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DOE OSTI2024

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid–base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of β-glucosidase function in Cellvibrio japonicus

Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene-deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics of C. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild-type strain as well as in β-glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non-diauxic growth and metabolite consumptions of the wild-type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially-useful strains. Importantly, the model is able to explain the role of the different β-glucosidases and their behavior under genetic perturbations. Here, this integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

From Optimization to Sampling Through Gradient Flows

Optimization and sampling algorithms play a central role in science and engineering as they enable finding optimal predictions, policies, and recommendations, as well as expected and equilibrium states of complex systems. The notion of “optimality” is formalized by the choice of an objective function, while the notion of an “expected” state is specified by a probabilistic model for the distribution of states. Optimizing rugged objective functions and sampling multimodal distributions is computationally challenging, especially in high-dimensional problems. Here, for this reason, many optimization and sampling methods have been developed by researchers working in disparate fields such as Bayesian statistics, molecular dynamics, genetics, quantum chemistry, machine learning, weather forecasting, econometrics, and medical imaging.

Trillos, N. García↗

DOE OSTI2020

Inferring the Dynamics of the State Evolution During Quantum Annealing

To solve an optimization problem using a commercial quantum annealer, one has to represent the problem of interest as an Ising or a quadratic unconstrained binary optimization (QUBO) problem and submit its coefficients to the annealer, which then returns a user-specified number of low-energy solutions. It would be useful to know what happens in the quantum processor during the anneal process so that one could design better algorithms or suggest improvements to the hardware. However, existing quantum annealers are not able to directly extract such information from the processor. Hence, in this work we propose to use advanced features of D-Wave 2000Q to indirectly infer information about the dynamics of the state evolution during the anneal process. Specifically, D-Wave 2000Q allows the user to customize the anneal schedule, that is, the schedule with which the anneal fraction is changed from the start to the end of the anneal. Furthermore, using this feature, we design a set of modified anneal schedules whose outputs can be used to generate information about the states of the system at user-defined time points during a standard anneal. With this process, called "slicing", we obtain approximate distributions of lowest-energy anneal solutions as the anneal time evolves. We use our technique to obtain a variety of insights into the annealer, such as the state evolution during annealing, when individual bits in an evolving solution flip during the anneal process and when they stabilize, and we introduce a technique to estimate the freeze-out point of both the system as well as of individual qubits.

42 ENGINEERING↗

DOE OSTI2023

Joint optimization of electric bus charging infrastructure, vehicle scheduling, and charging management

High upfront costs of vehicles and charging infrastructure as well as the lack of knowledge related to infrastructure planning and electric bus system operation are major obstacles to the implementation of battery electric buses (BEBs). To tackle the obstacles and promote BEB adoption, a comprehensive optimization framework was developed to address the combined charging infrastructure planning, vehicle scheduling, and charging management problem for BEB systems, with the goal to minimize the total cost of ownership. The problem was formulated as a mixed-integer non-linear problem. A genetic algorithm-based approach was then proposed to solve the problem. Last, three alternative scenarios based on a sub-transit network in Salt Lake City, Utah, were analyzed and compared with the optimal scenario results in the numerical experiments. Our comparison results demonstrate the effectiveness of the proposed model and solution algorithm in determining a cost-efficient planning strategy for BEB systems.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Deinococcus radiodurans UWO298 Dependence on Background Radiation for Optimal Growth

Ionizing radiation is a major environmental variable for cells on Earth, and so organisms have adapted to either prevent or to repair damages caused by it, primarily from the appearance and accumulation of reactive oxygen species (ROS). In this study, we measured the differential gene expression in Deinococcus radiodurans UWO298 cultures deprived of background ionizing radiation (IR) while growing 605 m underground at the Waste Isolation Pilot Plant (WIPP), reducing the dose rate from 72.1 to 0.9 nGy h –1 from control to treatment, respectively. This reduction in IR dose rate delayed the entry into the exponential phase of the IR-shielded cultures, resulting in a lower biomass accumulation for the duration of the experiment. The RNASeq-based transcriptome analysis showed the differential expression of 0.2 and 2.7% of the D. radiodurans genome after 24 and 34 h of growth in liquid culture, respectively. Gene expression regulation after 34 h was characterized by the downregulation of genes involved in folding newly synthesized and denatured/misfolded proteins, in the assimilation of nitrogen for amino acid synthesis and in the control of copper transport and homeostasis to prevent oxidative stress. We also observed the upregulation of genes coding for proteins with transport and cell wall assembly roles. These results show that D. radiodurans is sensitive to the absence of background levels of ionizing radiation and suggest that its transcriptional response is insufficient to maintain optimal growth.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

EVAP: A two-photon imaging tool to study conformational changes in endogenous Kv2 channels in live tissues

A primary goal of molecular physiology is to understand how conformational changes of proteins affect the function of cells, tissues, and organisms. Here, we describe an imaging method for measuring the conformational changes of the voltage sensors of endogenous ion channel proteins within live tissue, without genetic modification. We synthesized GxTX-594, a variant of the peptidyl tarantula toxin guangxitoxin-1E, conjugated to a fluorophore optimal for two-photon excitation imaging through light-scattering tissue. We term this tool EVAP (Endogenous Voltage-sensor Activity Probe). GxTX-594 targets the voltage sensors of Kv2 proteins, which form potassium channels and plasma membrane–endoplasmic reticulum junctions. GxTX-594 dynamically labels Kv2 proteins on cell surfaces in response to voltage stimulation. To interpret dynamic changes in fluorescence intensity, we developed a statistical thermodynamic model that relates the conformational changes of Kv2 voltage sensors to degree of labeling. We used two-photon excitation imaging of rat brain slices to image Kv2 proteins in neurons. We found puncta of GxTX-594 on hippocampal CA1 neurons that responded to voltage stimulation and retain a voltage response roughly similar to heterologously expressed Kv2.1 protein. Our findings show that EVAP imaging methods enable the identification of conformational changes of endogenous Kv2 voltage sensors in tissue.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A Unified Protocol for CRISPR/Cas9-Mediated Gene Knockout in Tephritid Fruit Flies Led to the Recreation of White Eye and White Puparium Phenotypes in the Melon Fly

Abstract Tephritid fruit flies are among the most invasive and destructive agricultural pests worldwide. Over recent years, many studies have implemented the CRISPR/Cas9 genome-editing technology to dissect gene functions in tephritids and create new strains to facilitate their genetics, management, and control. This growing literature allows us to compare diverse strategies for delivering CRISPR/Cas9 components into tephritid embryos, optimize procedures, and advance the technology to systems outside the most thoroughly studied species within the family. Here, we revisit five years of CRISPR research in Tephritidae and propose a unified protocol for candidate gene knockout in fruit flies using CRISPR/Cas9. We demonstrated the efficiency of our protocol by disrupting the eye pigmentation gene white eye (we) in the melon fly, Zeugodacus cucurbitae (Coquillett) (Diptera: Tephritidae). High rates of somatic and germline mutagenesis were induced by microinjecting pre-assembled Cas9-sgRNA complexes through the chorion of embryos at early embryogenesis, leading to the rapid development of new mutant lines. We achieved comparable results when targeting the we orthologue in the oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), illustrating the reliability of our methods when transferred to other related species. Finally, we functionally validated the recently discovered white pupae (wp) loci in the melon fly, successfully recreating the white puparium phenotype used in suppression programs of this and other major economically important tephritids. This is the first demonstration of CRISPR-based genome-editing in the genus Zeugodacus, and we anticipate that the procedures described here will contribute to advancing genome-editing in other non-model tephritid fruit flies.

Entomology↗

DOE OSTI2026

Data for Engineering and Evolution of Yarrowia lipolytica for Producing Lipids from Lignocellulosic Hydrolysates

Yarrowia lipolytica , an oleaginous yeast, shows promise for industrial fermentation due to its robust acetyl-CoA flux and well-developed genetic engineering tools. However, its lack of an active xylose metabolism restricts the conversion of cellulosic sugars to valuable products. To address this, metabolic engineering, and adaptive laboratory evolution (ALE) were applied to the Y. lipolytica PO1f strain, resulting in an efficient xylose-assimilating strain (XEV). Whole-genome sequencing (WGS) of the XEV followed by reverse engineering revealed that the amplification of the heterologous oxidoreductase pathway and a mutation in the GTPase-activating protein gene (YALI0B12100g) might be the primary reasons for improved xylose assimilation in the XEV strain. When a sorghum hydrolysate was used, the XEV strain showed superior xylose consumption and lipid production compared to its parental strain (X123). This study advances our understanding of xylose metabolism in Y. lipolytica and proposes effective metabolic engineering strategies for optimizing lignocellulosic hydrolysates.

Hydrolysate↗

DOE OSTI2025

An Innovative High Throughput Genome Releaser for Rapid and Efficient PCR Screening

High-throughput PCR screening is vital in synthetic biology and metabolic engineering as it allows researchers to rapidly analyze and detect numerous targeted genetic mutation in the genome. Current challenges for high-throughput PCR screening in synthetic biology include efficiently preparing genomic DNA, optimizing protocols for diverse sample types, managing contamination risks, and effectively analyzing the large volumes of data generated while ensuring consistent and accurate results. In this study, we present the development of a High Throughput Genome Releaser (HTGR), an innovative device addressing common challenges in screening PCR. This genome DNA releaser is designed based on a squash method for rapid, cost-effective, and efficient DNA release, optimized for subsequent PCR reactions. After experimenting with various synthetic materials, we selected a plastic that closely replicates the smooth surface and compression properties of microscope slides, ensuring reliable performance. We engineered a device featuring a 96-Well Plate and a shear applicator, operable both manually and automatically, and compatible with standard liquid-handling robot platform. This compatibility enhances ease of use in high-throughput PCR workflows. Additionally, we developed software to support its automatic functions. Our results demonstrated that the specially engineered 96-Well Plate and HTGR can effectively squash fungal spores , which release enough genome DNA for PCR screening. The genome releaser facilitates the preparation of PCR-amplifiable genomic DNA substrate from 96 samples within minutes, eliminates the need for extraction buffers, and is adaptable to a wide range of microorganisms and cells, which could significantly advance biomanufacturing processes.

Yuan, Guoliang [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2021

Understanding Multilevel Selection May Facilitate Management of Arbuscular Mycorrhizae in Sustainable Agroecosystems

Studies in natural ecosystems show that adaptation of arbuscular mycorrhizal (AM) fungi and other microbial plant symbionts to local environmental conditions can help ameliorate stress and optimize plant fitness. This local adaptation arises from the process of multilevel selection, which is the simultaneous selection of a hierarchy of groups. Studies of multilevel selection in natural ecosystems may inform the creation of sustainable agroecosystems through developing strategies to effectively manage crop microbiomes including AM symbioses. Field experiments show that the species composition of AM fungal communities varies across environmental gradients, and that the biomass of AM fungi and their benefits for plants generally diminish when fertilization and irrigation eliminate nutrient and water limitations. Furthermore, pathogen protection by mycorrhizas is only important in environments prone to plant damage due to pathogens. Consequently, certain agricultural practices may inadvertently select for less beneficial root symbioses because the conventional agricultural practices of fertilization, irrigation, and use of pesticides can make these symbioses superfluous for optimizing crop performance. The purpose of this paper is to examine how multilevel selection influences the flow of matter, energy, and genetic information through mycorrhizal microbiomes in natural and agricultural ecosystems, and propose testable hypotheses about how mycorrhizae may be actively managed to increase agricultural sustainability.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Genetic algorithm for demand response: a stackelberg game approach

Demand response (DR) has gained a significant recent interest due to its potential for mitigating many power system problems. Game theory is a very effective tool to be utilized in DR management. In this paper, the DR between a distribution system operator (DSO) and load aggregators (LAs) is designed as a Stackelberg game, where the DSO acts as the leader and LAs are regarded as the followers. Due to the limitations of the centralized solution approaches, a genetic algorithm-based decentralized approach is proposed. To demonstrate the proposed approach, a case study concerning a day-ahead optimization for a real-time pricing market with a single DSO and three LAs is designed and optimized. The proposed approach is able to shift the demand peaks and prove that it has a great potential to be used for the Stackelberg game between a DSO and multiple LAs to fully exploit the potential of DR.

Amasyali, Kadir↗

DOE OSTI2021

Cross-Feeding of a Toxic Metabolite in a Synthetic Lignocellulose-Degrading Microbial Community

The recalcitrance of complex organic polymers such as lignocellulose is one of the major obstacles to sustainable energy production from plant biomass, and the generation of toxic intermediates can negatively impact the efficiency of microbial lignocellulose degradation. Here, we describe the development of a model microbial consortium for studying lignocellulose degradation, with the specific goal of mitigating the production of the toxin formaldehyde during the breakdown of methoxylated aromatic compounds. Included are Pseudomonas putida, a lignin degrader; Cellulomonas fimi, a cellulose degrader; and sometimes Yarrowia lipolytica, an oleaginous yeast. Unique to our system is the inclusion of Methylorubrum extorquens, a methylotroph capable of using formaldehyde for growth. We developed a defined minimal “Model Lignocellulose” growth medium for reproducible coculture experiments. We demonstrated that the formaldehyde produced by P. putida growing on vanillic acid can exceed the minimum inhibitory concentration for C. fimi, and, furthermore, that the presence of M. extorquens lowers those concentrations. We also uncovered unexpected ecological dynamics, including resource competition, and interspecies differences in growth requirements and toxin sensitivities. Finally, we introduced the possibility for a mutualistic interaction between C. fimi and M. extorquens through metabolite exchange. This study lays the foundation to enable future work incorporating metabolomic analysis and modeling, genetic engineering, and laboratory evolution, on a model system that is appropriate both for fundamental eco-evolutionary studies and for the optimization of efficiency and yield in microbially-mediated biomass transformation.

Methylorubrum extorquens↗

DOE OSTI2021

Stellar and Accretion Disk Parameters of the Close Binary HD 50526

We present a photometric and spectroscopic study of HD 50526, an ellipsoidal binary member of the group Double Periodic Variable stars. Performing data mining in photometric surveys and conducting new spectroscopic observations with several spectrographs during 2008–2015, we obtained orbital and stellar parameters of the system. The radial velocities were analyzed with the genetic PIKAIA algorithm, whereas Doppler tomography maps for the Hα and Hβ lines were constructed with the Total Variation Minimization code. An optimized simplex algorithm was used to solve the inverse problem adjusting the light curve with the best stellar parameters for the system. We find an orbital period of 6.ͩ701 ± 0.ͩ001 and a long photometric cycle of 191 ± 2 days. We detected the spectral features of the coldest star and modeled it with a logg=2.79±0.02 dex giant of mass 1.13 ± 0.02 M {sub ⊙} and effective temperature 10500 ± 125 K. In addition, we determine a mass ratio q = 0.206 ± 0.033 and that the hot star is a B-type dwarf of mass 5.48 ± 0.02 M {sub ⊙}. The V-band orbital light curve can be modeled including the presence of an accretion disk around the hotter star. This fills the Roche lobe of the hotter star and has a radius 14.74 ± 0.02 R {sub ⊙} and the temperature at the outer edge is 9400 K. Two bright spots located in the disk account for the global morphology of the light curve. The Doppler tomography maps of Hα and Hβ reveal complex structures of mass fluxes in the system.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Optimizing Oil Production in Oleaginous Yeast by Cell-Wide Measurements and Genome-Based Models (Final Report)

The work funded under this DOE genomics grant focused on characterizing, modifying and developing genetic engineering tools for the then very promising but under-characterized oleaginous yeast Yarrowia lipolytica. To-date, all biodiesel production facilities rely on vegetable oils and animal fats as feedstocks, which are very limited. Nature, on the other hand, is very well equipped for making carbohydrates, which are very plentiful throughout the world and rather well distributed in various forms. While numerous (biochemical and thermochemical) technologies exist presently for carbohydrate conversion to alcohols, there is none available for the cost-efficient production of lipids from carbohydrate feedstocks. Such a technology would have wide-ranging implications in land use, renewable resource utilization and production of transportation fuels with minimal carbon footprint. The global research objectives of this award were to develop tools for cell-wide measurement of metabolites and lipids that, along with transcriptional data, will allow the construction of genome-scale metabolic models, as well as models of transcriptional regulation, that will guide the further metabolic engineering of Yarrowia lipolytica. To achieve these goals, a very strong international and diverse team consisting of research groups at MIT, UCLA, PNNL and Chalmers University was put together, whose specific goals, achievements and breakthroughs are included in this report.

10 SYNTHETIC FUELS↗

DOE OSTI2022

Comparison of Kill Switch Toxins in Plant-Beneficial Pseudomonas fluorescens Reveals Drivers of Lethality, Stability, and Escape

Kill switches provide a biocontainment strategy in which unwanted growth of an engineered microorganism is prevented by expression of a toxin gene. A major challenge in kill switch engineering is balancing evolutionary stability with robust cell killing activity in application relevant host strains. Understanding host-specific containment dynamics and modes of failure helps to develop potent yet stable kill switches. To guide the design of robust kill switches in the agriculturally relevant strain Pseudomonas fluorescens SBW25, we present a comparison of lethality, stability, and genetic escape of eight different toxic effectors in the presence of their cognate inactivators (i.e., toxin–antitoxin modules, polymorphic exotoxin–immunity systems, restriction endonuclease–methyltransferase pair). We find that cell killing capacity and evolutionary stability are inversely correlated and dependent on the level of protection provided by the inactivator gene. Decreasing the proteolytic stability of the inactivator protein can increase cell killing capacity, but at the cost of long-term circuit stability. By comparing toxins within the same genetic context, we determine that modes of genetic escape increase with circuit complexity and are driven by toxin activity, the protective capacity of the inactivator, and the presence of mutation-prone sequences within the circuit. Here, the results of our study reveal that circuit complexity, toxin choice, inactivator stability, and DNA sequence design are powerful drivers of kill switch stability and valuable targets for optimization of biocontainment systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Recent developments in pretreatment technologies on lignocellulosic biomass: Effect of key parameters, technological improvements, and challenges

Lignocellulosic biomass is an inexpensive renewable source that can be used to produce biofuels and bioproducts. The recalcitrance nature of biomass hampers polysaccharide accessibility for enzymes and microbes. Several pretreatment methods have been developed for the conversion of lignocellulosic biomass into value-added products. Yet, these pretreatment methods also produce a wide range of secondary compounds, which are inhibitory to enzymes and microorganisms. The selection of an effective and efficient pretreatment method discussed in the review and its process optimization can significantly reduce the production of inhibitory compounds and may lead to enhanced production of fermentable sugars and biochemicals. Furthermore, evolutionary and genetic engineering approaches are being used for the improvement of microbial tolerance towards inhibitors. Advancements in pretreatment and detoxification technologies may help to increase the productivity of lignocellulose-based biorefinery. Here, we discuss the recent advancements in lignocellulosic biomass pretreatment technologies and strategies for the removal of inhibitors.

09 BIOMASS FUELS↗

DOE OSTI2021

Hydrothermal pretreatment for valorization of genetically engineered bioenergy crop for lipid and cellulosic sugar recovery

Lipids accumulated in the vegetative tissues of cellulosic feedstocks can be a potential raw material for biodiesel and bioethanol production. In this work, bagasse of genetically engineered sorghum was subjected to liquid hot-water pretreatment at 170, 180, and 190°C for different reaction time. Under the optimal pretreatment condition (170°C, 20 min), the residue was enriched in glucan (57.39 ± 2.63 % w/w) and xylan (13.38 ± 0.49 % w/w). The total lipid content of the pretreated residue was 6.81% w/w, similar to that observed in untreated bagasse (6.30% w/w). Pretreatment improved the enzymatic digestibility of bagasse, allowing a recovery of 79% w/w and 86% w/w of glucose and xylose, respectively. The pretreatment and enzymatic saccharification resulted in a 2-fold increase in total lipid in enzymatic residue compared to the original bagasse. Furthermore, pretreatment and enzymatic hydrolysis enabled high sugar recovery while concentrating triglycerides and free fatty acids in the residue.

59 BASIC BIOLOGICAL SCIENCES↗