Plant Promoters and Terminators for High-Precision Bioengineering
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59 BASIC BIOLOGICAL SCIENCES↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Bioengineered algal lipids enriched in structured medium- and long-chain triacylglycerols, linoleate, and sn -2 palmitate for human milk fat substitutes
Human milk fat (HMF) contains triacylglycerol (TAG) as its primary component, providing over 50% of the calories for infant nutrition, along with structural and bioactive lipids that are important for immune and nervous system development. Palmitic acid, comprising 20-25% of the fatty acid complement of HMF, is predominantly esterified to the sn -2 position on the glycerol backbone. This regiospecific positioning facilitates absorption as 2-palmitoyl-monoacylglycerol after hydrolysis of the fatty acids at sn -1 and sn -2 by gut lipases. Other features of HMF include enrichment in structured medium- and long-chain triglycerides (MLCTs), and variation in the ratio of oleic acid to linoleic acid with maternal diet and geography. We have engineered Auxenochlorella, an oleaginous green alga, for biosynthesis of an MLCT- and sn -2 palmitate-enriched HMF substitute for infant formula, matching the regioisomeric composition and proportions of the most abundant fatty acids in HMF.
Lin, Jon Y-T [University of California, Berkeley;]↗
Capturing and modifying cell fates ? bioengineering interfaces and informatics.
Abstract not provided.
Krishnakumar, Raga↗
System and method for all optical electrode interface for bioengineering application
The present disclosure relates to an electro-optical modulator system having a source laser which generates an input optical signal. The input optical signal is received by an electro-optical module. The electro-optical module is implantable into an anatomy and includes a plurality of pixels. Each pixel has associated therewith an electrode and an optical modulator subsystem. The electrode receives electrical signals from the anatomy. The optical modulator subsystem receives the input optical signal and modulates the input optical signal to generate modulated optical output signals in relation to the received electrical signals. A detector subsystem may be used to receive and collect the modulated optical output signals.
Patra, Susant K.↗
AlgaeOrtho, a bioinformatics tool for processing ortholog inference results in algae
Introduction: Microalgae constitute a prominent feedstock for producing biofuels and biochemicals by virtue of their prolific reproduction, high bioproduct accumulation, and the ability to grow in brackish and saline water. However, naturally occurring wild type algal strains are rarely optimal for industrial use; therefore, bioengineering of algae is necessary to generate superior performing strains that can address production challenges in industrial settings, particularly the bioenergy and bioproduct sectors. One of the crucial steps in this process is deciding on a bioengineering target: namely, which gene/protein to differentially express. These targets are often orthologs which are defined as genes/proteins originating from a common ancestor in divergent species. Although bioinformatics tools for the identification of protein orthologs already exist, processing the output from such tools is nontrivial, especially for a researcher with little or no bioinformatics experience. Methods: The present study introduces AlgaeOrtho, a user-friendly tool that builds upon the SonicParanoid orthology inference tool (based on an algorithm that identifies potential protein orthologs based on amino acid sequences) and the PhycoCosm database from JGI (Joint Genome Institute) to help researchers identify orthologs of their proteins of interest in multiple diverse algal species. Results: The output of this application includes a table of the putative orthologs of their protein of interest, a heatmap showing sequence similarity (%), and an unrooted tree of the putative protein orthologs. Notably, the tool would be instrumental in identifying novel bioengineering targets in different algal strains, including targets in not-fully annotated algal species, since it does not depend on existing protein annotations. We tested AlgaeOrtho using three case studies, for which orthologs of proteins relevant to bioengineering targets, were identified from diverse algal species, demonstrating its ease of use and utility for bioengineering researchers. Discussion: This tool is unique in the protein ortholog identification space as it can visualize putative orthologs, as desired by the user, across several algal species.
09 BIOMASS FUELS↗
Optimization of Energy Flow Through Synthetic Metabolic Modules and Regulatory Networks in a Model Photosynthetic Eukaryotic Microbe
Diatoms are important oceanic photosynthetic microorganisms, responsible for approximately 20% of all carbon fixation on Earth. Additionally, diatoms are attractive metabolic engineering candidates. This investigation leveraged Systems Biology techniques to advance our understanding of and potential to engineer diatoms for bioproducts of societal interest, such as biofuels. Systems Biology combines computational and experimental techniques to understand an organism at the whole-cell level as well as characterize the interface between the organism and its environment. Our research resulted in major contributions to the understanding of systems-level light-driven metabolism. Diatoms have a unique evolutionary history. As a result, existing understanding of photosynthetic metabolism may not be applicable to diatom physiology. We generated, validated, and published a high-quality metabolic model of diatom metabolism facilitating novel insights into these unique organisms. Additionally, we overcame a consistent challenge in the modeling of photosynthetic organisms by integrating light into metabolic models. This result allows realistic assessments of how the light environment affects cell physiology and metabolism, a necessary advancement for bioengineering. Next, we generated several methodologies for integrating large-scale datasets with models of metabolism. These “big data” approaches begin to address a constant challenge in biology, which is translating data into information and understanding. Finally, we built web-based databases for housing, distributing, and visualizing systems-level biology data. These projects created an online ecosystem for sharing, analyzing, and characterizing metabolic information and is currently being used by public and private institutions. Photosynthetic bioengineering has the promise to create high-energy products with minimal inputs. Systems Biology has a long history of facilitating bioengineering of cellular metabolism. However, photosynthetic organisms are only sparsely present in this history. This project and its outputs have accelerated the application of proven Systems Biology techniques to light-driven metabolism. The resulting methodologies will accelerate public and private endeavors leveraging photosynthetic systems in the bioengineering of bioproducts such as biofuels or carbon sequestration.
09 BIOMASS FUELS↗
Direct SERDS sensing of molecular biomarkers in plants under field conditions
Molecular biomarkers such as microRNAs (miRNAs) play important roles in regulating various developmental processes in plants. Understanding these pathways will help bioengineer designing organisms for efficient biomass accumulation. Current methods for RNA analysis require sample extraction and multi-step sample analysis, hindering work in field studies. Recent work in the incorporation of nanomaterials for plant bioengineering research is leading the way of an agri-tech revolution. As an example, surface-enhanced Raman scattering (SERS)–based sensors can be used to monitor RNA in vivo. However, the use of SERS in the field has been limited due to issues with observing Raman signal over complex background. To this end, shifted-excitation Raman difference spectroscopy (SERDS) offers an effective solution to extract the SERS signal from high background based on a physical approach. In this manuscript, we report the first application of SERDS on SERS sensors. We investigated this technique on SERS sensor developed for the detection of a microRNA biomarker, miR858. We tested the technique on in vitro samples and validated the technique by detecting the presence of exogenous miR858 in plants directly under ambient light in a growth chamber. The possibility of moving the detection of nucleic acid targets outside the constraints of laboratory setting enables numerous important bioengineering applications. Finally, such applications can revolutionize biofuel production and agri-tech through the use of nanotechnology-based monitoring of plant growth, plant health, and exposure to pollution and pathogens.
09 BIOMASS FUELS↗
Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering
Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we showcase a set of computational tools that, combined, provide the ability to store, visualize, and leverage multiomics data to predict the outcome of bioengineering efforts. We show how to upload, visualize, and output multiomics data, as well as strain information, into online repositories for several isoprenol-producing strain designs. We then use these data to train machine learning algorithms that recommend new strain designs that are correctly predicted to improve isoprenol production by 23%. This demonstration is done by using synthetic data, as provided by a novel library, that can produce credible multiomics data for testing algorithms and computational tools. In short, this paper provides a step-by-step tutorial to leverage these computational tools to improve production in bioengineered strains.
09 BIOMASS FUELS↗
Two-Scale 13 C Metabolic Flux Analysis for Metabolic Engineering
Accelerating the Design-Build-Test-Learn (DBTL) cycle in synthetic biology is critical to achieving rapid and facile bioengineering of organisms for the production of, e.g., biofuels and other chemicals. The Learn phase involves using data obtained from the Test phase to inform the next Design phase. As part of the Learn phase, mathematical models of metabolic fluxes give a mechanistic level of comprehension to cellular metabolism, isolating the principle drivers of metabolic behavior from the peripheral ones, and directing future experimental designs and engineering methodologies. Furthermore, the measurement of intracellular metabolic fluxes is specifically noteworthy as providing a rapid and easy-to-understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis in the Learn phase of the DBTL cycle, where we show how one can take the isotope labeling data from a 13 C labeling experiment and immediately turn it into a determination of cellular fluxes that points in the direction of genetic engineering strategies that will advance the metabolic engineering process.For our modeling purposes we use the Joint BioEnergy Institute (JBEI) Quantitative Metabolic Modeling (jQMM) library, which provides an open-source, python-based framework for modeling internal metabolic fluxes and making actionable predictions on how to modify cellular metabolism for specific bioengineering goals. It presents a complete toolbox for performing different types of flux analysis such as Flux Balance Analysis, 13 C Metabolic Flux Analysis, and it introduces the capability to use 13 C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) [1]. In addition to several other capabilities, the jQMM is also able to predict the effects of knockouts using the MoMA and ROOM methodologies. The use of the jQMM library is illustrated through a step-by-step demonstration, which is also contained in a digital Jupyter Notebook format that enhances reproducibility and provides the capability to be adopted to the user's specific needs. As an open-source software project, users can modify and extend the code base and make improvements at will, providing a base for future modeling efforts.
13C Metabolic flux analysis↗
Cytoprotection, proliferation and epidermal differentiation of adipose tissue-derived stem cells on emu oil based electrospun nanofibrous mat
Electrospun nanofibrous scaffolds containing natural substances with wound healing properties such as Emu oil (EO) may have a great potential for increasing the efficiency of stem cell-based skin bioengineering. For this purpose, EO blended PCL/PEG electrospun nanofibrous mats were successfully fabricated and characterized using FE-SEM, FTIR and Universal Testing Machine. The efficiency of the scaffolds in supporting the adherence, cytoprotection, proliferation and differentiation of adipose tissue-derived stem cells (ADSCs) to keratinocyte was evaluated. GC/MS and HPLC were used to determine the composition of pure EO, which revealed to be mainly fatty acids and carotenoids. FE-SEM and cell proliferation assays showed that adhesion and proliferation of ADSCs on EO-PCL/PEG nanofibers was significantly higher than on PCL/PEG nanofibers. Additionally, EO-PCL/PEG nanofibers with free radical scavenging properties conferred a cytoprotective effect against cell-damaging free radicals, while the ability to support cell adhesion and growth was maintained or even improved. Immunostaining of ADSCs on EO-PCL/PEG nanofibers confirmed the change in morphology of ADSCs from spindle to polygonal shape suggesting their differentiation toward an epidermal linage. Moreover, the expression levels of the keratin 10, filaggrin, and involucrin that are involved in epidermal differentiation were upregulated in a stage-specific manner. This preliminary study shows that EO-PCL/PEG nanofibers could be a good candidate for the fabrication of wound dressings and skin bioengineered substitutes with ADSCs. - Highlights: • EO-PCL/PEG nanofibers showed excellent biocompatibility. • EO-PCL/PEG nanofibers conferred a cytoprotective effect against free radicals. • EO with nanofibrous scaffolds promoted epidermal differentiation of ADSCs. • EO-PCL/PEG with ADSCs provide an efficient cell–scaffold construct.
60 APPLIED LIFE SCIENCES↗
A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data
New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.
59 BASIC BIOLOGICAL SCIENCES↗
Enhancing lipid production in plant cells through automated high-throughput genome engineering and phenotyping
Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. Furthermore, these innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.
59 BASIC BIOLOGICAL SCIENCES↗
Towards a dynamic photosynthesis model to guide yield improvement in C4 crops
Summary The most productive C4 food and biofuel crops, such as Saccharum officinarum (sugarcane), Sorghum bicolor (sorghum) and Zea mays (maize), all use NADP‐ME‐type C4 photosynthesis. Despite high productivities, these crops fall well short of the theoretical maximum solar conversion efficiency of 6%. Understanding the basis of these inefficiencies is key for bioengineering and breeding strategies to increase the sustainable productivity of these major C4 crops. Photosynthesis is studied predominantly at steady state in saturating light. In field stands of these crops light is continually changing, and often with rapid fluctuations. Although light may change in a second, the adjustment of photosynthesis may take many minutes, leading to inefficiencies. We measured the rates of CO 2 uptake and stomatal conductance of maize, sorghum and sugarcane under fluctuating light regimes. The gas exchange results were combined with a new dynamic photosynthesis model to infer the limiting factors under non‐steady‐state conditions. The dynamic photosynthesis model was developed from an existing C4 metabolic model for maize and extended to include: (i) post‐translational regulation of key photosynthetic enzymes and their temperature responses; (ii) dynamic stomatal conductance; and (iii) leaf energy balance. Testing the model outputs against measured rates of leaf CO 2 uptake and stomatal conductance in the three C4 crops indicated that Rubisco activase, the pyruvate phosphate dikinase regulatory protein and stomatal conductance are the major limitations to the efficiency of NADP‐ME‐type C4 photosynthesis during dark‐to‐high light transitions. We propose that the level of influence of these limiting factors make them targets for bioengineering the improved photosynthetic efficiency of these key crops.
59 BASIC BIOLOGICAL SCIENCES↗
Distinct Physiological Roles of the Three Ferredoxins Encoded in the Hyperthermophilic Archaeon Thermococcus kodakarensis
Control of electron flux is critical in both natural and bioengineered systems to maximize energy gains. Both small molecules and proteins shuttle high-energy, low-potential electrons liberated during catabolism through diverse metabolic landscapes. Ferredoxin (Fd) proteins—an abundant class of Fe-S-containing small proteins—are essential in many species for energy conservation and ATP production strategies. It remains difficult to model electron flow through complicated metabolisms and in systems in which multiple Fd proteins are present. The overlap of activity and/or limitations of electron flux through each Fd can limit physiology and metabolic engineering strategies. Here we establish the interplay, reactivity, and physiological role(s) of the three ferredoxin proteins in the model hyperthermophile Thermococcus kodakarensis . We demonstrate that the three loci encoding known Fds are subject to distinct regulatory mechanisms and that specific Fds are utilized to shuttle electrons to separate respiratory and energy production complexes during different physiological states. The results obtained argue that unique physiological roles have been established for each Fd and that continued use of T. kodakarensis and related hydrogen-evolving species as bioengineering platforms must account for the distinct Fd partnerships that limit flux to desired electron acceptors. Extrapolating our results more broadly, the retention of multiple Fd isoforms in most species argues that specialized Fd partnerships are likely to influence electron flux throughout biology. High-energy electrons liberated during catabolic processes can be exploited for energy-conserving mechanisms. Maximal energy gains demand these valuable electrons be accurately shuttled from electron donor to appropriate electron acceptor. Proteinaceous electron carriers such as ferredoxins offer opportunities to exploit specific ferredoxin partnerships to ensure that electron flux to critical physiological pathways is aligned with maximal energy gains. Most species encode many ferredoxin isoforms, but very little is known about the role of individual ferredoxins in most systems. Our results detail that ferredoxin isoforms make largely unique and distinct protein interactions in vivo and that flux through one ferredoxin often cannot be recovered by flux through a different ferredoxin isoform. The results obtained more broadly suggest that ferredoxin isoforms throughout biological life have evolved not as generic electron shuttles, but rather serve as selective couriers of valuable low-potential electrons from select electron donors to desirable electron acceptors.
59 BASIC BIOLOGICAL SCIENCES↗
Kinetic Learning vo.1
New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. This software implements a method to effectively predict pathway dynamics in an automated fashion using a combination of machine learning and abundant multiomics data (proteomics and metabolomics). The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.
Martin, HectorGarcia↗
Data for "Enhancing Lipid Production in Plant Cells through Automated High-Throughput Genome Engineering and Phenotyping"
Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. These innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.
AI/ML↗
Methanotroph Immobilization in Polymeric Bioreactors to Increase Mass Transfer
Methane, a greenhouse gas, is produced naturally by volcanoes, bacteria, and wildfires. Yet, human activities such as landfills and rice patty fields have increased methane production at an alarming rate. The ability to convert methane to useful products without the use of expensive metal catalysts or elevated reaction conditions is highly sought-after. One such way is using methanotrophs, which naturally consume methane, to produce desired products. In this work, we strive to expedite biology and produce muconic acid via bioengineered methanotrophic bacteria, which can be used to synthesize adipic acid — a precursor to nylon. Positive outcomes have been observed previously by immobilizing non-bioengineered methanotroph strains in a hydrogel. Here, the hydrogel was embedded in polymer scaffolds to aid in mass transfer through the hydrogel. A silicone scaffold was chosen due to its gas permeability, biocompatibility, ease of loading, and chemical inertness.
36 MATERIALS SCIENCE↗
The Plasticity of Molecular Interactions Governs Bacterial Microcompartment Shell Assembly
Not Available
59 BASIC BIOLOGICAL SCIENCES↗