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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Description of a novel extremophile green algae, Chlamydomonas pacifica , and its potential as a biotechnology host

We present the comprehensive characterization of a newly identified microalga, Chlamydomonas pacifica , originally isolated from a soil sample in San Diego, CA, USA. This species showcases remarkable biological versatility, including a broad pH range tolerance (6–11.5), high thermal tolerance (up to 42 °C), and salinity resilience (up to 2 % NaCl). Its amenability to genetic manipulation and sexual reproduction via mating, particularly between the two opposing strains CC-5697 & CC-5699, now publicly available through the Chlamydomonas Resource Center, underscores its potential as a biotechnological chassis. The biological assessment of C. pacifica revealed versatile metabolic capabilities, including diverse nitrogen assimilation capability, motility and phototaxis. Genomic and transcriptomic analyses identified 17,829 genes within a 121 Mb genome, featuring a GC content of 61 %. The codon usage of C. pacifica closely mirrors that of C. reinhardtii , indicating a conserved genetic architecture that supports a trend in codon preference with minor variations. Phylogenetic analyses position C. pacifica within the core-Reinhardtinia clade yet distinct from known Volvocales species. The lipidomic data revealed an abundance of triacylglycerols (TAGs), promising for biofuel applications and lipids for health-related benefits. Our investigation lays the groundwork for exploiting C. pacifica in biotechnological applications, from biofuel generation to synthesizing biodegradable plastics, positioning it as a versatile host for future bioengineering endeavors.

Alkali tolerant↗
DOE OSTI2025

From clutter to clarity: Emergent neural operators via questionnaire metrics

Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.

42 ENGINEERING↗
DOE OSTI2025

ToF-SIMS spectral data analysis of Paenibacillus sp. 300A biofilms and planktonic cells

Analysis of bacterial biofilms is particularly challenging and important with diverse applications from systems biology to biotechnology. Among the variety of techniques that have been applied, time-of-flight secondary ion mass spectrometry (ToF-SIMS) has many promising features in studying the surface characteristics of biofilms. ToF-SIMS offers high spatial resolution and high mass accuracy, which permit surface sensitive analysis of biofilm components. Thus, ToF-SIMS provides a powerful solution to addressing the challenge of bacterial biofilm analysis. This dataset covers ToF-SIMS analysis of Paenibacillus sp. 300A (300A) isolated from the Hanford site in Richland, WA. The strain is known to have metal and sulfur reducing properties and can be used for bioremediation, wastewater treatment, bioengineering and technology development. There is a current need to identify small molecules and fragments produced from bacterial biofilms. Static ToF-SIMS spectra of 300A were obtained using an IONTOF TOF-SIMS V instrument equipped with a 25 keV Bi 3 + metal ion gun. Identified molecules and molecular fragments are compared against known biological databases and the reported peaks have at least 65 ppm mass accuracy. These molecules range from lipids and fatty acids to flavonoids, quinolones, and other naturally occurring organic compounds. It is anticipated that the spectral identification of key peaks will assist detection of metabolites, extracellular polymeric substance molecules like polysaccharides, and biologically relevant small molecules using ToF-SIMS in future surface and interface research of bacterial biofilms.

Biofilms↗
DOE OSTI2024

Anaerobic fungi contain abundant, diverse, and transcriptionally active Long Terminal Repeat retrotransposons

Long Terminal Repeat (LTR) retrotransposons are a class of repetitive elements that are widespread in the genomes of plants and many fungi. LTR retrotransposons have been associated with rapidly evolving gene clusters in plants and virulence factor transfer in fungal-plant parasite-host interactions. We report here the abundance and transcriptional activity of LTR retrotransposons across several species of the early-branching Neocallimastigomycota, otherwise known as the anaerobic gut fungi (AGF). The ubiquity of LTR retrotransposons in these genomes suggests key evolutionary roles in these rumen-dwelling biomass degraders, whose genomes also contain many enzymes that are horizontally transferred from other rumen-dwelling prokaryotes. Up to 10% of anaerobic fungal genomes consist of LTR retrotransposons, and the mapping of sequences from LTR retrotransposons to transcriptomes shows that the majority of clusters are transcribed, with some exhibiting expression greater than 104 reads per kilobase million mapped reads (rpkm). Many LTR retrotransposons are strongly differentially expressed upon heat stress during fungal cultivation, with several exhibiting a nearly three-log10 fold increase in expression, whereas growth substrate variation modulated transcription to a lesser extent. We show that some LTR retrotransposons contain carbohydrate-active enzymes (CAZymes), and the expansion of CAZymes within genomes and among anaerobic fungal species may be linked to retrotransposon activity. We further discuss how these widespread sequences may be a source of promoters and other parts towards the bioengineering of anaerobic fungi.

Genetics & Heredity↗
DOE OSTI2025

Conditional guide RNA deactivation by mRNA and small molecule triggers in Saccharomyces cerevisiae

CRISPR interference (CRISPRi) technologies have revolutionized bioengineering by providing precise tools for gene expression modulation, enabling targeted gene perturbation and metabolic pathway optimization. Despite these advances, achieving dynamic control over gene expression by CRISPR-based regulation remains a challenge due to its inherently static nature. Utilizing toehold-mediated strand displacement and ligand-responsive ribozymes (aptazymes), this study introduces switchable guide RNAs (gRNAs) that facilitate tunable gene expression mediated by mRNA or small molecule signals. We demonstrate complete silencing of gRNA via strategically designed 5’ or 3’ extensions that impede the gRNA spacer or the dCas9 handle, with subsequent restoration of function through sequestration or cleavage of the obstructive sequence. The resulting toehold-embedded or aptazyme-embedded gRNAs can be deactivated by specific signals, including two full-length translatable mRNAs and two small molecule triggers, thereby lifting CRISPRi repression on targeted genes. This modular approach allows for gRNA-based biocomputing through multi-layer or multi-input genetic logic gates in Saccharomyces cerevisiae . Offering a versatile strategy for post-CRISPR regulation in response to environmental signals or cellular states, this methodology expands the toolkit in eukaryotic systems for reversible control of gene expression.

Aptazyme↗
DOE OSTI2026

Artificial intelligence methods for protein structure and interaction prediction: Recent advances and challenges

Recent advances in artificial intelligence have introduced novel methods for high-accuracy prediction of protein tertiary structures, protein complex structures, and interactions between proteins and other biomolecules, such as small molecules and nucleic acids. Such advancements are accelerating biomedical research and the development of new protein design and bioengineering methods among many other important biotechnology applications. Here, in this review, we outline the recent advances in protein-centric biomolecular structure and interaction prediction, highlight some major challenges in the field, and discuss potential directions to address them.

Morehead, Alex [Lawrence Berkeley National Laborat↗
DOE OSTI2025

Molecular Modeling and Molecular Dynamics Simulation of a Packed and Intact Bacterial Microcompartment

Bacterial microcompartments (BMCs) are protein-bound organelles found in some bacteria which encapsulate enzymes for enhanced catalytic activity. These compartments spatially sequester enzymes within semipermeable shell proteins and are packed full of enzyme cargoes and metabolites as they fulfill their function. Coupling together recent SAXS and proteomics work, it is possible to develop molecular models for these microcompartments and interrogate enzyme and metabolite dynamics within. Our primary goal of this study is to quantify the permeability of metabolite glyceraldehyde-3-phosphate (G3P) and dihydroxyacetone phosphate (DHAP) across the BMC shell through classical molecular dynamics simulation. The Haliangium ochraceum model of BMC shell (PDB: 6MZX) was used to model an intact BMC of approximately 10 million atoms. Working at this scale presented its own challenges in managing large data sets, with multiple challenges and hardware advances discussed that facilitated this work. Over approximately 750 ns of aggregate simulation, we see multiple permeation events for these metabolites that were added at high concentration through the pores present within BMC shell tiles. When compared to independent permeability estimates for the same metabolites determined through replica exchange umbrella sampling simulations, the permeabilities varied by approximately 3 orders of magnitude. Regardless, the permeability coefficients for both G3P and DHAP are highly similar and very high, such that only very small concentration gradients can be maintained across the BMC shell between the cytosol and BMC interior. The large simulation systems also facilitated comparisons for molecular diffusivity in the crowded environment within the BMC shell. By our estimates, the viscosity within a packed BMC shell is at least 10-fold higher than it would be in neat solution and is the real driver for varying permeability estimates we obtained through simulation. These findings will be used as design inputs for future bioengineering efforts to make products from BMCs, highlighting how permeable BMC shells can be.

Diffusion↗
DOE OSTI2024

Toward a Circular Bioeconomy: Designing Microbes and Polymers for Biodegradation

Polymer production is rapidly increasing, but there are no large-scale technologies available to effectively mitigate the massive accumulation of these recalcitrant materials. One potential solution is the development of a carbon-neutral polymer life cycle, where microorganisms convert plant biomass to chemicals, which are used to synthesize biodegradable materials that ultimately contribute to the growth of new plants. Realizing a circular carbon life cycle requires the integration of knowledge across microbiology, bioengineering, materials science, and organic chemistry, which itself has hindered large-scale industrial advances. This review addresses the biodegradation status of common synthetic polymers, identifying novel microbes and enzymes capable of metabolizing these recalcitrant materials and engineering approaches to enhance their biodegradation pathways. Design considerations for the next generation of biodegradable polymers are also reviewed, and finally, opportunities to apply findings from lignocellulosic biodegradation to the design and biodegradation of similarly recalcitrant synthetic polymers are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Photosynthetic Biohybrid System for Enhanced Abiotic N 2 -to-NH 3 Conversion under Ambient Conditions

Photosynthetic biohybrid systems (PBSs) offer an eco-friendly approach to transforming solar energy into value-added products by integrating biological entities with inorganic semiconductors. However, the chemical conversion capacity of most PBSs has inherent limitations, as whole-cell bacteria and isolated enzymes require fine-tuning of environmental conditions. Here, in this study, we report a new PBS developed by introducing free-standing ceria nanoparticles into the purple membrane (PM) of Halobacterium salinarum archaea, which can unidirectionally transfer charge carriers in response to incident photons, even after separation from living archaea at various conditions. Our microscopy, spectroscopy, and synchrotron X-ray scattering analyses confirm that the electrostatic assembly between ceria and PM creates seamless interfacial contact, thereby enhancing the photocatalytic capacity of ceria. Although the conversion of dinitrogen (N 2 ) to ammonia (NH 3 ) is thermodynamically challenging due to the triple bond in N 2 and a series of charge-transfer reactions, our PM–ceria (PMC) hybrid nanoparticle efficiently produces NH 3 by reducing N 2 using solar energy even under atmospheric pressure and room temperature while simultaneously converting glycerol into value-added derivatives. Additionally, our PMC nanoparticle involves neither toxic/precious metals nor bioengineering processes to achieve enhanced photocatalytic N 2 -to-NH 3 conversion. This study sheds light on the new aspect of PBSs by employing PM to potentially resolve the global energy and environmental challenges posed by the conventional Haber–Bosch process.

Jang, Jinhyeong [Argonne National Laboratory (ANL)↗
DOE OSTI2024

A frugal CRISPR kit for equitable and accessible education in gene editing and synthetic biology

Equitable and accessible education in life sciences, bioengineering, and synthetic biology is crucial for training the next generation of scientists, fostering transparency in public decision-making, and ensuring biotechnology can benefit a wide-ranging population. As a groundbreaking technology for genome engineering, CRISPR has transformed research and therapeutics. However, hands-on exposure to this technology in educational settings remains limited due to the extensive resources required for CRISPR experiments. Here, we develop CRISPRkit, an affordable kit designed for gene editing and regulation in high school education. CRISPRkit eliminates the need for specialized equipment, prioritizes biosafety, and utilizes cost-effective reagents. By integrating CRISPRi gene regulation, colorful chromoproteins, cell-free transcription-translation systems, smartphone-based quantification, and an in-house automated algorithm (CRISPectra), our kit offers an inexpensive (~$2) and user-friendly approach to performing and analyzing CRISPR experiments, without the need for a traditional laboratory setup. Experiments conducted by high school students in classroom settings highlight the kit’s utility for reliable CRISPRkit experiments. Furthermore, CRISPRkit provides a modular and expandable platform for genome engineering, and we demonstrate its applications for controlling fluorescent proteins and metabolic pathways such as melanin production. We envision CRISPRkit will facilitate biotechnology education for communities of diverse socioeconomic and geographic backgrounds.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Comparative genomic analysis of thermophilic fungi reveals convergent evolutionary adaptations and gene losses

Thermophily is a trait scattered across the fungal tree of life, with its highest prevalence within three fungal families (Chaetomiaceae, Thermoascaceae, and Trichocomaceae), as well as some members of the phylum Mucoromycota. We examined 37 thermophilic and thermotolerant species and 42 mesophilic species for this study and identified thermophily as the ancestral state of all three prominent families of thermophilic fungi. Thermophilic fungal genomes were found to encode various thermostable enzymes, including carbohydrate-active enzymes such as endoxylanases, which are useful for many industrial applications. At the same time, the overall gene counts, especially in gene families responsible for microbial defense such as secondary metabolism, are reduced in thermophiles compared to mesophiles. We also found a reduction in the core genome size of thermophiles in both the Chaetomiaceae family and the Eurotiomycetes class. The Gene Ontology terms lost in thermophilic fungi include primary metabolism, transporters, UV response, and O-methyltransferases. Comparative genomics analysis also revealed higher GC content in the third base of codons (GC3) and a lower effective number of codons in fungal thermophiles than in both thermotolerant and mesophilic fungi. Furthermore, using the Support Vector Machine classifier, we identified several Pfam domains capable of discriminating between genomes of thermophiles and mesophiles with 94% accuracy. Using AlphaFold2 to predict protein structures of endoxylanases (GH10), we built a similarity network based on the structures. We found that the number of disulfide bonds appears important for protein structure, and the network clusters based on protein structures correlate with the optimal activity temperature. Thus, comparative genomics offers new insights into the biology, adaptation, and evolutionary history of thermophilic fungi while providing a parts list for bioengineering applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Mechanistic implications of excited high-spin states, spin–spin coupling, and differential [2Fe–2S] + cluster temperature relaxations in the electron-bifurcating NfnABC from Thermococcus sibiricus

Electron bifurcation (EB) is a mechanism of biological energy transduction in which multiple oxidation–reduction (redox) reactions are thermodynamically coupled within a single enzyme, enabling the enzyme to harness the excess free energy from an exergonic process to drive an endergonic process. Because of this unprecedented chemistry, there is interest to translate EB principles to artificial and bioengineered systems, but a hurdle is that knowledge pertaining to the fundamental design principles of EB enzymes remains scarce. Here, we investigated the fundamental physical and electronic properties of electron transfer sites in a spectroscopically uncharacterized member of the BfuABC family of EB enzymes, the NADH-dependent reduced-ferredoxin:NADP + oxidoreductase from Thermococcus sibiricus (Tsi NfnABC). Cryo-EM structures of Tsi NfnABC previously demonstrated that it contains twelve redox cofactors: two flavins (one FAD and one FMN), eight [4Fe–4S] clusters, and two [2Fe–2S] clusters. The FMN, one [4Fe–4S] cluster, and one [2Fe–2S] cluster comprise the bifurcating active site termed the electron-bifurcating flavobicluster (BF-FBC), which is found in all BfuABC family members. By using electron paramagnetic resonance spectroscopy, we identified spectral signatures originating from interactions between the FMN radical and [4Fe–4S] + cluster in the BF-FBC and observed temperature dependent behavior of the BF-FBC's [2Fe–2S] + cluster indicative of moderately slow spin–lattice relaxation. Additionally, we uncovered numerous spectral features corresponding to half-integer, S > ½ spin states of [4Fe–4S] + clusters, including one attributable to the consequences of lysine-ligation of a [4Fe–4S] cluster unique to NfnABC. We contextualize these findings to electron transfer theory and NfnABC's structure. Our insights further the understanding of how enzymes are designed to exert control over electron transfer to conduct thermodynamically challenging reactions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Bacterial microcompartments as a next-generation metabolic engineering tool: utilizing nature's solution for confining challenging catabolic pathways

Advancements in synthetic biology have facilitated the incorporation of heterologous metabolic pathways into various bacterial chassis, leading to the synthesis of targeted bioproducts. However, total output from heterologous production pathways can suffer from low flux, enzyme promiscuity, formation of toxic intermediates, or intermediate loss to competing reactions, which ultimately hinder their full potential. The self-assembling, easy-to-modify, protein-based bacterial microcompartments (BMCs) offer a sophisticated way to overcome these obstacles by acting as an autonomous catalytic module decoupled from the cell's regulatory and metabolic networks. More than a decade of fundamental research on various types of BMCs, particularly structural studies of shells and their self-assembly, the recruitment of enzymes to BMC shell scaffolds, and the involvement of ancillary proteins such as transporters, regulators, and activating enzymes in the integration of BMCs into the cell's metabolism, has significantly moved the field forward. These advances have enabled bioengineers to design synthetic multi-enzyme BMCs to promote ethanol or hydrogen production, increase cellular polyphosphate levels, and convert glycerol to propanediol or formate to pyruvate. These pioneering efforts demonstrate the enormous potential of synthetic BMCs to encapsulate non-native multi-enzyme biochemical pathways for the synthesis of high-value products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Targeted genetic manipulation and yeast-like evolutionary genomics in the green alga Auxenochlorella

Auxenochlorella spp. are diploid oleaginous green algae whose streamlined genomes can be readily manipulated by homologous recombination, making them highly amenable to discovery research and bioengineering. Vegetatively diploid organisms experience specific evolutionary phenomena, including allodiploid hybridization, mitotic recombination, loss-of-heterozygosity, and aneuploidy; however, studies of these forces have largely focused on yeasts. Here, we present a telomere-to-telomere phased diploid genome assembly of Auxenochlorella UTEX 250-A (haploid length 22 Mb) and introduce a genetic toolkit for site-specific manipulation of the nuclear genome in multiple strains, featuring several selectable markers, inducible promoters, and fluorescent reporters for protein localization. UTEX 250-A is an allodiploid hybrid of Auxenochlorella protothecoides and Auxenochlorella symbiontica, two species differentiated by extensive chromosomal rearrangements. UTEX 250-A haplotypes are a mosaic of each parental species following mitotic recombination, and two chromosomes are trisomic. Loss-of-heterozygosity events are pervasive across Auxenochlorella and can evolve rapidly in the laboratory. High-quality structural annotation yielded ∼7,500 genes per haplotype. Auxenochlorella have experienced gene family loss and reduction, including core photosynthesis genes, and exhibit periodic adenine and cytosine methylation at promoters and gene bodies, respectively. Approximately 10% of genes, especially those involved in DNA repair and sex, overlap antisense long noncoding RNAs, which may participate in a regulatory mechanism. We demonstrate the utility of Auxenochlorella for fundamental research by knockout of a chlorophyll biosynthesis enzyme, and confirm one trisomy by allele-specific transformation. These results demonstrate the generality of several evolutionary forces associated with vegetative diploidy and provide a foundation for the use of Auxenochlorella as a reference organism.

CHL27↗
DOE OSTI2025

Complete replacement of Arabidopsis oil-producing enzymes with heterologous diacylglycerol acyltransferases

Acyl-CoA:diacylglycerol acyltransferase 1 (DGAT1) and phospholipid:diacylglycerol acyltransferase 1 (PDAT1) share responsibility for triacylglycerol (TAG) biosynthesis, and their selectivities control TAG fatty acid (FA) compositions. For rational metabolic engineering of seed oils, replacing endogenous TAG biosynthesis with exogenous enzymes containing different substrate FA selectivities is desirable; however, the dgat1-1/pdat1-2 double mutant is pollen lethal. Here, we evaluated the ability of 3 DGAT1s, from phylogenetically diverse plants with distinct TAG assembly processes, to completely replace endogenous TAG biosynthesis in Arabidopsis ( Arabidopsis thaliana ). We transformed dgat1-1 mutant plants with expression constructs for DGAT1 s from Camelina sativa , Physaria fendleri , and castor ( Ricinus communis ). Transgene expression was properly “contextualized” by using a previously determined minimum necessary expression unit containing the promoter/5′ UTR and first intron of native AtDGAT1 ; both of these DNA elements are essential for pollen expression. Next, we crossed homozygous lines with a DGAT1/DGAT1/PDAT1/pdat1-2 parent. C. sativa and P. fendleri DGAT1s restored the FA compositions and transcriptional differences of dgat1-1 to near wild-type and rescued the dgat1-1/pdat1-2 pollen lethality. R. communis DGAT1 was active in dgat1-1 seeds but produced unique oil profiles and alterations in the expression of lipid metabolic genes; it also failed to rescue dgat1-1/pdat1-2 lethality. This study confirms that the promoter and first intron of AtDGAT1 can modulate the expression of foreign DGAT1 genes to fit the correct spatiotemporal profile necessary for completely replacing endogenous TAG biosynthesis. Furthermore, it demonstrates an additional layer of unexpected enzyme incompatibility between oilseed lineages, which may complicate bioengineering approaches that seek to replace essential genes with orthologs.

McGuire, Sean T. [Washington State Univ., Pullman,↗
DOE OSTI2024

Increasing thermostability of the key photorespiratory enzyme glycerate 3‐kinase by structure‐based recombination

As global temperatures rise, improving crop yields will require enhancing the thermotolerance of crops. One approach for improving thermotolerance is using bioengineering to increase the thermostability of enzymes catalysing essential biological processes. Photorespiration is an essential recycling process in plants that is integral to photosynthesis and crop growth. The enzymes of photorespiration are targets for enhancing plant thermotolerance as this pathway limits carbon fixation at elevated temperatures. We explored the effects of temperature on the activity of the photorespiratory enzyme glycerate kinase (GLYK) from various organisms and the homologue from the thermophilic alga Cyanidioschyzon merolae was more thermotolerant than those from mesophilic plants, including Arabidopsis thaliana. To understand enzyme features underlying the thermotolerance of C. merolae GLYK (CmGLYK), we performed molecular dynamics simulations using AlphaFold-predicted structures, which revealed greater movement of loop regions of mesophilic plant GLYKs at higher temperatures compared to CmGLYK. Based on these simulations, hybrid proteins were produced and analysed. These hybrid enzymes contained loop regions from CmGLYK replacing the most mobile corresponding loops of AtGLYK. Two of these hybrid enzymes had enhanced thermostability, with melting temperatures increased by 6 °C. One hybrid with three grafted loops maintained higher activity at elevated temperatures. Whilst this hybrid enzyme exhibited enhanced thermostability and a similar Km for ATP compared to AtGLYK, its Km for glycerate increased threefold. This study demonstrates that molecular dynamics simulation-guided structure-based recombination offers a promising strategy for enhancing the thermostability of other plant enzymes with possible application to increasing the thermotolerance of plants under warming climates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Genetic modification of the shikimate pathway to reduce lignin content in switchgrass ( Panicum virgatum L.) significantly impacts plant microbiomes

Switchgrass (Panicum virgatum L.) is considered a sustainable biofuel feedstock, given its fast-impact growth, low input requirements, and high biomass yields. Improvements in bioenergy conversion efficiency of switchgrass could be made by reducing its lignin content. Engineered switchgrass that expresses a bacterial 3-dehydroshikimate dehydratase (QsuB) has reduced lignin content and improved biomass saccharification due to the rerouting of the shikimate pathway towards the simple aromatic protocatechuate at the expense of lignin biosynthesis. However, the impacts of this QsuB trait on switchgrass microbiome structure and function remain unclear. To address this, wild-type and QsuB-engineered switchgrass were grown in switchgrass field soils, and samples were collected from inflorescences, leaves, roots, rhizospheres, and bulk soils for microbiome analysis. We investigated how QsuB expression influenced switchgrass-associated fungal and bacterial communities using high-throughput Illumina MiSeq amplicon sequencing of ITS and 16S rDNA. Compared to wild-type, QsuB-engineered switchgrass hosted different microbial communities in roots, rhizosphere, and leaves. Specifically, QsuB-engineered plants had a lower relative abundance of arbuscular mycorrhizal fungi (AMF). Additionally, QsuB-engineered plants had fewer Actinobacteriota in root and rhizosphere samples. These findings may indicate that changes in the plant metabolism impact both AMF and Actinobacteriota similarly or potential interactions between AMF and the bacterial community. This study enhances understanding of plant-microbiome interactions by providing baseline microbial data for developing beneficial bioengineering strategies and by assessing nontarget impacts of engineered plant traits on the plant microbiome.

09 BIOMASS FUELS↗
DOE OSTI2025

Kinetic Deep Learning v0.1

Here, we present a method that uses protein levels to predict times series of metabolite concentrations. Understanding this type of pathway dynamics is important in order to predict the behavior of the pathway and, more pragmatically, to be able to design biological systems (such as strains bioengineered to produce chemical products) reliably. Typically, for this purpose, kinetic models consisting of differential equations based on the Michaelis-Menten dynamics have been used in the past. However, these methods can rarely produce good fits to measured data time series. Possibly, this happens because the kinetic constants are unknown or are different from the ones measured in vivo, or perhaps because Michaelis-Menten dynamics is not a satisfactory description. In order to improve the predictive nature of these kinetic models we have eliminated the Michaelis-Menten description of pathway dynamics and we have substituted it by algorithms that automatically learn these dynamics from previously obtained metabolomics and proteomics data using machine learning approaches. Specifically, kinetic deep learning uses deep learning to map proteomics time series to metabolite concentration time series, instead of learning the first metabolite derivative and integrating in (as in the first version of kinetic learning). This approach is shown to provide good to excellent results with a data set specifically collected for this purpose.

Garcia Martin, Hector [Joint BioEnergy Institute (↗