Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.
Sandia National Laboratories will computationally evaluate several raceway pond design modifications for improved growth of Haematococcus pluvialis. Sandia National Laboratories will use the model to optimize design and growth conditions such as temperature, light, and CO2 to make design and condition modification recommendations to the Requestor.
Previous strain development efforts for cyanobacteria have failed to achieve the necessary productivities needed to support economic biofuel production. We proposed to develop CRISPR Engineering for Rapid Enhancement of Strains (CERES). We developed genetic and computational tools to enable future high-throughput screening of CRISPR interference (CRISPRi) libraries in the cyanobacterium Synechococcus sp. PCC 7002, including: (1) Operon- SEQer: an ensemble of algorithms for predicting operon pairs using RNA-seq data, (2) experimental characterization and machine learning prediction of gRNA design rules for CRISPRi, and (3) a shuttle vector for gene expression. These tools lay the foundation for CRISPR library screening to develop cyanobacterial strains that are optimized for growth or metabolite production under a wide range of environmental conditions. The optimization of cyanobacterial strains will directly advance U.S. energy and climate security by enabling domestic biofuel production while simultaneously mitigating atmospheric greenhouse gases through photoautotrophic fixation of carbon dioxide.
Industrial accidents, such as the Fukushima and Chernobyl disasters, release harmful chemicals into the environment, covering large geographical areas. Natural flora may serve as biological sensors for detecting metal contamination, such as cesium. Spectral detection of plant stresses typically employs a few select wavelengths and often cannot distinguish between different stress phenotypes. In this study, we apply hyperspectral reflectance imaging in the visible and near-infrared along with multivariate curve resolution (MCR) analysis to identify unique spectral signatures of three stresses in Arabidopsis thaliana: salt, copper, and cesium. While all stress conditions result in common stress physiology, hyperspectral reflectance imaging and MCR analysis produced unique spectral signatures that enabled classification of each stress. As the level of potassium was previously shown to affect cesium stress in plants, the response of A. thaliana to cesium stress under variable levels of potassium was also investigated. Increased levels of potassium reduced the spectral response of A. thaliana to cesium and prevented changes to chloroplast cellular organization. While metal stress mechanisms may vary under different environmental conditions, this study demonstrates that hyperspectral reflectance imaging with MCR analysis can distinguish metal stress phenotypes, providing the potential to detect metal contamination across large geographical areas.
Although chemically inert, Xe and other noble gases have been shown to have functional effects on biological systems. For example, Xe is a powerful anesthetic with neuroprotective properties. Recent reports have claimed that Xe inhibits the activity of tissue plasminogen activator (tPA) and urate oxidase (UOX), indicating that the use of Xe as an anesthetic may have undesirable side effects. Here, we revisited the methods used to demonstrate Xe inhibition of UOX and tPA, testing both indirect and direct gas delivery methods with variable bubble sizes and gas flowrates. Our results indicate that Xe or Kr do not affect the activity of UOX or tPA and that the previously reported inhibition is due to protein damage attendant to directly bubbling gases into protein solutions. The lack of evidence to support Xe inhibition of UOX or tPA alleviates concerns regarding possible side effects for the clinical application of Xe as an anesthetic. Furthermore, this study illustrates the importance of using indirect methods of gas dissolution for studying gas-protein interactions in aqueous solution.
Background: Successful implementation of modified cyanobacteria as hosts for industrial applications requires the development of a cyanobacterial chassis. The cyanobacterium Synechococcus sp. PCC 7002 embodies key attributes for an industrial host, including a fast growth rate and high salt, light, and temperature tolerances. This study addresses key limitations in the advancement of Synechococcus sp. PCC 7002 as an industrial chassis. Results: Tools for genome integration were developed and characterized, including several putative neutral sites for genome integration. The minimum homology arm length for genome integration in Synechococcus sp. PCC 7002 was determined to be approximately 250 bp. Three fluorescent protein reporters (hGFP, Ypet, and mOrange) were characterized for gene expression, microscopy, and flow cytometry applications in Synechococcus sp. PCC 7002. Of these three proteins, the yellow fluorescent protein (Ypet) had the best optical properties for minimal interference with the native photosynthetic pigments and for detection using standard microscopy and flow cytometry optics. Twenty-five native promoters were characterized as tools for recombinant gene expression in Synechococcus sp. PCC 7002 based on previous RNA-seq results. This characterization included comparisons of protein and mRNA levels as well as expression under both continuous and diurnal light conditions. Promoters A2520 and A2579 were found to have strong expression in Synechococcus sp. PCC 7002 while promoters A1930, A1961, A2531, and A2813 had moderate expression. Promoters A2520 and A2813 showed more than twofold increases in gene expression under light conditions compared to dark, suggesting these promoters may be useful tools for engineering diurnal regulation. Conclusions: The genome integration, fluorescent protein, and promoter tools developed in this study will help to advance Synechococcus sp. PCC 7002 as a cyanobacterial chassis. The long minimum homology arm length for Synechococcus sp. PCC 7002 genome integration indicates native exonuclease activity or a low efficiency of homologous recombination. Low correlation between transcript and protein levels in Synechococcus sp. PCC 7002 suggests that transcriptomic data are poor selection criteria for promoter tool development. Lastly, the conventional strategy of using promoters from photosynthetic operons as strong promoter tools is debunked, as promoters from hypothetical proteins (A2520 and A2579) were found to have much higher expression levels.
To determine the effect of Liquidoff on bacteria, three bacterial strains were tested: Escherichia coli DH5α, Synechococcus sp. PCC 7002, and Synechococcus elongatus PCC 7942. E. coli DH5α is a Gram-negative, aerobic bacterium that is often found in normal gut flora and is commonly used the laboratory due to its fast growth rate. Synechococcus sp. PCC 7002 and S. elongatus PCC 7942 are Gram-negative, aquatic, autophototrophic cyanobacteria. Synechococcus sp. PCC 7002 is a marine cyanobacterium isolated from ‘fish pens’ on Magueyes Island, Puerto Rico in 1962, while S. elongatus PCC 7942 is a freshwater cyanobacterium. It should be noted that no Gram-positive bacterium was tested in this study.
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.
The search is on for new renewable energy and algal-derived biofuel is a critical piece in the multi-faceted renewable energy puzzle. It has 30x more oil than any terrestrial oilseed crop, ideal composition for biodiesel, no competition with food crops, can be grown in waste water, and is cleaner than petroleum based fuels. This project discusses these three goals: (1) Conduct fundamental research into the effects that dynamic biotic and abiotic stressors have on algal growth and lipid production - Genomics/Transcriptomics, Bioanalytical spectroscopy/Chemical imaging; (2) Discover spectral signatures for algal health at the benchtop and greenhouse scale - Remote sensing, Bioanalytical spectroscopy; and (3) Develop computational model for algal growth and productivity at the raceway scale - Computational modeling.
Progress in algal biofuels has been limited by significant knowledge gaps in algal biology, particularly as they relate to scale-up. To address this we are investigating how culture composition dynamics (light as well as biotic and abiotic stressors) describe key biochemical indicators of algal health: growth rate, photosynthetic electron transport, and lipid production. Our approach combines traditional algal physiology with genomics, bioanalytical spectroscopy, chemical imaging, remote sensing, and computational modeling to provide an improved fundamental understanding of algal cell biology across multiple cultures scales. This work spans investigations from the single-cell level to ensemble measurements of algal cell cultures at the laboratory benchtop to large greenhouse scale (175 gal). We will discuss the advantages of this novel, multidisciplinary strategy and emphasize the importance of developing an integrated toolkit to provide sensitive, selective methods for detecting early fluctuations in algal health, productivity, and population diversity. Progress in several areas will be summarized including identification of spectroscopic signatures for algal culture composition, stress level, and lipid production enabled by non-invasive spectroscopic monitoring of the photosynthetic and photoprotective pigments at the single-cell and bulk-culture scales. Early experiments compare and contrast the well-studied green algae chlamydomonas with two potential production strains of microalgae, nannochloropsis and dunnaliella, under optimal and stressed conditions. This integrated approach has the potential for broad impact on algal biofuels and bioenergy and several of these opportunities will be discussed.