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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Photoprotection during iron deficiency is mediated by the bHLH transcription factors PYE and ILR3

Iron deficiency has many adverse effects on photosynthesis, which is not surprising given that 90% of foliar iron is found in the chloroplast. Although many iron-sparing mechanisms have been documented, a mechanistic understanding of how chloroplasts adapt to iron deficiency was lacking. Here, we show that the bHLH transcription factors ILR3 and PYE are required for photoprotection during iron deficiency. Changes in chloroplast morphology under the control of these transcription factors prevents the production of harmful reactive oxygen species and allows for repair of photosystem II. Understanding how plants adapt the photosynthetic machinery during iron deficiency may allow us to optimize plant growth in soils where iron is not bioavailable.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

SHP2 genetic variants in NSML-associated RASopathies disrupt the PZR–IRX transcription factor signaling axis

Noonan syndrome with multiple lentigines (NSML) is a rare autosomal dominant disorder caused by mutations inPTPN11(protein tyrosine phosphatase nonreceptor type 11) which encodes for the protein tyrosine phosphatase, SHP2. Approximately 85% of NSML patients develop hypertrophic cardiomyopathy (HCM). Here, we show that SHP2 is recruited to tyrosyl phosphorylated protein-zero related (PZR) in NSML mice. This recruitment is required for the Iroquois homeobox (IRX) transcription factors 3 and 5 to suppress BMP10 which negatively regulates postnatal cardiac growth. The protein expression of IRX3 and IRX5 was elevated in hypertrophied NSML hearts. IRX3 and IRX5 upregulation was rescued in NSML mice harboring a knock-in mutation of PZR that fails to become tyrosyl phosphorylated and recruit SHP2. NSML mice treated with low-dose dasatinib also exhibited normalized IRX3 and IRX5 expression levels. Consistent with this, BMP10 expression levels were reduced in NSML mice and rescued in PZR tyrosyl phosphorylation-deficient and low-dose dasatinib-treated NSML mice. A crystal structure of the tandem SH2 domains of SHP2 bound to tyrosyl phosphorylated PZR reveals that recruitment constrains the open SHP2 conformation to facilitate cellular-Src (c-Src) binding. Disruption of c-Src binding to SHP2 abolished IRX activation and failure to suppress BMP10. Hence, NSML-associated SHP2 genetic variants disrupt IRX transcription factor signaling to BMP10, implicating this axis as a target for RASopathy-associated HCM.

Science & Technology - Other Topics↗
DOE OSTI2019

Enhancing subtilisin thermostability through a modified normalized B-factor analysis and loop-grafting strategy

Rational design–guided improvement of protein thermostability typically requires identification of residues or regions contributing to instability and introduction of mutations into these residues or regions. One popular method, B-FIT, utilizes B-factors to identify unstable residues or regions and combines them with other strategies, such as directed evolution. Here, we performed structure-based engineering to improve the thermostability of the subtilisin E-S7 (SES7) peptidase. The B-value of each residue was redefined in a normalized B-factor calculation, which was implemented with a refined bioinformatics analysis strategy to identify the critical area (loop 158–162) related to flexibility and to screen for suitable thermostable motif sequences in the Protein Data Bank that can act as transplant loops. In total, we analyzed 445 structures and identified 29 thermostable motifs as candidates. Using these motifs as a starting point, we performed iterative homologous modeling to obtain a desirable chimera loop and introduced five different mutations into this loop to construct thermostable SES7 proteins. Differential scanning fluorimetry revealed increases of 7.3 °C in the melting temperature of an SES7 variant designated M5 compared with the WT. The X-ray crystallographic structure of this variant was resolved at 1.96 Å resolution. The crystal structure disclosed that M5 forms more hydrogen bonds than the WT protein, consistent with design and molecular dynamics simulation results. In summary, the modified B-FIT strategy reported here has yielded a subtilisin variant with improved thermostability and promising industrial applications, supporting the notion that this modified method is a powerful tool for protein engineering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

High-throughput small-angle X-ray scattering reveals effective structure factor transitions linked to high-concentration antibody viscosity

High-concentration monoclonal antibody (mAb) formulations are often constrained by elevated viscosity, largely driven by protein–protein interactions, which complicates manufacturing and limits subcutaneous delivery. Early viscosity risk assessment is essential during discovery, yet traditional measurements require large sample volumes, and lack high-throughput capability. Here, we develop a high-throughput small-angle X-ray scattering (SAXS) protocol to detect mAb self-association at dilute concentrations, enabling early predictive insights into high-concentration viscosity. Synchrotron SAXS measurements were conducted for 21 mAbs formulated in a histidine buffer at pH 6.0. An initial subset of 10 mAbs analyzed across 1–150 mg/mL revealed that effective structure factor transitions in the low-q region, indicative of interparticle interactions, consistently emerged below 25 mg/mL. Subsequently, 11 additional mAbs were analyzed at 1–25 mg/mL using automated liquid handling and flow cells to enable high-throughput screening. High-viscosity mAbs exhibited detectable low-q upturns at concentrations ≤10 mg/mL, whereas low-viscosity mAbs showed downturns. A classification criterion based on effective structure factor transitions accurately classified all high- and low-viscosity mAbs at 150 mg/mL, offering a scalable, sample-efficient alternative to conventional methods. These results extend recent findings on the concentration-dependent sensitivity of SAXS to short-range attractions, demonstrating that they can emerge at lower concentrations than previously reported. This study presents the most comprehensive and diverse SAXS dataset for mAbs reported to date within a single formulation, providing a valuable resource for developing and validating coarse-grained models that can more accurately capture intermolecular interactions governing high-concentration solution behavior, thereby enabling rational antibody engineering and improved developability.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Development of control quality factor for HVAC control loop performance assessment I—Methodology (ASHRAE RP-1587)

This article is the second paper from the ASHRAE research project RP-1587, focusing on the methodology of obtaining a control quality factor (CQF). This article presents a development of two CQFs for assessing the heating, ventilation, and air-conditioning (HVAC) control loop performance. Both CQFs are able to detect whether the control loop is able to maintain the setpoint and identify the loop's ability to handle disturbances. In addition, the reversal behaviors are assessed as well. The first CQF (CQF-Harris) is proposed based on the normalized Harris index using the recursive least squares method. This recursive least squares method is selected because of its computational efficiency compared with the maximum likelihood estimation method. The second CQF (CQF-EWMA) is based on the exponentially weighted moving average of the error ratio. The assessment scale of excellent, good, fair, bad, and failed, which indicates the quality of the HVAC control loops, is established as well. The sensitivity analysis for both CQFs is also conducted, and it provides insights on choosing the appropriate parameters to compute such CQFs. Such parameters include the sampling frequency, the length of the moving window, and the variance of the unmeasured disturbance. The field evaluations and tests of the proposed CQFs for simulated control loops and real control loops can be found in the companion paper with the title 'Development of Control Quality Factor for HVAC Control Loop Performance Assessment - III: Field Testing and Results (ASHRAE RP-1587).'

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

S -factor and scattering-parameter extractions from ${}^{3}\mathrm{He}+{}^{4}\mathrm{He}{ \rightarrow }^{7}\mathrm{Be}+\gamma $

Previous studies of the reaction ${}^{3}\mathrm{He}+{}^{4}\mathrm{He}\to {}^{7}\mathrm{Be}+\gamma $ have mainly focused on providing the best central value and error bar for the S factor at solar energies. Experimental measurements of this capture reaction at higher energies, the ${}^{3}\mathrm{He}$–${}^{4}\mathrm{He}$ scattering phase shifts, as well as properties of ${}^{7}\mathrm{Be}$ and its excited state, have been used to constrain the theoretical models employed for this purpose. Here we show that much more information than was previously appreciated can be extracted from angle-integrated capture data alone. We use the next-to-leading-order (NLO) amplitude in an effective field theory (EFT) for ${}^{3}\mathrm{He}+{}^{4}\mathrm{He}\to {}^{7}\mathrm{Be}+\gamma $ to perform the extrapolation. At this order the EFT describes the capture process using an s-wave scattering length and effective range, the asymptotic properties of ${}^{7}\mathrm{Be}$ and its excited state, and short-distance contributions to the E1 capture amplitude. We extract the multi-dimensional posterior of all these parameters via a Bayesian analysis that uses capture data below 2 MeV. We find that properties of the ${}^{7}\mathrm{Be}$ ground and excited states are well constrained. The total S factor $S(0)\,={0.577}_{-0.016}^{+0.015}$ keV b, while the branching ratio for excited- to ground-state capture at zero energy, ${Br}(0)={0.406}_{-0.011}^{+0.013}$, both at 68% degree of belief. This S(0) is broadly consistent with other recent evaluations, and agrees with the previously recommended value $S(0)=0.56\pm 0.03\,\,\mathrm{keV}$ b, but has a smaller error bar. We also find significant constraints on ${}^{3}\mathrm{He}$–${}^{4}\mathrm{He}$ scattering parameters, and we obtain constraints on the angular distribution of capture gamma rays, which is important for interpreting experiments. The path forward for this reaction seems to lie with better measurements of the scattering phase shifts and S(E)'s angular dependence away from zero energy, together with better understanding of the asymptotic normalization coefficients of the ${}^{7}$Be bound states' wave functions. Lastly, data on these could further reduce the uncertainty on S(0).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Simultaneous control of the electron temperature and safety factor profiles in DIII-D using model-based optimal control techniques

Future tokamak power plants will likely operate using a single, well-defined plasma scenario, either in steady state or for very long pulse lengths. In order to enhance the robustness of the scenario, feedback controllers for a variety of plasma properties will be necessary to counteract any disturbances and ensure safe operation. However, only a limited set of actuators will be available to control many different quantities. Because of this, it is necessary to develop controllers that are able to regulate multiple plasma properties using a limited set of actuators. To this end, a controller has been developed for the simultaneous regulation of both the electron temperature and safety factor profiles in DIII-D. This algorithm uses a linear quadratic integral control synthesis approach based on a linearized model of the dynamics of the two profiles. Two neural network surrogate models, NubeamNet and MMMnet, are included to improve the fidelity of the model. Furthermore, the controller has been tested in simulation using COTSIM, and has demonstrated the ability to simultaneously track changes in both the electron temperature and safety factor targets, including changes in both the magnitude and the shape of the profiles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Decoherence factor as a convolution: an interplay between a Gaussian and an exponential coherence loss

Abstract We identify and investigate the origin and nature of the transition between Gaussian and exponential forms of decoherence: the decoherence factor (that controls the time dependence of the off-diagonal terms of the density matrix expressed in the pointer basis representation) is the convolution of the Fourier transforms of the spectral density and of the overlap (between the eigenstates the environment with and without couplings to the system). Spectral density alone tends to lead to the (approximately) Gaussian decay of coherence while the overlap alone results in a (largely) exponential decay. We show that these two contributions combine as a convolution, their relative importance controlled by the strength of the system-environment coupling. The resulting decoherence factor in the strong and weak coupling limits leads to predominantly Gaussian or exponential decay, respectively, as is demonstrated with two paradigmatic examples of decoherence—a spin-bath model and the quantum Brownian motion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Velocity-dependent J-factors for annihilation radiation from cosmological simulations

Abstract We determine the dark matter pair-wise relative velocity distribution in a set of Milky Way-like halos in the Auriga and APOSTLE simulations. Focusing on the smooth halo component, the relative velocity distribution is well-described by a Maxwell-Boltzmann distribution over nearly all radii in the halo. We explore the implications for velocity-dependent dark matter annihilation, focusing on four models which scale as different powers of the relative velocity: Sommerfeld, s-wave, p-wave, and d-wave models. We show that the J-factors scale as the moments of the relative velocity distribution, and that the halo-to-halo scatter is largest for d-wave, and smallest for Sommerfeld models. The J-factor is strongly correlated with the dark matter density in the halo, and is very weakly correlated with the velocity dispersion. This implies that if the dark matter density in the Milky Way can be robustly determined, one can accurately predict the dark matter annihilation signal, without the need to identify the dark matter velocity distribution in the Galaxy.

Astronomy & Astrophysics↗
DOE OSTI2023

Velocity-dependent J-factors for Milky Way dwarf spheroidal analogues in cosmological simulations

We study the impact of the dark matter velocity distribution modelling on signals from velocity-dependent dark matter annihilation in Milky Way dwarf spheroidal galaxies. Using the high resolution APOSTLE simulations, we identify analogues corresponding to Milky Way dwarf spheroidal galaxies, and from these directly determine the dark matter pair-wise relative velocity distribution, and compare to best-fitting Maxwell-Boltzmann distribution models. For three velocity-dependent annihilation models, p-wave, d-wave, and the Sommerfeld model, we quantify the errors introduced when using the Maxwell-Boltzmann parameterization. We extract a simple power-law relation between the maximum circular velocity of the dwarf spheroidal analogue and the peak speed of the Maxwell-Boltzmann distribution. We show that this relation can be used to accurately calculate the dark matter relative velocity distribution, and find that it allows us to estimate the dark matter annihilation signal without the need to directly calculate the relative velocity distribution for each galaxy. The scatter in the J-factors calculated from the analogues dominates the uncertainty obtained when compared to the J-factor as determined from the observational data for each dwarf spheroidal, with the largest scatter from d-wave models and the smallest from Sommerfeld models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Hybrid model predictive control techniques for safety factor profile and stored energy regulation while incorporating NBI constraints

Abstract A novel hybrid Model Predictive Control (MPC) algorithm has been designed for simultaneous safety factor ( q ) profile and stored energy ( w ) control while incorporating the pulse-width-modulation constraints associated with the neutral beam injection (NBI) system. Regulation of the q -profile has been extensively shown to be a key factor for improved confinement as well as non-inductive sustainment of the plasma current. Simultaneous control of w is necessary to prevent the triggering of pressure-driven magnetohydrodynamic instabilities as the controller shapes the q profile. Conventional MPC schemes proposed for q -profile control have considered the NBI powers as continuous-time signals, ignoring the discrete-time nature of these actuators and leading in some cases to performance loss. The hybrid MPC scheme in this work has the capability of incorporating the discrete-time actuator dynamics as additional constraints. In nonlinear simulations, the proposed hybrid MPC scheme demonstrates improved q -profile+ w control performance for NSTX-U operating scenarios.

Physics↗
DOE OSTI2025

On infinite tensor networks, complementary recovery and type II factors

We initiate a study of local operator algebras at the boundary of infinite tensor networks, using the mathematical theory of inductive limits. In particular, we consider tensor networks in which each layer acts as a quantum code with complementary recovery, a property that features prominently in the bulk-to-boundary maps intrinsic to holographic quantum error-correcting codes. In this case, we decompose the limiting Hilbert space and the algebras of observables in a way that keeps track of the entanglement in the network. As a specific example, we describe this inductive limit for the holographic Harlow-Pastawski-Preskill-Yoshida code model and relate its algebraic and error-correction features. We find that the local algebras in this model are given by the hyperfinite type II$_\infty$ factor. Next, we discuss other networks that build upon this framework and comment on a connection between type II factors and stabilizer circuits. We conclude with a discussion of multiscale entanglement renormalization ansatz networks in which complementary recovery is broken. We argue that this breaking possibly permits a limiting type III von Neumann algebra, making them more suitable ansätze for approximating subregions of quantum field theories.

holographic dualities↗
DOE OSTI2020

A neural network for determination of latent dimensionality in Nonnegative Matrix Factorization

Non-negative Matrix Factorization (NMF) has proven to be a powerful unsupervised learning method for uncovering hidden features in complex and noisy datasets with applications in data mining, text recognition, dimension reduction, face recognition, anomaly detection, blind source separation, and many other fields. An important input for NMF is the latent dimensionality of the data, that is, the number of hidden features, K, present in the explored dataset. Unfortunately, and this quantity is rarely known a priori. The existing methods for determining latent dimensionality, such as Automatic Relevance Determination (ARD), are mostly heuristic and utilize different characteristics to estimate the number of hidden features. However, all of them require human presence to make a final determination of K. Here we utilize a supervised machine learning approach in combination with a recent method for model determination, called NMFk, to determine the number of hidden features automatically. NMFk performs a set of NMF simulations on an ensemble of matrices, obtained by bootstrapping the initial dataset, and estimates which K produces stable groups of latent features that reconstruct the initial dataset well. We then train a Multi-Layter Perceptron (MLP) classifier network to determine the correct number of latent features utilizing the statistics and characteristics of the NMF solution, obtained from NMFk. In order to train the MLP classifier, a training set of 58,660 matrices with predetermined latent features were factorized with NMFk. The MLP classifier in conjunction with NMFk maintains a greater than 95% success rate when applied to a held out test set. Additionally, when applied to two well-known benchmark datasets, the swimmer and MIT face data, NMFk/MLP correctly recovers the established number of hidden features. Finally, we compare the accuracy of our method to the ARD, AIC and Stability-based methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A web-based tool for the prediction of rice transcription factor function

Transcription factors (TFs) are an important class of regulatory molecules. Despite their importance, only a small number of genes encoding TFs have been characterized in Oryza sativa (rice), often because gene duplication and functional redundancy complicate their analysis. To address this challenge, we developed a web-based tool called the Rice Transcription Factor Phylogenomics Database (RTFDB) and demonstrate its application for predicting TF function. The RTFDB hosts transcriptome and co-expression analyses. Sources include high-throughput data from oligonucleotide microarray (Affymetrix and Agilent) as well as RNA-Seq-based expression profiles. We used the RTFDB to identify tissue-specific and stress-related gene expression. Subsequently, 273 genes preferentially expressed in specific tissues or organs, 455 genes showing a differential expression pattern in response to 4 abiotic stresses, 179 genes responsive to infection of various pathogens and 512 genes showing differential accumulation in response to various hormone treatments were identified through the meta-expression analysis. Pairwise Pearson correlation coefficient analysis between paralogous genes in a phylogenetic tree was used to assess their expression collinearity and thereby provides a hint on their genetic redundancy. Integrating transcriptome with the gene evolutionary information reveals the possible functional redundancy or dominance played by paralog genes in a highly duplicated genome such as rice. With this method, we estimated a predominant role for 83.3% (65/78) of the TF or transcriptional regulator genes that had been characterized via loss-of-function studies. In this regard, the proposed method is applicable for functional studies of other plant species with annotated genome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Identification of genetic and environmental factors influencing aerial root traits that support biological nitrogen fixation in sorghum

Abstract Plant breeding and genetics play a major role in the adaptation of plants to meet human needs. The current requirement to make agriculture more sustainable can be partly met by a greater reliance on biological nitrogen fixation by symbiotic diazotrophic microorganisms that provide crop plants with ammonium. Select accessions of the cereal crop sorghum (Sorghum bicolor (L.) Moench) form mucilage-producing aerial roots that harbor nitrogen-fixing bacteria. Breeding programs aimed at developing sorghum varieties that support diazotrophs will benefit from a detailed understanding of the genetic and environmental factors contributing to aerial root formation. A genome-wide association study of the sorghum minicore, a collection of 242 landraces, and 30 accessions from the sorghum association panel was conducted in Florida and Wisconsin and under 2 fertilizer treatments to identify loci associated with the number of nodes with aerial roots and aerial root diameter. Sequence variation in genes encoding transcription factors that control phytohormone signaling and root system architecture showed significant associations with these traits. In addition, the location had a significant effect on the phenotypes. Concurrently, we developed F2 populations from crosses between bioenergy sorghums and a landrace that produced extensive aerial roots to evaluate the mode of inheritance of the loci identified by the genome-wide association study. Furthermore, the mucilage collected from aerial roots contained polysaccharides rich in galactose, arabinose, and fucose, whose composition displayed minimal variation among 10 genotypes and 2 fertilizer treatments. These combined results support the development of sorghums with the ability to acquire nitrogen via biological nitrogen fixation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The influence of physical and algorithmic factors on simulated far-field waveforms and source–time functions of underground explosions using unsupervised machine learning

SUMMARY Characterizing explosion sources and differentiating between earthquake and underground explosions using distributed seismic networks becomes non-trivial when explosions are detonated in cavities or heterogeneous ground material. Moreover, there is little understanding of how changes in subsurface physical properties affect the far-field waveforms we record and use to infer information about the source. Simulations of underground explosions and the resultant ground motions can be a powerful tool to systematically explore how different subsurface properties affect far-field waveform features, but there are added variables that arise from how we choose to model the explosions that can confound interpretation. To assess how both subsurface properties and algorithmic choices affect the seismic wavefield and the estimated source functions, we ran a series of 2-D axisymmetric non-linear numerical explosion experiments and wave propagation simulations that explore a wide array of parameters. We then inverted the synthetic far-field waveform data using a linear inversion scheme to estimate source–time functions (STFs) for each simulation case. We applied principal component analysis (PCA), an unsupervised machine learning method, to both the far-field waveforms and STFs to identify the most important factors that control variance in the waveform data and differences between cases. For the far-field waveforms, the largest variance occurs in the shallower radial receiver channels in the 0–50 Hz frequency band. For the STFs, both peak amplitude and rise times across different frequencies contribute to the variance. We find that the ground equation of state (i.e. lithology and rheology) and the explosion emplacement conditions (i.e. tamped versus cavity) have the greatest effect on the variance of the far-field waveforms and STFs, with the ground yield strength and fracture pressure being secondary factors. Differences in the PCA results between the far-field waveforms and STFs could possibly be due to near-field non-linearities of the source that are not accounted for in the estimation of STFs and could be associated with yield strength, fracture pressure, cavity radius and cavity shape parameters. Other algorithmic parameters are found to be less important and cause less variance in both the far-field waveforms and STFs, meaning algorithmic choices in how we model explosions are less important, which is encouraging for the further use of explosion simulations to study how physical Earth properties affect seismic waveform features and estimated STFs.

58 GEOSCIENCES↗
DOE OSTI2018

Distinctive structural basis for DNA recognition by the fission yeast Zn 2 Cys 6 transcription factor Pho7 and its role in phosphate homeostasis

Pho7, a member of the Zn 2 Cys 6 family of fungal transcription factors, is the key transcriptional activator underlying fission yeast phosphate homeostasis, a physiological response to phosphate starvation in which the pho1, pho84 and tgp1 genes are upregulated. Here, we delineated a minimized 61-amino-acid Pho7 DNA-binding domain (DBD) and determined the 1.7 Å crystal structure of the DBD at its target site in the tgp1 promoter. Two distinctive features of the Pho7 DBD are: it binds DNA as a monomer, unlike most other fungal zinc-cluster factors that bind as homodimers; and it makes extensive interactions with its asymmetric target sequence over a 14-bp footprint that entails hydrogen bonding to 13 individual bases within, and remote from, the CGG triplet typically recognized by other Zn 2 Cys 6 DBDs. Base pair substitutions at Pho7 sites in the tgp1 and pho1 promoters highlight the importance of the 5'-CGG triplet for Pho7 binding in vitro and Pho7-dependent gene expression in vivo. We identify several DBD amino acids at which alanine substitution effaced or attenuated the pho1 phosphate starvation response and concordantly reduced Pho7 binding to a pho1 promoter site.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Structural and functional insight into the Mycobacterium tuberculosis protein PrpR reveals a novel type of transcription factor

The pathogenicity of Mycobacterium tuberculosis depends upon its ability to catabolize host cholesterol. Upregulation of the methylcitrate cycle (MCC) is required to assimilate and detoxify propionyl-CoA, a cholesterol degradation product. The transcription of key genes prpC and prpD in MCC is activated by MtPrpR, a member of a family of prokaryotic transcription factors whose structures and modes of action have not been clearly defined. We show that MtPrpR has a novel overall structure and directly binds to CoA or short-chain acyl-CoA derivatives to form a homotetramer that covers the binding cavity and locks CoA tightly inside the protein. The regulation of this process involves a [4Fe4S] cluster located close to the CoA-binding cavity on a neighboring chain. Mutations in the [4Fe4S] cluster binding residues rendered MtPrpR incapable of regulating MCC gene transcription. The structure of MtPrpR without the [4Fe4S] cluster-binding region shows a conformational change that prohibits CoA binding. The stability of this cluster means it is unlikely a redox sensor but may function by sensing ambient iron levels. These results provide mechanistic insights into this family of critical transcription factors who share similar structures and regulate gene transcription using a combination of acyl-CoAs and [4Fe4S] cluster.

59 BASIC BIOLOGICAL SCIENCES↗