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At least 109 records · Page 6

Synthetic Design of Microorganisms for Lignin Fuel

Despite the proof-of-the-concept for lignin bioconversion at the time when the project was first funded, the conversion efficiency and titer were extremely low for bioconversion of lignin into lipid. The low conversion efficiency is due the inherent recalcitrance of lignin and limited understanding of lignin chemistry. In nature, lignin is a complex aromatic heteropolymer composed of phenylpropane units cross-linked via a variety of chemically stable bonds, which makes it particularly difficult for microbial degradation. In addition to the recalcitrance, lignin as a biorefinery waste is also highly heterogeneous in terms of chemical linkage abundance, functional group distribution, molecular weight and uniformity. In order to achieve better lignin bioconversion, it is critical to understand how lignin chemistry impacts the conversion performance. Throughout the project, we have clearly found that ‘not all lignin are created equal’, in particular, for the lignin out of different biochemical conversion processes. In addition, we have clearly shown that lignin chemistry defines its functionality and processiblity for bioconversion. In particular, the S/G/H ratio, functional groups, chemical linkages and molecular weight defines the reactivity of lignin during bioconversion. Throughout the project, we have advanced the understanding for the impact of lignin chemistry on reactivity, developed a series of lignin 3 fractionation technologies to improve lignin processibility, and ultimately advanced biorefinery design holistically for simultaneous optimization of carbohydrate release and lignin reactivity. In addition to lignin reactivity, another important consideration for lignin bioconversion is the available microbial strains for bioconversion. Natural biomass utilization systems evolved mechanisms for lignin degradation, often with at least three sequential steps: lignin depolymerization into aromatic compounds, subsequent degradation of aromatic compounds into common intermediates like acetyl-CoA, and eventually the conversion of central metabolites into target bioproducts like lipid and PHA. Extensive work has gone into understanding the mechanisms of lignin depolymerization in model systems such as white rot fungi and termites. Despite interest, no fungus-based commercial process for lignin depolymerization has been developed, due in large part to the practical challenges of fungal genetic manipulations. In addition to white rot fungi, a few members of the Actinobacteria, α-Proteobacteria, and γ-Proteobacteria (including Rhodococcus putida) have lignin degradation activity in vitro. However, most of lignindegrading bacteria has very low lignin conversion efficiency, in particular, for lignin depolymerization. The project focused on addressing the issue with systems biology-guided microbial design to improve lignin bioconversion. The project first established that laccase enzyme can synergize with bacteria to promote lignin depolymerization, and further utilized synthetic biology approach to designed functional modules for lignin depolymerization, aromatic compound utilization, and bioproduct accumulation. The integration of biorefinery design, synthetic biology, and fermentation optimization eventually allowed us to achieve the target yield of 10g/L lipid production from lignin-containing biorefinery waste. In this summary, we hereby briefly review the major discoveries from the project with reference to different sections of this report.

09 BIOMASS FUELS↗

Computational Framework for Machine-Learning-Enabled 13 C Fluxomics

13 C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13 C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13 C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.

13C metabolic flux analysis↗

Rational Control of Off‐State Heterogeneity in a Photoswitchable Fluorescent Protein Provides Switching Contrast Enhancement**

Abstract Reversibly photoswitchable fluorescent proteins are essential markers for advanced biological imaging, and optimization of their photophysical properties underlies improved performance and novel applications. Here we establish a link between photoswitching contrast, one of the key parameters that dictate the achievable resolution in nanoscopy applications, and chromophore conformation in the non‐fluorescent state of rsEGFP2, a widely employed label in REversible Saturable OpticaL Fluorescence Transitions (RESOLFT) microscopy. Upon illumination, the cis chromophore of rsEGFP2 isomerizes to two distinct off ‐state conformations, trans1 and trans2 , located on either side of the V151 side chain. Reducing or enlarging the side chain at this position (V151A and V151L variants) leads to single off ‐state conformations that exhibit higher and lower switching contrast, respectively, compared to the rsEGFP2 parent. The combination of structural information obtained by serial femtosecond crystallography with high‐level quantum chemical calculations and with spectroscopic and photophysical data determined in vitro suggests that the changes in switching contrast arise from blue‐ and red‐shifts of the absorption bands associated to trans1 and trans2 , respectively. Thus, due to elimination of trans2 , the V151A variants of rsEGFP2 and its superfolding variant rsFolder2 display a more than two‐fold higher switching contrast than their respective parent proteins, both in vitro and in E. coli cells. The application of the rsFolder2‐V151A variant is demonstrated in RESOLFT nanoscopy. Our study rationalizes the connection between structural and photophysical chromophore properties and suggests a means to rationally improve fluorescent proteins for nanoscopy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Separation of radium and actinium in acetic acid/acetate solutions using TK101 resin

Here, the uptake and elution behavior of Ra and Ac was studied on TK101 resin using acetic acid and acetate buffer (acetic acid/lithium acetate) solutions. There is high extraction of Ra (>10 3 ) over a wide range of acetic acid concentrations (0.02 to 4 M) with slightly lower extraction (>10 2 ) in the acetate buffer solutions (pH 3.9 to 5.6). The Ac extraction is considerably lower with a maximum D w of ∼100, and negligible extraction at high acetic acid concentrations (> 1 M) and in the acetate buffer solutions. The difference in D w can be leveraged for highly efficient separations of Ac from Ra, with no detectable Ra in the Ac fractions and high Ac yields (∼100%). These separations may be useful for radiopharmaceutical applications as the Ac purity is extremely high and the elution conditions can be optimized to be biologically safe and appropriate for chelation to molecules relevant to targeted alpha therapy.

and nuclear chemistry↗

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii , a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii , we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii 's network in dealing with varied environmental challenges that the species may face. As a result, the defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

54 ENVIRONMENTAL SCIENCES↗

Impact of Nd : YAG laser radiation (λ = 1.44 mm) on myocardial tissue in the treatment of coronary heart disease by transmyocardial laser revascularisation

We have studied the impact of Nd : YAG laser radiation with a wavelength of λ = 1.44 μm on the myocardial tissue for the formation of channels in the treatment of coronary heart disease by transmyocardial laser revascularisation (TMLR). The mechanism of perforation of the myocardial tissue by a laser pulse is presented and an experimental setup for TMLR is developed. In experiments on biological objects, the optimal parameters of radiation energy and duration necessary for the formation of channels of a given size in a single laser pulse are determined. The exposure regime ensures the adequate character of myocardial tissue destruction, assessed by such criteria as the perforation force, exposure energy, size of the coagulation zone, and level of carbonisation. (laser medicine)

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Microbiome Adaptation Could Amplify Modeled Projections of Global Soil Carbon Loss With Climate Warming

Warming alters soil microbial traits through ecological and evolutionary processes, directly influencing the decomposition of organic matter, which significantly affects global soil carbon emissions. Yet, soil carbon models largely ignore these processes and their implications for global responses to warming. Here, we incorporate eco-evolutionary theory into a mechanistic model describing microbial soil carbon decomposition to address the question of whether such processes could have consequential effects on climate carbon feedbacks globally. We assume that a key trait of microbes, their resource allocation to production of exoenzymes (which facilitate decomposition of organic matter)—is optimized to environmental temperatures by natural selection. We find that eco-evolutionary optimization results in microbes allocating more resources to enzyme production under warming. When applied at the global scale, eco-evolutionary optimization enhances the biological realism of soil carbon models and significantly amplifies global soil carbon loss by 2100. Our results highlight the significant potential of microbial eco-evolutionary responses to influence carbon cycle feedbacks to climate change, and motivate an urgent need for more comprehensive data to accurately quantify the adaptive potential of microbiomes in response to climate change.

Biodiversity & Conservation↗

WE-EF-303-11: Activity Distributions of Positron-Emitting-Isotopes Activated by Modulated Spot-Scanning Particle Beams

Purpose: To present the characteristics of positron-emitting-isotope (PEI) activity distributions on homogeneous and CIRS thorax phantoms irradiated by modulated spot-scanning carbon-ions and protons in Siemens IONTRIS system. Methods and Materials: Cylindrical homogeneous wax was irradiated by carbon-ions and protons of 10cm uniform square field with single-energy-layer ranges of 7cm, 16cm and 29cm. Images of PEI distributions were acquired over a period of 30 minutes at 10 minutes after each irradiation. Optimized uniform biological-equivalent doses of carbon-ions over a 5.0cm spherical target was delivered to CIRS phantom. Doses with range shifts in depths from 1mm to 6mm were also calculated and delivered to evaluate the accuracy of measuring activated PEI distributions. Results: Activated PEI distribution on homogeneous phantoms showed sharper peak near distal edge with flatter distribution toward proximal depth by carbon-ions than by protons. For depths near at Bragg-peak, penetration-depth spreading for energies from 10MeV/u to 60 MeV/u are from 0.4mm to 10.3mm, and 1.2mm to 30.4mm for projectile-fragments by carbon-ions and target-fragments by protons, respectively. For depths proximal to Bragg-peak, target-fragments for both carbon-ions and protons had minimal energy dependence in depth. For complex dose distributions delivered to CIRS thorax phantom, similar PEI peak-like distributions were still observed for carbon-ions. And, the changes at locations and edges of measured PEI peak were correlated within 0.5mm of shifted ranges. Mean activities over 5.0cm spherical target were similar for CISR phantom irradiated by doses with various range shifts. Conclusions: Peak-like PEI distributions by carbon-ions with good correlation to distal edges in both homogeneous and CIRS thorax phantoms present the potential for the in-vivo range verification while additional efforts is needed for protons. Due to flat distributions for depths at 6mm proximal to distal edges, mean activity at target might be used for in-vivo dose verification of target.

60 APPLIED LIFE SCIENCES↗

Hsp100/ClpB Chaperone Function and Mechanism

The supported research investigated the mechanism of action of a unique class of molecular chaperones in higher plants, the Hsp100/ClpB proteins, with the ultimate goal of defining how these chaperones influence plant growth, development, stress tolerance and productivity. Molecular chaperones are essential effectors of cellular “protein quality control”, which comprises processes that ensure the proper folding, localization, activation and turnover of proteins. Hsp100/ClpB proteins are required for temperature acclimation in plants, optimal seed yield, and proper chloroplast development. The model plant Arabidopsis thaliana and genetic and molecular approaches were used to investigate two of the three members of the Hsp100/ClpB proteins in plants, cytosolic AtHsp101 and chloroplast-localized AtClpB-p. Investigating the chaperone activity of the Hsp100/ClpB proteins addresses DOE goals in that this activity impacts how “plants generate and assemble components” as well as “allowing for their self repair”. Additionally, Hsp100/ClpB protein function in plants is directly required for optimal “utilization of biological energy” and is involved in “mechanisms that control the architecture of energy transduction systems”.

59 BASIC BIOLOGICAL SCIENCES↗

New measurements for hadrontherapy and space radiation: biology

The dual goals of optimizing clinical efficacy of hadrontherapy and determining radiation risk estimates for space research have intersected to a common focus for investigation of the biological effects of charged particles. This paper briefly highlights recent international progress at accelerator facilities engaged in both biological and clinical studies of the effects of particle beams, primarily protons, carbon and iron ions. Basic mechanisms of molecular, cellular and tissue responses continue under investigation for radiations with a range of ionization densities. Late normal tissue effects, including the risk of cancer in particular, are of importance for both research fields. International cooperation has enhanced the rate of progress as evidenced by recent publications. Specific areas of biomedical research related to the biological radiotoxicity of critical organs (especially the central nervous system), individual radiosensitivities to radiation carcinogenesis, and the analysis of effects in mixed radiation fields still require more research. Recommendations for addressing these issues are made.

Review↗

Normal Tissue Injury Induced by Photon and Proton Therapies: Gaps and Opportunities

Despite technological advances in radiation therapy (RT) and cancer treatment, patients still experience adverse effects. Proton therapy (PT) has emerged as a valuable RT modality that can improve treatment outcomes. Normal tissue injury is an important determinant of the outcome; therefore, for this review, we analyzed 2 databases: (1) clinical trials registered with ClinicalTrials.gov and (2) the literature on PT in PubMed, which shows a steady increase in the number of publications. Most studies in PT registered with ClinicalTrials.gov with results available are nonrandomized early phase studies with a relatively small number of patients enrolled. From the larger database of nonrandomized trials, we listed adverse events in specific organs/sites among patients with cancer who are treated with photons and protons to identify critical issues. The present data demonstrate dosimetric advantages of PT with favorable toxicity profiles and form the basis for comparative randomized prospective trials. A comparative analysis of 3 recently completed randomized trials for normal tissue toxicities suggests that for early stage non-small cell lung cancer, no meaningful comparison could be made between stereotactic body RT and stereotactic body PT due to low accrual (NCT01511081). In addition, for locally advanced non-small cell lung cancer, a comparison of intensity modulated RT with passive scattering PT (now largely replaced by spot-scanned intensity modulated PT), PT did not provide any benefit in normal tissue toxicity or locoregional failure over photon therapy. Finally, for locally advanced esophageal cancer, proton beam therapy provided a lower total toxicity burden but did not improve progression-free survival and quality of life (NCT01512589). The purpose of this review is to inform the limitations of current trials looking at protons and photons, considering that advances in technology, physics, and biology are a continuum, and to advocate for future trials geared toward accurate precision RT that need to be viewed as an iterative process in a defined path toward delivering optimal radiation treatment. A foundational understanding of the radiobiologic differences between protons and photons in tumor and normal tissue responses is fundamental to, and necessary for, determining the suitability of a given type of biologically optimized RT to a patient or cohort.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Distribution of Micronuclei in Human Fibroblasts across the Bragg Curve of Light and Heavy Ions

The space environment consists of energetic particles of varying mass and energy, and understanding the :biological Bragg curve" is essential in optimizing shielding effectiveness against space radiation induced biological impacts. The "biological Bragg curve" is dependent on the energy and the type of the primary particle, and may vary for different biological endpoints. Previously, we studied the induction of micronuclei (MN) across the Bragg curve of energetic Fe and Si ions, and observed no increased yield of MN at the location of the Bragg peak. However, the ratio of mono- to bi-nucleated cells, which indicates inhibition of cell progression, was found higher at the Bragg peak location in comparison to the plateau region of the Bragg curve. Here, we report the induction of MN in normal human fibroblast cells across the Bragg curve of incident protons generated at Loma Linda University. Similar to Si and Fe ions, the ratio of mono- to bi-nucleated cells showed a clear spike as the protons reached the Bragg peak. Unlike the two heavy ions, however, the MN yield also increased at the Bragg peak location. These results confirm the hypothesis that severely damaged cells at the Bragg peak of heavy, but not light ions are more likely to go through reproductive death and not be evaluated for micronuclei.

Hada, M.↗

Optimal Transport as a Tool for Scientific Discovery in Radiation Biology

This report summarizes findings from research conducted for the “Exploration of the Poten tial for Artificial Intelligence and Machine Learning to Advance Low-Dose Radiation Biology Re search” (RadBio-AI) program, supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under Awards KP1601011/FWP CC121 and KP1601017/FWP CC121. The research reported here was undertaken in an effort to assess the potential of optimal measure transport methods as components within the larger scope of a com putational framework envisioned to support research in the radiation biology domain. Within this effort, our interest centered on enabling a unified generic framework where probabilistic modeling, inference, and statistical learning can be carried out for a wide range of data distributions. As described next in Section 1 (and in more detail in our original publication), optimal measure transport offers the possibility of such unified approach.

97 MATHEMATICS AND COMPUTING↗

Toward the Validation of Maternal Embryonic Leucine Zipper Kinase: Discovery, Optimization of Highly Potent and Selective Inhibitors, and Preliminary Biology Insight

MELK kinase has been implicated in playing an important role in tumorigenesis. Our previous studies suggested that MELK is involved in the regulation of cell cycle and its genetic depletion leads to growth inhibition in a subset of high MELK-expressing basal-like breast cancer cell lines. Herein we describe the discovery and optimization of novel MELK inhibitors 8a and 8b that recapitulate the cellular effects observed by short hairpin ribonucleic acid (shRNA)-mediated MELK knockdown in cellular models. We also discovered a novel fluorine-induced hydrophobic collapse that locked the ligand in its bioactive conformation and led to a 20-fold gain in potency. Here, these novel pharmacological inhibitors achieved high exposure in vivo and were well tolerated, which may allow further in vivo evaluation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Biochemical Production with Purified Cell-Free Systems

Bioprocesses offer a powerful set of opportunities for current chemical process innovation. Such conversions provide opportunities to employ process intensification strategies and the right characteristics to scale down and harness the power of modular process design, but strain engineering and the use of microbial systems for industrial biochemicals production remain unpredictable and expensive. Purified cell-free technologies, in which complex enzyme reaction cascades are used to catalyze bioconversions of interest, can be designed to mitigate these limitations. Such systems allow predictive kinetic modeling and full control of reaction conductions, enabling a dramatically increased design-built-test cycle iteration rate. When sufficiently robust engineered enzymes are used, opportunities to leverage harsh reaction conditions, long-term recycling strategies, and hybridization with chemical catalysis can be used to dramatically improve the value proposition of biochemicals production. As a result, further research is required to realize the potential of these approaches and to address the current platform-limiting challenges around cofactor cost and reuse.

09 BIOMASS FUELS↗

Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization

The design priority for manned space exploration missions is almost always placed on human safety. Proposed manned surface exploration tasks (lunar, asteroid sample returns, Mars) have the possibility of astronauts traveling several kilometers away from a home base. Deviations from preplanned paths are expected while exploring. In a time-critical emergency situation, there is a need to develop an optimal home base return path. The return path may or may not be similar to the outbound path, and what defines optimal may change with, and even within, each mission. A novel path planning algorithm and prototype program was developed using biologically inspired particle swarm optimization (PSO) that generates an optimal path of traversal while avoiding obstacles. Applications include emergency path planning on lunar, Martian, and/or asteroid surfaces, generating multiple scenarios for outbound missions, Earth-based search and rescue, as well as human manual traversal and/or path integration into robotic control systems. The strategy allows for a changing environment, and can be re-tasked at will and run in real-time situations. Given a random extraterrestrial planetary or small body surface position, the goal was to find the fastest (or shortest) path to an arbitrary position such as a safe zone or geographic objective, subject to possibly varying constraints. The problem requires a workable solution 100% of the time, though it does not require the absolute theoretical optimum. Obstacles should be avoided, but if they cannot be, then the algorithm needs to be smart enough to recognize this and deal with it. With some modifications, it works with non-stationary error topologies as well.

Birge, Brian↗

SU-E-T-54: Benefits of Biological Cost Functions

Purpose: To verify the benefits of the biological cost functions. Methods: TG166 patients were used for the test case scenarios. Patients were planned using Monaco V5.0 (CMS/Elekta, St.Louis, MO) Monaco has 3 biological and 8 physical CFs. In this study the plans were optimized using 3 different scenarios. 1- Biological CFs only 2-Physical CFs only 3- Combination of Physical and Biological CFsMonaco has 3 biological CFs. Target EUD used for the targets, derived from the poisson cell kill model, has an α value that controls the cold spots inside the target. α values used in the optimization were 0.5 and 0.8. if cold spots needs to be penalized α value increased. Serial CF: it's called serial to mimic the behaviour of the serial organs, if a high k value like 12 or 14 is used it controls the maximum dose. Serial CF has a k parameter that is used to shape the whole dvh curve. K value ranges between 1–20. k:1 is used to control the mean dose, lower k value controls the mean dose, higher k value controls the higher dose, using 2 serial CFs with different k values controls the whole DVH. Paralel CF controls the percentage of the volume that tolerates higher doses than the reference dose to mimic the behaviour of the paralel organs. Results: It was possible to achive clinically accepted plans in all 3 scenarios. The benefit of the biological cost functions were to control the mean dose for target and OAR, to shape the DVH curve using one EUD value and one k value simplifies the optimization process. Using the biological CFs alone, it was hard to control the dose at a point. Conclusion: Biological CFs in Monaco doesn't require the ntcp/tcp values from the labs and useful to shape the whole dvh curve. I work as an applications support specialist for Elekta and I am a Ph.D. Student in Istanbul University for radiation therapy physics.

60 APPLIED LIFE SCIENCES↗

Surface Biology and Geology Imaging Spectrometer: A Case Study to Optimize the Mission Design Using Intrinsic Dimensionality

The information content that can be derived from spectroscopic imagery tends to increase with finer ground sampling distance, finer spectral sampling, more frequent revisit, and higher signal-to-noise ratios (SNRs). However, these parameters are not independent, and it is thus impossible to design a space-borne imaging spectrometer to maximize all of them simultaneously. We present an instrument model and simulation environment that enable us to find the optimal combination of these four mission design parameters, using intrinsic dimensionality (ID) as the metric. ID is the size of the signal subspace – the maximum degrees of freedom when noise can be disregarded – and is a metric that is independent of any one particular algorithm or application area. This study is important for upcoming missions such as NASA's Earth System Observatory mission to study the Earth's Surface Biology and Geology (SBG), which will comprise a visible to shortwave infrared spectrometer in addition to a multi-channel thermal radiometer on a separate platform. When evaluating a desert site and a tropical forested site, we find that spectral resolution drives information content, with a significant drop in normalized ID (15–45% decrease) when simulating 15 nm spectral sampling as opposed to 10 nm spectral sampling. However, there was some variation between sites, with the forested site benefiting from 5 nm spectral sampling, whereas the desert site had poorer results at this resolution, due to the impact on noise. At 10 nm spectral sampling, ground sampling distances in the range 30–50 m provided the optimal balance between spatial resolution and SNR, although more frequent revisit, potentially by combining data from multiple missions, would maximize total information content.

Mission design↗