{
"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST001199","ANALYSIS_ID":"AN001995","VERSION":"1","CREATED_ON":"June 18, 2019, 9:51 pm"},

"PROJECT":{"PROJECT_TITLE":"A comprehensive time-course metabolite profiling of the model cyanobacterium Synechocystis sp. PCC 6803 under diurnal light:dark cycles","PROJECT_SUMMARY":"Cyanobacteria are a model photoautotroph and a chassis for the sustainable production of fuels and chemicals. Yet, knowledge of photoautotrophic metabolism in the natural environment of day/night cycles is lacking yet has implications for improved yield from plants, algae, and cyanobacteria. Here, a thorough approach to characterizing diverse metabolites—including carbohydrates, lipids, amino acids, pigments, co-factors, nucleic acids and polysaccharides—in the model cyanobacterium Synechocystis sp. PCC 6803 (S. 6803) under sinusoidal diurnal light-dark cycles was developed and applied. A custom photobioreactor and novel multi-platform mass spectrometry workflow enabled metabolite profiling every 30-120 minutes across a 24-hour diurnal sinusoidal LD (“sinLD”) cycle peaking at 1,600 mol photons m 2 s-1. We report widespread oscillations across the sinLD cycle with 90%, 94%, and 40% of the identified polar/semi-polar, non-polar, and polymeric metabolites displaying statistically significant oscillations, respectively. Microbial growth displayed distinct lag, biomass accumulation, and cell division phases of growth. During the lag phase, amino acids (AA) and nucleic acids (NA) accumulated to high levels per cell followed by decreased levels during the biomass accumulation phase, presumably due to protein and DNA synthesis. Insoluble carbohydrates displayed sharp oscillations per cell at the day-to-night transition. Potential bottlenecks in central carbon metabolism are highlighted. Together, this report provides a comprehensive view of photosynthetic metabolite behavior with high temporal resolution, offering insight into the impact of growth synchronization to light cycles via circadian rhythms. Incorporation into computational modeling and metabolic engineering efforts promises to improve industrially-relevant strain design.","INSTITUTE":"Colorado State University","DEPARTMENT":"Chemical and","LAST_NAME":"Peebles","FIRST_NAME":"Christie","ADDRESS":"700 Meridian Ave, Fort Collins, CO 80523 USA","EMAIL":"wernerajz@gmail.com","PHONE":"2699981811"},

"STUDY":{"STUDY_TITLE":"Non-targeted LC-MS Analysis of Soluble Metabolites in the Non-Polar MTBE Phase (part-V)","STUDY_SUMMARY":"Cyanobacteria are a model photoautotroph and a chassis for the sustainable production of fuels and chemicals. Yet, knowledge of photoautotrophic metabolism in the natural environment of day/night cycles is lacking yet has implications for improved yield from plants, algae, and cyanobacteria. Here, a thorough approach to characterizing diverse metabolites—including carbohydrates, lipids, amino acids, pigments, co-factors, nucleic acids and polysaccharides—in the model cyanobacterium Synechocystis sp. PCC 6803 (S. 6803) under sinusoidal diurnal light-dark cycles was developed and applied. A custom photobioreactor and novel multi-platform mass spectrometry workflow enabled metabolite profiling every 30-120 minutes across a 24-hour diurnal sinusoidal LD (“sinLD”) cycle peaking at 1,600 mol photons m 2 s-1. We report widespread oscillations across the sinLD cycle with 90%, 94%, and 40% of the identified polar/semi-polar, non-polar, and polymeric metabolites displaying statistically significant oscillations, respectively. Microbial growth displayed distinct lag, biomass accumulation, and cell division phases of growth. During the lag phase, amino acids (AA) and nucleic acids (NA) accumulated to high levels per cell followed by decreased levels during the biomass accumulation phase, presumably due to protein and DNA synthesis. Insoluble carbohydrates displayed sharp oscillations per cell at the day-to-night transition. Potential bottlenecks in central carbon metabolism are highlighted. Together, this report provides a comprehensive view of photosynthetic metabolite behavior with high temporal resolution, offering insight into the impact of growth synchronization to light cycles via circadian rhythms. Incorporation into computational modeling and metabolic engineering efforts promises to improve industrially-relevant strain design.","INSTITUTE":"Colorado State University","DEPARTMENT":"Chemical and Biological Engineering","LAST_NAME":"Peebles","FIRST_NAME":"Christie","ADDRESS":"700 Meridian Ave, Fort Collins, CO 80523","EMAIL":"christie.peebles@colostate.edu","PHONE":"970-491-6779"},

"SUBJECT":{"SUBJECT_TYPE":"Bacteria","SUBJECT_SPECIES":"Synechocystis sp. PCC 6803","TAXONOMY_ID":"1148","GENOTYPE_STRAIN":"NCBI:txid1148","CELL_BIOSOURCE_OR_SUPPLIER":"ATCC"},
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"COLLECTION":{"COLLECTION_SUMMARY":"For each metabolomics time-point, a 10 mL culture were rapidly sampled via sterile on-reactor syringes into a pre-weighed centrifuge tube, quenched in -4°C 1X PBS, spun at 3,000g for 5 min., decanted, frozen in liquid nitrogen, and lyophilized at -50°C. The workflow from sampling to centrifugation took < 2 minutes; lyophilized samples were stored at -80°C for < 1 month prior to extraction. A biphasic extraction from lyophilized cell pellets was performed via a 2:1:1.6 MTBE:MeOH:H2O biphasic extraction, modified from the protocol developed by Salem et al. (Salem et al., 2016) resulting in a top layer of MTBE with non-polar soluble metabolites, a lower layer of MeOH:H2O with polar and semi-polar soluble metabolites, and an insoluble pellet. Each liquid layer was transferred to a fresh glass vial and dried under nitrogen gas overnight. The MTBE layer was resuspended in 1:1 toluene:MeOH and analyzed via Q-TOF-MS with a UPLC Phenyl Hexyl column (“RP-MS”). The MeOH:H2O layer was resuspended in 1:1 H2O:MeOH, split evenly and subjected to either i) derivatization in methoxyamine HCl and MSTFA followed by GC-MS analysis, or ii) targeted SRM analysis on a tandem quadrupole-MS equipped with a HILIC column. The insoluble pellet was hydrolyzed with a hydrochloric acid (HCl) based on previously published protocols (Fountoulakis and Lahm, 1998) (Huang, Kaiser and Benner, 2012) to analyze individual amino acids, nucleoside, and carbohydrate content of the insoluble polymers utilizing MTBSTFA derivatization for insoluble amino acids. Of the soluble phases, 10 µL were removed from each sample and pooled to create a QC sample, mixed, and aliquoted into thirteen vials. A QC sample was run after every sixth injection.","SAMPLE_TYPE":"Bacterial cells"},

"TREATMENT":{"TREATMENT_SUMMARY":"Synechocystis sp. PCC 6803 [N-1] (ATCC 27184, NCBI Taxonomy ID: 1080229) was utilized for all experiments. A light-emitting diode photobioreactor (LED PBR) was engineered to provide a rectified sinusoidal waveform light profile which (results in the negative half-cycle being set to zero) via two custom 4000K White LED panels (Reliance Laboratories, Port Townsend WA) arranged opposite a water bath facing inwards, 5% CO2 at 200 mL min-1 via in-house gas mixing and custom aerators to provide sufficient mixing, 27-30°C temperature control via a Huber Ministat and custom water bath (Midwest Custom Aquarium, Starbuck MN), and improved light penetration at high volumes via custom flat-panel reactors (FPRs) built in a circular geometry to maximize mixing (Allen Scientific Glass, Boulder CO) (Figure S1). At the peak, 1,600 mol photons m-2s-1 (E) was provided as measured by LightScout Quantum Meter (Model: 3415FXSE). . A single LED-PBR was inoculated and entrained to sinLD cycles for two days; this entrained culture was then use inoculated three biological triplicate FPRs in the LED PBR (Figure S2). Reactors were cultivated under the sinLD cycle profile for an additional day of entrainment prior to sampling (total of 3 days of entrainment)."},

"SAMPLEPREP":{"SAMPLEPREP_SUMMARY":"Briefly, 6 mL of 75% methanol (MeOH) was added to pellets, vortexed, and transferred to glass vials. 9 mL of 100% methyl tert-butyl ether (MTBE) was added, vortexed for 30 seconds, placed on automatic shaker for 1.5 hours at 4 ºC, and sonicated for 15 minutes. 3.75 mL of water was added, each extraction was vortexed by hand for 1 minute, and centrifuged for 10 minutes at 3,270g at 4ºC. A biphasic solution with a pellet formed: the top, green MTBE layer and the bottom, clear MeOH:H2O layer were separated into separate tubes and dried under N2,gas overnight. The pellet was stored at -80 ºC. After drying, the MTBE layer was resuspended in 100 uL 1:1 toluene:MeOH, transferred to a LC-MS vial insert, and stored at -80C for <1 month prior to MS analysis. The MeOH:H2O layer was resuspended in 1 mL of 1:1 H2O:MeOH, transferred to a 1.7 mL centrifuge tube and spun at 15,000g for 2 minutes at 4 ºC. The supernatant was split into two 465 µL aliquots—one for GCMS and one for LC(HILIC)MS—in glass vials and dried under N2,gas. The protocol outlined above is suitable for filter-quenched cyanobacteria samples and centrifuged cell pellets. The polar methanol/water fraction resulting from the biphasic extraction was processed for analysis by hydrophilic interaction liquid chromatography (HILIC) LC-MS. Dried samples were resuspended in 100 µL 1:1 H2O:MeOH and 10 µL were aliquoted into a pooled QC sample. Samples were stored at -80 ºC until analysis. The pooled QC sample was mixed and aliquoted into twelve vials. A QC injection was run every tenth injection. The dried polar fraction for analysis by GC-MS was stored at -80 ºC until derivatization, immediately prior to MS analysis. Samples were derivatized in 30 uL methoxyamine HCl and 30 uL MSTFA, as specified in the following section. Ten microliters were removed from each sample to create a pooled QC sample, mixed, and aliquoted into thirteen vials. A QC sample was run after every sixth injection. The non-polar MTBE phase was processed for non-targeted LC-MS analysis. Twenty microliters from each sample were pooled, mixed, and aliquoted into thirteen pooled QC samples. QC injections were placed after every sixth injection."},

"CHROMATOGRAPHY":{"CHROMATOGRAPHY_SUMMARY":"For non-targeted LC-MS experiments, two microliters of extract were injected onto a Waters Acquity UPLC system in discrete, randomized blocks with a pooled QC injection after every 6 sample injections and separated using a Waters Acquity UPLC CSH Phenyl Hexyl column (1.7 µM, 1.0 x 100 mm), using a gradient from solvent A (2mM ammonium hydroxide, 0.1% formic acid) to solvent B (Acetonitrile, 0.1% formic acid). Injections were made in 100% A, held at 100% A for 1 min, ramped to 98% B over 12 minutes, held at 98% B for 3 minutes, and then returned to starting conditions over 0.05 minutes and allowed to re-equilibrate for 3.95 minutes, with a 200 µL/min constant flow rate. The column and samples were held at 65 °C and 6 °C, respectively. The column eluent was infused into a Waters Xevo G2 Q-TOF-MS with an electrospray source in positive mode, scanning 50-2000 m/z at 0.2 seconds per scan, alternating between MS (6 V collision energy) and MSE mode (15-30 V ramp). Calibration was performed using sodium iodide with 1 ppm mass accuracy. The capillary voltage was held at 2200 V, source temp at 150 °C, and nitrogen desolvation temp at 350 °C with a flow rate of 800 L/hr.","CHROMATOGRAPHY_TYPE":"Reversed phase","INSTRUMENT_NAME":"Waters Xevo G2","COLUMN_NAME":"Waters Acquity UPLC CSH Phenyl Hexyl (1.7 uM, 1.0 x 100 mm)"},

"ANALYSIS":{"ANALYSIS_TYPE":"MS"},

"MS":{"INSTRUMENT_NAME":"Waters Xevo QS","INSTRUMENT_TYPE":"QTOF","MS_TYPE":"ESI","ION_MODE":"POSITIVE","MS_COMMENTS":"Compounds were created by clustering features using RAMClustR (Broeckling et al. 2014). RAMClustR uses a similarity matric which calculates feature correlation across samples and retention time correlation between features. Hierarchical clustering of the similarity matrix was computed via the fastcluter package (Müllner 2013). The resulting clustered dendrogram is cut using DynamicTreeCut and spectra are created with clusters and features abundances from input data (Langfelder, Zhang, and Horvath 2008). The abundance for each mass in spectra is a weighted mean of feature intensity. The RAMClustR outputs are compounds (clusters of correlated features) and intensities for each sample; spectral abundance intensities reflect weighted mean of all features within the compound.","MS_RESULTS_FILE":"ST001199_AN001995_Results.txt UNITS:spectral abundance per cell Has m/z:No Has RT:No RT units:No RT data"}

}