While CRISPRi was previously established in Synechococcus sp. PCC 7002 (hereafter 7002), the design principles for guide RNA (gRNA) effectiveness remain largely unknown. Here, 76 strains of 7002 were constructed with gRNAs targeting three reporter systems to evaluate features that impact gRNA efficiency. Correlation analysis of the data revealed that important features of gRNA design include the position relative to the start codon, GC content, protospacer adjacent motif (PAM) site, minimum free energy, and targeted DNA strand. Unexpectedly, some gRNAs targeting upstream of the promoter region showed small but significant increases in reporter expression, and gRNAs targeting the terminator region showed greater repression than gRNAs targeting the 3 ' end of the coding sequence. Machine learning algorithms enabled prediction of gRNA effectiveness, with Random Forest having the best performance across all training sets. This study demonstrates that high-density gRNA data and machine learning can improve gRNA design for tuning gene expression in 7002.

While algae demonstrate potential as a sustainable fuel source, low productivities limit the economic realization of algal biofuels. High-throughput strain engineering, omics-informed genome-scale modeling, and microbiome engineering are key technologies for enabling algal biofuels. High-throughput strain engineering efforts generate improved traits, including high biomass productivity and lipid content, in diverse algal species. Genome-scale models, constructed with the aid of omics data, provide insight into metabolic limitations and guide rational algal strain engineering efforts. As outdoor cultivation systems introduce exogenous organisms, microbiome engineering seeks to eliminate harmful organisms and introduce beneficial species. Optimizing algal biomass production and lipid content using these technologies may overcome the productivity barrier for the commercialization of algal biofuels.

Expanded industrial globalization has resulted in the release of high concentrations of heavy metals into environmental water sources and soils. Phytoremediation may help to remove these heavy metals from contaminated soils. Tall fescue (Festuca arundinacea Shreb.) exhibits phytoremediation potential due to its endurance and high stress tolerances. Here, we report photochemical and structural responses in tall fescue to acute and chronic doses of heavy metals, copper (Cu) and hexavalent chromium (Cr(VI)). Visual signs of stress and decreased photosynthetic yield measurements were detected for both the acute and chronic exposures. To gain insight into stress responses at the cellular level, structural and pigment changes in tall fescue in response to Cu and Cr(VI) stress were assessed with brightfield and confocal fluorescence imaging. While brightfield images showed qualitative changes in plant tissue structure, the quantification of changes were not statistically significant due to high variability between leaf blades. Fluorescence imaging confirmed decreasing total chlorophyll content in tall fescue cross-sections in response to Cr(VI) and Cu exposure. To spectrally separate the closely related chlorophyll pigments (Chl-a, Chl-b, and Chl in photosystem I) and visualize their relative localizations within the plant tissue, hyperspectral confocal fluorescence microscopy was conducted with multivariate curve resolution (MCR) analysis of the data. These results determined that Chl-a and Chl-b were more significantly reduced than Chl associated with photosystem I. Additionally, a new spectral component was identified. A broad autofluorescence (AF) feature appeared in the late stress response of both acute and chronically exposed tall fescue and was localized in globular bodies. While the identity of the broad AF feature remains to be identified, we hypothesize that it may be associated with degraded chlorophyll components in autophagic bodies. If confirmed, this would indicate that autophagy is a stress response to heavy metal exposure in tall fescue.

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.

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.

Expanded industrial globalization has resulted in the release of high concentrations of heavy metal ions into environmental water sources and soils. Phytoremediation has attracted increasing attention because it can safely remove environmental contaminates via plant uptake, accumulation, and harvesting. Tall fescue (Festuca arundinaceaShreb.) is the predominant temperate perennial grass in the United States and serves as soil erosion prevention and livestock feed, but also exhibits phytoremediation potential due to its endurance and high stress tolerances. Here, we discovered unique degradation bioindicators of photochemical and structural responses in tall fescue to heavy metals copper and chromium (VI) exposure. To quantify structural and pigment changes in tall fescue in response to copper and hexavalent chromium stresses, we performed brightfield and confocal fluorescence imaging. Fluorescence images were collected over a 9-day period, which confirmed decreasing total chlorophyll content in tall fescue cross-sections in response to Cr(VI) and Cu exposure. Brightfield fluorescence microscopy images presented multiple structural features of tall fescue: rib cross-section, vascular fiber bunder, adaxial fiber bundle, abaxial fiber bundle, metaxylem element 1, and metaxylem element 2. Observations from structural analysis indicated that the copper and chromium exposed plants became compromised by their respective stressor after 4 days of exposure resulting in varying plant survival responses. On day 9 copper and chromium treated plants displayed near-complete collapse of the plant vascular systems. To spectrally separate the closely related chlorophyll pigments (Chl-a, Chl-b, and Chl in Photosystem I) and visualize their relative localizations within the plant tissue, hyperspectral confocal fluorescence microscopy was also

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.

Through mathematical modeling, we determine that the small cellular volume in prokaryotes is the primary reason that no N-terminal helix is found in these enzymes, as fewer enzymes are mistargeted into the small cytoplasmic space.

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.

The anthropogenic release of toxic metals into the environment poses danger to the health of both humans and the local ecosystem. Biosensors for the detection of metals have been developed to improve our ability to monitor these environmental contaminants, yet most of these sensors use heterotrophic bacterial hosts, which require a fixed carbon source and do not typically grow in natural waterways. In this study, we constructed and characterized metal sensors for development of a photoautotrophic biosensor using Synechococcus sp. PCC 7002. We characterized gold and copper sensors based on modified MerR transcriptional activators: GolSA113T, with improved gold binding, and GolSCL, containing the metal-binding loop from CueR which binds both gold and copper. The metal-sensing constructs were first optimized and characterized in Escherichia coli MG1655. The addition of a strong ribosome binding site to the optical reporter protein increased translation of the fluorescent reporter, and expression of golSA113T from the rbc promoter of Synechococcus sp. PCC 7002 improved the response to gold in MG1655. In rich medium, the GolSA113T-based E. coli sensor detected gold at concentrations as low as 100 nM, while the GolSCL-based E. coli sensor detected gold and copper at sensitivities of 100 nM and 10 μM, respectively. Both E. coli sensors responded to gold and copper yet showed no detectable response to other metals. Abiotic factors, such as medium complexity, were found to influence the response of the E. coli sensors, with minimal medium resulting in higher sensitivities of detection. Expression of the GolSA113T- and GolSCL-based sensor constructs in the cyanobacterium Synechococcus sp. PCC 7002 resulted in photoautotrophic gold sensors, but these biosensors failed to produce a significant response to copper. Moreover, the fluorescence response of the cyanobacterial sensors to gold was significantly reduced compared to that of analogous E. coli sensors. While this effort demonstrates feasibility for the development of photoautotrophic biosensors, additional efforts to optimize sensor performance will be required.

Free fatty acids (FFAs) are essential cellular components and also potential precursors for biofuel production. The modification of cyanobacteria for FFA production allows for the production of this high density energy molecule from CO2 and sunlight as the main carbon and energy sources. Efforts to engineer cyanobacteria for FFA production have provided a proof-of-concept demonstration for this approach, yet FFA yields are too low to support industrial-scale production for biofuel applications. This chapter highlights previous successes in engineering cyanobacterial FFA production, possible targets for future metabolic engineering efforts, and the many challenges that must be overcome. Due to the essential nature of fatty acid biosynthesis, modification of this major metabolic pathway includes interactions with many other pathways of carbon metabolism and complex regulatory mechanisms. In addition, cyanobacterial FFA production has been shown to result in cellular stress responses affecting growth, membrane integrity and composition, and photosynthesis. These complex interactions necessitate the use of systems biology approaches, such as omics and computational modelling, to understand and effectively manipulate cyanobacteria for enhanced FFA production. While there are few examples of the application of systems biology to FFA production in cyanobacteria, a review of technologies and tools developed for cyanobacteria is presented to guide future efforts in this area. The proposed integration of metabolic engineering and systems biology approaches may advance our understanding of cyanobacterial fatty acid biosynthesis and overcome current barriers in cyanobacterial FFA production.

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.

EDITORIAL article Front. Bioeng. Biotechnol., 02 February 2016Sec. Synthetic Biology Volume 4 - 2016 | https://doi.org/10.3389/fbioe.2016.00007

Cyanobacteria have been shown to be capable of producing a variety of advanced biofuels; however, product yields remain well below those necessary for large scale production. New genetic tools and high throughput metabolic engineering techniques are needed to optimize cyanobacterial metabolisms for enhanced biofuel production. Towards this goal, this project advances the development of a multiple promoter replacement technique for systems-level optimization of gene expression in a model cyanobacterial host: Synechococcus sp. PCC 7002. To realize this multiple-target approach, key capabilities were developed, including a high throughput detection method for advanced biofuels, enhanced transformation efficiency, and genetic tools for Synechococcus sp. PCC 7002. Moreover, several additional obstacles were identified for realization of this multiple promoter replacement technique. The techniques and tools developed in this project will help to enable future efforts in the advancement of cyanobacterial biofuels.

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.

While recent studies have demonstrated direct photosynthetic production of biofuels via engineered cyanobacteria, biofuel yields from cyanobacteria remain at low levels. As with heterotrophic biofuel production, product toxicity is likely a limiting factor. Some mechanisms of toxicity may be similar to those studied in common heterotrophic hosts; however, the photosynthesis-dependent pathways for carbon fixation and energy production in cyanobacteria present unique targets for biofuel toxicity. This study investigates biofuel toxicity for three model cyanobacterial strains: Synechococcus elongatus PCC 7942, Synechocystis sp. PCC 6803, and Synechococcus sp. PCC 7002. While cyanobacterial biofuel tolerances were generally lower than that of heterotrophic hosts, the marine strain Synechococcus sp. PCC 7002 showed increased tolerance to short chain alcohols, and long-chain biofuel products, such as fatty alcohols, saturated free fatty acids, alkanes, and alkenes had minimal toxicity for all three cyanobacteria. Targeted mutants were generated to explore natural mechanisms of biofuel tolerance in cyanobacteria, such as cell membrane composition, reactive oxygen species degradation, and efflux pumps. These mutants confirmed the influence of cell membrane composition on cyanobacterial tolerance to short-chain alcohols. This study provides data to guide both biofuel product and cyanobacterial host selection and further identifies potential targets for improving biofuel tolerance in cyanobacteria.