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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

G4Boost: a machine learning-based tool for quadruplex identification and stability prediction

Background: G-quadruplexes (G4s), formed within guanine-rich nucleic acids, are secondary structures involved in important biological processes. Although every G4 motif has the potential to form a stable G4 structure, not every G4 motif would, and accurate energy-based methods are needed to assess their structural stability. Here, we present a decision tree-based prediction tool, G4Boost, to identify G4 motifs and predict their secondary structure folding probability and thermodynamic stability based on their sequences, nucleotide compositions, and estimated structural topologies. Results: G4Boost predicted the quadruplex folding state with an accuracy greater then 93% and an F1-score of 0.96, and the folding energy with an RMSE of 4.28 and R 2 of 0.95 only by the means of sequence intrinsic feature. G4Boost was successfully applied and validated to predict the stability of experimentally-determined G4 structures, including for plants and humans. Conclusion: G4Boost outperformed the three machine-learning based prediction tools, DeepG4, Quadron, and G4RNA Screener, in terms of both accuracy and F1-score, and can be highly useful for G4 prediction to understand gene regulation across species including plants and humans.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

From 2D to 4D: a containerized workflow and browser to explore dynamic chromatin architecture

Background Characterizing the physical organization of the genome is essential for understanding long-range gene regulation, chromatin compartmentalization, and epigenetic accessibility. Hi-C experiments generate two-dimensional (2D) genome-wide contact maps of chromatin interactions by capturing the spatial proximity between genomic loci, which reveal interaction frequencies but lack the spatial resolution needed to interpret the three-dimensional (3D) genome structure(s). Emerging evidence suggests that epigenetic regulation is closely linked to 3D genome architecture, and that structural changes over time (4D) drive key biological processes in development, disease, and environmental response. Thus, integrating 3D structure with functional data is critical for a more complete understanding of genome regulation. Previous work, most notably the 4DHiC chromosome modeling framework, has shown that physical multi-dimensional modeling approaches rooted in polymer physics and molecular dynamics can resolve these structures at biologically meaningful resolutions by integrating temporal Hi-C data with physical constraints to uncover dynamic chromosome reorganization. Thus, molecular dynamics simulations, constrained by Hi-C contact matrices, can resolve fine-scale structural changes and reveal functionally significant transitions in chromatin conformation. Results Herein, we present the 4D Genome Browser Workflow (4DGBWorkflow) and the 4D Genome Browser (4DGB). The algorithm is based on the 4DHiC method, and the containerized tool is an end-to-end workflow that can transform, filter, and view 4D epigenomics and chromatin datasets, allowing non-specialists to apply three-dimensional modeling principles to diverse datasets and experimental conditions. The software executes on a laptop running macOS, Linux or Windows. From input Hi-C files (.hic), the 4DGBWorkflow produces 3D reconstructions of chromosomes, integrates the reconstruction with track data (e.g., epigenetic marks, transcriptome profiles), and provides comparative visualization of the results in a single workflow. Conclusions The 4DGBWorkflow and 4D Genome Browser are open-source tools for comparative analysis and visualization of 4D chromosome datasets, including chromatin architecture and epigenomic signals. Automatic integration of Hi-C data with molecular dynamics democratizes the construction of time resolved 3D genome structures, simplifying complex simulations and data integration schemes.

3D Genome Browser↗
DOE OSTI2022

Adapted laboratory evolution of Thermotoga sp. strain RQ7 under carbon starvation

Abstract Objective Adaptive laboratory evolution (ALE) is an effective approach to study the evolution behavior of bacterial cultures and to select for strains with desired metabolic features. In this study, we explored the possibility of evolving Thermotoga sp. strain RQ7 for cellulose-degrading abilities. Results Wild type RQ7 strain was subject to a series of transfers over six and half years with cellulose filter paper as the main and eventually the sole carbon source. Each transfer was accompanied with the addition of 50 μg of Caldicellulosiruptor saccharolyticus DSM 8903 genomic DNA. A total of 331 transfers were completed. No cellulose degradation was observed with the RQ7 cultures. Thirty three (33) isolates from six time points were sampled and sequenced. Nineteen (19) of the 33 isolates were unique, and the rest were duplicated clones. None of the isolates acquired C. saccharolyticus DNA, but all accumulated small-scale mutations throughout their genomes. Sequence analyses revealed 35 mutations that were preserved throughout the generations and another 15 mutations emerged near the end of the study. Many of the affected genes participate in phosphate metabolism, substrate transport, stress response, sensory transduction, and gene regulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models

Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources. We argue that these models are the equivalent of foundation models in natural language processing in their utility, as they encode within them chromatin state in its different aspects, providing useful representations that allow quick deployment of accurate models of gene regulation. We demonstrate this premise by leveraging the recently created Sei model to develop simple, interpretable models of intron retention, and demonstrate their advantage over models based on the DNA language model DNABERT-2. Our work also demonstrates the impact of chromatin state on the regulation of intron retention. Using representations learned by Sei, our model is able to discover the involvement of transcription factors and chromatin marks in regulating intron retention, providing better accuracy than a recently published custom model developed for this purpose.

Biochemistry & Molecular Biology↗
DOE OSTI2023

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor’s sensitivity (EC 50 ) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC 50 . Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Validation and functional characterization of transcription factors in wheat using cell-free protein expression and high-throughput sequencing technologies

Transcription factors (TFs) are critical biomolecules that control and regulate gene expression in every organism. In wheat, there are 3,606 genes annotated as putative TFs which are primarily uncharacterized. In this work, using a DAP-seq approach, we have validated and characterized a select number of TFs (5bl and 5dl) belonging to GeBP family of proteins, responsible for plant cellular growth, development, and differentiation. We found that top DNA binding motifs for 5bl and 5dl are (G/T)N(T/G)GTGGT and (C/G)AA(C/G)AA respectively. These motifs/peaks are enriched in the promoter regions of the genes, which are associated with rRNA biogenesis and protein maturation pathways. This study is also a first demonstration that DAP-seq can be applied for such a complex and large genome such as wheat.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

New principles of self‐organization created through the interplay of DNA condensates, microtubules, and motors

Bioinspired design—which holds great promise for a new generation of materials that are robust to defects, scalable under green manufacture, environmentally responsive, and programmably reconfigurable—requires mastery over molecular self-organization. Yet, from its specific mechanisms to most general architectures, the principles governing self-organization remain poorly understood and not even fully enumerated. For living systems, one obvious architectural principle is the modular reuse of a few simple molecular components in myriad combinations to achieve more complex phenomena. For example, the mechanical tasks of a cell are driven by the nonequilibrium dynamics of cytoskeletal filaments and molecular motors—the same filaments and motors, reprogrammed by a variety of modulators, perform tasks ranging from cell movement to division. Similarly, many compartmentalization tasks are performed by liquid-like condensates of simple components, which act as membraneless organelles to localize particular molecules in space and time (e.g. for gene regulation or RNA processing). In a few cases, condensates combine and interact with the cytoskeleton to create still more complex phenomena, e.g. the nucleation of microtubule asters from the centrosome (a protein condensate) to form the mitotic spindle during cell division. Very little is known about the fundamental mechanisms of such filament-plus-condensate phenomena. Despite few examples, the landscape of behaviors that can be achieved through the combination of condensates, filaments, and motors appears vast. However, exploration has been hindered by a lack of systems that have sufficiently programmable and dynamically tunable interactions between component condensates, filaments, and motors. We proposed to combine programmable DNA condensates, filamentous microtubules, and light-controlled motors into self-organizing systems whose principles go beyond those that have been observed in nature. In one limit, our systems will use microtubules and motors to create the molecular analog of a network of roads, which will organize droplets of DNA condensates capable of carrying molecular cargo. DNA condensates coupled to motors will flow from one microtubule aster hub to another, with their direction and timing controlled by DNA circuits. In another limit, microtubules will swim through bulk DNA condensates and exhibit strong interactions with boundaries between different types of condensates. Microtubule swimmers will reflect, get trapped, or refract at boundaries, under a mechanical analog of the classical optical index of refraction. DNA condensates having different mechanical indexes of refraction will be used to construct the analog of optical lenses, so that microtubule swimmers can be manipulated like light—collimated, diffracted, focused, and sorted based on properties analogous to wavelength. These two limits define two new architectures, within which multiple new mechanistic principles for self-organization will be discovered and explored. To explore these architectures, the motor-based coupling between DNA condensates and filaments will be controlled in time and space through the use of opto-proteins that create reversible links between DNA condensates and motors upon illumination. For each principle of interest, patterns of light will create virtual experiments by defining patterns of activity where DNA condensates walk along filaments, or filaments swim through condensates, and patterns of inactivity which will serve either as controls, or as boundary conditions vital to create the desired phenomena. This research serves the goals of Basic Energy Sciences Biomolecular Material Program by elucidating the principles by which the emergent, nonequilibrium behavior of collections of DNA condensates, motors, and microtubules can be programmed by environmental light patterns to create complex motion and materials transport. Because DNA condensates can be readily coupled to virtually any high performance nanomaterial, from carbon nanotubes, to metal nanoparticles, to light harvesting systems, this work provides a path to the construction, self-maintenance and reconfiguration of materials relevant to the Department of Energy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Human RNome Project draft human RNome sequence of GM12878, B-cell line, obtained by mass-spectrometry sequencing, long-read sequencing and short-read sequencing.

Here we report the first draft of the human RNome sequence, a reference map of RNA chemical modifications in a human B-cell line. RNA carries a diverse repertoire of chemical modifications that regulate gene expression, cellular function, and responses to physiological and pathological cues. Yet, unlike the genome, no reference map of RNA modifications is available for any human cell. To generate this resource, the Human RNome Project Consortium analyzed a shared RNA preparation from the well-characterized GM12878 B-cell line using short-read sequencing, long-read direct RNA sequencing, and mass spectrometry, generating more than 7.1 billion sequencing reads spanning approximately 1.2 trillion nucleotides. The resulting maps of the human RNome reveal that RNA modifications are organized according to function, transcript architecture, and cellular identity. Modifications concentrate at functional centers of ribosomal and transfer RNAs, follow the canonical topology of N6-methyladenosine in coding transcripts, and form coordinated hotspots in immune regulatory genes. This first reference human RNome provides a foundation for understanding how RNA chemistry shapes cellular identity, human disease, and the development of RNA-based therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Acidification in the U.S. Southeast: Causes, Potential Consequences and the Role of the Southeast Ocean and Coastal Acidification Network

Coastal acidification in southeastern U.S. estuaries and coastal waters is influenced by biological activity, run-off from the land, and increasing carbon dioxide in the atmosphere. Acidification can negatively impact coastal resources such as shellfish, finfish, and coral reefs, and the communities that rely on them. Organismal responses for species located in the U.S. Southeast document large negative impacts of acidification, especially in larval stages. For example, the toxicity of pesticides increases under acidified conditions and the combination of acidification and low oxygen has profoundly negative influences on genes regulating oxygen consumption. In corals, the rate of calcification decreases with acidification and processes such as wound recovery, reproduction, and recruitment are negatively impacted. Minimizing the changes in global ocean chemistry will ultimately depend on the reduction of carbon dioxide emissions, but adaptation to these changes and mitigation of the local stressors that exacerbate global acidification can be addressed locally. The evolution of our knowledge of acidification, from basic understanding of the problem to the emergence of applied research and monitoring, has been facilitated by the development of regional Coastal Acidification Networks (CANs) across the United States. This synthesis is a product of the Southeast Coastal and Ocean Acidification Network (SOCAN). SOCAN was established to better understand acidification in the coastal waters of the U.S. Southeast and to foster communication among scientists, resource managers, businesses, and governments in the region. Here we review acidification issues in the U.S. Southeast, including the regional mechanisms of acidification and their potential impacts on biological resources and coastal communities. We recommend research and monitoring priorities and discuss the role SOCAN has in advancing acidification research and mitigation of and adaptation to these changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Metagenomic Insights Into the Microbial Iron Cycle of Subseafloor Habitats

Microbial iron cycling influences the flux of major nutrients in the environment (e.g., through the adsorptive capacity of iron oxides) and includes biotically induced iron oxidation and reduction processes. The ecological extent of microbial iron cycling is not well understood, even with increased sequencing efforts, in part due to limitations in gene annotation pipelines and limitations in experimental studies linking phenotype to genotype. This is particularly true for the marine subseafloor, which remains undersampled, but represents the largest contiguous habitat on Earth. To address this limitation, we used FeGenie, a database and bioinformatics tool that identifies microbial iron cycling genes and enables the development of testable hypotheses on the biogeochemical cycling of iron. Herein, we survey the microbial iron cycle in diverse subseafloor habitats, including sediment-buried crustal aquifers, as well as surficial and deep sediments. We inferred the genetic potential for iron redox cycling in 32 of the 46 metagenomes included in our analysis, demonstrating the prevalence of these activities across underexplored subseafloor ecosystems. We show that while some processes (e.g., iron uptake and storage, siderophore transport potential, and iron gene regulation) are near-universal, others (e.g., iron reduction/oxidation, siderophore synthesis, and magnetosome formation) are dependent on local redox and nutrient status. Additionally, we detected niche-specific differences in strategies used for dissimilatory iron reduction, suggesting that geochemical constraints likely play an important role in dictating the dominant mechanisms for iron cycling. Overall, our survey advances the known distribution, magnitude, and potential ecological impact of microbe-mediated iron cycling and utilization in sub-benthic ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

RNA-Seq Reveals that Light and Darkness Are Different Stimuli in Freshwater Heterotrophic Actinobacteria

Light is a ubiquitous source of both energy and information in surface environments, and regulates gene expression not only in photosynthetic microorganisms, but in a broad range of photoheterotrophic and heterotrophic microbes as well. Actinobacteria are keystone species in surface freshwater environments, where the ability to sense light could allow them to coordinate periods of nutrient uptake and metabolic activity with primary production. The model freshwater Actinobacteria Rhodoluna (R.) lacicola strain MWH-Ta8 and Aurantimicrobium(A.) photophilum strain MWH-Mo1 grow faster in the light than in the dark, but do not use light energy to support growth. Here, we characterize transcription throughout a light-dark cycle in R. lacicola and A. photophilum. In both species, some genes encoding carbohydrate metabolism and storage are upregulated in the light. However, expression of genes of the TCA cycle is only coordinated with light availability in R. lacicola. In fact, the majority of genes that respond to light and darkness in these two species are different, even though their light-responsive phenotypes are similar. The ability to respond to light and darkness may be widespread in freshwater Actinobacteria, but the genetic networks controlled by these two stimuli may vary significantly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The PagKNAT2/6b-PagBOP1/2a Regulatory Module Controls Leaf Morphogenesis in Populus

Leaf morphogenesis requires precise regulation of gene expression to achieve organ separation and flat-leaf form. The poplar KNOTTED-like homeobox gene PagKNAT2/6b could change plant architecture, especially leaf shape, in response to drought stress. However, its regulatory mechanism in leaf development remains unclear. In this work, gene expression analyses of PagKNAT2/6b suggested that PagKNAT2/6b was highly expressed during leaf development. Moreover, the leaf shape changes along the adaxial-abaxial, medial-lateral, and proximal-distal axes caused by the mis-expression of PagKNAT2/6b demonstrated that its overexpression (PagKNAT2/6b OE) and SRDX dominant repression (PagKNAT2/6b SRDX) poplars had an impact on the leaf axial development. The crinkle leaf of PagKNAT2/6b OE was consistent with the differential expression gene PagBOP1/2a (BLADE-ON-PETIOLE), which was the critical gene for regulating leaf development. Further study showed that PagBOP1/2a was directly activated by PagKNAT2/6b through a novel cis-acting element “CTCTT”. Together, the PagKNAT2/6b-PagBOP1/2a module regulates poplar leaf morphology by affecting axial development, which provides insights aimed at leaf shape modification for further improving the drought tolerance of woody plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Structure and Function of the Zinc Binding Protein ZrgA from Vibrio cholerae

ATP binding cassette (ABC) transporters are the primary means by which bacteria acquire trace elements from the environment. They rely on solute binding proteins (SBPs) to bind the relevant substrate and deliver it to the integral membrane permease for ATP-powered import into the cytoplasm. SBPs of cluster A-I are known to facilitate the transport of essential metals zinc, manganese, and iron, and many have been characterized to date. A group of ABC transporter operons dubbed zinc-regulated genes (zrg) have recently been shown to transport zinc with putative SBPs (zrgA) bearing no homology to the classical cluster A-I family, and a recent crystal structure of a representative protein from Pseudomonas aeruginosa shows no structural similarity to classical SBPs. Thus, the ZrgA proteins appear to represent a newly discovered family of zinc SBPs widespread among Gram-negative bacteria, including human pathogens. Here, we have determined the crystal structure of ZrgA from Vibrio cholerae and characterized its zinc binding in vitro and function in vivo. We also assessed the role of a histidine-rich sequence that appears to be a hallmark of ZrgA proteins that is particularly long in V. cholerae ZrgA. The results show that the zrgA gene is critical to the function of the operon, consistent with a function as an SBP in this system. Further, the His-rich region is not essential to the function of ZrgA, but it does provide additional zinc binding sites in vitro. The structure and zinc binding data for ZrgA reveal interesting differences between it and its homologue from P. aeruginosa, illustrating diversity within this little-studied protein family.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Effects of Perfluorinated Alkyl Substances (PFAS) on Amphibian Body and Liver Conditions: Is Lipid Metabolism Being Perturbed throughout Metamorphosis?

Per- and polyfluoroalkyl substances (PFAS) may interact with peroxisome proliferator activated receptors (PPARs) and alter lipid homeostasis. Using Xenopus laevis, we investigated the effect of PFAS on (a) lipid homeostasis and whether this correlated to changes in body and hepatic condition; (b) the expression of hepatic genes regulated by PPAR; and (c) the hepatic lipidome. We chronically exposed tadpoles to 0.5 µg/L of either PFOS, PFHxS, PFOA, PFHxA, a binary mixture of PFOS and PFHxS (0.5 µg/L of each), or a control, from NF stage 52 through metamorphic climax. Growth, development, and survival were not affected, but we detected a sex-specific decrease in body condition at NF 66 (6.8%) and in hepatic condition (16.6%) across metamorphic climax for male tadpoles exposed to PFOS. We observed weak evidence for the transient downregulation of apolipoprotein-V (apoa5) at NF 62 in tadpoles exposed to PFHxA. Acyl-CoA oxidase 1 (acox1) was downregulated only in males exposed to PFHxS (Ln(Fold Change) = −0.54). We detected PFAS-specific downregulation of structural glycerophospholipids, while semi-quantitative profiling detected the upregulation in numerous glycerophospholipids, sphingomyelins, and diglycerides. Overall, our findings indicate that PFAS can induce sex-specific effects that change across larval development and metamorphosis. We demonstrate that PFAS alter lipid metabolism at environmentally relevant concentrations through divergent mechanisms that may not be related to PPARs, with an absence of effects on body condition, demonstrating the need for more molecular studies to elucidate mechanisms of PFAS-induced lipid dysregulation in amphibians and in other taxa.

Bushong, Anna (ORCID:0000000249630578)↗
DOE OSTI2021

Isolation of Histone from Sorghum Leaf Tissue for Top Down Mass Spectrometry Profiling of Potential Epigenetic Markers

Histones belong to a family of highly conserved proteins in eukaryotes. They pack DNA into nucleosomes as functional units of chromatin. Post-translational modifications (PTMs) of histones, which are highly dynamic and can be added or removed by enzymes, play critical roles in regulating gene expression. In plants, epigenetic factors including histone PTMs are related to their adaptive responses to the environment. Understanding the molecular mechanisms of epigenetic control can bring unprecedented opportunities for engineering solutions to increase the resilience of crops to climate change. Herein, we describe a protocol to isolate the nuclei and purify histones from sorghum leaf tissue. The extracted histones can be analyzed as their intact forms by top-down mass spectrometry (MS) coupled to online reversed-phase (RP) liquid chromatography (LC). Combinations and stoichiometry of multiple PTMs on the same histone proteoform can be readily identified. In addition, histone tail clipping can be detected using the top-down LC-MS workflow thus yielding the global PTM profile of core histones (H4, H2A, H2B, H3). By comparing the PTM profile among samples corresponding to different conditions (e.g. drought vs. control), potential epigenetic marks can be discovered as targets for further characterization using approaches such as chromatin immunoprecipitation – sequencing (ChIP-seq).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Light-Controlled Fermentations for Microbial Chemical and Protein Production

Microbial cell factories offer a sustainable alternative for producing chemicals and recombinant proteins from renewable feedstocks. However, overburdening a microorganism with genetic modifications can reduce host fitness and productivity. This problem can be overcome by using dynamic control: inducible expression of enzymes and pathways, typically using chemical- or nutrient-based additives, to balance cellular growth and production. Optogenetics offers a non-invasive, highly tunable, and reversible method of dynamically regulating gene expression. Here, we describe how to set up light-controlled fermentations of engineered Escherichia coli and Saccharomyces cerevisiae for the production of chemicals or recombinant proteins. We discuss how to apply light at selected times and dosages to decouple microbial growth and production for improved fermentation control and productivity, as well as the key optimization considerations for best results. Additionally, we describe how to implement light controls for lab-scale bioreactor experiments. As a result, these protocols facilitate the adoption of optogenetic controls in engineered microorganisms for improved fermentation performance.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1999

Gravisensing, apoptosis, and drug recovery in Taxus cell suspensions

Haploid and diploid cell suspensions of Taxus spp. were examined for their adaptive plasticity in response to simulated microgravity, unit gravity, and hypergravity. Cell suspensions produced the taxane, paclitaxel, (TAXOL (R)), which is useful for the treatment of various cancers. Amyloplasts contributed to taxane ring biosynthesis and to drug release at the cell wall. Drug-producing cells reacted as gravisensing osmotic tensiometers. In stressed cells, amyloplasts docked and fused in clusters to sites on the plasmalemma before taxane discharge into the culture medium. In simulated microgravity and compared to all other treatments, taxane production was reduced nearly 100-fold. The percent paclitaxel of total taxanes remained 3-to 6-fold greater, and biomass doubled. When p53-independent programmed cell death was induced, taxanes were released into the culture medium as free molecules (soluble and insoluble) or bound to membranes, nuclear fragments, xylan residues, and other particulate materials. Unit gravity and especially hypergravity promoted xylogenesis and significant drug overproduction. A model relating families of >touch = (TCH), taxane early response (TER), nuclear cycling, and apoptosis-regulating genes to gravisensing, cell wall modifications, and to taxane recovery accounted for most but not all of the observations.

Paclitaxel/biosynthesis↗