Major support for this work was provided by the NYUAD Center for Genomics and Systems Biology (CGSB), funded by Tamkeen under New York University Abu Dhabi Research Institute grant (73 71210 CGSB9) and NYU Abu Dhabi Faculty Research Funds (AD060). W.F. was additionally supported by the Hundred Talents Program of Zhejiang University. We thank Ashish Jaiswal for help in recording the video. We thank Hong Cai for generating the metabolic phenotype data.

1. Phenotype Microarray Experiments
<ol>
	<li>Obtain C. reinhardtii strain CC-503 from the Chlamydomonas Resource Center at the University of Minnesota, USA (https://www.chlamycollection.org).</li>
	<li>Grow the cells in fresh Tris-Acetate-Phosphate (TAP) media19&#160;with final concentrations of 400 &#181;g/mL timentin, 50 &#181;g/mL ampicillin, and 100 &#181;g/mL kanamycin (to inhibit bacterial growth) under 400 micromol photons/m2s, at 25 &#176;C, for two days to mid-log phase.</li>
	<li>Spin down the culture at 2,000 x g for 10 min at 22 &#176;C and discard the supernatant without disturbing the pellet.</li>
	<li>Prepare fresh TAP media containing 0.1% tetrazolium violet dye &#34;D&#34;. 
	NOTE: Modify TAP media in this step to exclude some nutrients depending on the metabolite category tested in each plate (e.g., exclude ammonium chloride for nitrogen source plates).</li>
	<li>Resuspend the pellet in fresh TAP media prepared (from step 1.2) to a final concentration of 1 x 106 cells/mL.</li>
	<li>Use chemical compound array assay plates (carbon sources, nitrogen sources, phosphorus and sulfur sources plates, and the peptide nitrogen sources).</li>
	<li>Inoculate a 100 &#181;L aliquot of cell-containing media into each well of the assay plates. 
	NOTE: Make sure to duplicate the assays.</li>
	<li>Streak cells on yeast extract/peptone plates and perform gram staining, as in Smith et al.20&#160;before and after the assay to monitor bacterial contamination.</li>
	<li>Insert the chemical compound array assay plates into the microplate reader system.</li>
	<li>Incubate all the plates at 30 &#176;C for up to 7 days and program the microplate reader system to read the dye color change every 15 min. 
	NOTE: As most microplate readers do not provide a source of continuous light during incubation, the algae should be able to carry out heterotrophic respiration.</li>
</ol>
2. Data Analysis
<ol>
	<li>Export the raw kinetic data from the microplate reader as CSV files, which will subsequently be used as input to the Omnilog Phenotype Microarray (OPM) package in R. Add the biological information as metadata (e.g., strain designation, growth media, temperature, etc.).
	<ol>
		<li>Using the PM Kinetic data converter software; load the D5E data files, and convert them to OKA files using the following command lines in the PM kinetic analysis software: 
		Load | Import (locate the folder of the OKA files) | Populate Filters | Import | Add All Plates | Close. 
		&#8203;Export | choose read data (Kinetic), choose format (CSV) (Tabulate Header), and choose plates (every plate (individual Files)) | Export data | Save.</li>
		<li>To carry out the Phenotype Microarray (PM) data analysis, use the OPM software package21,22&#160;that runs within the R software environment. The package, tutorial, and reference documentation&#160;are available at:&#160;http://www.goeker.org/opm/. In RStudio, a graphical user interface for R, install the opm package and its dependencies using the following commands: 
		source (http://www.goeker.org/opm/install_opm.R) 
		library (opm)</li>
		<li>Navigate to the directory that contains the CSV files of the kinetic data and import the data using the read_opm function: 
		&#8203;x &#60;- read_opm(&#34;.&#34;, convert=&#34;grp&#34;, include=list (&#34;csv:&#34;))</li>
		<li>Aggregate and discretize the kinetic data using curve-parameter estimation. 
		For (i in 1 :length(x)) { 
		x[[i]] &#60;I do _aggre(x[[i]], boot = 0 L, cores = 1 L, method =&#34;splines&#34;, options = set_spline_options(&#34;smooth.spline&#34;)) 
		x[[i]] &#60;- do_disc(x[[i]], cutoff = FALSE) 
		} 
		#Collection of the metadata 
		metadata &#60;- collect_template(&#34;.&#34;) 
		metadata$Strain &#60;- c(&#34;BLANK&#34;,&#34;CC- 503&#34;) 
		for (I in 1 :length(x)) {x[[i]] &#60;- include_metadata(x[[i]], md = metadata, replace = TRUE)}</li>
		<li>Use the function xy_plot to map the respiration (or growth) measurements (y-axis) as a function of time (x-axis) for the assayed 96-well plates. 
		print (xy_plot(x[[ 1 ]], include =&#34;Strain&#34;, theor.max = FALSE))</li>
		<li>Visualize the data as a heat map using the function level_plot to allow for a quick comparative overview of the kinetic data. 
		level_plot(x, main = list(), colors = opm_opt(&#34;color.borders&#34;), panel.headers = metadata$Strain, cex = NULL, strip.fmt = list(), striptext.fmt = list(), legend.sep =&#34; &#34;, space =&#34;Lab&#34;, bias = 0.7 , num.colors = 200 L)</li>
		<li>Extract important biological information, the curve parameters, from the raw kinetic curves and include the lag phase (&#955;), the growth rate (&#956;), the maximum cell respiration (A), and the area under the curve (AUC)21. To identify positive metabolites, use the A values of the negative control, which represents the abiotic reactivity of the dye with the medium, in addition to the blank of each microwell plate as background subtraction values. The extract function is used to obtain the A parameter. 
		opm_opt(&#34;curve.param&#34;) 
		param &#60;- extract (x, as.labels = list(&#34;Strain&#34;)))</li>
	</ol>
	</li>
</ol>
3. Identification of Reactions and Genes Associated with New Metabolites
<ol>
	<li>Search KEGG(Kyoto Encyclopedia of Genes and Genomes) (http://www.genome.jp/kegg/)&#160;and MetaCyc (http://metacyc.org/) to identify Enzyme Commission numbers (ECs) for reactions using metabolites found from chemical compound arrays23,2423,24.</li>
	<li>Use the identified EC numbers as a search basis in multiple available algal annotation resources such as Joint Genome Institute (JGI), Phytozome (http://www.phytozome.net), and peer-reviewed publications23,25,26,27.</li>
	<li>If a query returns no genetic evidence for a given EC number, identify the relevant associated proteins in other organisms, starting with species closest to the C. reinhardtii, then carry out a profile-based search using the NCBI PSI-BLAST server with default settings and use non-redundant proteins (nr) in C. reinhardtii (taxid:3055) to identify candidate genes associated to the reaction12.</li>
	<li>Manually curate PSI-BLAST hits with E values of&#160;&#60; 0.05 for relevance to the searched EC number through querying those BLAST hits through EMBL-EBI Pfam (http://pfam.xfam.org/search), or InterPro (http://www.ebi.ac.uk/interpro/) protein domain prediction servers. Note that the latter two scans are critical steps to ensure the identification of the correct enzymatic activity for the protein.</li>
</ol>
4. Model Refinement and Evaluation
<ol>
	<li>Use the latest COBRA Toolbox v.3.028&#160;in MATLAB29,30&#160;platform to carry out the following steps for model refinement. The COBRA Toolbox can be installed by following the steps in: https://opencobra.github.io/cobratoolbox/stable/installation.html . Alternatively, note that the COBRA Toolbox is also implemented across other open-source programming languages, such as Python (COBRApy31) and is available at: https://opencobra.github.io/cobrapy/ .
	<ol>
		<li>After installing the COBRA Toolbox v.3.0, open MATLAB and execute the following command to initialize the toolbox: 
		&#8203;initCobraToolbox;</li>
		<li>Add the identified reactions with their associated genes to the metabolic model, such as iRC1080, using the COBRA Toolbox functions addReaction and changeGeneAssociation. Navigate to the directory that contains the iRC1080 model, downloaded from http://bigg.ucsd.edu/models/iRC1080 and execute the following commands to load the model, rename it, and add a new reaction and its associated gene. 
		Load(&#39;iRC 1080 .mat&#39;) 
		modelNew = iRC 1080; 
		modelNew = addReaction(modelNew, &#39;D-ALA 2&#39; , &#8230; 
		{&#39;d-ala[c]&#39; , &#39;atp[c]&#39; , &#8230; 
		&#39;D-aladata[c]&#39; , &#39;adp[c]&#39; , &#39;pi[c]&#39; , &#8230; 
		&#39;h[c]&#39; },[- 2 - 1 1 1 1 1 ],false); 
		modelNEW = changeGeneAssociation(modelNew, &#8230; 
		&#39;D-ALA 2&#39;,&#39;au.g 14655 _t 1&#39; );</li>
		<li>In some cases when the metabolite is not produced intracellularly but is taken up from the medium, add transport reactions for the new metabolites to the model. These transport reactions represent passive diffusion of a metabolite from the extracellular medium to the cytosol. In addition, add a corresponding artificial exchange reaction using the addExchangeRxn function to input or output the metabolite into the extracellular medium. 
		modelNew = addReaction(modelNew, &#39;CYCPt&#39; , &#8230; 
		{&#39;cycp[e]&#39;,&#39;cycp[c]&#39; },[- 1 1 ],true)&#39; 
		modelNew = addExchangeRxn(modelNew, &#39;cycp[e]&#39; ,- 1000 ,1000 );</li>
		<li>Test the behavior of the new resultant model, e.g., iBD1106, by carrying out flux balance analysis (FBA) using the function optimizeCbModel under light and dark conditions for the maximization of biomass as the objective function. For light growth, set the lower and upper bounds of the PRISM solar litho&#39; light reactions to 646.07 (maximum rate). For dark growth, set the bounds of all PRISM light reactions to zero. Use the Biomass function defined previously10&#160;for growth under dark and light conditions. The FBA solution will output two vectors corresponding to reaction fluxes (solution.v) and reduced cost (solution.w), as well as one vector corresponding to metabolites&#39; shadow prices (solution.y). 
		%Simulate growth under light condition: 
		modelNew = changeRxnBounds(modelNew,{ &#8230; 
		% &#39;PRISM_solar_litho&#39; , &#8230; 
		&#39;PRISM_solar_exo&#39; , &#8230; 
		&#39;PRISM_incandescent_ 60 W&#39; , &#8230; 
		&#39;PRISM_fluorescent_cool_ 215 W&#39; , &#8230; 
		&#39;PRISM_metal_halide&#39; , &#8230; 
		&#39;PRISM_high_pressure_sodium&#39; , &#8230; 
		&#39;PRISM_growth_room&#39; , &#8230; 
		&#39;PRISM_white_LED&#39; , &#8230; 
		&#39;PRISM_red_LED_array_ 653 nm&#39; , &#8230; 
		&#39;PRISM_red_LED_ 674 nm&#39; , &#8230; 
		&#39;PRISM_fluorescent_warm_ 18 W&#39; , &#8230; 
		&#39;PRISM_design_growth&#39; , &#8230; 
		}, 0, &#39;b&#39; ); 
		modelNew = changeObjective(modelNew, &#39;BIOMASS_Chlamy_mixo&#39;); 
		FBAsolutionNew = optimizeCbModel(modelNew, &#39;max&#39;);</li>
		<li>Repeat step 4.1.4 for iRC1080 to compare FBA solutions obtained for iBD1106 with those obtained for iRC1080.</li>
		<li>There is a range of COBRA methods available that can be used to compare models (e.g., flux variability analysis, gene deletion studies, robustness analyses, flux split predictions, FBA, sampling, etc.). Detailed tutorials can be found at&#160;https://opencobra.github.io/cobratoolbox/stable/tutorials/index.html. Here, an example is provided where the&#160;iRC1080 model is compared with its refined version, iBD1106, by obtaining the shadow prices (sensitivity of the biomass objective function to changes in system variable) of the metabolites accounted for in each model. 
		Obtain the shadow prices for the metabolites: 
		shadowPrices = table(modelNew.mets, &#8230; 
		modelNew.metNames, FBAsolutionNew.y);</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

Metabolic phenotyping of the green microalga, C. reinhardtii, was described here using high throughput PM assay plates and an unmodified PMI. The assays were utilized for a total of 190 carbon sources (PM01 and PM02), 95 nitrogen sources (PM03), 59 phosphorus sources, and 35 sulfur sources (PM04), along with peptide nitrogen sources (PM06-08). Positive respiration was observed for 148 nutrients (one positive assay for C-source utilization, four positive assays for each the S-source and P-source utilizations, and 139 positive assays for N-source utilization). The substrates or nutrients (carbon, nitrogen, phosphorus, or sulfur) component of the media should not be added to the defined medium when applied to the relevant PM microplates that test for each of those sources.
The method shown here is effective for characterizing metabolic microalgae phenotypes that can be used to extend existing metabolic network models or direct the reconstruction of new models. Further, as the&#160;nutritional requirements of most microalgae are not known, this platform can be used to define these rapidly. Nelson et al.43&#160;had successfully applied these methods to identify new compounds that support the growth of the microalgae Chloroidium sp. UTEX 3007 and used the obtained information to define the species entry metabolites, which, unlike Chlamydomonas, include&#160;40 different carbon sources.
One major limitation of the PM for profiling microalgae is that the PMI has no illumination in the incubation chamber, and the microalgae need to be able to carry out heterotrophic metabolism. The absence of light could affect the interpretation of models that incorporate light to calculate metabolic fluxes. Gene pairs with coordinating functions have co-evolved to constitute metabolic network hubs, and the distinction between photosynthetic and non-photosynthetic network hubs can be made44. In general, photosynthetic network hubs (i.e., highly-connected nodes in the model) would be left out of heterotrophic models. For practical purposes, modeling heterotrophism in mixotrophic species should omit reactions known to be driven by light and account for the energy balance differences between conditions. Thus, modeling light-dependent and light-independent metabolism is standard practice in Chlamydomonas metabolic modeling6,45.
Some green microalgae, like Trebouxiophytes, are known to assimilate a variety of carbon molecules for growth, and this is thought to have arisen from their long evolutionary history as members of lichens46. While chlorophytes like Chlamydomonas can use acetate for growth, the brown marine microalga Tisochrysis lutea, known for its potential to commercially produce very-long-chain polyunsaturated fatty acids (VLC-PUFAs), cannot use acetate but can use glycerol for growth47. Biomass concentration of more than 100 g l&#8722;1 dry cell weight has been achieved with Chlorella with optimized addition of organic carbon sources in a fed-batch mode48. Further, the addition of sugar to Chlorellavulgaris can elevate its sequestration of CO2, thus providing an additive benefit during photosynthetic growth49. Most heterotrophic microalgae can also grow mixotrophically, but the Chlorophyte Chromochloris zofingiensis has been shown to shut off photosynthesis upon the addition of sugar50.
Diatoms, belonging to the division Bacillariophyta, are a major group of phytoplankton. Although most of the diatoms can only grow photoautotrophically, some of them can be cultivated mixotrophically or heterotrophically51. For example, glycerol was found to support growth in the light in the absence of CO2&#160;in some diatoms, including the model species Phaeodactylum tricornutum52. Also, some benthic diatoms like Nitzschia linearis can grow on carbohydrates in the dark53. It is likely to extend the PM assays to diatoms and other algal groups by supplementing suitable organic carbon sources to enable the cells to grow heterotrophically, and a mixotrophy strategy can also be potentially used for the obligate autotrophic microalgae providing a minimally required light supply.
To assess the reproducibility of the data, it is highly recommended to carry out duplicate assays for all plates. An assay may be considered positive only if, after subtraction from the negative control and the respective blank wells, the absorbance (PMI value) is positive. This description, in the presence of the tested compound, is a reflection of the dye&#39;s abiotic reaction with the medium.

Optimizing algal metabolism for enhanced and stable production of targeted metabolites requires the development of complex metabolic engineering strategies through systems-level analyses of metabolic networks. Metabolic network models can guide the rational designs for the rapid development of optimization strategies1,2,3,4. Although approximately 160 microalgal species have been sequenced5, there are, to our knowledge, only 44 algal metabolic models available4,6,7. Due to the difficulty in obtaining high-throughput metabolic phenotypic data for experimental validation of genomic information, the reconstruction of high-quality network models lags behind the rapid development of algal genome sequencing.
C. reinhardtii is an attractive model system for algal-based studies. This species can grow photoautotrophically or heterotrophically and has been widely used as a model organism in basic and applied research. Its genome sequence was published in 20078, with genome-scale metabolic models subsequently reconstructed for the species9,10,11. The genome-scale model for C. reinhardtii (iRC1080) was reconstructed by Chang et al.10&#160;based on genomic and literature evidence (entailing ~250 sources). It has 1,706 metabolites with 2,190 reactions10; however, the completeness of the model could not be verified beyond the available published experimental evidence at the time.
The phenotype microarrays (PMs) technology is a high-throughput platform that can provide metabolic profiling information for heterotrophic microorganisms as well as tissue-culture cells. In particular, it can be used to address the phenotype-to-genotype knowledge gap in microalgae, as first reported for Chlamydomonas reinhardtii12&#160;and subsequently for a species of Chloroidium13&#160;and Chlorella14. By studying cell responses to thousands of metabolites, signaling molecules, osmolytes, and effector molecules, the PM assays can provide functional metabolic profiling and offer insights into the function, metabolism, and environmental sensitivity15,16,17. Specifically, PM assays detect cells metabolite utilization in 96-well microplates with different nutrients, metabolites, or osmolytes contained in each well. Moreover, it is also possible to assay bioactive molecules, such as antibiotics and hormones. As determined by the intensity of color production by the NADH reduction of a tetrazolium-based redox dye, the metabolic utilization of substrates is evaluated in terms of cell respiration15,16,17. The experiments in 96-well microplates can be monitored and determined automatically over time with the phenotype microarray instrument (PMI) platform. Twenty 96-well microplates are designed to represent the common set metabolites to study cellular phenotypes to utilize carbon, nitrogen, sulfur, and phosphorus sources, along with different osmotic/ion and pH effects. The PM technology has been successfully used for updating and upgrading a number of existing genome-scale metabolic models for microorganisms15,16,17,18.
The protocol and data shown here are based on previously published work by Chaiboonchoe et al.12&#160;The presented work details the use of the PM assay method to characterize the metabolic phenotypes of microalgae and to expand an existing algal metabolic model of C. reinhardtii as well as to guide the reconstruction of new metabolic models.

<table><tbody><tr><td>Ampicillin</td><td>VWR</td><td>97062-796</td><td></td></tr><tr><td>Biolog assay plates [ PM01-08]</td><td>Biolog, Hayward, CA, USA</td><td></td><td></td></tr><tr><td>Biolog Omnilog Instrument</td><td>Biolog, Hayward, CA, USA</td><td></td><td></td></tr><tr><td>Chlamydomonas reinhardtii strain CC-503</td><td>Chlamydomonas Resource Center at the University of Minnesota, USA.</td><td></td><td>Regents of the University of Minnesota</td></tr><tr><td>Kanamycin</td><td>VWR</td><td>0408-EU-10G</td><td></td></tr><tr><td>Tetrazolium Violet Dye &amp;ldquo;D&amp;rdquo;</td><td>Biolog, Hayward, CA, USA</td><td></td><td></td></tr><tr><td>Timentin</td><td>GlaxoSmithKline Australia Pty Ltd</td><td>42010012-2</td><td></td></tr></tbody></table>

high-throughput metabolic profiling for model refinements of microalgae

Metabolic models are reconstructed based on an organism&#39;s available genome annotation and provide predictive tools to study metabolic processes at a systems-level. Genome-scale metabolic models may include gaps as well as reactions that are unverified experimentally. Reconstructed models of newly isolated microalgal species will result in weaknesses due to these gaps, as there is usually sparse biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology is an effective, high-throughput method that functionally determines cellular metabolic activities&#160;in response to a wide array of entry metabolites. Combining the high throughput phenotypic assays with metabolic modeling can allow existing metabolic network models to be rapidly reconstructed or optimized by providing biochemical evidence to support and expand genomic evidence. This work will show the use of PM assays for the study of microalgae by using the green microalgal model species Chlamydomonas reinhardtii as an example. Experimental evidence for over 254 reactions obtained by PM was used in this study to expand and refine a genome-scale C. reinhardtii metabolic network model, iRC1080, by approximately 25 percent. The protocol created here can be used as a basis for functionally profiling the metabolism of other microalgae, including known microalgae mutants and new isolates.

Watch this Scientific Journal Video about High-Throughput Metabolic Profiling for Model Refinements of Microalgae at JoVE.com

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

This protocol demonstrates the use of a phenotype microarray (PM) technology platform to define metabolic requirements of Chlamydomonas reinhardtii, a green microalga, and refine an existing metabolic network model.

Metabolic models are reconstructed based on an organism&#39;s available genome annotation and provide predictive tools to study metabolic processes at a ...

high-throughput-metabolic-profiling-for-model-refinements

Research

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Biology

3.7K Views.  New York University Abu Dhabi Research Institute. De fenotype micro-array technologie is een effectieve high-throughput methode die functioneel cellulaire metabole activiteiten bepaalt als reactie op een breed scala aan entry metabolieten. Het gebruik in deze technologie wordt gemeten in de vorm van cellulaire ademhaling bepaald door de hoeveelheid kleurontwikkeling geproduceerd door de NADH-reductie van een op tetrazolium gebaseerde redoxkleurstof. In dit werk zullen we het gebruik van fenotype micro array assays introduceren in de context ...

Video: High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Analysis of Fatty Acid Content and Composition in Microalgae

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of microalgal biomass. These fatty acids can be present in different acyl-lipid classes. Especially the fatty acids present in triacylglycerol (TAG) are of commercial interest, because they can be used for production of transportation fuels, bulk chemicals, nutraceuticals (&omega;-3 fatty acids), and food commodities. To develop commercial applications, reliable analytical methods for quantification of fatty acid content and composition are needed. Microalgae are single cells surrounded by a rigid cell wall. A fatty acid analysis method should provide sufficient cell disruption to liberate all acyl lipids and the extraction procedure used should be able to extract all acyl lipid classes.
With the method presented here all fatty acids present in microalgae can be accurately and reproducibly identified and quantified using small amounts of sample (5 mg) independent of their chain length, degree of unsaturation, or the lipid class they are part of.
This method does not provide information about the relative abundance of different lipid classes, but can be extended to separate lipid classes from each other.
The method is based on a sequence of mechanical cell disruption, solvent based lipid extraction, transesterification of fatty acids to fatty acid methyl esters (FAMEs), and quantification and identification of FAMEs using gas chromatography (GC-FID). A TAG internal standard (tripentadecanoin) is added prior to the analytical procedure to correct for losses during extraction and incomplete transesterification.

A method for the determination of fatty acid content and composition in microalgae based on mechanical cell disruption, solvent based lipid extraction, transesterification, and quantification and identification of fatty acids using gas chromatography is described. A tripentadecanoin internal standard is used to compensate for the possible losses during extraction and incomplete transesterification.

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of ...

Environment

Quantification of Heavy Metals and Other Inorganic Contaminants on the Productivity of Microalgae

Increasing demand for renewable fuels has researchers investigating the feasibility of alternative feedstocks, such as microalgae. Inherent advantages include high potential yield, use of non-arable land and integration with waste streams. The nutrient requirements of a large-scale microalgae production system will require the coupling of cultivation systems with industrial waste resources, such as carbon dioxide from flue gas and nutrients from wastewater. Inorganic contaminants present in these wastes can potentially lead to bioaccumulation in microalgal biomass negatively impact productivity and limiting end use. This study focuses on the experimental evaluation of the impact and the fate of 14 inorganic contaminants (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Sb, Se, Sn, V and Zn) on Nannochloropsis salina growth. Microalgae were cultivated in photobioreactors illuminated at 984 &#181;mol m-2 sec-1 and maintained at pH 7 in a growth media polluted with inorganic contaminants at levels expected based on the composition found in commercial coal flue gas systems. Contaminants present in the biomass and the medium at the end of a 7 day growth period were analytically quantified through cold vapor atomic absorption spectrometry for Hg and through inductively coupled plasma mass spectrometry for As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Sb, Se, Sn, V and Zn. Results show N. salina is a sensitive strain to the multi-metal environment with a statistical decrease in biomass yieldwith the introduction of these contaminants. The techniques presented here are adequate for quantifying algal growth and determining the fate of inorganic contaminants.

Integration of microalgal cultivation with industrial flue gas will ultimately introduce heavy metals and other inorganic compounds into the growth media. This study presents a procedure used to determine the end fate and impact of heavy metals and inorganic contaminants on the growth of Nannochloropsis salina grown in photobioreactors.

Increasing demand for renewable fuels has researchers investigating the feasibility of alternative feedstocks, such as microalgae. Inherent advantages ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Cultivation of Green Microalgae in Bubble Column Photobioreactors and an Assay for Neutral Lipids

There is significant interest in the study of microalgae for engineering applications such as the production of biofuels, high value products, and for the treatment of wastes. As most new research efforts begin at laboratory scale, there is a need for cost-effective methods for culturing microalgae in a reproducible manner. Here, we communicate an effective approach to culture microalgae in laboratory-scale photobioreactors, and to measure the growth and neutral lipid content of that algae. Instructions are also included on how to set up the photobioreactor system. Although the example organisms are species of Chlorella and Auxenochlorella, this system can be adapted to cultivate a wide range of microalgae, including co-cultures of algae with non-algae species. Stock cultures are first grown in bottles to produce inoculum for the photobioreactor system. Algae inoculum is concentrated and transferred to photobioreactors for cultivation in batch mode. Samples are collected daily for the optical density readings. At the end of the batch culture, cells are harvested by centrifuge, washed, and freeze dried to obtain a final dry weight concentration. The final dry weight concentration is used to create a correlation between the optical density and the dry weight concentration. A modified Folch method is subsequently used to extract total lipids from the freeze-dried biomass and the extract is assayed for its neutral lipid content using a microplate assay. This assay has been published previously but protocol steps were included here to highlight critical steps in the procedure where errors frequently occur. The bioreactor system described here fills a niche between simple flask cultivation and fully-controlled commercial bioreactors. Even with only 3-4 biological replicates per treatment, our approach to culturing algae leads to tight standard deviations in the growth and lipid assays.

Here, we present a protocol to construct lab-scale bubble column photobioreactors and use them to culture microalgae. It also provides a method for the determination of the culture growth rate and neutral lipid content.

There is significant interest in the study of microalgae for engineering applications such as the production of biofuels, high value products, and for the ...

Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases

Photobioreactors are illuminated cultivation systems for experiments on phototrophic microorganisms. These systems provide a sterile environment for microalgal cultivation with temperature, pH, and gas composition and flow rate control. At bench-scale, photobioreactors are advantageous to researchers studying microalgal properties, productivity, and growth optimization. At industrial scales, photobioreactors can maintain product purity and improve production efficiency. The video describes the preparation and use of a bench-scale photobioreactor for microalgal cultivation, including the safe use of corrosive gas inputs, and details relevant biomass measurements and biomass productivity calculations. Specifically, the video illustrates microalgal culture storage and preparation for inoculation, photobioreactor assembly and sterilization, biomass concentration measurements, and a logistic model for microalgal biomass productivity with rate calculations including maximum and overall biomass productivities. Additionally, since there is growing interest in experiments to cultivate microalgae using simulated or real waste gas emissions, the video will cover the photobioreactor equipment adaptations necessary to work with corrosive gases and discuss safe sampling in such scenarios.

Bench-scale, axenic cultivation facilitates microalgal characterization and productivity optimization before subsequent process scale-up. Photobioreactors provide the necessary control for reliable and reproducible microalgal experiments and can be adapted to safely cultivate microalgae with the corrosive gases (CO2, SO2, NO2) from municipal or industrial combustion emissions.

Photobioreactors are illuminated cultivation systems for experiments on phototrophic microorganisms. These systems provide a sterile environment for ...

Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation

In the United States, 35% of the total carbon dioxide (CO2) emissions come from the electrical power industry, of which 30% represent natural gas electricity generation. Microalgae can biofix CO2 10 to 15 times faster than plants and convert algal biomass to products of interest, such as biofuels. Thus, this study presents a protocol that demonstrates the potential synergies of microalgae cultivation with a natural gas power plant situated in the southwestern United States in a hot semi-arid climate. State-of-the-art technologies are used to enhance carbon capture and utilization via the green algal species Chlorella sorokiniana, which can be further processed into biofuel. We describe a protocol involving a semi-automated open raceway pond and discuss the results of its performance when it was tested at the Tucson Electric Power plant, in Tucson, Arizona. Flue gas was used as the main carbon source to control pH, and Chlorella sorokiniana was cultivated. An optimized medium was used to grow the algae. The amount of CO2 added to the system as a function of time was closely monitored. Additionally, other physicochemical factors affecting algal growth rate, biomass productivity, and carbon fixation were monitored, including optical density, dissolved oxygen (DO), electroconductivity (EC), and air and pond temperatures. The results indicate that a microalgae yield of up to 0.385 g/L ash-free dry weight is attainable, with a lipid content of 24%. Leveraging synergistic opportunities between CO2 emitters and algal farmers can provide the resources required to increase carbon capture while supporting the sustainable production of algal biofuels and bioproducts.

A protocol is described to utilize the carbon dioxide in natural gas power plant flue gas to cultivate microalgae in open raceway ponds. Flue gas injection is controlled with a pH sensor, and microalgae growth is monitored with real time measurements of optical density.

In the United States, 35% of the total carbon dioxide (CO2) emissions come from the electrical power industry, of which 30% represent natural gas ...

Microalgal Cultivation for Biomethane Obtention from Swine Waste Biogas

Biogas Purification through the use of a Microalgae-Bacterial System in Semi-Industrial High Rate Algal Ponds

In recent years, a number of technologies have emerged to purify biogas into biomethane. This purification entails a reduction in the concentration of polluting gases such as carbon dioxide and hydrogen sulfide to increase the content of methane. In this study, we used a microalgal cultivation technology to treat and purify biogas produced from organic waste from the swine industry to obtain ready-to-use biomethane. For cultivation and purification, two 22.2 m3 open-pond photobioreactors coupled with an absorption-desorption column system were set up in San Juan de los Lagos, Mexico. Several recirculation liquid/biogas ratios (L/G) were tested to obtain the highest removal efficiencies; other parameters, such as pH, dissolved oxygen (DO), temperature, and biomass growth, were measured. The most efficient L/Gs were 1.6 and 2.5, resulting in a treated biogas effluent with a composition of 6.8%vol and 6.6%vol in CO2, respectively, and removal efficiencies for H2S up to 98.9%, as well as maintaining O2 contamination values of less than 2%vol. We found that pH greatly determines CO2 removal, more so than L/G, during cultivation because of its participation in the photosynthetic process of microalgae and its ability to vary pH when solubilized due to its acidic nature. DO, and temperature oscillated as expected from the light-dark natural cycles of photosynthesis and the time of day, respectively. Biomass growth varied with CO2 and nutrient feeding as well as reactor harvesting; however, the trend remained primed for growth.

Author Spotlight: Scaling Microalgal Biotechnology for Enhanced Biomethane Production 

Air pollution impacts the quality of life of all organisms. Here, we describe the use of microalgae biotechnology for the treatment of biogas (simultaneous removal of carbon dioxide and hydrogen sulfide) and the production of biomethane through semi-industrial open high-rate algal ponds and subsequent analysis of treatment efficiency, pH, dissolved oxygen, and microalgae growth.

In recent years, a number of technologies have emerged to purify biogas into biomethane. This purification entails a reduction in the concentration of ...

Qualitative Characterization of the Aqueous Fraction from Hydrothermal Liquefaction of Algae Using 2D Gas Chromatography with Time-of-flight Mass Spectrometry

Two-dimensional gas chromatography coupled with time-of-flight mass spectrometry is a powerful tool for identifying and quantifying chemical components in complex mixtures. It is often used to analyze gasoline, jet fuel, diesel, bio-diesel and the organic fraction of bio-crude/bio-oil. In most of those analyses, the first dimension of separation is non-polar, followed by a polar separation. The aqueous fractions of bio-crude and other aqueous samples from biofuels production have been examined with similar column combinations. However, sample preparation techniques such as derivatization, solvent extraction, and solid-phase extraction were necessary prior to analysis. In this study, aqueous fractions obtained from the hydrothermal liquefaction of algae were characterized by two-dimensional gas chromatography coupled with time-of-flight mass spectrometry without prior sample preparation techniques using a polar separation in the first dimension followed by a non-polar separation in the second. Two-dimensional plots from this analysis were compared with those obtained from the more traditional column configuration. Results from qualitative characterization of the aqueous fractions of algal bio-crude are discussed in detail. The advantages of using a polar separation followed by a non-polar separation for characterization of organics in aqueous samples by two-dimensional gas chromatography coupled with time-of-flight mass spectrometry are highlighted.

A two-dimensional gas chromatography-time-of-flight mass spectrometry method is described for characterization of the aqueous fraction of bio-crude produced from hydrothermal liquefaction of algae. This protocol can also be employed to analyze the aqueous fraction of liquid products from fast pyrolysis, catalytic fast pyrolysis, catalytic deoxygenation and hydro-treating.

Two-dimensional gas chromatography coupled with time-of-flight mass spectrometry is a powerful tool for identifying and quantifying chemical components in ...

Bioengineering

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

Microalgae have been the focus of research for their applications in the production of high value compounds, food and fuel. Moreover, they are valuable photosynthetic models facilitating the understanding of the basic cellular processes. System wide studies enable comprehensive and in-depth understanding of molecular functions of the organisms. However, multiple independent samples and protocols are required for proteomics, lipidomics and metabolomics studies introducing higher error and variability. A robust high throughput extraction method for the simultaneous extraction of chlorophyll, lipids, metabolites, proteins and starch from a single sample of the green alga Chlamydomonas reinhardtii is presented here. The illustrated experimental setup is for Chlamydomonas cultures synchronized using 12 h/12 h light/dark conditions. Samples were collected over a 24 h cell cycle to demonstrate that the metabolites, lipids and starch data obtained using various analytical platforms are well conformed. Furthermore, protein samples collected using the same extraction protocol were used to conduct detailed proteomics analysis to evaluate their quality and reproducibility. Based on the data, it can be inferred that the illustrated method provides a robust and reproducible approach to advance understanding of various biochemical pathways and their functions with greater confidence for both basic and applied research.

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

System-wide analysis of multiple biomolecules is crucial to gain functional and mechanistic insights into biological processes. Hereby, an extensive protocol is described for high throughput extraction of lipids, metabolites, proteins and starch from a single sample harvested from synchronized Chlamydomonas culture.

Microalgae have been the focus of research for their applications in the production of high value compounds, food and fuel. Moreover, they are valuable ...

Developmental Biology

Construction and Setup of a Bench-scale Algal Photosynthetic Bioreactor with Temperature, Light, and pH Monitoring for Kinetic Growth Tests

The optimal design and operation of photosynthetic bioreactors (PBRs) for microalgal cultivation is essential for improving the environmental and economic performance of microalgae-based biofuel production. Models that estimate microalgal growth under different conditions can help to optimize PBR design and operation. To be effective, the growth parameters used in these models must be accurately determined. Algal growth experiments are often constrained by the dynamic nature of the culture environment, and control systems are needed to accurately determine the kinetic parameters. The first step in setting up a controlled batch experiment is live data acquisition and monitoring. This protocol outlines a process for the assembly and operation of a bench-scale photosynthetic bioreactor that can be used to conduct microalgal growth experiments. This protocol describes how to size and assemble a flat-plate, bench-scale PBR from acrylic. It also details how to configure a PBR with continuous pH, light, and temperature monitoring using a data acquisition and control unit, analog sensors, and open-source data acquisition software.

This paper describes the assembly process and operation of a bench-scale photosynthetic bioreactor that can be used, in conjunction with other methods, to estimate pertinent kinetic growth parameters. This system continuously monitors the pH, light, and temperature using sensors, a data acquisition and control unit, and open-source data acquisition software.

The optimal design and operation of photosynthetic bioreactors (PBRs) for microalgal cultivation is essential for improving the environmental and economic ...

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

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