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At least 685 records · Page 38

Rewinding evolution in planta: A Rubisco-null platform validates high-performance ancestral enzymes

Improving the photosynthetic enzyme Rubisco is a key target for enhancing C3 crop productivity, but progress has been hampered by the difficulty of evaluating engineered variants in planta without interference from the native enzyme. Here, we report the creation of a Rubisco-null Nicotiana tabacum platform by using CRISPR-Cas9 to knock out all 11 nuclear-encoded small subunit (rbcS) genes. Knockout was achieved in a line expressing cyanobacterial Rubisco from the plastid genome, allowing the recovery of viable plants. We then developed a chloroplast expression system for coexpressing both large and small subunits from the plastid genome. We expressed two resurrected ancestral Rubiscos from the Solanaceae family. The resulting transgenic plants were phenotypically normal and accumulated Rubisco to wild-type levels. Importantly, kinetic analyses of the purified ancestral enzymes revealed they possessed a 16 to 20% higher catalytic efficiency (k cat,air /K c,air ) under ambient conditions, driven by a significantly faster turnover rate (k cat,air ). We have demonstrated that our system allows robust in vivo assessment of novel Rubiscos and that ancestral reconstruction is a powerful strategy for identifying superior enzymes to improve photosynthesis in C3 crops.

59 BASIC BIOLOGICAL SCIENCES↗

Biologically Diverse Genetic Engineering Platform

This project focused on methods to genetically edit arbitrary prokaryotes to convert non-agricultural biomass to industrially relevant chemicals at scale and at a price point that would be competitive with petroleum. Our target chemical for this demonstration was succinic acid, a polymer precursor that could theoretically be produced at scale from biomass. We demonstrated genetic control over a selected bacterial type strain: we can make permanent and directed changes to its genome. After thoroughly characterizing its behavior, and the circumstances under which it produced the most succinic acid, the researchers found a wild-type isolate that produces at least eight times more succinic acid than the type strain.

09 BIOMASS FUELS↗

Targeted repression of AXIN2 and MYC gene expression using designer TALEs

Highlights: • We designed TALE–SID fusion proteins to target AXIN2 and MYC. • TALE–SIDs bound the chromosomal AXIN2 and MYC genes and repressed their expression. • TALE–SIDs repress β-catenin{sup S45F}-dependent AXIN2 and MYC transcription. - Abstract: Designer TALEs (dTALEs) are chimeric transcription factors that can be engineered to regulate gene expression in mammalian cells. Whether dTALEs can block gene transcription downstream of signal transduction cascades, however, has yet to be fully explored. Here we tested whether dTALEs can be used to target genes whose expression is controlled by Wnt/β-catenin signaling. TALE DNA binding domains were engineered to recognize sequences adjacent to Wnt responsive enhancer elements (WREs) that control expression of axis inhibition protein 2 (AXIN2) and c-MYC (MYC). These custom DNA binding domains were linked to the mSin3A interaction domain (SID) to generate TALE–SID chimeric repressors. The TALE–SIDs repressed luciferase reporter activity, bound their genomic target sites, and repressed AXIN2 and MYC expression in HEK293 cells. We generated a novel HEK293 cell line to determine whether the TALE–SIDs could function downstream of oncogenic Wnt/β-catenin signaling. Treating these cells with doxycycline and tamoxifen stimulates nuclear accumulation of a stabilized form of β-catenin found in a subset of colorectal cancers. The TALE–SIDs repressed AXIN2 and MYC expression in these cells, which suggests that dTALEs could offer an effective therapeutic strategy for the treatment of colorectal cancer.

60 APPLIED LIFE SCIENCES↗

Biomolecular Analysis Capability for Cellular and Omics Research on the International Space Station

International Space Station (ISS) assembly complete ushered a new era focused on utilization of this state-of-the-art orbiting laboratory to advance science and technology research in a wide array of disciplines, with benefits to Earth and space exploration. ISS enabling capability for research in cellular and molecular biology includes equipment for in situ, on-orbit analysis of biomolecules. Applications of this growing capability range from biomedicine and biotechnology to the emerging field of Omics. For example, Biomolecule Sequencer is a space-based miniature DNA sequencer that provides nucleotide sequence data for entire samples, which may be used for purposes such as microorganism identification and astrobiology. It complements the use of WetLab-2 SmartCycler"TradeMark", which extracts RNA and provides real-time quantitative gene expression data analysis from biospecimens sampled or cultured onboard the ISS, for downlink to ground investigators, with applications ranging from clinical tissue evaluation to multigenerational assessment of organismal alterations. And the Genes in Space-1 investigation, aimed at examining epigenetic changes, employs polymerase chain reaction to detect immune system alterations. In addition, an increasing assortment of tools to visualize the subcellular distribution of tagged macromolecules is becoming available onboard the ISS. For instance, the NASA LMM (Light Microscopy Module) is a flexible light microscopy imaging facility that enables imaging of physical and biological microscopic phenomena in microgravity. Another light microscopy system modified for use in space to image life sciences payloads is initially used by the Heart Cells investigation ("Effects of Microgravity on Stem Cell-Derived Cardiomyocytes for Human Cardiovascular Disease Modeling and Drug Discovery"). Also, the JAXA Microscope system can perform remotely controllable light, phase-contrast, and fluorescent observations. And upcoming confocal microscopy capability will allow for optical sectioning of biological tissues to determine microanatomical localization of biomarkers. Furthermore, NASA's geneLAB effort addresses integration of genomic, epigenomic, transcriptomic, proteomic and metabolomic datasets, by applying an innovative open source science platform for multi-investigator high throughput utilization of the ISS. In sum, the expanding ISS capability for analysis of biomolecules is enabling innovative research in a broad spectrum of areas such as cellular and molecular biology, biotechnology, tissue engineering, biomedicine, and Omics, providing manifold benefits for humanity.

Guinart-Ramirez, Y.↗

Comparative Genomics of Saccharomyces cerevisiae Natural Isolates for Bioenergy Production

Lignocellulosic plant material is a viable source of biomass to produce alternative energy including ethanol and other biofuels. However, several factors—including toxic by products from biomass pretreatment and poor fermentation of xylose and other pentose sugars—currently limit the efficiency of microbial biofuel production. To begin to understand the genetic basis of desirable traits, we characterized three strains of Saccharomyces cerevisiae with robust growth in a pretreated lignocellulosic hydrolysate or tolerance to stress conditions relevant to industrial biofuel production, through genome and transcriptome sequencing analysis. All stress resistant strains were highly mosaic, suggesting that genetic admixture may contribute to novel allele combinations underlying these phenotypes. Strain-specific gene sets not found in the lab strain were functionally linked to the tolerances of particular strains. Furthermore, genes with signatures of evolutionary selection were enriched for functional categories important for stress resistance and included stress-responsive signaling factors. Comparison of the strains’ transcriptomic responses to heat and ethanol treatment—two stresses relevant to industrial bioethanol production—pointed to physiological processes that were related to particular stress resistance profiles. Many of the genotype-by-environment expression responses occurred at targets of transcription factors with signatures of positive selection, suggesting that these strains have undergone positive selection for stress tolerance. Our results generate new insights into potential mechanisms of tolerance to stresses relevant to biofuel production, including ethanol and heat, present a backdrop for further engineering, and provide glimpses into the natural variation of stress tolerance in wild yeast strains.

59 BASIC BIOLOGICAL SCIENCES↗

Probing the limits of genetic recoding using multi-omics-guided evolution

Engineering the genetic code—by reassigning multiple of the 64 natural codons—enables making organisms resistant to all viruses, preventing genetic information exchange, and allowing the biosynthesis of genetically encoded unnatural polymers. However, synonymous codon replacement—recoding—is frequently lethal, and how recoding impacts fitness remains poorly explored. Here, we explore these effects using genome synthesis, directed evolution, and genome-transcriptome-translatome-proteome co-profiling on multiple synthetic Escherichia coli genomes. We construct six partially recoded E. coli strains bearing up to 45.8% of a synthetic genome with a deleterious 57-codon genetic code. As our analyses revealed widespread defects—including unassigned codons in Syn61 and Syn57—we apply multi-omics to revise our genome design and mitigate defects. Using multi-omics, we show that recoding induces transcriptional and translational changes leading to fitness defects under hundreds of conditions. Finally, we develop a multi-omics-guided evolution strategy that rapidly restores fitness, enabling genome synthesis with radical changes.

Nyerges, Akos [Harvard Medical School, Boston, MA ↗

EvoNet: A phylogenomic and systems biology approach to identify genes underlying plant survival in marginal, low‐N soils

The DOE‐BER “EvoNet” project investigates the genetic and molecular basis of plant resilience in extreme environments. We do this by identifying key genes that enable “extreme survivor” species to thrive in the nitrogen-poor soils of Chile’s hyper-arid Atacama Desert. Our collections focus on 32 Atacama extremophile species, including seven grass species with potential biofuel applications. To identify genes-of-importance to survival we compared genomic and transcriptomic profiles of extremophile species that thrive in the Atacama to those of closely related “sister” species from nitrogen-rich arid and mesic regions of California. Deep RNA sequencing and de novo transcriptome assembly across these triplet species sets supported a phylogenomic framework for identifying positively selected genes associated with adaptive divergence. Our integrative analysis combined ecological and environmental data, metagenomics, evolutionary and systems biology, and metabolomics. This enabled us to create an unprecedented framework for systematically understanding how non-model plants have adapted to survive in extreme conditions. Our resulting database of positively selected ortholog groups in the extremophile plants offers promising targets for engineering crop and biofuel species with enhanced resilience to drought and extreme weather. Additionally, our newest dataset explores and exploits a complementary metabolomic approach. This new aspect provides innovative strategies to manipulate plant cell metabolism, further supporting efforts to improve agricultural productivity in the face of extreme climates. Importantly, our combined evolutionary- and metabolomic-based strategies focused on convergent patterns of adaptation, providing a genetic and metabolomic toolkit for improving crop and biofuel resilience across diverse plant species. Finally, our novel exploration of ecological and evolutionary dynamics delivered to the community a phylogenomic computational pipeline called “PhyloGeneious.” Our continued adaptations of this pipeline are publicly available to expedite evolutionary genomic research for future scientific discoveries. In total, our DOE-BER has provided genomic, metabolomic, and computational strategies to understand how extremophile plants provide evolutionary and physiological targets for improving agricultural and biofuel production.

59 BASIC BIOLOGICAL SCIENCES↗

Yield, chemistry, and TEA/LCA data for 84 switchgrass (Panicum virgatum L.) variants

Switchgrass yield, chemistry, technoeconomic assessment (TEA), and life cycle assessment (LCA) data referenced in the manuscript "Economics and Sustainability impacts of yield and composition variation in bioenergy crops: Panicum virgatum L.", Happs, et. al. (manuscript in revision at ACS Sustainable Chemistry & Engineering). These results are based on analysis of yield data generated from a set of switchgrass common garden experiments established by the Center for Bioenergy Innovation (CBI; https://cbi.ornl.gov/). These data include estimates of commercial-scale yields and characterization of biomass chemistry (fermentable carbohydrate fraction) for 84 different switchgrass variants from a genome-wide association study population, as well as estimates of minimum fuel selling price and various life-cycle footprint metrics for cellulosic ethanol produced from that switchgrass biomass.

switchgrass, biofuels↗

Reactive transport benchmarks for subsurface environmental simulation

Over the last 20 years, we have seen firsthand the evolution of multicomponent reactive transport modeling and the expanding range and increasing complexity of subsurface applications it is being used to address. There is a growing reliance on reactive transport modeling (RTM) to address some of the most compelling issues facing our planet: climate change, nuclear waste management, contaminant remediation, and pollution prevention. While these issues are motivating the development of new and improved capabilities for subsurface environmental modeling using RTM (e.g., biogeochemistry from cell-scale physiology to continental-scale terrestrial ecosystems, nonisothermal multiphase conditions, coupled geomechanics), there remain longstanding challenges in characterizing the natural variability of hydrological, biological, and geochemical properties in subsurface environments and limited success in transferring models between sites and across scales. An equally important trend over the last 20 years is the evolution of modeling from a service sought out after data has been collected to a multifaceted research approach that provides (1) an organizing principle for characterization and monitoring activities; (2) a systematic framework for identifying knowledge gaps, developing and integrating new knowledge; and (3) a mechanistic understanding that represents the collective wisdom of the participating scientists and engineers. There are now large multidisciplinary projects where the research approach is model-driven, and the principal product is a holistic predictive simulation capability that can be used as a test bed for alternative conceptualizations of processes, properties, and conditions. Much of the future growth and expanded role for RTM will depend on its continued ability to exploit technological advancements in the earth and environmental sciences. Advances in measurement technology, particularly in molecular biology (genomics), isotope fractionation, and high-resolution X-ray spectroscopy, have created new lines of research that can be used to inform the conceptualization of reactions and rate laws and validate mechanistic models. For example, spectroscopy has identified the oxidation states of key components and elemental distributions at increasingly smaller scales and lower concentrations; molecular biology has progressed from identifying the presence of microbes to characterization of which microbial communities are active and what they are doing (i.e., microbial function), which has led in turn to the identification of active processes under conditions beyond what analytical chemistry can discern; isotope ratios in pore water and solid phases that can be used to distinguish between biotic from abiotic processes, sorption from precipitation, and origin and age of groundwater. The other noteworthy development that is expanding the role of RTM in subsurface environmental modeling is he advance in computational technology that is enabling the simulation of more coupled processes with increasing mechanistic detail. In some cases, this involves the inclusion of more reactive species and/or microbial populations in the simulations; in other cases, the impact is through the ability to achieve high resolution of property distributions over longer simulated times. To achieve these ambitious objectives for subsurface reactive transport simulation, the subsurface science and engineering community is being driven to provide accurate assessments of engineering performance and risk for important issues with far-reaching consequences. As a result, the complexity and detail of subsurface processes, properties, and conditions that can be simulated have significantly expanded. This expansion was enabled, in part, by advances in measurement technology, computing technology, and numerical techniques.

benchmarking↗

Artificial intelligence–powered biofoundries for protein engineering and metabolic engineering

Synthetic biology is rapidly evolving through the integration of artificial intelligence (AI) and automated biofoundries. This convergence accelerates the design–build–test–learn cycle, shifting protein engineering and metabolic engineering from labor-intensive manual experimentation to autonomous experimentation. This review summarizes recent advances in workflow development, AI models, and their integration with biofoundries for automated or autonomous protein engineering and metabolic engineering. Particularly, we highlight the potential of AI-powered biofoundries for accelerated scientific discovery and innovation in synthetic biology.

Chen, Junyu [Univ. of Illinois at Urbana-Champaign↗

Phenylpropanoid methyl esterase unlocks catabolism of aromatic biological nitrification inhibitors

Microbial nitrification of fertilizers represents is a significant global source of greenhouse gas emissions. This process increases emissions, fosters toxic algal blooms, and raises crop production costs. Some plants naturally release biological nitrification inhibitors to suppress ammonium-oxidizing microbes and reduce nitrification. Engineering nitrification inhibitor production into food and bioenergy crops via synthetic biology offers a promising mitigation strategy, but its success depends on addressing gaps in our understanding of inhibitor degradation in soil. This study begins to fill this gap by identifying a previously unknown microbial pathway for degrading phenylpropanoid methyl esters, a key class of aromatic nitrification inhibitors. Using transcriptomics and high-throughput functional genomics, we discovered genes essential for phenylpropanoid methyl ester degradation. Genetic and biochemical analyses revealed two novel enzymes, including a newly identified phenylpropanoid methyl esterase, that direct phenylpropanoid methyl esters into known metabolic pathways. Importantly, transferring these genes into bacteria capable of metabolizing other phenylpropanoids enabled them to use the methyl esters as a carbon source. This work provides critical insights into microbial nitrification inhibitor degradation, a poorly understood element of the nitrification cycle.

Genetic Engineering↗

Advanced genetic tools enable synthetic biology in the oleaginous microalgae Nannochloropsis sp.

Nannochloropsis is a genus of fast-growing microalgae that are regularly used for biotechnology applications. Nannochloropsis species have a high triacylglycerol content and their polar lipids are rich in the omega-3 long-chain polyunsaturated fatty acid, eicosapentaenoic acid. Placed in the heterokont lineage, the Nannochloropsis genus has a complex evolutionary history. Genome sequences are available for several species, and a number of transcriptomic datasets have been produced, making this genus a facile model for comparative genomics. There is a growing interest in Nannochloropsis species as models for the study of microalga lipid metabolism and as a chassis for synthetic biology. Recently, techniques for gene stacking, and targeted gene disruption and repression in the Nannochloropsis genus have been developed. These tools enable gene-specific, mechanistic studies and have already allowed the engineering of improved Nannochloropsis strains with superior growth, or greater bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗

GeneDrugRepo.sol

An entry to the IDASH PRIVACY & SECURITY WORKSHOP 2019 - secure genome analysis competition, Track I: Distributed Gene-Drug Interaction Data Sharing based on Blockchain and Smart Contracts. The entire codebase is comprised of a single solidity file, GeneDrugRepo.sol. Solidity is a smart contract language for the Ethereum blockchain platform. SAND2019-13419 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Pattengale, Nicholas↗

The Artificial Intelligence Ontology: LLM-Assisted Construction of AI Concept Hierarchies

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).

Joachimiak, Marcin P. [Biosystems Data Science Dep↗

Engineering Anaerobic Gut Fungi for Lignocellulose Breakdown

Biofuels derived from plant biomass (lignocellulose) are attractive alternatives to petroleum-based products, as they avoid many of the economic and environmental problems that plague traditional energy sources. Breakdown of plant waste into simple sugars would translate into the production of renewable fuels via microbial fermentation. However, existing technologies are insufficient to allow for industrial-scale production of these products due to difficulties associated with the recalcitrance of crude lignin-rich biomass, and the high cost/poor performance of known cellulolytic enzymes (cellulases). Therefore, there is a critical need to develop new technologies to break down lignocellulosic biomass into fermentable sugars for downstream fuel development. Towards this goal, much can be learned by studying how anaerobic microbes depolymerize lignocellulose in biomass-rich environments, such as the digestive tract of large herbivores. Anaerobic gut fungi are native to the gut and rumen of these animals, where they have evolved unique abilities to break down lignocellulosic biomass through invasive growth and the secretion of powerful enzymes. The goal of this project was to engineer anaerobic gut fungi as novel platform organisms for biofuel production from plant material. To accomplish this goal, a panel of anaerobic fungi was isolated form different herbivores and screened for their ability to degrade lignocellulose. Four novel strains were isolated and genetically sequenced, including Piromyces finnis, Neocallimastix californiae, Anaeromyces robustus, and Caecomyces churrovis. Transcriptomic and genomic characterization of these anaerobic fungi revealed that they harbor the largest collection of biomass-degrading enzymes of any sequenced fungus, and delineated the basic metabolic networks that govern lignocellulose hydrolysis within anaerobic fungi, RNA sequencing enabled the dynamics and mechanism associated with carbon catabolite repression to be delineated, which revealed how important enzyme groups are coordinated during breakdown. Through cluster analysis, a set of putative novel enzymes associated with lignocellulose breakdown was discovered, which also revealed a set of proteins critical to the formation of fungal cellulosomes (multi-enzyme complexes). Using this information, genetic transformation strategies to manipulate gut fungi were developed, which would endow them with enhanced functionality against a range of industrially relevant substrates. Collectively, this information will establish the molecular framework for anaerobic fungal hydrolysis, and will guide in the development of lignocellulosic biofuels.

09 BIOMASS FUELS↗

CceR and AkgR Regulate Central Carbon and Energy Metabolism in Alphaproteobacteria

Many pathways of carbon and energy metabolism are conserved across the phylogeny, but the networks that regulate their expression or activity often vary considerably among organisms. In this work, we show that two previously uncharacterized transcription factors (TFs) are direct regulators of genes encoding enzymes of central carbon and energy metabolism in the alphaproteobacterium Rhodobacter sphaeroides. The LacI family member CceR (RSP_1663) directly represses genes encoding enzymes in the Entner-Doudoroff pathway, while activating those encoding the F 1 F 0 ATPase and enzymes of the tricarboxylic acid (TCA) cycle and gluconeogenesis, providing a direct transcriptional network connection between carbon and energy metabolism. We identified bases that are important for CceR DNA binding and showed that DNA binding by this TF is inhibited by 6-phosphogluconate. We also showed that the GntR family TF AkgR (RSP_0981) directly activates genes encoding several TCA cycle enzymes, and we identified conditions where its activity is increased. The properties of single and double ΔCceR and ΔAkgR mutants illustrate that these 2 TFs cooperatively regulate carbon and energy metabolism. Comparative genomic analysis indicates that CceR and AkgR orthologs are found in other alphaproteobacteria, where they are predicted to have a conserved function in regulating central carbon metabolism. Our characterization of CceR and AkgR has provided important new insight into the networks that control central carbon and energy metabolism in alphaproteobacteria that can be exploited to modify or engineer new traits in these widespread and versatile bacteria.

Microbiology↗

The survivor strain: isolation and characterization of Phormidium yuhuli AB48, a filamentous phototactic cyanobacterium with biotechnological potential

Despite their recognized potential, current applications of cyanobacteria as microbial cell factories remain in early stages of development. This is partly due to the fact that engineered strains are often difficult to grow at scale. This technical challenge contrasts with the dense and highly productive cyanobacteria populations thriving in many natural environments. It has been proposed that the selection of strains pre-adapted for growth in industrial photobioreactors could enable more productive cultivation outcomes. Here, we described the initial morphological, physiological, and genomic characterization of Phormidium yuhuli AB48 isolated from an industrial photobioreactor environment. P. yuhuli AB48 is a filamentous phototactic cyanobacterium with a growth rate comparable to Synechocystis sp . PCC 6803. The isolate forms dense biofilms under high salinity and alkaline conditions and manifests a similar nutrient profile to Arthrospira platensis ( Spirulina ). We sequenced, assembled, and analyzed the P. yuhuli AB48 genome, the first closed circular isolate reference genome for a member of the Phormidium genus. We then used cultivation experiments in combination with proteomics and metabolomics to investigate growth characteristics and phenotypes related to industrial scale cultivation, including nitrogen and carbon utilization, salinity, and pH acclimation, as well as antibiotic resistance. These analyses provide insight into the biological mechanisms behind the desirable growth properties manifested by P. yuhuli AB48 and position it as a promising microbial cell factory for industrial-scale bioproduction[221, 1631].

59 BASIC BIOLOGICAL SCIENCES↗