Publications

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Algal biofuels are a renewable energy source with the potential to replace conventional petroleum-based fuels, while simultaneously reducing greenhouse gas emissions. The economic feasibility of commercial algal fuel production, however, is limited by low productivity of the natural algal strains. The project described in this SAND report addresses this low algal productivity by genetically engineering cyanobacteria (i.e. blue-green algae) to produce free fatty acids as fuel precursors. The engineered strains were characterized using Sandias unique imaging capabilities along with cutting-edge RNA-seq technology. These tools are applied to identify additional genetic targets for improving fuel production in cyanobacteria. This proof-of-concept study demonstrates successful fuel production from engineered cyanobacteria, identifies potential limitations, and investigates several strategies to overcome these limitations. This project was funded from FY10-FY13 through the President Harry S. Truman Fellowship in National Security Science and Engineering, a program sponsored by the LDRD office at Sandia National Laboratories.

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Magnetic resonance spectroscopy has been used in a high-risk, high-payoff search for neuronal current (NC) signals in the free induction decay (FID) data from the visual cortex of human subjects during visual stimulation. If successful, this approach could make possible the detection of neuronal currents in the brain at high spatial and temporal resolution. Our initial experiments indicated the presence of a statistically significant change in the FID containing the NC relative to FIDs with the NC absent, and this signal was consistent with the presence of NC. Unfortunately, two follow-on experiments were not able to confirm or replicate the positive findings of the first experiment. However, even if the result from the first experiment were evidence of NC in the FID, it is clear that its effect is so small, that a true NC imaging experiment would not be possible with the current instrumentation and experimental protocol used here.

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The direct conversion of carbon dioxide into biofuels by photosynthetic microorganisms is a promising alternative energy solution. In this study, a model cyanobacterium, Synechococcus elongatus PCC 7942, is engineered to produce free fatty acids (FFA), potential biodiesel precursors, via gene knockout of the FFA-recycling acyl-ACP synthetase and expression of a thioesterase for release of the FFA. Similar to previous efforts, the engineered strains produce and excrete FFA, but the yields are too low for large-scale production. While other efforts have applied additional metabolic engineering strategies in an attempt to boost FFA production, we focus on characterizing the engineered strains to identify the physiological effects that limit cell growth and FFA synthesis. The strains engineered for FFA-production show reduced photosynthetic yields, chlorophyll-a degradation, and changes in the cellular localization of the light-harvesting pigments, phycocyanin and allophycocyanin. Possible causes of these physiological effects are also identified. The addition of exogenous linolenic acid, a polyunsaturated FFA, to cultures of S. elongatus 7942 yielded a physiological response similar to that observed in the FFA-producing strains with only one notable difference. In addition, the lipid constituents of the cell and thylakoid membranes in the FFA-producing strains show changes in both the relative amounts of lipid components and the degree of saturation of the fatty acid side chains. These changes in lipid composition may affect membrane integrity and structure, the binding and diffusion of phycobilisomes, and the activity of membrane-bound enzymes including those involved in photosynthesis. Thus, the toxicity of unsaturated FFA and changes in membrane composition may be responsible for the physiological effects observed in FFA-producing S. elongatus 7942. These issues must be addressed to enable the high yields of FFA synthesis necessary for large-scale biofuel production. © 2012 Wiley Periodicals, Inc.

Multivariate curve resolution (MCR) is a useful and important analysis tool for extracting quantitative information from hyperspectral image data. However, in the case of hyperspectral fluorescence microscope images acquired with CCD-type technologies, cosmic spikes and the presence of detector artifacts in the spectral data can make the extraction of the pure-component spectra and their relative concentrations challenging when applying MCR to the images. In this paper, we present new generalized and automated approaches for preprocessing spectral image data to improve the robustness of the MCR analysis of spectral images. These novel preprocessing steps remove cosmic spikes, correct for the presence of detector offsets and structured noise as well as select spectral and spatial regions to reduce the detrimental effects of detector noise. These preprocessing and MCR analysis techniques incorporate the use of an optical filter to prevent light from impinging on a small number of spectral pixels in the CCD detector. This dark spectral region can be incorporated into any spectral imaging system to enhance modeling of detector offset and structured noise components as well as the automated selection of spatial regions to restrict the analysis to only those regions containing viable spectral information. The success of these automated preprocessing methods combined with new MCR modeling approaches are demonstrated with realistically simulated data derived from spectral images of macrophage cells with green fluorescence protein (GFP). Further, we demonstrate using spectral images from the green alga, Chlorella, approaches for the analyses when fluorescent species with widely different relative spectral intensities are present in the image. We believe that the preprocessing and MCR approaches introduced in this paper can be generalized to several other hyperspectral image technologies and can improve the success of automated MCR analyses with little or no a priori information required about the spectral components present in the samples. © 2012 Elsevier B.V.

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