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Results for “Genetic interaction”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dual transposon sequencing profiles the genetic interaction landscape in bacteria

Gene redundancy complicates systematic characterization of gene function as single-gene deletions may not produce discernible phenotypes. We report dual transposon sequencing (dual Tn-seq), a platform for assaying the fitness of a comprehensive double mutant pool in parallel. Dual Tn-seq couples random barcode transposon site sequencing with the Cre-lox system, enabling deep sampling of 73% of the 1.3 million possible double gene deletions in Streptococcus pneumoniae. The genetic interactions identified span a wide range of biochemical processes, revealing new factors in presumably well-studied pathways, exemplified by a cytidine triphosphate synthase PyrJ. Moreover, this approach should permit further investigation of growth condition–specific genetic interactions. Because dual Tn-seq does not require the construction of a large array of single mutants, it should be readily adaptable to various microorganisms.

CTP synthesis↗
DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗
DOE OSTI2025

Deep Active Learning based Experimental Design

This project is an implementation of the Deep Active Learning (DeepAL) framework from the paper Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics

Zhu, Haonan [Lawrence Livermore National Laborator↗
DOE OSTI2026

Adsorption Hysteresis Under Control: Tuning Host–Guest Interactions via a Genetic Algorithm

Mesoporous adsorbent materials offer a large volumetric capacity; however, cyclic adsorption/desorption processes in these systems often suffer from hysteresis and may require a significant pressure swing to access this capacity. To mitigate hysteresis, a proposed strategy is to include nucleation sites on the walls of the mesoporous material to facilitate droplet and bubble formation, lowering the free energy barriers to the respective phase transitions. It is unclear, however, what combination of adsorbate− adsorbent interactions and spatial patterning would be beneficial for a given application, considering that improvements to some sorption properties may come at the expense of other attributes. To understand these interconnected observables, we examine two model systems, planar-slit and cylindrical pores with tunable interaction sites, using GPU-accelerated transition matrix Monte Carlo simulations. The simulations provide a free energy map of the pressure−adsorption space in a matter of minutes, which we use to track adsorption isotherm characteristics as a function of adsorbent properties. We then leverage the rapid acquisition of simulation data to construct a genetic algorithm to iteratively modify interaction sites of the slit-pore wall to minimize the hysteresis of this system without sacrificing uptake. We find that the adsorption branch of the isotherm is easily modulated via the average host−guest interaction strength, but desorption is only adjustable if there is a suitable bubble nucleation site. Within the context of a slit-pore system, we identify relative interaction strengths and patch sizes required to gain control over both branches of the hysteresis loop.

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

Genetic variations and their interaction with thirdhand smoke exposure on anxiety and memory in Collaborative Cross mice

Thirdhand smoke (THS) is linked to adverse health effects, but the effect of genetic variations on behavioral outcomes is poorly understood. To investigate this, we assessed anxiety- and memory-related behaviors in 820 mice from 21 strains of the genetically diverse Collaborative Cross (CC) mouse that were exposed to THS from 4 through 10 weeks of age. Anxiety was evaluated with a light/dark box assay with a previously established risk score system. Females were generally more sensitive: THS reduced anxiety risk in strains CC013, CC019, and CC051, but increased risk in CC036 and CC061, while males showed no significant effects. Memory was tested using passive avoidance: impairments were observed in both sexes in CC016 and CC019, with sex-dependent effects in CC002 and CC051. A genome-wide association study identified 2,347 SNPs associated with anxiety and 1,568 SNPs with memory, with 32 and 85 SNPs, respectively, interacting with THS exposure. Enrichment analyses revealed distinct biological processes underlying susceptibility, including axonogenesis, synapse organization, cognition, and learning and memory. KEGG pathway analysis identified distinct genetic pathways, including GTPase binding and GTPase regulatory activity, that act as critical molecular switches in the brain that regulate synaptic plasticity, dendritic spine structure, and neuronal signaling, directly influencing anxiety-like behaviors and memory formation. These findings show that THS exposure affects neurobehavioral outcomes in a sex- and genotype-dependent manner, highlighting critical gene-environment interactions and providing a foundation for mechanistic insights into THS neurotoxicity

Anxiety↗
DOE OSTI2026

Predictive models of the genetic bases underlying budding yeast fitness in multiple environments

Abstract The ability of organisms to adapt and survive depends on the effects of genes and the environment on fitness. However, the multigenic nature of fitness and genotype-by-environment interactions hinder our understanding of the genetic basis of fitness. Here, we established fitness prediction models for 35 environments using machine learning and existing fitness data and different genetic variant types for a Saccharomyces cerevisiae population. Models revealed that the predictive ability of genetic variants varied across environments, with copy number variants explaining the majority of fitness variation in most cases. Model interpretation showed that different variant types identified distinct gene sets associated with predictive variants. These gene sets were significantly enriched in experimentally validated genes affecting fitness in only a subset of environments, indicating that many genes influencing fitness remain unexplored. Notably, non-experimentally validated genes were more important than validated ones for fitness predictions. Gene contributions to predictions were both isolate- and environment-dependent, pointing to gene-by-gene and gene-by-environment interactions. Furthermore, models uncovered experimentally validated and novel candidate genetic interactions for a well-characterized stress, the fungicide benomyl. These findings highlight the feasibility of identifying the genetic basis of fitness by using different genetic variant types and offer novel targets for future functional analysis.

DNA copy number variations↗
DOE OSTI2025

Analysis of biofilm assembly by large area automated AFM

Biofilms are complex microbial communities critical in medical, industrial, and environmental contexts. Understanding their assembly, structure, genetic regulation, interspecies interactions, and environmental responses is key to developing effective control and mitigation strategies. While atomic force microscopy (AFM) offers critically important high-resolution insights on structural and functional properties at the cellular and even sub-cellular level, its limited scan range and labor-intensive nature restricts the ability to link these smaller scale features to the functional macroscale organization of the films. We begin to address this limitation by introducing an automated large area AFM approach capable of capturing high-resolution images over millimeter-scale areas, aided by machine learning for seamless image stitching, cell detection, and classification. Large area AFM is shown to provide a very detailed view of spatial heterogeneity and cellular morphology during the early stages of biofilm formation which were previously obscured. Using this approach, we examined the organization of Pantoea sp. YR343 on PFOTS-treated glass surfaces. Our findings reveal a preferred cellular orientation among surface-attached cells, forming a distinctive honeycomb pattern. Detailed mapping of flagella interactions suggests that flagellar coordination plays a role in biofilm assembly beyond initial attachment. Additionally, we use large-area AFM to characterize surface modifications on silicon substrates, observing a significant reduction in bacterial density. This highlights the potential of this method for studying surface modifications to better understand and control bacterial adhesion and biofilm formation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Correlational selection and genetic architecture shape the evolution of the leaf economics spectrum in a perennial grass

The generality of the worldwide leaf economics spectrum (LES) has made it a pillar of trait-based ecological research. Yet, few studies have examined the processes shaping the evolution of the LES within species, in part, because most species occupy only a small portion of the LES. Here, to address this gap, we took advantage of the distinct leaf economics strategies present in different ecotypes of the phenotypically diverse perennial grass Panicum virgatum (switchgrass) to generate a genetic mapping population, which we planted in common gardens at three sites spanning 12 degrees of latitude in the central United States. With this genetic mapping population, we evaluated two potentially interacting causes of LES evolution: 1) genetic architecture, where multiple traits are influenced by either the same gene (pleiotropy) or by genes in close physical proximity (genetic linkage), and 2) correlational selection, where selection acts on traits in combination rather than in isolation. We found that shared genetic architecture influenced covariation between photosynthetic rate (A MASS ) and leaf nitrogen (N MASS ) and between A MASS and leaf mass per area (LMA). We also found that correlational selection favored the trait combinations predicted by the LES (e.g., high LMA with low N MASS or low LMA with high N MASS ) and disfavored other, mismatched trait combinations at two of the three sites. Together, these results demonstrate how the evolution of an integrated LES within species can arise from multiple evolutionary causes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue (multi-agent, multi-turn interactions) improves the quality of NLP-based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an genetic algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and a novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing empirical evidence for dialogue-driven hazard analysis.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗
DOE OSTI2025

Rational Modulation of Plant Root Development Using Engineered Cytokinin Regulators

Achieving precise control over quantitative developmental phenotypes is a key objective in plant biology. Recent advances in synthetic biology have enabled tools to reprogram entire developmental pathways; however, the complexity of designing synthetic genetic programs and the inherent interactions between various signaling processes remains a critical challenge. Here, we leverage Type-B response regulators to modulate the expression of genes involved in cytokinin-dependent growth and development processes. We rationally engineered these regulators to modulate their transcriptional activity (i.e., repression or activation) and potency while reducing their sensitivity to cytokinin. By localizing the expression of these engineered transcription factors using tissue-specific promoters, we can predictably tune cytokinin-regulated traits. As a proof of principle, we deployed this synthetic system in Arabidopsis thaliana to either decrease or increase the number of lateral roots. The simplicity and modularity of our approach makes it an ideal system for controlling other developmental phenotypes of agronomic interest in plants.

Cell signaling↗
DOE OSTI2025

Enhancing Hydrogen Production from Bioenergy Crops via Photoreforming

Photoreforming perennial bioenergy crops (willow, Miscanthus , and poplar) has the potential to produce H 2 with reduced environmental impacts. To understand the compositional effects of the biomass on the average rate of H 2 production over the first 30 min of reaction ( r H 2 ), the r H 2 values of model biomass component (i.e., cellulose, hemicellulose, and lignin) mixtures were compared with those from the raw biomass. The higher cellulose or hemicellulose content in multicomponent mixtures resulted in higher r H 2 , whereas lignin reduced the hydrogen production rate. However, with raw biomass, the ratio of biomass components alone did not determine the r H 2 via photoreforming, with rates of hydrogen production for different varieties of willow ranging between 1.9 μmol h -1 and 12.3 μmol h -1 , 11.8 μmol h -1 for a poplar, and 6.8 μmol h -1 for a miscanthus biomass. In addition, comparable r H 2 values of raw poplar and its extracted cellulose via an IonoSolv treatment indicated the possibility of using raw biomass materials without delignification for generating H 2 via photoreforming. Importantly, r H 2 was positively correlated with the interaction between water and the biomass, as assessed by NMR relaxation via an examination of the T 1 /T 2 ratio. A stronger water-biomass interaction resulted in a higher r H 2 . Genetic modification of biomass has been suggested as a putative way to improve the r H 2 of biomass with an enhanced interaction with water. This research enhances the understanding of factors influencing H 2 production from lignocellulosic biomass by photoreforming and supports the breeding and management of perennial biomass crops to maximize H 2 yields while minimizing land area requirements.

biomass↗
DOE OSTI2025

Atmospheric methane consumption in arid ecosystems acts as a reverse chimney and is accelerated by plant-methanotroph biomes

Drylands cover one-third of the Earth’s surface and are one of the largest terrestrial sinks for methane. Understanding the structure–function interplay between members of arid biomes can provide critical insights into mechanisms of resilience toward anthropogenic and climate-change-driven environmental stressors—water scarcity, heatwaves, and increased atmospheric greenhouse gases. This study integrates in situ measurements with culture-independent and enrichment-based investigations of methane-consuming microbiomes inhabiting soil in the Anza-Borrego Desert, a model arid ecosystem in Southern California, United States. The atmospheric methane consumption ranged between 2.26 and 12.73 μmol m 2 h −1 , peaking during the daytime at vegetated sites. Metagenomic studies revealed similar soil-microbiome compositions at vegetated and unvegetated sites, with Methylocaldum being the major methanotrophic clade. Eighty-four metagenome-assembled genomes were recovered, six represented by methanotrophic bacteria (three Methylocaldum , two Methylobacter , and uncultivated Methylococcaceae ). The prevalence of copper-containing methane monooxygenases in metagenomic datasets suggests a diverse potential for methane oxidation in canonical methanotrophs and uncultivated Gammaproteobacteria. Five pure cultures of methanotrophic bacteria were obtained, including four Methylocaldum . Genomic analysis of Methylocaldum isolates and metagenome-assembled genomes revealed the presence of multiple stand-alone methane monooxygenase subunit C paralogs, which may have functions beyond methane oxidation. Furthermore, these methanotrophs have genetic signatures typically linked to symbiotic interactions with plants, including tryptophan synthesis and indole-3-acetic acid production. Based on in situ fluxes and soil microbiome compositions, we propose the existence of arid-soil reverse chimneys, an empowered methane sink represented by yet-to-be-defined cooperation between desert vegetation and methane-consuming microbiomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Exploring phage–host interactions in Burkholderia cepacia complex bacterium to reveal host factors and phage resistance genes using CRISPRi functional genomics and transcriptomics

Complex interactions of bacteriophages with their bacterial hosts determine phage host range and infectivity. While phage defense systems and host factors have been identified in model bacteria, they remain challenging to predict in non-model bacteria. In this paper, we integrate functional genomics and transcriptomics to investigate phage–host interactions, revealing active phage resistance and host factor genes in Burkholderia cenocepacia K56-2. Burkholderia cepacia complex species are commonly found in soil and are opportunistic pathogens in immunocompromised patients. We studied infection of B. cenocepacia K56-2 with Bcep176, a temperate phage isolated from Burkholderia multivorans. A genome-wide dCas9 knockdown library targeting B. cenocepacia K56-2 was constructed, and a pooled infection experiment identified 63 novel genes or operons coding for candidate host factors or phage resistance genes. The activities of a subset of candidate host factor and resistance genes were validated via single-gene knockdowns. Transcriptomics of B. cenocepacia K56-2 during Bcep176 infection revealed that expression of genes coding for host factor and resistance candidates identified in this screen was significantly altered during infection by 4 h post-infection. Identifying which bacterial genes are involved in phage infection is important to understand the ecological niches of B. cenocepacia and its phages, and for designing phage therapies.

Bacterial Pathogenesis↗
DOE OSTI2025

Intraspecific Reaction Norm Variation Controls the Eco-Evolutionary Consequences of Environmental Change

As environmental change accelerates globally, understanding concurrent organismal, species, and community responses is increasingly vital. Here, we examine these collective responses by incorporating genotype-specific thermal reaction norms into an eco-evolutionary predator-prey model, allowing us to track simultaneous phenotypic, ecological, and evolutionary responses to environmental change within ecological communities. We show that the reaction norms expressed by genotypes within a population determine how a community switches between different eco-evolutionary outcomes with changes in temperature. We identify how different components of phenotypic variation in thermal reaction norms—environmental (E), additive environmental and genetic (E + G), and gene-by-environment interactions (G × E)—influence eco-evolutionary dynamics and outcomes as temperature changes. Furthermore, our findings underscore how complex eco-evolutionary responses to environmental change ultimately emerge from variation in reaction norms among genotypes, offering new mechanistic insights into environmental impacts on adaptation, the maintenance of phenotypic and genetic variation, and ecological stability, which is crucial for understanding and predicting eco-evolutionary effects of rapid environmental change in the future.

Eco-evolutionary↗
DOE OSTI2024

Connectivity, Pathology, and ApoE4 Interactions Predict Longitudinal Tau Spatial Progression and Memory

ABSTRACT Tau pathology spread into neocortex indicates a transition from healthy aging to Alzheimer's disease (AD). Connectivity between tau epicenters and later accumulating regions of cortex has been proposed as a mechanism of tau spread, but how this relationship changes with greater AD pathology burden or genotype is not understood. We investigated tau accumulation in two key regions, precuneus and inferior temporal cortex, using resting state functional connectivity (rsFC) and longitudinal PET imaging from a multicohort sample of cognitively unimpaired older adults. We examined how baseline tau PET, Aβ PET, and ApoE4 genotype status interact with rsFC between hippocampus and these downstream regions to predict rate of tau accumulation in neocortex. We found that the 3‐way interaction between connectivity, baseline tau, and baseline Aβ or ApoE4 status was associated with neocortical tau accumulation in precuneus and inferior temporal cortex. In addition, baseline tau, Aβ, and ApoE4 status also moderated the association between connectivity and rate of memory decline. Together, these results suggest that the extent and distribution of future tau accumulation may be predicted by the interaction of baseline connectivity, AD pathology, and genetic risk.

Neurosciences & Neurology↗
DOE OSTI2025

Bacteria-mediated dsRNA delivery for mosquito-borne virus control

Mosquito-borne viruses represent an increasing global public health threat, exacerbated by urbanisation and climate change, thus making effective mosquito control essential. RNA interference (RNAi), a sequence-specific gene regulation mechanism, can be a flexible vector control tool. RNAi effectors, such as double-stranded RNA (dsRNA), can target mosquito genes or the viruses they carry, disrupting development or suppressing infection. However, current RNAi delivery methods are ineffective. Engineered bacterial symbionts offer a promising alternative for delivery, as they can produce dsRNA directly within mosquitoes. However, bacterial RNAi delivery in mosquitoes remains underexplored. We review emerging genetic tools, insights from RNAi and bacteria–mosquito interactions to outline priorities for realising bacterial RNAi as an efficient and sustainable vector control strategy.

Biological and medical sciences↗
DOE OSTI2025

MPK6-mediated phosphorylation destabilizes MYC2 and attenuates its transcriptional activity in jasmonate signaling

Given the role of MYC2 as a pivotal regulator in the jasmonate (JA) signaling pathway, influencing the expression of a multitude of downstream genes (Zander et al., 2020), understanding the regulatory dynamics of MYC2 is essential for unraveling plant responses to stress conditions and hormonal signals. Previous studies on the regulation of MYC2 by MPK6 have reported conflicting findings. Takahashi et al. (2007) reported that MPK6 acts as a negative regulator of MYC2, whereas Sethi et al. (2014) proposed a positive regulatory role. Despite these findings, the precise genetic and biochemical mechanisms underlying the MPK6–MYC2 interaction remain poorly understood, highlighting the need for further investigation.

Im, Jong Hee [Michigan State University, East Lans↗
DOE OSTI2025

Covalent Drug Binding in Live Cells Monitored by Mid-Infrared Quantum Cascade Laser Spectroscopy: Photoactive Yellow Protein as a Model System

The detection of drug-target interactions in live cells enables analysis of therapeutic compounds in a native cellular environment. Recent advances in spectroscopy and molecular biology have facilitated the development of genetically encoded vibrational probes like nitriles that can sensitively report on molecular interactions. Nitriles are powerful tools for measuring electrostatic environments within condensed media like proteins, but such measurements in live cells have been hindered by low signal-to-noise ratios. In this study, we design a spectrometer based on a double-beam quantum cascade laser (QCL)-based transmission infrared (IR) source with balanced detection that can significantly enhance sensitivity to nitrile vibrational probes embedded in proteins within cells compared to a conventional FTIR spectrometer. Here, using this approach, we detect small-molecule binding in Escherichia coli, with particular focus on the interaction between para-Coumaric acid (pCA) and nitrile-incorporated photoactive yellow protein (PYP). This system effectively serves as a model for investigating covalent drug binding in a cellular environment. Notably, we observe large spectral shifts of up to 15 cm –1 for nitriles embedded in PYP between the unbound and drug-bound states directly within bacteria, in agreement with observations for purified proteins. Such large spectral shifts are ascribed to the changes in the hydrogen-bonding environment around the local environment of nitriles, accurately modeled through high-level molecular dynamics simulations using the AMOEBA force field. Our findings underscore the QCL spectrometer’s ability to enhance sensitivity for monitoring drug–protein interactions, offering new opportunities for advanced methodologies in drug development and biochemical research.

chromophores↗