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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Arabidopsis SnRK1 negatively regulates phenylpropanoid metabolism via Kelch domain-containing F-box proteins

Phenylpropanoid metabolism represents a substantial metabolic sink for the photosynthetically fixed carbon. The evolutionarily conserved Sucrose Non-Fermenting Related Kinase 1 (SnRK1) is a major metabolic sensor that reprograms metabolism upon carbon deprivation. However, it is not clear if and how SnRK1-mediated sugar signaling pathway controls phenylpropanoid metabolism. Here, we show that Arabidopsis SnRK1 negatively regulates phenylpropanoid biosynthesis via a group of Kelch domain-containing F-Box (KFB) proteins that are responsible for the ubiquitination and degradation of phenylalanine ammonia lyase (PAL). Down-regulation of AtSnRK1 significantly promoted the accumulation of soluble phenolics and lignin polymers and drastically increased PAL cellular accumulation but only slightly altered its transcription level. Co-expression of SnRK1α with PAL in Nicotiana benthamiana leaves resulted in the severe attenuation of the latter's protein level, but protein interaction assays suggested PAL is not a direct substrate of SnRK1. Furthermore, up- or down-regulation of AtSnRK1 positively affected KFB PALs gene expression, and energy starvation upregulated KFB PAL expression, which partially depends on AtSnRK1. Collectively, our study reveals that SnRK1 negatively regulates phenylpropanoid biosynthesis, and KFB PALs act as regulatory components of the SnRK1 signaling network, transcriptionally regulated by SnRK1 and subsequently mediating proteasomal degradation of PAL in response to the cellular carbon availability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Dynamics of ER stress-induced gene regulation in plants

Endoplasmic reticulum (ER) stress is a potentially lethal condition that is induced by the abnormal accumulation of unfolded or misfolded secretory proteins in the ER. In eukaryotes, ER stress is managed by the unfolded protein response (UPR) through a tightly regulated, yet highly dynamic, reprogramming of gene transcription. Although the core principles of the UPR are similar across eukaryotes, unique features of the plant UPR reflect the adaptability of plants to their ever-changing environments and the need to balance the demands of growth and development with the response to environmental stressors. Here, the past decades have seen notable progress in understanding the mechanisms underlying ER stress sensing and signaling transduction pathways, implicating the UPR in the effects of physiological and induced ER stress on plant growth and crop yield. Facilitated by sequencing technologies and advances in genetic and genomic resources, recent efforts have driven the discovery of transcriptional regulators and elucidated the mechanisms that mediate the dynamic and precise gene regulation in response to ER stress at the systems level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A suberized exodermis is required for tomato drought tolerance

Plant roots integrate environmental signals with development using exquisite spatiotemporal control. This is apparent in the deposition of suberin, an apoplastic diffusion barrier, which regulates flow of water, solutes and gases, and is environmentally plastic. Suberin is considered a hallmark of endodermal differentiation but is absent in the tomato endodermis. Instead, suberin is present in the exodermis, a cell type that is absent in the model organism Arabidopsis thaliana. Here we demonstrate that the suberin regulatory network has the same parts driving suberin production in the tomato exodermis and the Arabidopsis endodermis. Despite this co-option of network components, the network has undergone rewiring to drive distinct spatial expression and with distinct contributions of specific genes. Functional genetic analyses of the tomato MYB92 transcription factor and ASFT enzyme demonstrate the importance of exodermal suberin for a plant water-deficit response and that the exodermal barrier serves an equivalent function to that of the endodermis and can act in its place.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcriptome and quantitatively describe regulatory activity under diverse conditions, creating an unbiased summary of gene expression. We obtain 83 independently modulated gene sets that explain most of the variance in expression and demonstrate that 76% of them represent the effects of known regulators. The TRN structure and its condition-dependent activity uncover putative or recently discovered roles for at least five regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states. As this TRN covers the majority of the transcriptome and concisely characterizes the global expression state, it could inform research on nearly every aspect of transcriptional regulation in B. subtilis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data for Characterization of the Ghd8 Flowering Time Gene in a Mini-Core Collection of Miscanthus sinensis

The optimal flowering time for bioenergy crop miscanthus is essential for environmental adaptability and biomass accumulation. However, little is known about how genes controlling flowering in other grasses contribute to flowering regulation in miscanthus. Here, we report on the sequence characterization and gene expression of Miscanthus sinensisGhd8 , a transcription factor encoding a HAP3/NF-YB DNA-binding domain, which has been identified as a major quantitative trait locus in rice, with pleiotropic effects on grain yield, heading date and plant height. In M. sinensis , we identified two homoeologous loci, MsiGhd8A located on chromosome 13 and MsiGhd8B on chromosome 7, with one on each of this paleo-allotetraploid species’ subgenomes. A total of 46 alleles and 28 predicted protein sequence types were identified in 12 wild-collected accessions. Several variants of MsiGhd8 showed a geographic and latitudinal distribution. Quantitative real-time PCR revealed that MsiGhd8 expressed under both long days and short days, and MsiGhd8B showed a significantly higher expression than MsiGhd8A . The comparison between flowering time and gene expression indicated that MsiGhd8B affected flowering time in response to day length for some accessions. This study provides insight into the conserved function of Ghd8 in the Poaceae, and is an important initial step in elucidating the flowering regulatory network of Miscanthus.

Feedstock Production↗
DOE OSTI2022

Disrupting the ArcA Regulatory Network Amplifies the Fitness Cost of Tetracycline Resistance in Escherichia coli

There is an urgent need for strategies to discover secondary drugs to prevent or disrupt antimicrobial resistance (AMR), which is causing >700,000 deaths annually. Here, we demonstrate that tetracycline-resistant (Tet R ) Escherichia coli undergoes global transcriptional and metabolic remodeling, including downregulation of tricarboxylic acid cycle and disruption of redox homeostasis, to support consumption of the proton motive force for tetracycline efflux. Using a pooled genome-wide library of single-gene deletion strains, at least 308 genes, including four transcriptional regulators identified by our network analysis, were confirmed as essential for restoring the fitness of Tet R E. coli during treatment with tetracycline. Targeted knockout of ArcA, identified by network analysis as a master regulator of this new compensatory physiological state, significantly compromised fitness of Tet R E. coli during tetracycline treatment. A drug, sertraline, which generated a similar metabolome profile as the arcA knockout strain, also resensitized Tet R E. coli to tetracycline. We discovered that the potentiating effect of sertraline was eliminated upon knocking out arcA, demonstrating that the mechanism of potential synergy was through action of sertraline on the tetracycline-induced ArcA network in the Tet R strain. Our findings demonstrate that therapies that target mechanistic drivers of compensatory physiological states could resensitize AMR pathogens to lost antibiotics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Optimal dimensionality selection for independent component analysis of transcriptomic data

Independent component analysis is an unsupervised machine learning algorithm that separates a set of mixed signals into a set of statistically independent source signals. Applied to high-quality gene expression datasets, independent component analysis effectively reveals both the source signals of the transcriptome as co-regulated gene sets, and the activity levels of the underlying regulators across diverse experimental conditions. Two major variables that affect the final gene sets are the diversity of the expression profiles contained in the underlying data, and the user-defined number of independent components, or dimensionality, to compute. Availability of high-quality transcriptomic datasets has grown exponentially as high-throughput technologies have advanced; however, optimal dimensionality selection remains an open question. We computed independent components across a range of dimensionalities for four gene expression datasets with varying dimensions (both in terms of number of genes and number of samples). We computed the correlation between independent components across different dimensionalities to understand how the overall structure evolves as the number of user-defined components increases. We then measured how well the resulting gene clusters reflected known regulatory mechanisms, and developed a set of metrics to assess the accuracy of the decomposition at a given dimension. We found that over-decomposition results in many independent components dominated by a single gene, whereas under-decomposition results in independent components that poorly capture the known regulatory structure. From these results, we developed a new method, called OptICA, for finding the optimal dimensionality that controls for both over- and under-decomposition. Specifically, OptICA selects the highest dimension that produces a low number of components that are dominated by a single gene. We show that OptICA outperforms two previously proposed methods for selecting the number of independent components across four transcriptomic databases of varying sizes. OptICA avoids both over-decomposition and under-decomposition of transcriptomic datasets resulting in the best representation of the organism’s underlying transcriptional regulatory network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Adaptive laboratory evolution and metabolic engineering of Cupriavidus necator for improved catabolism of volatile fatty acids

Bioconversion of high-volume waste streams into value-added products will be an integral component of the growing bioeconomy. Volatile fatty acids (VFAs) (e.g., butyrate, valerate, and hexanoate) are an emerging and promising waste-derived feedstock for microbial carbon upcycling. Cupriavidus necator H16 is a favorable host for conversion of VFAs into various bioproducts due to its diverse carbon metabolism, ease of metabolic engineering, and use at industrial scales. Here, in this study, we report that a common strategy to improve product titers in C. necator, deletion of the polyhydroxybutyrate (PHB) biosynthetic operon, results in a significant growth defect on VFA substrates. Using adaptive laboratory evolution, we identify mutations to the regulator gene phaR, the two-component response regulator-histidine kinase pair encoded by H16_A1372/H16_A1373, and the tripartite transporter assembly encoded by H16_A2296-A2298 as causative for improved growth on VFA substrates. Deletion of phaR and H16_A1373 led to significantly reduced NADH abundance accompanied by large changes to expression of genes involved in carbon metabolism, balance of electron carriers, and oxidative stress tolerance that may be responsible for improved growth of these engineered strains. These results provide insight into the role of PHB biosynthesis in carbon and energy metabolism and highlight a key role for the regulator PhaR in global regulatory networks. By combining mutations, we generated platform strains with significant growth improvements on VFAs, which can enable improved conversion of waste-derived VFA substrates to target bioproducts.

09 BIOMASS FUELS↗
DOE OSTI2024

maranasgroup/yeastsMFA

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

Dinh, Hoang↗
DOE OSTI2023

maranasgroup/k-sacce306

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

Dinh, Hoang↗
DOE OSTI2023

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13 C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Spurious regulatory connections dictate the expression-fitness landscape of translation factors

During steady-state cell growth, individual enzymatic fluxes can be directly inferred from growth rate by mass conservation, but the inverse problem remains unsolved. Perturbing the flux and expression of a single enzyme could have pleiotropic effects that may or may not dominate the impact on cell fitness. Here, we quantitatively dissect the molecular and global responses to varied expression of translation termination factors (peptide release factors, RFs) in the bacterium Bacillus subtilis. While endogenous RF expression maximizes proliferation, deviations in expression lead to unexpected distal regulatory responses that dictate fitness reduction. Molecularly, RF depletion causes expression imbalance at specific operons, which activates master regulators and detrimentally overrides the transcriptome. Through these spurious connections, RF abundances are thus entrenched by focal points within the regulatory network, in one case located at a single stop codon. Such regulatory entrenchment suggests that predictive bottom-up models of expression-fitness landscapes will require near-exhaustive characterization of parts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Relevance of the Unfolded Protein Response to Spaceflight-Induced Transcriptional Reprogramming in Arabidopsis

Plants are primary producers of food and oxygen on Earth and will likewise be indispensable to the establishment of large-scale sustainable ecosystems and human survival in space. To contribute to the understanding of how plants respond to spaceflight stress, we examined the significance of the unfolded protein response (UPR), a conserved signaling cascade that responds to a number of unfavorable environmental stresses, in the model plant Arabidopsis thaliana. To do so, we performed a large-scale comparative transcriptome profiling in wild type and various UPR-defective mutants during the SpaceX-CRS12 mission to the International Space Station. We established that orbital culture substantially alters the expression of hundreds of stress-related genes compared with ground control conditions. Although expression of those genes varied in the UPR mutants on the ground, it was largely similar across the genotypes in the spaceflight condition. Furthermore, our results have yielded new information on how plants respond to growth in orbit and support the hypothesis that spaceflight induces the activation of signaling pathways that compensate for the loss of UPR regulators in the control of downstream transcriptional regulatory networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Lignin’s Indispensable role in orchestrating seed stone formation: Insights from jujuba, peach and pear with future prospective on pitless fruits

A hard endocarp (i.e., stone) inside fruit is a characteristic of drupe fruits such as jujube, peach, mango, etc. Hard stone significantly affects the quality and downstream processing of fruits. The complex aromatic polymer lignin deposition in the secondary cell wall determines stone hardness. Lignin comprises phenylpropanoid units formed by hydroxycinnamoyl alcohol, which includes coniferyl, sinapyl, and p-coumaroyl alcohols. Lignin biosynthesis pathway involves a series of complex enzymatic reactions initiated from phenylalanine ammonia lyase and ends up polymerizing lignin monomers by laccase and peroxidase enzymes. Phytohormones, particularly auxin, gibberellins, and Ca²⁺ signaling, further modulate endocarp lignification by regulating transcriptional networks and lignin biosynthetic genes, thereby fine-tuning secondary cell wall thickening and stone hardness in drupe fruits. Lignin biosynthesis is controlled by both structural genes and transcriptional regulators. The structural genes encoding lignin biosynthetic enzymes include LAC12–1, PAL2, C4H, C3H, CSE, CCoAOMT, F5H, CAD, and PRX1. In addition, several transcription factors regulating secondary cell wall and lignin deposition, such as MYB24, bZIP48, and bZIP33 play key regulatory roles. Conversely, delignification or suppression of stone formation is associated with transcription factors (Pistillata, MYB32, FUL, and REPLUMLESS) and post-transcriptional regulators, including miR397a, miR31-3p, and miR8-5p. Accurate alteration in the expression of these genes will result in the attainment of stoneless fruits for cheap and hazel-free downstream processing.

Fruit endocarp↗
DOE OSTI2021

Heterospecific Neighbor Plants Impact Root Microbiome Diversity and Molecular Function of Root Fungi

Within the forest community, competition and facilitation between adjacent-growing conspecific and heterospecific plants are mediated by interactions involving common mycorrhizal networks. The ability of plants to alter their neighbor’s microbiome is well documented, but the molecular biology of plant-fungal interactions during competition and facilitation has not been previously examined. We used a common soil-plant bioassay experiment to study molecular plant-microbial interactions among rhizosphere communities associated with Pinus taeda (native host) and Populus trichocarpa (non-native host). Gene expression of interacting fungal and bacterial rhizosphere communities was compared among three plant-pairs: Populus growing with Populus , Populus with Pinus , and Pinus with Pinus . Our results demonstrate that heterospecific plant partners affect the assembly of root microbiomes, including the changes in the structure of host specific community. Comparative metatranscriptomics reveals that several species of ectomycorrhizal fungi (EMF) and saprotrophic fungi exhibit different patterns of functional and regulatory gene expression with these two plant hosts. Heterospecific plants affect the transcriptional expression pattern of EMF host-specialists (e.g., Pinus -associated Suillus spp.) on both plant species, mainly including the genes involved in the transportation of amino acids, carbohydrates, and inorganic ions. Alteration of root microbiome by neighboring plants may help regulate basic plant physiological processes via modulation of molecular functions in the root microbiome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Regulatory response to a hybrid ancestral nitrogenase in Azotobacter vinelandii

Biological nitrogen fixation, the microbial reduction of atmospheric nitrogen to bioavailable ammonia, represents both a major limitation on biological productivity and a highly desirable engineering target for synthetic biology. However, the engineering of nitrogen fixation requires an integrated understanding of how the gene regulatory dynamics of host diazotrophs respond across sequence-function space of its central catalytic metalloenzyme, nitrogenase. Here, we interrogate this relationship by analyzing the transcriptome of Azotobacter vinelandii engineered with a phylogenetically inferred ancestral nitrogenase protein variant. The engineered strain exhibits reduced cellular nitrogenase activity but recovers wild-type growth rates following an extended lag period. We find that expression of genes within the immediate nitrogen fixation network is resilient to the introduced nitrogenase sequence-level perturbations. Rather the sustained physiological compatibility with the ancestral nitrogenase variant is accompanied by reduced expression of genes that support trace metal and electron resource allocation to nitrogenase. Our results spotlight gene expression changes in cellular processes adjacent to nitrogen fixation as productive engineering considerations to improve compatibility between remodeled nitrogenase proteins and engineered host diazotrophs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Physiological roles of an Acinetobacter -specific σ factor

ABSTRACT The Gram-negative pathogen Acinetobacter baumannii is considered an “urgent threat” to human health due to its propensity to become antibiotic resistant. Understanding the distinct regulatory paradigms used by A. baumannii to mitigate cellular stresses may uncover new therapeutic targets. Many γ-proteobacteria use the extracytoplasmic function (ECF) σ factor, RpoE, to invoke envelope homeostasis networks in response to stress. Acinetobacter species contain the poorly characterized ECF “SigAb”; however, it is unclear if SigAb has the same physiological role as RpoE. Here, we show that SigAb is a metal stress-responsive ECF that appears unique to Acinetobacter species and distinct from RpoE-like ECFs. We combine promoter mutagenesis, motif scanning, and chromatin immunoprecipitation-sequencing (ChIP-seq) to define the direct SigAb regulon, which consists of genes encoding SigAb itself, the stringent response mediator, RelA, and the uncharacterized small RNA, “SabS.” However, RNA-seq of strains overexpressing SigAb revealed a large, indirect regulon containing hundreds of genes. Metal resistance genes are key elements of the indirect regulon, as CRISPRi knockdown of sigAb or sabS resulted in increased copper sensitivity and excess copper-induced SigAb-dependent transcription. Furthermore, we found that two uncharacterized genes in the sigAb operon, “ aabA ” and “ aabB ,” have anti-SigAb activity. Finally, employing a targeted Tn-seq approach that uses CRISPR-associated transposons, we show that sigAb , aabA , and aabB are important for fitness even during optimal growth conditions. Our work reveals new physiological roles for SigAb and SabS, provides a novel approach for assessing gene fitness, and highlights the distinct regulatory architecture of A. baumannii . IMPORTANCE Acinetobacter baumannii is a hospital-acquired pathogen, and many strains are resistant to multiple antibiotics. Understanding how A. baumannii senses and responds to stress may uncover novel routes to treat infections. Here, we examine how the Acinetobacter -specific transcription factor, SigAb, mitigates stress. We find that SigAb directly regulates only a small number of genes, but indirectly controls hundreds of genes that have substantial impacts on cell physiology. We show that SigAb is required for maximal growth, even during optimal conditions, and is acutely required during growth in the presence of elevated copper. Given that copper toxicity plays roles in pathogenesis and on copper-containing surfaces in hospitals, we speculate that SigAb function may be important in clinically relevant contexts.

Bacon, Emily E. (ORCID:0000000180907689)↗
DOE OSTI2023

TroR is the primary regulator of the iron homeostasis transcription network in the halophilic archaeon Haloferax volcanii

Abstract Maintaining the intracellular iron concentration within the homeostatic range is vital to meet cellular metabolic needs and reduce oxidative stress. Previous research revealed that the haloarchaeon Halobacterium salinarum encodes four diphtheria toxin repressor (DtxR) family transcription factors (TFs) that together regulate the iron response through an interconnected transcriptional regulatory network (TRN). However, the conservation of the TRN and the metal specificity of DtxR TFs remained poorly understood. Here we identified and characterized the TRN of Haloferax volcanii for comparison. Genetic analysis demonstrated that Hfx. volcanii relies on three DtxR transcriptional regulators (Idr, SirR, and TroR), with TroR as the primary regulator of iron homeostasis. Bioinformatics and molecular approaches revealed that TroR binds a conserved cis-regulatory motif located ∼100 nt upstream of the start codon of iron-related target genes. Transcriptomics analysis demonstrated that, under conditions of iron sufficiency, TroR repressed iron uptake and induced iron storage mechanisms. TroR repressed the expression of one other DtxR TF, Idr. This reduced DtxR TRN complexity relative to that of Hbt. salinarum appeared correlated with natural variations in iron availability. Based on these data, we hypothesize that variable environmental conditions such as iron availability appear to select for increasing TRN complexity.

59 BASIC BIOLOGICAL SCIENCES↗