Name: a tandem liquid chromatography–mass spectrometry-based approach for metabolite analysis of staphylococcus aureus
Uploaded: 2017-03-28T15:00:00.000+00:00
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Description: Watch this Scientific Journal Video about A Tandem Liquid Chromatographyâ€“Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus at JoVE.com

This work was funded in part by an NIH Pathway to Independence Award (grant GM 099893) and faculty startup funds to SRB, as well as a Research Project Grant (grant GM 042219). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

1. Preparation of Buffer Solutions
<ol>
	<li>Prepare phosphate-buffered saline (PBS; pH 7.4) by diluting a stock solution of 10x PBS to a final concentration of 1x with ultrapure (distilled and deionized) water.</li>
	<li>Prepare quenching solution by combining 2 mL of acetonitrile, 2 mL of methanol, 1 mL of ultrapure H2O, and 19 &#181;L (0.1 mM final concentration) of formic acid.</li>
	<li>Prepare LC-MS solvent A by adding formic acid (0.2% [v/v] final concentration) to ultrapure water.</li>
	<li>Prepare LC-MS solvent B by adding formic acid (0.2% [v/v] final concentration) to acetonitrile. 
	NOTE: All solutions should be prepared using the highest-purity reagents available (generally high-performance liquid chromatography grade). Solutions should be prepared fresh before each experiment and stored on ice prior to use.</li>
</ol>
2. Establishment of Steady-state S. aureus Growth
<ol>
	<li>Streak S. aureus strains of interest for isolation on tryptic soy agar (TSA) from a frozen glycerol stock. Incubate at 37 &#176;C for 16-24 h.</li>
	<li>Inoculate 4 mL of tryptic soy broth (TSB) or another suitable medium in sterile glass incubation tubes with single colonies of each strain. Incubate inclined (~70&#176; angle) with rotation at 60 rotations per min (rpm) at 37 &#176;C for 16-20 h. 
	NOTE: Overnight cultures are prone to oxygen gradients when using standard methods, including those described in step 2.2, which affect cellular physiology. Thus, we employ a multiple back-dilution strategy to ensure biological steady state (see steps 2.4-3.2, below).</li>
	<li>Use a spectrophotometer to measure the optical density of the cultures from step 2.2 at 600 nm (OD600). Use sterile medium as an optical reference (blank). Dilute these cells to an OD600 of 0.05 in 50 mL of sterile TSB medium (pre-warmed to 37 &#176;C) in separate, 250 mL DeLong flasks.</li>
	<li>Incubate the cultures at 37 &#176;C in a water bath with shaking at 280 rpm.</li>
	<li>Every 30 min, take OD600 measurements; as the optical densities increase, it may become necessary to dilute the cultures with TSB so that they remain within the linear absorbance range of the spectrophotometer.</li>
	<li>When cultures from step 2.5 achieve an OD600 of ~0.8-1.0, subculture them into 50 mL of 37 &#176;C TSB to an OD600 of 0.01-0.05 and repeat steps 2.4 and 2.5.</li>
</ol>
3. Sample Collection Setup
<ol>
	<li>Prepare a bed of crushed dry ice in an appropriate vessel (e.g., glass dish, ice bucket, or cooler).</li>
	<li>As the optical densities of the cultures approach the desired harvest point, add 1 mL of quenching solution to a 35 mm untreated Petri dish and pre-cool on dry ice for &#8805;5 min. 
	NOTE: The &#34;desired harvest point&#34; will vary depending on experimental goals. For example, if one were to examine metabolites during aerobic growth, key indicators of this state include acetate excretion and re-assimilation of the acetate during the post-exponential growth phase32,33. Generally, this point should be within a specific growth stage (e.g., exponential phase). The specific OD600 values associated with this stage may vary between different bacterial strains and growth media.</li>
	<li>Place a stainless steel filter frit (pre-chilled to -20 &#176;C) in a rubber stopper and place it atop a vacuum flask attached to a house vacuum or vacuum pump.</li>
	<li>Apply the vacuum and place a mixed cellulose ester membrane (0.22 &#181;m pore size) on top. 
	NOTE: It is critical to use a filter with a diameter equal to that of the frit and to properly center this filter to ensure that the sample is drawn through the filter rather than over the edge. Wetting the membrane with ice-cold, sterile H2O may help with positioning the membrane.</li>
</ol>
4. Sample Harvest
<ol>
	<li>At an OD600 of ~0.4-0.5, use a serological pipette to remove 13 mL of culture from the flask and to apply the sample to the filter.</li>
	<li>After the entire sample has been filtered, immediately wash the filter with &#8805;5 mL of ice-cold PBS to wash away medium-associated metabolites.</li>
	<li>Disconnect the vacuum and use a pair of sterile tweezers to remove the filter from the frit. Invert the filter (cell-side down) into the pre-chilled quench solution. 
	NOTE: It is important to perform the above steps quickly (i.e., within seconds) and as soon as the liquid has been removed to ensure the rapid quenching of the cells, arresting metabolic activity.</li>
	<li>Incubate the filter in quench solution on dry ice for &#8805;20 min.</li>
	<li>Using sterile tweezers, invert the filter (cell-side up) in the petri dish and use a micropipette to rinse the cells off of the membrane into the quench solution.</li>
	<li>Re-suspended the cells in quench solution and then transfer the cell suspension to a sterile 2 mL impact-resistant tube containing ~100 &#181;L of 0.1 mm silica beads. Store this on dry ice or at -80 &#176;C.</li>
</ol>
5. Metabolite Extraction
<ol>
	<li>Thaw samples on wet ice and disrupt the cells in a homogenizer with four 30 s bursts at 6,000 rpm, with 2 min cooling periods on dry ice between cycles.</li>
	<li>Clarify the lysates for 15 min in a pre-chilled, refrigerated microcentrifuge at maximum speed (i.e., 18,213 x g at &#8804;4 &#176;C).</li>
	<li>Transfer the supernatant to a clean microcentrifuge tube.</li>
	<li>Using a micropipette, transfer a small portion of the sample to a microcentrifuge tube for the quantification of residual peptide content in step 6; store the remainder at -80 &#176;C. 
	NOTE: The volume of reserved sample varies, depending on the BCA assay used in step 6.1. This sample should be stored on wet ice for immediate analysis or frozen at -80 &#176;C.</li>
</ol>
6. Bicinchoninic Acid (BCA) Assay
<ol>
	<li>Perform a BCA assay as recommended by the kit manufacturer, using samples from step 5.4 to determine the residual peptide concentration for each sample.</li>
</ol>
7. LC-MS
<ol>
	<li>Mix 75 &#181;L of S. aureus extract with 75 &#181;L of LC-MS solvent B, prepared in step 1.4.</li>
	<li>Vortex to mix and spin at 13,000 x g for 5 min.</li>
	<li>Place 100 &#181;L of supernatant into a liquid chromatography (LC) vial and cap it. Ensure that no air bubbles are trapped in the sample.</li>
	<li>Load the LC vials onto the LC-MS autosampler and edit the running list in the software &#34;Offline Worklist Editor.&#34;
	<ol>
		<li>Fill out the &#34;Sample Name&#34; (e.g., wild-type-1), &#34;Sample Position&#34; (e.g., P1-A1), &#34;Method&#34; (e.g., Formic Acid-Negative Method), and &#34;Data File&#34; (e.g., wild-type-1) columns. Click the button &#34;Save Worklist&#34; button. Open the &#34;Mass Spectrometry Data Acquisition Workstation&#34; software and input the previously saved worklist. Click the &#34;Start Worklist Run&#34; button to start the continuous LC-MS measurement.</li>
	</ol>
	</li>
	<li>Separate the samples on a column, link the column to a time of flight (TOF) spectrometer, and couple the TOF spectrometer with the LC system. Use a mobile-phase gradient as follows: 0-2 min, 85% solvent B; 3-5 min, 80% solvent B; 6-7 min, 75% solvent B; 8-9 min, 70% solvent B; 10-11.1 min, 50% solvent B; 11.1-14 min, 20% solvent B; and 14.1-24 min, 5% solvent B; end with a 10 min re-equilibration period at 85% solvent B and a flow rate of 0.4 mL min-1.</li>
	<li>Using an isocratic pump, infuse a reference mass solution with the run to allow for simultaneous mass axis calibration. 
	NOTE: This step is based on the standard TOF spectrometer manual.
	<ol>
		<li>Use the mixture of acetic acid D4 and hexakis (1H,1H,3H-tetrafluoropropoxy) phosphazine as the reference mass solution to perform the real-time calibration. Use the isocratic pump with the flow rate of 2.5 mL min-1 for the infusion.</li>
	</ol>
	</li>
</ol>
8. Batch Correction of Ion Counts
<ol>
	<li>Designate any sample to serve as a reference sample for batch correction (e.g., wild-type, replicate 1).</li>
	<li>Calculate the sum of the ion counts for all metabolites within the reference sample. Repeat this calculation for all samples.</li>
	<li>Divide the total ion count of each sample by the total ion count of the reference sample to generate a ratio.</li>
	<li>Divide the ion count for each metabolite within a sample by the sample/reference ratio to obtain a batch-corrected ion count for each metabolite.</li>
</ol>
9. Peptide Normalization
<ol>
	<li>Divide the batch-corrected ion count values for each sample obtained in step 8 by the peptide concentration determined with the BCA assay in step 6 to yield a normalized value for each metabolite. 
	NOTE: The normalized, batch-batch corrected ion counts for each metabolite obtained in step 9.1 can be directly compared between strains and subjected to statistical analysis (e.g., a Mann-Whitney U-test). Alternatively, a metabolite known to be unchanged either by the treatment or genetic background may be used as a normalizer to detect changes due to metabolite decomposition. The inclusion of a known amount of L-norvaline or gluraric acid in the extraction buffer can be used to correct for loss during sample processing34.</li>
</ol>

The authors declare that they have no competing financial interests.

All small-molecule metabolites are connected to one another through their common origins in central metabolic pathways. During exponential growth, bacterial cells are at biological and metabolic steady state, providing a snapshot of the physiological state under specific conditions. CodY monitors nutrient sufficiency by responding to ILV and GTP. As ILV and GTP pools drop, CodY activity is likely progressively reduced, adjusting the expression of its target genes to adapt to increasing nutrient depletion30. A CodY-deficient strain behaves as though ILV and GTP are depleted from the environment but only exhibits a very mild difference in growth behavior relative to the CodY-proficient strain (Figure 1). Thus, the comparison of steady-state pools of metabolites in these strains provides us with a unique opportunity to reveal the extent to which metabolism is reconfigured when nutrients are scarce. It should be noted that our experiments investigate metabolite abundances when CodY activity is maximized (ILV and GTP are most abundant in exponential phase). However, other questions may be addressed in other phases of growth. For example, in exponential phase, tricarboxylic acid (TCA) cycle activity is very low; in post-exponential phase, the TCA cycle is activated36. A number of phenotypes, including metabolite abundances, are dependent upon this activation. Additionally, the agr quorum sensing system becomes active during the transition from exponential growth to stationary phase37. Collecting samples in post-exponential or stationary phase may be more relevant for studies addressing these topics. Regardless of when the samples are harvested, it is important to collect, wash, and transfer the samples to the extraction buffer as quickly as possible (i.e., within s) to minimize the metabolite turnover that occurs when steady state is perturbed.
The chromatographic method described here is especially suited to analyzing polar and non-polar amino acids and central carbon metabolism compounds. However, additional class-specific methods can be used to quantify specific compounds of interest not chromatographically resolved by this method. For example, altering the pH of the chromatographic mobile phase by replacing formic acid with acetic acid as an additive has been reported to enable the resolution of isoleucine and leucine38. Modifying the extraction procedure can similarly enable the recovery and quantification of labile metabolites that are sensitive to the extraction method used here. For example, compounds such as cysteine that are prone to disulfide bond formation can potentially be recovered following derivatization with Ellman&#39;s reagent39. Sparging extraction buffers with nitrogen gas can preserve NAD+ and NADH ratios, allowing for an assessment of the cellular redox state40. Nucleoside triphosphates can decompose in basic or unbuffered solutions; acidifying the extraction solution can improve the recovery of these molecules41.
In an exponentially growing bacterial culture, the depletion of one or more nutrients leads to the transition to the post-exponential and stationary phases of growth. These growth phases are characterized by distinct metabolic states42. Chemostat-based cultures generate a virtually continuous biological steady state ideal for gene expression and physiological studies. However, the method requires specialized equipment, is technically demanding, and necessitates that a nutrient limitation be imposed to maintain a stable population of cells under flow. The latter requirement causes transcriptional and physiological perturbations due to factors other than the variable or regulator being analyzed. To ensure that our flask-based results are representative of cells growing exponentially at steady state and not of a transition between two phases, we employ a double-back dilution strategy with a consistent flask:volume ratio (slight changes in oxygen levels can lead to altered metabolism36,42). After an initial dilution of overnight cultures, these cells are grown to an OD600 of ~1.0, back-diluted to an OD600 of ~0.05, and harvested when they reach an OD600 of ~0.5. Such a method also dilutes cytoplasmic molecules that accumulate during overnight growth, including stable RNAs. Indeed, RNAIII, the effector of the agr quorum sensing system, is one such RNA and regulates the expression of some of the same gene targets as CodY25,43,44. Accumulated RNAIII can mask CodY-dependent regulation, leading to an underestimate of the strength of repression or stimulation by CodY (Sharma and Brinsmade, unpublished results).
One limitation of this analysis is that it provides an instantaneous glimpse of metabolite abundances within the cell;&#160;no conclusions can be drawn from the results regarding changes in flux through any given pathway. For example, the abundances of lysine and methionine between the two strains examined did not change, despite de-repression of biosynthetic enzymes in the codY-null strain (Figures 2 and 3). The codY-null strain may in fact be generating more lysine and methionine, but they may be rapidly converted into other compounds; thus, these molecules do not accumulate. Using 13C- or 15N-labeled carbon or nitrogen sources would allow us to follow carbon and nitrogen skeletons through major metabolic junctions45,46.
We have used the described method to elucidate changes in metabolite pools in S. aureus, B. subtilis29, Mycobacterium tuberculosis47, and Enterococcus faecium48, but the method may be applied to other Gram-positive and Gram-negative bacteria, including other human pathogens easily cultivated in the laboratory. Indeed, integrating metabolomic&#160;and transcriptomic information can reveal unexpected connections between metabolism and virulence, which could lead to novel strategies to treat infections.

Bacterial pathogens must contend with many challenges within the host environment. In addition to direct attack by immune cells, the host also sequesters nutrients essential for bacterial survival and replication, generating nutritional immunity1,2. To survive these hostile environments, bacterial pathogens deploy virulence factors. Some of these factors allow the bacteria to evade the immune response; other factors include secreted digestive enzymes, such as hyaluronidase, thermonuclease, and lipase, which may enable the bacteria to replenish missing nutrients by consuming tissue-derived constituents3,4,5. Indeed, bacteria have evolved regulatory systems that tie the physiological state of the cell to the production of virulence factors6,7,8,9,10.
A growing body of evidence points to CodY as a critical regulator linking metabolism and virulence. Although first discovered in Bacillus subtilis as a repressor of the dipeptide permease (dpp) gene11, CodY is now known to be produced by nearly all the low G+C Gram-positive bacteria12,13 and regulates dozens of genes involved in carbon and nitrogen metabolism14,15,16,17,18,19. In pathogenic species, CodY also controls the expression of some of the most important virulence genes20,21,22,23,24,25,26,27. CodY is activated as a DNA-binding protein by two classes of ligands: branched-chain amino acids (BCAAs; isoleucine, leucine, and valine [ILV]) and GTP. When these nutrients are abundant, CodY represses (or in some cases, stimulates) transcription. As these nutrients become limited, CodY activity is progressively reduced, resulting in a graded transcriptional response that re-routes precursors through various metabolic pathways connected to central metabolism28,29,30. 
Tandem liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful technique that can accurately identify and quantify small-molecule intracellular metabolites31. When paired with transcriptome analysis (e.g., RNA-Seq), this analytical workflow can provide insight into the physiological changes that occur in response to environmental or nutritional stress. Here, we present a method for metabolite extraction from Staphylococcus aureus cells and subsequent analysis via LC-MS. This approach has been used to demonstrate the pleiotropic effects of CodY on S. aureus physiology.

<table><tbody><tr><td>&lt;strong&gt;Material/Equipment&lt;sup&gt;a&lt;/sup&gt;&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>DeLong Culture Flask (250 mL)</td><td>Belco</td><td>2510-00250</td><td></td></tr><tr><td>Sidearm Flask, 500 mL</td><td>Pyrex</td><td>5340</td><td></td></tr><tr><td>3-hole Rubber Stopper, #7</td><td>Fisher</td><td>14-131E</td><td></td></tr><tr><td>Stainless Steel Filter holder/frit</td><td>VWR</td><td>89428-936</td><td></td></tr><tr><td>Petri Dish, 35 mm</td><td>Corning</td><td>430588</td><td>Not tissue culture treated</td></tr><tr><td>Mixed cellulose ester membrane, 0.22 &amp;mu;m pore size</td><td>Millipore</td><td>GSWP02500</td><td></td></tr><tr><td>Impact-resistant tubes, 2 mL</td><td>USA Scientific</td><td>1420-9600</td><td></td></tr><tr><td>Silica Beads, 0.1 mm</td><td>Biospec Products Inc</td><td>11079101Z</td><td></td></tr><tr><td>Precellys 24 homogenizer</td><td>Bertin Instruments</td><td>EQ03119-200-RD000.0</td><td></td></tr><tr><td>Micro BCA Protein Assay Kit</td><td>Pierce (Thermo Scientific)</td><td>23235</td><td></td></tr><tr><td>Cogent Diamond hydride type C column</td><td>Agilent</td><td>70000-15P-2</td><td></td></tr><tr><td>Accurate-Mass Time-of-Flight (TOF) LC-MS, 6200 Series</td><td>Agilent</td><td>G6230B</td><td></td></tr><tr><td>Quat Pump, 1290 Series</td><td>Agilent</td><td>G4204A&amp;nbsp;</td><td></td></tr><tr><td>Bin Pump, 1290 Series</td><td>Agilent</td><td>G4220A&amp;nbsp;</td><td></td></tr><tr><td>Valve Drive, 1290 Series</td><td>Agilent</td><td>G1107A&amp;nbsp;</td><td></td></tr><tr><td>Isocratic Pump, 1290 Series</td><td>Agilent</td><td>G1310B&amp;nbsp;</td><td></td></tr><tr><td>TCC, 1290 Series</td><td>Agilent</td><td>G1316C&amp;nbsp;</td><td></td></tr><tr><td>Sampler, 1290 Series</td><td>Agilent</td><td>G4226A&amp;nbsp;</td><td></td></tr><tr><td>Thermostat, 1290 Series</td><td>Agilent</td><td>G1330B&amp;nbsp;</td><td></td></tr><tr><td>&lt;strong&gt;Chemical&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Tryptic Soy Broth</td><td>Becton Dickinson</td><td>211825</td><td></td></tr><tr><td>Difco Agar, Granulated</td><td>Becton Dickinson</td><td>214530</td><td>Solid media contains 1.5% [w/v] agar</td></tr><tr><td>Phosphate-buffered saline (pH 7.4) 10x</td><td>Ambion</td><td>AM9624</td><td>Dilute fresh to 1x with ultra-pure water</td></tr><tr><td>Acetonitrile</td><td>Fisher Scientific</td><td>A955-500</td><td>Optima LC-MS</td></tr><tr><td>Methanol</td><td>Fisher Scientific</td><td>A456-500</td><td>Optima LC-MS; toxic</td></tr><tr><td>Formic Acid</td><td>Sigma Aldrich</td><td>94318</td><td>For mass spectrometry, 98%</td></tr><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>MassHunter</td><td>Agilent</td><td>G3337AA</td><td></td></tr><tr><td>&lt;strong&gt;Bacterial Strain&lt;/strong&gt;</td><td>&lt;strong&gt;Species&lt;/strong&gt;</td><td>&lt;strong&gt;Strain&lt;/strong&gt;</td><td>&lt;strong&gt;Genotype&lt;/strong&gt;</td></tr><tr><td>SRB 337</td><td>Staphylococcus aureus</td><td>USA200 MSSA UAMS-1</td><td>wild type</td></tr><tr><td>SRB 372</td><td>Staphylococcus aureus</td><td>USA200 MSSA UAMS-1</td><td>&amp;Delta;codY::erm</td></tr><tr><td colspan="4">&lt;sup&gt;a&lt;/sup&gt;Chemicals and materials listed are specific to the method described and do not include standard laboratory chemicals or supplies.</td></tr></tbody></table>

a tandem liquid chromatography&#8211;mass spectrometry-based approach for metabolite analysis of staphylococcus aureus

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the abundances of key metabolites to which regulatory factors respond, adjusting cellular metabolism. In recent years, a number of proteins and RNA have emerged as important regulators of virulence gene expression. For example, the CodY protein responds to levels of branched-chain amino acids and GTP and is widely conserved in low G+C Gram-positive bacteria. As a global regulator in Staphylococcus aureus, CodY controls the expression of dozens of virulence and metabolic genes. We hypothesize that S. aureus uses CodY, in part, to alter its metabolic state in an effort to adapt to nutrient-limiting conditions potentially encountered in the host environment. This manuscript describes a method for extracting and analyzing metabolites from S. aureus using liquid chromatography coupled with mass spectrometry, a protocol that was developed to test this hypothesis. The method also highlights best practices that will ensure rigor and reproducibility, such as maintaining biological steady state and constant aeration without the use of continuous chemostat cultures. Relative to the USA200 methicillin-susceptible S. aureus isolate UAMS-1 parental strain, the isogenic codY mutant exhibited significant increases in amino acids derived from aspartate (e.g., threonine and isoleucine) and decreases in their precursors (e.g., aspartate and O-acetylhomoserine). These findings correlate well with transcriptional data obtained with RNA-seq analysis: genes in these pathways were up-regulated between 10- and 800-fold in the codY null mutant. Coupling global analyses of the transcriptome and the metabolome can reveal how bacteria alter their metabolism when faced with environmental or nutritional stress, providing potential insight into the physiological changes associated with nutrient depletion experienced during infection. Such discoveries may pave the way for the development of novel anti-infectives and therapeutics.

We have analyzed intracellular metabolite pools in S. aureus during in vitro growth in a rich, complex medium. As proof of principle, we compared metabolite profiles between the methicillin-susceptible S. aureus osteomyelitis isolate UAMS-1 (wild-type [WT]) and an isogenic strain lacking the global transcriptional regulator CodY (&#916;codY)26. Steady-state, exponential cultures of the WT and codY strains were established in TSB medium, as described in step 2 of the protocol. The growth behavior of the wild-type and codY-null mutant cultures were similar, with only mild differences in growth yield and rate (Figure 1). Using RNA-Seq and microarray technology, we and others revealed that multiple genes coding for enzymes involved in the biosynthesis of amino acids derived from aspartate were de-repressed in the codY-null mutant compared to WT cells during in vitro growth in TSB (Figure 2)25,27,30. Moreover, brnQ1 and brnQ2, which code for branched-chain amino acid permeases, are overexpressed in the codY-null mutant30,35.
To determine the extent to which steady-state intracellular abundances of metabolites associated with this pathway are altered in the null mutant, we performed LC-MS-based metabolite profiling. WT and codY-null mutant cells were grown to biological steady state and were sampled as described in step 4 of the protocol. We determined metabolite abundances by integrating the peak area ion intensity for each chromatographically resolved metabolite using an analytical software package (see Materials List). We corrected for differences in biomass by normalizing metabolite abundances to the residual peptide concentration of each sample. We further corrected these values for potential batch effects between samples by calculating the average ion count for all metabolites within each sample and by using the wild-type sample as the reference value. This approach enabled inter-sample comparisons of metabolite abundances across conditions. Inter-metabolite comparisons within a given sample can similarly be achieved by first converting normalized metabolite abundances from ion counts to molar quantities using the method of standard addition.
We have compared the levels of key intermediates in the aspartate pathway in UAMS-1 and its codY-null mutant. As seen in Figure 3, the end products of this pathway (e.g., threonine and (iso)-leucine) are more abundant in codY-null mutant cells, while precursors (e.g., aspartate and o-acetyl homoserine) are more abundant in WT cells. The combined upregulation of BrnQ permeases36 and the ILV biosynthetic pathway likely leads to increases in isoleucine and leucine30. Although the differences are relatively small (&#60;4-fold), LC-MS-based quantitation and batch correction reveal robust and statistically significant changes that are consistent with transcriptional alterations mediated by CodY.
<img alt="Figure 1" src="/files/ftp_upload/55558/55558fig1.jpg" /> 
Figure 1: Growth behavior of S. aureus UAMS-1 and an isogenic codY-null mutant in TSB. To extend the time the cells spent in steady-state exponential growth, cultures were back-diluted to an optical density of 0.05 in fresh medium after the pre-cultures achieved an OD600 of ~1. Samples for LC-MS metabolite analysis were collected from experimental cultures at an optical density of ~0.5 (arrows). The data shown are representative of three biological replicates.
<img alt="Figure 2" src="/files/ftp_upload/55558/55558fig2.jpg" /> 
Figure 2: Schematic of selected metabolites derived from aspartate. Genes and operons whose products catalyze the synthesis of aspartate-family amino acids are indicated in italics; the increase in transcript abundance in a codY-null mutant compared to wild type, as determined by RNA-seq analysis29, is also noted. DHP, 2,6-diaminoheptanedioate (2,6-diaminopimelate).
<img alt="Figure 3" src="/files/ftp_upload/55558/55558fig3.jpg" /> 
Figure 3: Abundances of metabolites in the aspartate family are altered in a codY mutant. The log2-fold changes of selected metabolites in the codY-null mutant compared to UAMS-1 (WT) are shown. The change was determined by dividing the average abundance of three biological replicates of the codY-null strain by the average abundance of three biological replicates of the WT strain. The standard error between biological replicates for each metabolite was &#60;35%. The dashed lines indicate a log2 1.5-fold change, the cutoff used in this experiment.

Watch this Scientific Journal Video about A Tandem Liquid Chromatographyâ€“Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus at JoVE.com

A Tandem Liquid Chromatography&#8211;Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus

Here we describe a protocol for the extraction of metabolites from Staphylococcus aureus and their subsequent analysis via liquid chromatography and mass spectrometry.

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the ...

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Nanyang Technological University

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JoVE Journal

Biochemistry

10.0K Views.  Georgetown University.  Hier beschrijven we een protocol voor extractie van metabolieten van Staphylococcus aureus en de daaropvolgende analyse via vloeistofchromatografie en massaspectrometrie. 

Video: A Tandem Liquid Chromatography–Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus

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Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to ...

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Untargeted metabolomics provides a hypothesis generating snapshot of a metabolic profile. This protocol will demonstrate the extraction and analysis of metabolites from cells, serum, or tissue. A range of metabolites are surveyed using liquid-liquid phase extraction, microflow ultraperformance liquid chromatography/high-resolution mass spectrometry (UPLC-HRMS) coupled to differential analysis software.

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first ...

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Lipids are structurally diverse amphipathic molecules that are insoluble in water. Lipids are essential contributors to the organization and function of biological membranes, energy storage and production, cellular signaling, vesicular transport of proteins, organelle biogenesis, and regulated cell death. Because the budding yeast Saccharomyces cerevisiae is a unicellular eukaryote amenable to thorough molecular analyses, its use as a model organism helped uncover mechanisms linking lipid metabolism and intracellular transport to complex biological processes within eukaryotic cells. The availability of a versatile analytical method for the robust, sensitive, and accurate quantitative assessment of major classes of lipids within a yeast cell is crucial for getting deep insights into these mechanisms. Here we present a protocol to use liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for the quantitative analysis of major cellular lipids of S. cerevisiae. The LC-MS/MS method described is versatile and robust. It enables the identification and quantification of numerous species (including different isobaric or isomeric forms) within each of the 10 lipid classes. This method is sensitive and allows identification and quantitation of some lipid species at concentrations as low as 0.2 pmol/&#181;L. The method has been successfully applied to assessing lipidomes of whole yeast cells and their purified organelles. The use of alternative mobile phase additives for electrospray ionization mass spectrometry in this method can increase the efficiency of ionization for some lipid species and can be therefore used to improve their identification and quantitation.

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

We present a protocol using liquid chromatography coupled with tandem mass spectrometry to identify and quantify major cellular lipids in Saccharomyces cerevisiae. The described method for a quantitative assessment of major lipid classes within a yeast cell is versatile, robust, and sensitive.

Lipids are structurally diverse amphipathic molecules that are insoluble in water. Lipids are essential contributors to the organization and function of ...

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Understanding the interactions between genes, the environment and management in agricultural practice could allow more accurate prediction and management of product yield and quality. Metabolomics data provides a read-out of these interactions at a given moment in time and is informative of an organism&#39;s biochemical status. Further, individual metabolites or panels of metabolites can be used as precise biomarkers for yield and quality prediction and management. The plant metabolome is predicted to contain thousands of small molecules with varied physicochemical properties that provide an opportunity for a biochemical insight into physiological traits and biomarker discovery. To exploit this, a key aim for metabolomics researchers is to capture as much of the physicochemical diversity as possible within a single analysis. Here we present a liquid chromatography-mass spectrometry-based untargeted metabolomics method for the analysis of field-grown wheat grain. The method uses the liquid chromatograph quaternary solvent manager to introduce a third mobile phase and combines a traditional reversed-phase gradient with a lipid-amenable gradient. Grain preparation, metabolite extraction, instrumental analysis and data processing workflows are described in detail. Good mass accuracy and signal reproducibility were observed, and the method yielded approximately 500 biologically relevant features per ionization mode. Further, significantly different metabolite and lipid feature signals between wheat varieties were determined.

A method for the untargeted analysis of wheat grain metabolites and lipids is presented. The protocol includes an acetonitrile metabolite extraction method and reversed phase liquid chromatography-mass spectrometry methodology, with acquisition in positive and negative electrospray ionization modes.

Understanding the interactions between genes, the environment and management in agricultural practice could allow more accurate prediction and management ...

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Metabolomics is a methodology used for the identification and quantification of many low-molecular-weight intermediates and products of metabolism within a cell, tissue, organ, biological fluid, or organism. Metabolomics traditionally focuses on water-soluble metabolites. The water-soluble metabolome is the final product of a complex cellular network that integrates various genomic, epigenomic, transcriptomic, proteomic, and environmental factors. Hence, the metabolomic analysis directly assesses the outcome of the action for all these factors in a plethora of biological processes within various organisms. One of these organisms is the budding yeast Saccharomyces cerevisiae, a unicellular eukaryote with the fully sequenced genome. Because S. cerevisiae is amenable to comprehensive molecular analyses, it is used as a model for dissecting mechanisms underlying many biological processes within the eukaryotic cell. A versatile analytical method for the robust, sensitive, and accurate quantitative assessment of the water-soluble metabolome would provide the essential methodology for dissecting these mechanisms. Here we present a protocol for the optimized conditions of metabolic activity quenching in and water-soluble metabolite extraction from S. cerevisiae cells. The protocol also describes the use of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for the quantitative analysis of the extracted water-soluble metabolites. The LC-MS/MS method of non-targeted metabolomics described here is versatile and robust. It enables the identification and quantification of more than 370 water-soluble metabolites with diverse structural, physical, and chemical properties, including different structural isomers and stereoisomeric forms of these metabolites. These metabolites include various energy carrier molecules, nucleotides, amino acids, monosaccharides, intermediates of glycolysis, and tricarboxylic cycle intermediates. The LC-MS/MS method of non-targeted metabolomics is sensitive and allows the identification and quantitation of some water-soluble metabolites at concentrations as low as 0.05 pmol/&#181;L. The method has been successfully used for assessing water-soluble metabolomes of wild-type and mutant yeast cells cultured under different conditions.

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

We present a protocol for the identification and quantitation of major classes of water-soluble metabolites in the yeast Saccharomyces cerevisiae. The described method is versatile, robust, and sensitive. It allows the separation of structural isomers and stereoisomeric forms of water-soluble metabolites from each other.

Metabolomics is a methodology used for the identification and quantification of many low-molecular-weight intermediates and products of metabolism within ...

Extraction of Non-Protein Amino Acids from Cyanobacteria for Liquid Chromatography-Tandem Mass Spectrometry Analysis

Non-protein amino acids (NPAAs) are a large class of amino acids (AAs) that are not genetically encoded for translation into proteins. The analysis of NPAAs can provide crucial information about cellular uptake and/or function, metabolic pathways, and potential toxicity. &#946;-methylamino-L-alanine (BMAA) is a neurotoxic NPAA produced by various algae species and is associated with an increased risk for neurodegenerative diseases, which has led to significant research interest. There are numerous ways to extract AAs for analysis, with liquid chromatography-tandem mass spectrometry being the most common, requiring protein precipitation followed by acid hydrolysis of the protein pellet. Studies on the presence of BMAA in algal species provide contradictory results, with the use of unvalidated sample preparation/extraction and analysis a primary cause. Like most NPAAs, protein precipitation in 10% aqueous TCA and hydrolysis with fuming HCl is the most appropriate form of extraction for BMAA and its isomers aminoethylglycine (AEG) and 2,4-diaminobutyric acid (2,4-DAB). The present protocol describes the steps in a validated NPAA extraction method commonly used in research and teaching laboratories.

The present protocol describes the extraction of non-protein amino acids from biological matrices via trichloroacetic acid (TCA) protein precipitation and acid hydrolysis before analysis using liquid chromatography-tandem mass spectrometry.

Non-protein amino acids (NPAAs) are a large class of amino acids (AAs) that are not genetically encoded for translation into proteins. The analysis of ...

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the cell&#39;s survival requirements. Alternatively, carrying out DBTL cycles in cell-free environments can accelerate this process and alleviate concerns with host compatibility. A promising approach to cell-free metabolic engineering (CFME) leverages metabolically active crude cell extracts as platforms for biomanufacturing and for rapidly discovering and prototyping modified proteins and pathways. Realizing these capabilities and optimizing CFME performance requires methods to characterize the metabolome of lysate-based cell-free platforms. That is, analytical tools are necessary for monitoring improvements in targeted metabolite conversions and in elucidating alterations to metabolite flux when manipulating lysate metabolism. Here, metabolite analyses using high-performance liquid chromatography (HPLC) coupled with either optical or mass spectrometric detection were applied to characterize metabolite production and flux in E. coli S30 lysates. Specifically, this report describes the preparation of samples from CFME lysates for HPLC analyses using refractive index detection (RID) to quantify the generation of central metabolic intermediates and by-products in the conversion of low-cost substrates (i.e., glucose) to various high-value products. The analysis of metabolite conversion in CFME reactions fed with 13C-labeled glucose through reversed-phase liquid chromatography coupled to tandem mass spectrometry (MS/MS), a powerful tool for characterizing specific metabolite yields and lysate metabolic flux from starting materials, is also presented. Altogether, applying these analytical methods to CFME lysate metabolism enables the advancement of these systems as alternative platforms for executing faster or novel metabolic engineering tasks.

The protocols describe high-performance liquid chromatography methods coupled to refractive index or mass spectrometric detection for studying metabolic reactions in complex lysate-based cell-free systems.

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the ...

Bioengineering

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously.&#160; Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. Utilizing normal-phased liquid chromatography coupled to high-resolution mass spectrometry with polarity switching and a rapid duty cycle, we describe a protocol to analyze the polar metabolic composition of biological material with high sensitivity, accuracy, and resolution.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting ...

Biology

Capture Compound Mass Spectrometry - A Powerful Tool to Identify Novel c-di-GMP Effector Proteins

Considerable progress has been made during the last decade towards the identification and characterization of enzymes involved in the synthesis (diguanylate cyclases) and degradation (phosphodiesterases) of the second messenger c-di-GMP. In contrast, little information is available regarding the molecular mechanisms and cellular components through which this signaling molecule regulates a diverse range of cellular processes. Most of the known effector proteins belong to the PilZ family or are degenerated diguanylate cyclases or phosphodiesterases that have given up on catalysis and have adopted effector function. Thus, to better define the cellular c-di-GMP network in a wide range of bacteria experimental methods are required to identify and validate novel effectors for which reliable in silico predictions fail.
We have recently developed a novel Capture Compound Mass Spectrometry (CCMS) based technology as a powerful tool to biochemically identify and characterize c-di-GMP binding proteins. This technique has previously been reported to be applicable to a wide range of organisms1. Here we give a detailed description of the protocol that we utilize to probe such signaling components. As an example, we use Pseudomonas aeruginosa, an opportunistic pathogen in which c-di-GMP plays a critical role in virulence and biofilm control. CCMS identified 74% (38/51) of the known or predicted components of the c-di-GMP network. This study explains the CCMS procedure in detail, and establishes it as a powerful and versatile tool to identify novel components involved in small molecule signaling.

The ubiquitous second messenger c-di-GMP controls growth and behavior of many bacteria. We have developed a novel Capture Compound Mass Spectrometry based technology to biochemically identify and characterize c-di-GMP binding proteins in virtually any bacterial species.

Considerable progress has been made during the last decade towards the identification and characterization of enzymes involved in the synthesis ...

High-throughput and Deep-proteome Profiling by 16-plex Tandem Mass Tag Labeling Coupled with Two-dimensional Chromatography and Mass Spectrometry

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an expanded 16-plex TMT method has been introduced, which further increases the throughput of proteomic studies. In this manuscript, we present an optimized protocol for 16-plex TMT-based deep-proteome profiling, including protein sample preparation, enzymatic digestion, TMT labeling reaction, two-dimensional reverse-phase liquid chromatography (LC/LC) fractionation, tandem mass spectrometry (MS/MS), and computational data processing. The crucial quality control steps and improvements in the process specific for the 16-plex TMT analysis are highlighted. This multiplexed process offers a powerful tool for profiling a variety of complex samples such as cells, tissues, and clinical specimens. More than 10,000 proteins and posttranslational modifications such as phosphorylation, methylation, acetylation, and ubiquitination in highly complex biological samples from up to 16 different samples can be quantified in a single experiment, providing a potent tool for basic and clinical research.

Presented here is an optimized high-throughput protocol developed with 16-plex tandem mass tag reagents, enabling quantitative proteome profiling of biological samples. Extensive basic pH fractionation and high-resolution LC-MS/MS mitigate ratio compression and provide deep proteome coverage.

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an ...

A Tandem Liquid Chromatography–Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus

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