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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The impact of G-quadruplex dynamics on inter-tetrad electronic couplings: a hybrid computational study

The G-quadruplex is a fascinating nucleic acid motif with implications in biology, medicine, and nanotechnologies. G-quadruplexes can form in the telomeres at the edges of chromosomes and in other guanine-rich regions of the genome. They can also be engineered for exploitation as biological materials for nanodevices. Their higher stiffness and higher charge transfer rates make them better candidates in nanodevices than duplex DNA. For the development of molecular nanowires, it is important to optimize electron transport along the wire axis. One powerful basis to do so is by manipulating the structure, based on known effects that structural changes have on electron transport. Here, for this work, we investigate such effects, by a combination of classical simulations of the structure and dynamics and quantum calculations of electronic couplings. We find that this structure–function relationship is complex. A single helix shape parameter alone does not embody such complexity, but rather a combination of distances and angles between stacked bases influences charge transfer efficiency. By analyzing linear combinations of shape descriptors for different topologies, we identify the structural features that most affect charge transfer efficiency. We discuss the transferability of the proposed model and the limiting effects of inherent flexibility.

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

Resilient Crop Production for the Bioeconomy: Frontier Science for the Bioeconomy Workshop Series

A bioeconomy based on energy, chemicals, and bioproducts derived from nonfood crops promotes U.S. prosperity, energy independence, and national security. Crop productivity (i.e., yield) affects profitability for farmers, whereas biomass and seed oil quality affects profitability for biorefineries through the conversion efficiency of feedstock crops into energy, chemicals, and bioproducts. Scientific breakthroughs in plant genetics and genomics, ecological processes, bioprocessing, microbial engineering and design, and conversion technologies have improved the potential market viability of products derived from nonfood crops specifically cultivated as biomass and seed oil feedstocks. Improving understanding of a plant’s complex responses to withstand or recover from stress is crucial to maintaining feedstock yield and quality, particularly in challenging production zones. Stress may be related to biotic or abiotic (i.e., nonliving) environmental conditions that negatively affect plant growth, development, and productivity. Research and development to enhance the environmental resilience of feedstock crops will enable improved production by minimizing losses during stress and improving production on currently economically nonviable marginal lands.

09 BIOMASS FUELS↗
DOE OSTI2024

CRISPR-Cas9/Cas12a systems for efficient genome editing and large genomic fragment deletions in Aspergillus niger

CRISPR technology has revolutionized fungal genetic engineering by accelerating the pace and expanding the feasible scope of experiments in this field. Among various CRISPR-Cas systems, Cas9 and Cas12a are widely used in genetic and metabolic engineering. In filamentous fungi, both Cas9 and Cas12a have been utilized as CRISPR nucleases. In this work we first compared efficacies and types of genetic edits for CRISPR-Cas9 and -Cas12a systems at the polyketide synthase (albA) gene locus in Aspergillus niger. By employing a tRNA-based gRNA polycistronic cassette, both Cas9 and Cas12a have demonstrated equally remarkable editing efficacy. Cas12a showed potential superiority over Cas9 protein when one gRNA was used for targeting, achieving an editing efficiency of 86.5% compared to 31.7% for Cas9. Moreover, when employing two gRNAs for targeting, both systems achieved up to 100% editing efficiency for single gene editing. In addition, the CRISPR-Cas9 system has been reported to induce large genomic deletions in various species. However, its use for engineering large chromosomal segments deletions in filamentous fungi still requires optimization. Here, we engineered Cas9 and -Cas12a-induced large genomic fragment deletions by targeting various genomic regions of A. niger ranging from 3.5 kb to 40 kb. Our findings demonstrate that targeted engineering of large chromosomal segments can be achieved, with deletions of up to 69.1% efficiency. Furthermore, by targeting a secondary metabolite gene cluster, we show that fragments over 100 kb can be efficiently and specifically deleted using the CRISPR-Cas9 or -Cas12a system. Overall, in this paper, we present an efficient multi-gRNA genome editing system utilizing Cas9 or Cas12a that enables highly efficient targeted editing of genes and large chromosomal regions in A. niger.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Application of functional genomics for domestication of novel non-model microbes

Abstract With the expansion of domesticated microbes producing biomaterials and chemicals to support a growing circular bioeconomy, the variety of waste and sustainable substrates that can support microbial growth and production will also continue to expand. The diversity of these microbes also requires a range of compatible genetic tools to engineer improved robustness and economic viability. As we still do not fully understand the function of many genes in even highly studied model microbes, engineering improved microbial performance requires introducing genome-scale genetic modifications followed by screening or selecting mutants that enhance growth under prohibitive conditions encountered during production. These approaches include adaptive laboratory evolution, random or directed mutagenesis, transposon-mediated gene disruption, or CRISPR interference (CRISPRi). Although any of these approaches may be applicable for identifying engineering targets, here we focus on using CRISPRi to reduce the time required to engineer more robust microbes for industrial applications. One-Sentence Summary The development of genome scale CRISPR-based libraries in new microbes enables discovery of genetic factors linked to desired traits for engineering more robust microbial systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Rapid Annotation of Photosynthetic Systems (RAPS): automated algorithm to generate genome-scale metabolic networks from algal genomes

Algae have mounting potential to serve as platform strains for engineering efforts, but tools for these species lag behind traditional model systems like E. coli and yeast. Metabolic models are one such tool that would enhance our ability to rationally engineer algae but current automated algorithms to build models do not perform well for algal genomes. We introduce a novel software pipeline, Rapid Annotation of Photosynthetic Systems (RAPS), which leverages manual curation efforts of published models to create high quality first draft metabolic networks for new species. We compared models produced by our pipeline to published models and found that these models are able to capture more genes than the published models and are more predictive of experimentally determined growth rate. We used RAPS to automate the creation of 8 first draft metabolic models of new species to enable the further study of metabolism in these species.

09 BIOMASS FUELS↗
DOE OSTI2019

Functional genomics of the rapidly replicating bacterium Vibrio natriegens by CRISPRi

We present that the fast-growing Gram-negative bacterium Vibrio natriegens is an attractive microbial system for molecular biology and biotechnology due to its remarkably short generation time and metabolic prowess. However, efforts to uncover and utilize the mechanisms underlying its rapid growth are hampered by the scarcity of functional genomic data. Here, we develop a pooled genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) interference (CRISPRi) screen to identify a minimal set of genes required for rapid wild-type growth. Targeting 4,565 (99.7%) of predicted protein-coding genes, our screen uncovered core genes comprising putative essential and growth-supporting genes that are enriched for respiratory pathways. We found that 96% of core genes were located on the larger chromosome 1, with growth-neutral duplicates of core genes located primarily on chromosome 2. Lastly, our screen also refines metabolic pathway annotations by distinguishing functional biosynthetic enzymes from those predicted on the basis of comparative genomics. Taken together, this work provides a broadly applicable platform for high-throughput functional genomics to accelerate biological studies and engineering of V. natriegens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Fatty Acid and Alcohol Metabolism in Pseudomonas putida: Functional Analysis Using Random Barcode Transposon Sequencing.

With its ability to catabolize a wide variety of carbon sources and a growing engineering toolkit, Pseudomonas putida KT2440 is emerging as an important chassis organism for metabolic engineering. Despite advances in our understanding of the organism, many gaps remain in our knowledge of the genetic basis of its metabolic capabilities. The gaps are particularly noticeable in our understanding of both fatty acid and alcohol catabolism, where many paralogs putatively coding for similar enzymes coexist, making biochemical assignment via sequence homology difficult. To rapidly assign function to the enzymes responsible for these metabolisms, we leveraged random barcode transposon sequencing (RB-Tn-Seq). Global fitness analyses of transposon libraries grown on 13 fatty acids and 10 alcohols produced strong phenotypes for hundreds of genes. Fitness data from mutant pools grown on fatty acids of varying chain lengths indicated specific enzyme substrate preferences and enabled us to hypothesize that DUF1302/DUF1329 family proteins potentially function as esterases. From the data, we also postulate catabolic routes for the two biogasoline molecules isoprenol and isopentanol, which are catabolized via leucine metabolism after initial oxidation and activation with coenzyme A (CoA). Because fatty acids and alcohols may serve as both feedstocks and final products of metabolic-engineering efforts, the fitness data presented here will help guide future genomic modifications toward higher titers, rates, and yields. IMPORTANCE To engineer novel metabolic pathways into P. putida , a comprehensive understanding of the genetic basis of its versatile metabolism is essential. Here, we provide functional evidence for the putative roles of hundreds of genes involved in the fatty acid and alcohol metabolism of the bacterium. These data provide a framework facilitating precise genetic changes to prevent product degradation and to channel the flux of specific pathway intermediates as desired.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Validating genome-wide CRISPR-Cas9 function improves screening in the oleaginous yeast Yarrowia lipolytica

Genome-wide mutational screens are central to understanding the genetic underpinnings of evolved and engineered phenotypes. The widespread adoption of CRISPR-Cas9 genome editing has enabled such screens in many organisms, but identifying functional sgRNAs still remains a challenge. Here, we developed a methodology to quantify the cutting efficiency of each sgRNA in a genome-scale library, and in doing so improve screens in the biotechnologically important yeast Yarrowia lipolytica. Screening in the presence and absence of native DNA repair enabled high-throughput quantification of sgRNA function leading to the identification of high efficiency sgRNAs that cover 94% of genes. Library validation enhanced the classification of essential genes by identifying inactive guides that create false negatives and mask the effects of successful disruptions. Quantification of guide effectiveness also creates a dataset from which determinants of CRISPR-Cas9 can be identified. Finally, application of the library identified novel mutations for metabolic engineering of high lipid accumulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Maize tissue culture, transformation, and genome editing

The importance of maize (Zea mays L.) to global agriculture, world economy, and food security is widely known and increasing. Current maize breeding programs are deeply integrated with recent and rapid technological advances in genome sequencing, computational biology, and new genotyping and phenotyping technologies. Transformation and genome editing capabilities are a central hub to an array of advanced molecular and breeding approaches to crop improvement. Tissue culture and somatic embryogenesis play essential and central roles in maize transformation biology. Synergistic applications of maize transformation, advanced genomics, and genome editing provide a potent interdependent triad for functional genomics research and advanced molecular breeding. Implementation of advanced capabilities to transform maize and conduct genome editing will profoundly influence the dynamics of global agriculture ushering in a new era of varietal development and molecular breeding. With over 60.9 Mha planted in 2019 alone, biotech maize accounts for 31% of the world’s maize production. Up to 10% higher yields are achieved using new varieties generated using genetic modification technologies compared to similar conventional varieties. By extension, the impact new varietal releases developed through genome editing will likely be more significant. Further, advances in transformation and genome editing technologies will facilitate an even wider applicability for the development of new varieties with increasingly complex traits. The introduction of biochemical pathways and the use of synthetic biology have become increasingly more attainable. The future is of genotype independent maize transformation and genome editing, as the working platform will impact world agriculture, global food security, and plant science well into the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Genome sequence and analysis of a stress-tolerant, wild-derived strain of Saccharomyces cerevisiae used in biofuels research

The genome sequences of more than 100 strains of the yeast Saccharomyces cerevisiae have been published. Unfortunately, most of these genome assemblies contain dozens to hundreds of gaps at repetitive sequences, including transposable elements, tRNAs, and subtelomeric regions, which is where novel genes generally reside. Relatively few strains have been chosen for genome sequencing based on their biofuel production potential, leaving an additional knowledge gap. Here, we describe the nearly complete genome sequence of GLBRCY22-3 (Y22-3), a strain of S. cerevisiae derived from the stress-tolerant wild strain NRRL YB-210 and subsequently engineered for xylose metabolism. After benchmarking several genome assembly approaches, we developed a pipeline to integrate Pacific Biosciences (PacBio) and Illumina sequencing data and achieved one of the highest quality genome assemblies for any S. cerevisiae strain. Specifically, the contig N50 is 693 kbp, and the sequences of most chromosomes, the mitochondrial genome, and the 2-micron plasmid are complete. Our annotation predicts 92 genes that are not present in the reference genome of the laboratory strain S288c, over 70% of which were expressed. We predicted functions for 43 of these genes, 28 of which were previously uncharacterized and unnamed. Remarkably, many of these genes are predicted to be involved in stress tolerance and carbon metabolism and are shared with a Brazilian bioethanol production strain, even though the strains differ dramatically at most genetic loci. Lastly, the Y22-3 genome sequence provides an exceptionally high-quality resource for basic and applied research in bioenergy and genetics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Metabolic Differentiation of Co-occurring Accumulibacter Clades Revealed through Genome-Resolved Metatranscriptomics

Natural microbial communities consist of closely related taxa that may exhibit phenotypic differences and inhabit distinct niches. However, connecting genetic diversity to ecological properties remains a challenge in microbial ecology due to the lack of pure cultures across the microbial tree of life. “Candidatus Accumulibacter phosphatis” (Accumulibacter) is a polyphosphate-accumulating organism that contributes to the enhanced biological phosphorus removal (EBPR) biotechnological process for removing excess phosphorus from wastewater and preventing eutrophication from downstream receiving waters. Distinct Accumulibacter clades often coexist in full-scale wastewater treatment plants and laboratory-scale enrichment bioreactors and have been hypothesized to inhabit distinct ecological niches. However, since individual strains of the Accumulibacter lineage have not been isolated in pure culture to date, these predictions have been made solely on genome-based comparisons and enrichments with varying strain compositions. Here, we used genome-resolved metagenomics and metatranscriptomics to explore the activity of coexisting Accumulibacter strains in an engineered bioreactor environment. We obtained four high-quality genomes of Accumulibacter strains that were present in the bioreactor ecosystem, one of which is a completely contiguous draft genome scaffolded with long Nanopore reads. We identified core and accessory genes to investigate how gene expression patterns differed among the dominating strains. Using this approach, we were able to identify putative pathways and functions that may confer distinct functions to Accumulibacter strains and provide key functional insights into this biotechnologically significant microbial lineage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Machine learning framework for assessment of microbial factory performance

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2023

Evolutionary Flexibility and Rigidity in the Bacterial Methylerythritol Phosphate (Mep) Pathway

Terpenoids are a diverse class of compounds with wide-ranging uses including as industrial solvents, pharmaceuticals, and fragrances. Efforts to produce terpenoids sustainably by engineering microbes for fermentation are ongoing, but industrial production still largely relies on nonrenewable sources. The methylerythritol phosphate (MEP) pathway generates terpenoid precursor molecules and includes the enzyme Dxs and two iron–sulfur cluster enzymes: IspG and IspH. IspG and IspH are rate limiting-enzymes of the MEP pathway but are challenging for metabolic engineering because they require iron–sulfur cluster biogenesis and an ongoing supply of reducing equivalents to function. Therefore, identifying novel alternatives to IspG and IspH has been an on-going effort to aid in metabolic engineering of terpenoid biosynthesis. We report here an analysis of the evolutionary diversity of terpenoid biosynthesis strategies as a resource for exploration of alternative terpenoid biosynthesis pathways. Using comparative genomics, we surveyed a database of 4,400 diverse bacterial species and found that some may have evolved alternatives to the first enzyme in the pathway, Dxs making it evolutionarily flexible. In contrast, we found that IspG and IspH are evolutionarily rigid because we could not identify any species that appear to have enzymatic routes that circumvent these enzymes. The ever-growing repository of sequenced bacterial genomes has great potential to provide metabolic engineers with alternative metabolic pathway solutions. With the current state of knowledge, we found that enzymes IspG and IspH are evolutionarily indispensable which informs both metabolic engineering efforts and our understanding of the evolution of terpenoid biosynthesis pathways.

Bailey Marshall↗
DOE OSTI2015

A Method to Constrain Genome-Scale Models with 13 C Labeling Data

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13 C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13 C labeling data. A comparison shows that the results of this new method are similar to those found through 13 C Metabolic Flux Analysis ( 13 C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing

Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O 2 ). Furthermore this review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate "Learn and Design" for strain development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Integrating CO2 Electrolysis with Gas Fermentation to Produce Valuable Fuels and Chemicals

Many industrial activities squander CO2, decreasing process yield. We envision a future where this waste carbon is instead captured, upgraded, and valorized directly at the point of emission. Within the CO2 Reduction and Upgrading Consortium (a collaboration of seven US national laboratories and industrial partners), we are pursuing this goal by developing and de-risking new technologies for low temperature CO2 electrolysis, coupled with biological upgrading of intermediates into more valuable compounds. One such process involves electrocatalytic reduction of CO2 to generate carbon monoxide (CO). As both a carbon and energy source, CO represents an attractive feedstock for microbial upgrading by certain syngas-fermenting species, such as the autotrophic bacterium Clostridium autoethanogenum. Our team has developed new genetic tools and optimized cultivation techniques to enhance C. autoethanogenum as a platform host for the biological conversion of syngas into value-added products. For example, we have created novel CRISPR-based genetic engineering techniques to build new, genome-reduced, platform strains of C. autoethanogenum with improved growth rates. Further, we ve introduced heterologous biochemical pathways into C. autoethanogenum to enable the production of high-value compounds from syngas, such as the isoprenoid precursor mevalonic acid. With the tools of electrochemistry and synthetic biology, there is virtually no limit to the spectrum of products that could be sustainably manufactured from CO2.

09 BIOMASS FUELS↗
DOE OSTI2018

Active site residue identity regulates cleavage preference of LAGLIDADG homing endonucleases

LAGLIDADG homing endonucleases (meganucleases) are site-specific mobile endonucleases that can be adapted for genome-editing applications. However, one problem when reprogramming meganucleases on non-native substrates is indirect readout of DNA shape and flexibility at the central 4 bases where cleavage occurs. To understand how the meganuclease active site regulates DNA cleavage, we used functional selections and deep sequencing to profile the fitness landscape of 1600 I-LtrI and I-OnuI active site variants individually challenged with 67 substrates with central 4 base substitutions. The wild-type active site was not optimal for cleavage on many substrates, including the native I-LtrI and I-OnuI targets. Novel combinations of active site residues not observed in known meganucleases supported activity on substrates poorly cleaved by the wild-type enzymes. Strikingly, combinations of E or D substitutions in the two metal-binding residues greatly influenced cleavage activity, and E184D variants had a broadened cleavage profile. Analyses of I-LtrI E184D and the wild-type proteins co-crystallized with the non-cognate AACC central 4 sequence revealed structural differences that correlated with kinetic constants for cleavage of individual DNA strands. Optimizing meganuclease active sites to enhance cleavage of non-native central 4 target sites is a straightforward addition to engineering workflows that will expand genome-editing applications.

59 BASIC BIOLOGICAL SCIENCES↗