Rational engineering enables co-crystallization and structural determination of the HIV-1 matrix-tRNA complex
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Bou-Nader, Charles (ORCID:0000000309550204)↗
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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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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↗
Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection
Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.
59 BASIC BIOLOGICAL SCIENCES↗
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↗
Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction
TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a Transcription Factor (TF)-gene regulatory network (TRN), which is modeled via a Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. Here we have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.
59 BASIC BIOLOGICAL SCIENCES↗
Technical Resilience Navigator
The Technical Resilience Navigator (TRN) is a systematic approach to identifying vulnerabilities with energy and water systems, and prioritizing solutions that reduce risk. The ultimate outcome of the TRN is a set of actionable resilience solutions that address the site's most important gaps in resilience and enhance the ability to maintain mission continuity. The TRN is designed to step users through this planning process, providing a framework to: assign roles and responsibilities; collect and document information and data; document key inputs and outputs for each of the TRN modules (Site Level Planning, Baseline Development, Risk Assessment, Solution Development, and Solution Prioritization); document prioritized list of resilience solutions; and track progress through the entire process. Currently "software as a service" at the listed website.
Rotondo, Julia↗
Federal Energy Management Program’s Technical Resilience Navigator: Communications Plan
The Federal Energy Management Program is working to develop a standard, agency-neutral energy and water technical resilience navigator (TRN) to assist federal agencies with their resilience planning efforts, building on agency-expressed needs. The TRN Communications Plan defines how the project team and FEMP will generate awareness of the TRN, recruit validators, encourage its use across federal agencies, increase awareness among various audiences, and solicit user feedback on its usefulness including suggestions for improvement. In support of this purpose, the communications plan characterizes the key audiences, messages, communications channels, and activities needed to help ensure success. The role of communications is to define strategy and ensure leadership and consistency in messaging, writing, outreach, media relations, editing, and design related to the TRN.
99 GENERAL AND MISCELLANEOUS↗
The Technical Resilience Navigator: Risk-Informed Decision Making to Support Resilience Planning
The Technical Resilience Navigator (TRN) helps organizations manage the risk to critical functions at a site from disruptions in energy and water services. Developed in partnership by the Pacific Northwest National Laboratory and the National Renewable Energy Laboratory, under direction and funding from the Department of Energy’s Federal Energy Management Program, the TRN helps organizations enhance their resilience to a variety of disruptive events, both natural and human-caused, that could interrupt normal operations for an unknown period of time. This report provides an overview of the TRN’s risk-informed approach to resilience planning and formally describes how it uses a streamlined risk model to identify effective strategies for improving resilience.
99 GENERAL AND MISCELLANEOUS↗
Privacy-Preserving Real-Time Action Detection in Intelligent Vehicles Using Federated Learning-Based Temporal Recurrent Network
This study introduces a privacy-preserving approach for the real-time action detection in intelligent vehicles using a federated learning (FL)-based temporal recurrent network (TRN). This approach enables edge devices to independently train models, enhancing data privacy and scalability by eliminating central data consolidation. Our FL-based TRN effectively captures temporal dependencies, anticipating future actions with high precision. Extensive testing on the Honda HDD and TVSeries datasets demonstrated robust performance in centralized and decentralized settings, with competitive mean average precision (mAP) scores. The experimental results highlighted that our FL-based TRN achieved an mAP of 40.0% in decentralized settings, closely matching the 40.1% in centralized configurations. Notably, the model excelled in detecting complex driving maneuvers, with mAPs of 80.7% for intersection passing and 78.1% for right turns. These outcomes affirm the model’s accuracy in action localization and identification. The system showed significant scalability and adaptability, maintaining robust performance across increased client device counts. The integration of a temporal decoder enabled predictions of future actions up to 2 s ahead, enhancing the responsiveness. Our research advances intelligent vehicle technology, promoting safety and efficiency while maintaining strict privacy standards.
33 ADVANCED PROPULSION SYSTEMS↗
Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network
Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida KT2440, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida KT2440 and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.
09 BIOMASS FUELS↗
Genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma
Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to multiple lines of therapies. Therefore, we not only need the ability to predict which patients are at high risk for disease progression but also a means to understand the mechanisms underlying their risk. Here, we report a transcriptional regulatory network (TRN) for MM inferred from cross-sectional multi-omics data from 881 patients that predicts how 124 chromosomal abnormalities and somatic mutations causally perturb 392 transcription regulators of 8549 genes to manifest in distinct clinical phenotypes and outcomes. We identified 141 genetic programs whose activity profiles stratify patients into 25 distinct transcriptional states and proved to be more predictive of outcomes than did mutations. The coherence of these programs and accuracy of our network-based risk prediction was validated in two independent datasets. We observed subtype-specific vulnerabilities to interventions with existing drugs and revealed plausible mechanisms for relapse, including the establishment of an immunosuppressive microenvironment. Investigation of the t(4;14) clinical subtype using the TRN revealed that 16% of these patients exhibit an extreme-risk combination of genetic programs (median progression-free survival of 5 months) that create a distinct phenotype with targetable genes and pathways.
59 BASIC BIOLOGICAL SCIENCES↗
Revealing systematic changes in the transcriptome during the transition from exponential growth to stationary phase
ABSTRACT The composition of bacterial transcriptomes is determined by the transcriptional regulatory network (TRN). The TRN regulates the transition from one physiological state to another. Here, we use independent component analysis to monitor the composition of the transcriptome during the transition from the exponential growth phase to the stationary phase. With Escherichia coli K-12 MG1655 as a model strain, we trigger the transition using carbon, nitrogen, and sulfur starvation. We find that (i) the transition to the stationary phase accompanies common transcriptome changes, including increased stringent responses and reduced production of cellular building blocks and energy regardless of the limiting element; (ii) condition-specific changes are strongly associated with transcriptional regulators ( e.g. , Crp, NtrC, CysB, Cbl) responsible for metabolizing the limiting element; and (iii) the shortage of each limiting element differentially affects the production of amino acids and extracellular polymers. This study demonstrates how the combination of genome-scale datasets and new data analytics reveals the fundamental characteristics of a key transition in the life cycle of bacteria. IMPORTANCE Nutrient limitations are critical environmental perturbations in bacterial physiology. Despite its importance, a detailed understanding of how bacterial transcriptomes are adjusted has been limited. By utilizing independent component analysis (ICA) to decompose transcriptome data, this study reveals key regulatory events that enable bacteria to adapt to nutrient limitations. The findings not only highlight common responses, such as the stringent response, but also condition-specific regulatory shifts associated with carbon, nitrogen, and sulfur starvation. The insights gained from this work advance our knowledge of bacterial physiology, gene regulation, and metabolic adaptation.
Lim, Hyun Gyu (ORCID:0000000204692388)↗
Incorporating climate change into risk-informed resilience planning
In response to the development of portfolio-wide Climate Action Plans by federal agencies, federal sites are working to incorporate the impacts of climate change into their resilience assessments. However, it can be challenging to incorporate climate change scenarios into resilience assessments given the uncertainty inherent in climate change modeling. Incorporating these factors into a resilience plan requires an understanding of what the different climate scenarios mean, as well as how to estimate potential impacts of climate change on hazard occurrence on a regional, or even local, scale under different scenarios. We discuss approaches to incorporating this data into risk-informed resilience assessment processes, such as those implemented in the Department of Energy’s (DOE) Federal Energy Management Program’s (FEMP) Technical Resilience Navigator (TRN) and the Sustainability Performance Division’s (SPD) Vulnerability Assessment and Resilience Plan (VARP) Risk Assessment Tool. We also describe the climate scenarios and the availability of hazard data for site resilience planning, based on modeling included in the International Panel on Climate Change (IPCC), the National Climate Assessment (NCA), and state-level reports. We present examples from the TRN risk assessment and the VARP Risk Assessment Tool to illustrate how sensitivity analysis can be used to incorporate climate change projections into the resilience planning process.
Rabinowitz, Hannah S.↗
Terrain-Relative Navigation with Neuro-Inspired Elevation Encoding
Terrain-relative navigation (TRN) encompasses a wide variety of algorithms that perform localization with respect to the terrain below a flying vehicle. In traditional approaches, measurements of the terrain are matched to a map carried onboard. This work presents a terrain-relative navigation filter with a position measurement inspired by neural activity associated with positioning in nature. Here, the filter is shown to produce accurate position measurements that outperform popular optimization and template matching methods given poor prior knowledge of the position. The proposed method is also better-suited to distributed implementation than optimization-based methods.
33 ADVANCED PROPULSION SYSTEMS↗
Audits and Energy Efficiency in Support of Resilience
Presentation provides an overview of how facility energy and water efficiency audits can provide resilience benefits, how NREL's REopt Lite web tool optimizes the economic and resilience benefits of distributed energy resources, and how FEMP's Technical Resilience Navigator assesses risk to a site's critical functions and prioritizes solutions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗