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At least 1,711 records · Page 95

The Need for Earth-Based Experiments to Inform Microbial Evolution on Planetary Surfaces

Introduction: Historically, the focus of planetary protection at NASA has been on unmanned, robotic missions. Such missions have paved the way for understanding how to implement planetary protection in a feasible and cost-sensitive way. However, with the introduction of crewed missions to Mars in the not-sodistant future, there is a need to better define and understand how to implement planetary protection under new circumstances, as well as understand the risk of contaminating Mars. One unavoidable fact is that microbes will go where humans go. Therefore, it is critical to understand how these microbes may (and will) impact our ability to conduct meaningful, reliable astrobiological science. Microorganisms have spent millions of years evolving to survive in extreme environments here on Earth. Already there are indications that microbes aboard the International Space Station evolve and adapt to life in low earth orbit. The microbes that are eventually taken to Mars with humans will also adapt, potentially causing harmful effects to crew and/or the planetary or astrobiological science conducted. Therefore, it is of critical interest that we evaluate and characterize the potential risks of microbial evolution on Mars. It is expected that microbes carried by humans will begin to evolve to new environments even before landing on Mars, during the several month cruise phase. Once landed, microbes will encounter different stressors within the crew habitats on Mars. During extravehicular activities, venting, or other release events, microbes will find their way out onto the Martian surface. The induced environments around crewed systems will create potentially-favorable conditions for microbes to continue evolving on Mars. Eventually, microbes may find their way beyond the close confines of the crewed area and continue evolving so as to fill new or distant niches on the Martian surface. It is challenging to replicate Martian environments here on Earth, making it nearly impossible to predict the evolutionary changes that microbes would undergo on Mars. But this work is critical. Serial passaging experiments performed by Richard Lenski on E. coli show the dramatic changes microbes can undergo even within a laboratory setting. Furthermore, experiments performed by Michael Baym also demonstrate the power of single mutations in microbial development of antibiotic resistance [3]. Long duration experiments should be performed on a suite of microbes exposed to environments likely to be experienced on the Martian surface. While simulating space environments can be challenging, facilities exist that can achieve individual and combinatorial environmental conditions to simulate space and planetary conditions. Such chambers should be employed for microbial studies. Currently, at the Marshall Space Flight Center, we have used various stressors like drying, vacuum, proton radiation, and ultraviolet light both separately and in combination, to evaluate the survival of cleanroom microbes. Shockingly, several non-spore forming isolates have demonstrated the ability to survive many extreme conditions (manuscript in preparation). These short duration exposures must be augmented with larger and more gradual studies to replicate what microbes might experience in the transition from cruise, to surface habitats, to induced surface environments, and finally true Martian environments. While no Earth-based experiment can perfectly replicate the Martian environment, nor could we test every possible microbe in simulation experimental regimes, efforts should be made to examine the evolutionary potential of the “usual suspects” seen on the ISS or in other crewed environments to begin to fill this important knowledge gap.

Chelsi D. Cassilly↗

Life Finds A Way, the Dynamics of E. Coli Evolution in Microgravity

Investigating the evolutionary dynamics of Escherichia coli in microgravity offers a unique opportunity to understand microbial adaptation to extreme environments. Here, we explored the effects of simulated microgravity (SµG) on gene expression and genome evolution of Escherichia coli REL606, a strain continuously evolved and documented terrestrially for 35 years. We used transcriptomic profiling over a 24-hour growth cycle to examine how short-term exposure to SµG under glucose-limiting and glucose-replete conditions may influence the genetic adaptations in microbial populations. Pathway analyses of differentially expressed genes suggest that SµG may alter cell membrane structure and function across all conditions, while changes to protein synthesis machinery were uniquely observed in glucose-replete samples. Furthermore, altered expression of several prophage genes across conditions in SµG samples and upregulation of general stress response factors hints at the potential for stress-induced mutagenesis in response to microgravity. We further investigated the impact of long-term exposure to SµG on genome evolution and observed a more rapid accumulation of base substitutions and deletions in SµG sample genomes across time. Specifically, mutations in the mraZ and elyC genes suggest a mechanism for increased production of peptidoglycan in the cell membrane. These findings offer insights into bacterial adaptations in long-term microgravity environments and pave the way for further detailed investigations.

Brittney Lozzi↗

Engineering an increase in ethanol production by altering cofactor specificity

The present invention provides for the manipulation of cofactor usage in a recombinant host cell to increase the formation of desirable products. In some embodiments, the invention provides for a recombinant microorganism comprising a mutation in one or more native enzymes such that their cofactor specificity is altered in such a way that overall cofactor usage in the cell is balanced for a specified pathway and there is an increase in a specific product formation within the cell. In some embodiments, endogenous enzymes are replaced by enzymes with an alternate cofactor specificity from a different species.

09 BIOMASS FUELS↗

Source identification by non-negative matrix factorization combined with semi-supervised clustering

Machine-learning methods and apparatus are provided to solve blind source separation problems with an unknown number of sources and having a signal propagation model with features such as wave-like propagation, medium-dependent velocity, attenuation, diffusion, and/or advection, between sources and sensors. In exemplary embodiments, multiple trials of non-negative matrix factorization are performed for a fixed number of sources, with selection criteria applied to determine successful trials. A semi-supervised clustering procedure is applied to trial results, and the clustering results are evaluated for robustness using measures for reconstruction quality and cluster separation. The number of sources is determined by comparing these measures for different trial numbers of sources. Source locations and parameters of the signal propagation model can also be determined. Disclosed methods are applicable to a wide range of spatial problems including chemical dispersal, pressure transients, and electromagnetic signals, and also to non-spatial problems such as cancer mutation.

97 MATHEMATICS AND COMPUTING↗

Fuzzified PaCcET for Economic-Emission Scheduling of Microgrids

In this paper, a new approach is proposed to solve a multi-objective economic-emission scheduling problem in microgrids (MGs) by simultaneously minimizing the energy and emission costs of the MG with various distributed energy resources (DERs). The proposed approach is an extension of a computationally effective multiobjective optimization technique, Pareto concavity elimination transformation (PaCcET). The proposed approach, referred to as Fuzzified-PaCcET, employs a fuzzy logic controller to dynamically revise crossover and mutation rates in the original PaCcET leading to the faster convergence of the solution. The proposed approach finds the best Pareto front, also referred to as a Non-dominated set (NDS) of solutions, instead of finding a single optimal solution. In order to find the solutions on concave areas of the Pareto front, an iterative objective space transformation is performed in the PaCcET algorithm to allow a linear combination of objective functions (in the transformed objective space). The proposed Fuzzified-PaCcET-based scheduling is implemented on a MG with various dispatchable and non-dispatchable DERs to find the set of optimal solutions according to the total fuel cost of DERs, as well as the most optimum environmental cost. In order to extract the best compromise solution (BCS) among NDS of solutions, a fuzzy-based method is implemented. The comparison of the simulation results of the Fuzzified-PaCcET with that of PaCcET shows that Fuzzified-PaCcET can generate better solution with less computational burden.

42 ENGINEERING↗

Viral inhibitory nucleotide sequences and vaccines

The invention relates to inhibitory nucleotide signal sequences or “INS” sequences in the genomes of lentiviruses. In particular the invention relates to the AGG motif present in all viral genomes. The AGG motif may have an inhibitory effect on a virus, for example by reducing the levels of, or maintaining low steady-state levels of, viral RNAs in host cells, and inducing and/or maintaining in viral latency. In one aspect, the invention provides vaccines that contain, or are produced from, viral nucleic acids in which the AGG sequences have been mutated. In another aspect, the invention provides methods and compositions for affecting the function of the AGG motif, and methods for identifying other INS sequences in viral genomes.

Rabadan, Raul↗

BETO 2021 Peer Review - Improving Formate Upgrading by Cupriavidus Necator 2.3.2.111

Formate can be generated by electrocatalytic reduction of CO2 and has been proposed as a soluble intermediate for the storage of carbon and energy. Biological systems capable of assimilating formate could enable conversion of formate generated from low-cost renewable energy and waste CO2 to myriad fuels and chemicals. To that end, the goal of this project is to develop the natural formatotroph Cupriavidus necator as a robust microbial chassis for efficient conversion of formate to value-added products. Using a combination of laboratory evolution and rational engineering, we have identified mutations and genetic deletions that improve its growth rate and biomass yield on formate by 1.2X. Using RNA-seq transcriptomics, we are elucidating the mechanisms that underly these improvements and anticipate this will contribute to our understanding of formate conversion. We have also engineered C. necator to convert formate to the potential polymer precursor 2-hydroxymuconate semialdehyde (2HMS) by introducing an exogenous pathway and deleting competing pathways and demonstrated production and robust growth using a pH-stat bioreactor. Final techno-economic analysis and life-cycle assessment on biological production of 2HMS from formate will enable the DOE and other stake holders to evaluate the economic and environmental impact of this project and related technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Synthetic transcription factor and uses thereof

The present invention provides for a synthetic transcription factor (TF) comprising a first peptide capable of binding a target ligand, a second peptide capable of binding a target DNA, and a peptide linker linking the first and second peptides. The present invention also provide for a system for modulating the mutagenesis frequency of a host cell. The host cell has a mutator rate (R) which is inversely proportional to a phenotypic trait (P).

Chou, Howard H.↗

Spent enrichment media for growth of gene-matched microorganisms

A method for amplifying and detecting microorganisms, such as species of Listeria, is described. The method utilizes gene-matched enrichment media and PCR-based detection. The enrichment media is spent media produced using a modified microorganism containing a plurality of mutations in a selected gene such that the modified microorganism does not contain the PCR signature. Thus, PCR detects only the amplified microorganism of interest, not the modified microorganism. Exemplary methods and kits for amplification and detection of Listeria species are described.

Ozanich, Richard M.↗

Optimizing neural networks

A system and method design and optimize neural networks. The system and method include a data store that stores a plurality of gene vectors that represent diverse and distinct neural networks and an evaluation queue stored with the plurality of gene vectors. Secondary nodes construct, train, and evaluate the neural network and automatically render a plurality of fitness values asynchronously. A primary node executes a gene amplification on a select plurality of gene vectors, a crossing-over of the amplified gene vectors, and a mutation of the crossing-over gene vectors automatically and asynchronously, which are then transmitted to the evaluation queue. The process continuously repeats itself by processing the gene vectors inserted into the evaluation queue until a fitness level is reached, a network's accuracy level plateaus, a processing time period expires, or when some stopping condition or performance metric is met or exceeded.

97 MATHEMATICS AND COMPUTING↗

Electrons to Molecules by Engineering and Evolution: Biological Upgrading of Formate by Cupriavidus necator

Waste carbon from industrial point sources can be captured, stored, and/or transformed using electrochemical conversion or "electrons to molecules" technologies using low-cost renewable electricity. One such process involves electrocatalytic reduction of CO2 to generate formate/formic acid, a C1 carboxylic acid. Formate is a promising potential feedstock for microbial upgrading, as it is water soluble and can be consumed as the sole source of carbon and energy by some microbial species, such as the soil bacterium Cupriavidus necator. Here we will present progress toward improving C. necator as a host for biological conversion of formate to value-added products. Using the power of adaptive laboratory evolution, we were able to isolate mutants of C. necator with significantly faster growth rates on formate. We then sequenced the genomes of these strains, elucidated the metabolic role of the mutations we found, and then used these insights to build rationally engineered strains that outperform even the best evolved isolates. These results highlight the utility of "genome streamlining" as a route for generating platform strains with potential industrial applications.

adaptive laboratory evolution↗

Reagent to label proteins via lysine isopeptide bonds

Covalently cross-linked pilus polymers displayed on the cell surface of Gram-positive bacteria are assembled by class C sortase enzymes. These pilus-specific transpeptidases located on the bacterial membrane catalyze a two-step protein ligation reaction—first, cleaving the LPXTG motif of one pilin protomer to form an acyl-enzyme intermediate, and second, joining the terminal threonine to the nucleophilic lysine residue residing within the pilin motif of another pilin protomer. Informed by the high-resolution crystal structures of corynebacterial pilus-specific sortase (SrtA) and by developing structural variants of the sortase enzyme whose catalytic pocket has been unmasked by activating mutations, we have developed new reagents capable of forming isopeptide bonds in vitro. The reagents disclosed herein can catalyze ligation of isolated SpaA domains in vitro provide a facile and versatile new platform for protein engineering and bio-conjugation that has major implications for biotechnology.

Clubb, Robert T.↗

Combustion control using spiking neural networks

A system that controls a combustion engine stores network vectors in a memory that represent diverse and distinct spiking neural networks. The system decodes the network vectors and trains and evaluates the spiking neural networks. The system duplicates selected network vectors and crosses-over the duplicated network vectors that represent modified spiking neural networks. The system mutates the crossed-over duplicated network vectors by randomly modifying one or more portions of the crossing-over duplicated network vectors. The system meter exhaust gas into an intake manifold when an engine temperature exceeds a threshold, an engine load exceeds a threshold, an engine's rotation-per-minute rate exceeds a threshold, and a fuel flow exceeds a threshold. The system modifies fuel flow into an engine's combustion chamber on a cycle-to-cycle basis by the trained spiking neural network.

Puente, Brian P. Maldonado↗

Small molecule biosensors

Biosensors for small molecules can be used in applications that range from metabolic engineering to orthogonal control of transcription. Biosensors are produced based on a ligand-binding domain (LBD) using a method that, in principle, can be applied for any target molecule. The LBD is fused to either a fluorescent protein or a transcriptional activator and is destabilized by mutation such that the fusion accumulates only in cells containing the target ligand. The power of this method is illustrated by developing biosensors for digoxin and progesterone. Addition of ligand to cells expressing a biosensor activates transcription in yeast, mammalian cells and plants, with a dynamic range of up to about 100-fold or more. The biosensors are used to improve the biotransformation of pregnenolone to progesterone in yeast and to regulate CRISPR activity in mammalian cells. This work provides a general methodology to develop biosensors for a broad range of molecules.

Mandell, Daniel J.↗

Modified biosensors and biocatalysts and methods of use

Biosensors including a nucleic acid encoding a PcaU protein, a PobR protein, a CatM protein, a PcaR protein, or a TphR protein are provided. In some examples, the biosensors include a promoter regulated by the sensed molecule operably linked to a reporter gene. The biosensors may be included in a vector or in cells including one or more of the biosensors or vectors. Modified chorismate pyruvate lyase (UbiC) and modified paraoxonase (PON1) proteins including one or more amino acid substitutions are provided. Finally, methods of selecting biocatalysts with increased activity including transforming a library of cells expressing a biosensor with one or more nucleic acids encoding one or more mutations in a gene involved in a biosynthesis pathway, determining activity of the reporter protein; and selecting a cell with increased reporter protein activity as expressing a biocatalyst with increased activity are provided.

Jha, Ramesh K.↗

Source identification by non-negative matrix factorization combined with semi-supervised clustering

Machine-learning methods and apparatus are provided to solve blind source separation problems with an unknown number of sources and having a signal propagation model with features such as wave-like propagation, medium-dependent velocity, attenuation, diffusion, and/or advection, between sources and sensors. In exemplary embodiments, multiple trials of non-negative matrix factorization are performed for a fixed number of sources, with selection criteria applied to determine successful trials. A semi-supervised clustering procedure is applied to trial results, and the clustering results are evaluated for robustness using measures for reconstruction quality and cluster separation. The number of sources is determined by comparing these measures for different trial numbers of sources. Source locations and parameters of the signal propagation model can also be determined. Disclosed methods are applicable to a wide range of spatial problems including chemical dispersal, pressure transients, and electromagnetic signals, and also to non-spatial problems such as cancer mutation.

Alexandrov, Boian S.↗

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗