that for the model-free requirement, both NODEs and SysId can provide accurate state estimates; however, with increasing model complexity, NODEs can be a better choice for model identification. Similarly, the results from the FFR comparative study show that the SAC RL-based FFR, once trained, outperforms MPC with better control signals and faster computation time, making the SAC RL-based FFR better option for providing FFR to the power system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Metalloproteomics Reveals Multi-Level Stress Response in Escherichia coli When Exposed to Arsenite

The arsRBC operon encodes a three-protein arsenic resistance system. ArsR regulates the transcription of the operon, while ArsB and ArsC are involved in exporting trivalent arsenic and reducing pentavalent arsenic, respectively. Previous research into Agrobacterium tumefaciens 5A has demonstrated that ArsR has regulatory control over a wide range of metal-related proteins and metabolic pathways. We hypothesized that ArsR has broad regulatory control in other Gram-negative bacteria and set out to test this. Here, we use differential proteomics to investigate changes caused by the presence of the arsR gene in human microbiome-relevant Escherichia coli during arsenite (AsIII) exposure. We show that ArsR has broad-ranging impacts such as the expression of TCA cycle enzymes during AsIII stress. Additionally, we found that the Isc [Fe-S] cluster and molybdenum cofactor assembly proteins are upregulated regardless of the presence of ArsR under these same conditions. An important finding from this differential proteomics analysis was the identification of response mechanisms that were strain-, ArsR-, and arsenic-specific, providing new clarity to this complex regulon. Given the widespread occurrence of the arsRBC operon, these findings should have broad applicability across microbial genera, including sensitive environments such as the human gastrointestinal tract.

Biochemistry & Molecular Biology↗