Name: high-throughput metabolic profiling for model refinements of microalgae
Uploaded: 2021-12-04T13:00:00
Duration: 11 min 7 s
Description: Watch this Scientific Journal Video about High-Throughput Metabolic Profiling for Model Refinements of Microalgae at JoVE.com

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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 &#8220;D&#8221;</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 ...

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