The authors are grateful for the financial support from CONACYT, Mexico (CB- 2016-01-286449), and UACJ-PIVA (Projects 313-17-16 and 335-18-13). OAMB wishes to thank CONACYT for his Ph.D. scholarship. Technical support from the Multimedia Production office from UACJ is gratefully acknowledged.

The plasma samples used in this protocol were collected in a previous study following all the ethical guidelines and approved by the Institutional Ethics and Bioethics Committee (CIEB-2018-1-37) from the Universidad Aut&#243;noma de Ciudad Ju&#225;rez. The complete protocol for the extraction and identification of the phenolic compounds and metabolites in plasma by UPLC-MS/MS is represented in Figure 1.
<img alt="Figure 1...

<ol>
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Targeted analysis of precursor compounds, intermediate metabolites and end-products.</a> Food Chemistry. 131 (1), 337-347 (2012).</li><li>Zhang, X., Sandhu, A., Edirisinghe, I., Burton-Freeman, B. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Plasma+and+urinary+(poly)phenolic+profiles+after+4-week+red+raspberry+(Rubus+idaeus+L.)+intake+with+or+without+fructo-oligosaccharide+supplementation.">Plasma and urinary (poly)phenolic profiles after 4-week red raspberry (Rubus idaeus L.) intake with or without fructo-oligosaccharide supplementation.</a> Molecules. 25 (20), (2020).</li></ol>

All authors declare no conflict of interest.

The identification and quantification of the bioactive phytochemicals that are absorbed after consumption of a food or food supplement are crucial for demonstrating and understanding the health benefits of these compounds and the foods containing them. In the present work, the UPLC-MS/MS method was developed, aimed only at the identification of the main phenolic compounds and their metabolites that increased in concentration in plasma after a 30-day nutritional intervention with two food products specially formulated for...

Sarcopenia is a progressive skeletal disorder related to an accelerated loss of muscle in the elderly population. This condition increases the risk of falls and leads to limited activities of daily living. Sarcopenia is present in about 5%-10% of persons over 65 years old and about 50% of persons aged 80 years or older1. No specific drugs have been approved for the treatment of sarcopenia, so prevention with physical activity and a well-balanced diet is important1,2. Nutritional interventions with specially formulated foods enriched with dairy protein and essential amino acids have shown positive results in preventing sarcopenia2. In other studies, authors have included vitamins and antioxidants, like vitamin E and isoflavones, in the diet, increasing the benefits for muscle gain on the waist and hips3.
Brosimum alicastrum Sw. (Ram&#243;n) is a tree that grows in the Mexican tropical regions; it has been consumed by Mayan cultures due to its high nutritional value4. It is a good source of protein, fiber, minerals, and phenolic antioxidants, such as chlorogenic acid5. Since it can be ground into powder and used in baking products or consumed in beverages, recent studies have evaluated the incorporation of Ram&#243;n seed flour (RSF) into different foods to improve their nutritional value. An RSF-supplemented cappuccino-flavored beverage was formulated, which was high in dietary fiber and had more than 6 g of protein per serving, and was highly accepted by consumers; thus, it was considered a potential alternative for meeting special dietary requirements6. In a follow-up study, RSF was also used to formulate a muffin and a new beverage rich in protein, dietary fiber, micronutrients, and phenolic antioxidants. The muffin and beverage were used in a dietary intervention for elderly individuals, who consumed both products twice per day for 30 days. After this period, the nutritional and sarcopenic status of the participants improved, and the total phenolic content of plasma increased7. However, the determination of total phenolic compounds in plasma was carried out by a spectrophotometric method, so identification of the actual phenolic compounds that were absorbed was not possible; moreover, this method is not completely specific for phenolic compounds, so some overestimation may occur8.
Identification and quantification of the phenolic compounds that are absorbed after consumption of foods rich in these antioxidants is a difficult task but is necessary to demonstrate the biological activity of these phytochemicals. The bioavailability of most phenolic compounds is low; less than 5% of them can be found without structural transformation in plasma. Phenolic compounds undergo several biotransformations, such as methylation, sulfonation, or glucuronidation, which are carried out by enterocytes and hepatocytes9. Phenolic compounds are also biotransformed by the microbiota into bacterial catabolites that may exert their beneficial effects in the body after being absorbed into the plasma10. For example, phenylacetic acid is a product of the bacterial transformation of flavonoids and oligomeric proanthocyanidins, which can inhibit up to 40% of bacteria (Escherichia coli) adhesion in the urinary tract after cranberry consumption11.
The structural diversity of naturally occurring phenolic compounds, added to the diversity of their metabolites and their low bioavailability, makes their identification in plasma even more challenging. Metabolomic profiling, using spectroscopic analysis platforms like nuclear magnetic resonance (NMR) and tandem mass spectroscopy (MS/MS), is probably the best approach to achieve this goal; unfortunately, the equipment is not easily accessible, and the development of analysis protocols is still limited12. Several studies have reported MS/MS coupled with a separation system (such as liquid chromatography) as a strategy for reducing the complexity of mass spectra in metabolomic studies. The recent introduction of ultra-high-performance liquid chromatography (UPLC) separation methods has reduced the time of analysis and increased the resolution and sensitivity compared with conventional high-performance liquid protocols, so UPLC-MS/MS systems have rapidly been widely accepted by the analytical metabolomics community13. In this way, some studies have investigated phenolic metabolites and detected glucuronidated derivatives from caffeic acid, quercetin, and ferulic acid, as well as sulfonated derivatives from syringic and vanillic acid in the plasma of individuals after cranberry intake14. Previous protocols have intended to find phenolic compounds and phenolic metabolites in biofluids such as plasma. These protocols were based on identification and quantification by high-performance liquid chromatography (HPLC) coupled to a UV-vis detector15. Nevertheless, such protocols require the use of authentic standards to assess absolute identification and accurate quantification. A wide range of studies have identified the most common metabolites in biofluids (sulphonated, glucuronidated, and methylated forms) by UPLC-MS and UPLC-MS/MS; however, a large part of the bacterial metabolites has not been reported due to the lack of databases that contain their complete information16. Metabolite identification is complicated by the cost and commercial availability of metabolite standards. Therefore, the best strategy may be untargeted or semi-targeted MS/MS metabolite analysis, which relies on the use of molecular feature information (m/z, monoisotopic exact mass, isotopic distribution, and fragmentation pattern) to determine the chemical identity and compares it with freely available online databases that contain polyphenol metabolites identified in biofluids after the consumption of polypolyphenol-richts12. The most important databases used in UPLC-MS/MS studies for the identification of phenolic compounds and their metabolites are the Human Metabolome Database (HMDB), LipidBlast Library, METLIN Library, and other complementary databases, such as PubChem, ChemSpider, and Phenol Explorer17.
In the present study, a semi-targeted UPLC-MS/MS method was developed to analyze the plasma samples of the group of elderly persons involved in the RSF-containing muffin and beverage consumption study7. Data from different free online databases of plasma metabolites were collected and integrated into a specialized database. This database can be accessed automatically by the equipment software to identify the polyphenolic metabolites in the five plasma samples before and after the 30-day nutritional intervention. This is done to identify the main phenolic compounds, or their metabolites, that are absorbed from the specially formulated functional foods designed for the prevention of sarcopenia.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Acetonitrile</td>
			<td>Tedia</td>
			<td>Al1129-001</td>
			<td>LC Mass spectrometry</td>
		</tr>
		<tr>
			<td>Autosampler</td>
			<td>Agilent Technologies</td>
			<td>G4226A</td>
			<td>1290 Infinity series</td>
		</tr>
		<tr>
			<td>C18 reverse phase column</td>
			<td>Agilent Technologies</td>
			<td>959757-902</td>
			<td>Zorbax Eclipse plus C18 2.1x50 mm, 1.8 &#956;m; Rapid resolution HD</td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>Eppendorf</td>
			<td>5452000018</td>
			<td>Mini Spin; Rotor F-45-12-11</td>
		</tr>
		<tr>
			<td>Column compartment with thermostat</td>
			<td>Agilent Technologies</td>
			<td>G1316C</td>
			<td>1290 Infinity series</td>
		</tr>
		<tr>
			<td>Diode Array Detector (UV-Vis)</td>
			<td>Agilent Technologies</td>
			<td>G4212B</td>
			<td>1260 Infinity series</td>
		</tr>
		<tr>
			<td>Electrospray ionnization source</td>
			<td>Agilent Technologies</td>
			<td>G3251B</td>
			<td>Dual sprayer ESI source</td>
		</tr>
		<tr>
			<td>Formic acid</td>
			<td>J.T. Baker</td>
			<td>0128-02</td>
			<td>Baker reagent, ACS</td>
		</tr>
		<tr>
			<td>Mass Hunter Data Acquisition</td>
			<td>Agilent Technologies</td>
			<td>G3338AA</td>
			<td></td>
		</tr>
		<tr>
			<td>Mass Hunter Personal Compound Datbase and Library Manager</td>
			<td>Agilent Technologies</td>
			<td>G3338AA</td>
			<td></td>
		</tr>
		<tr>
			<td>Mass Hunter Qualitative Analysis</td>
			<td>Agilent Technologies</td>
			<td>G3338AA</td>
			<td></td>
		</tr>
		<tr>
			<td>Microcentrifuge tube</td>
			<td>Brand</td>
			<td>BR780546</td>
			<td>Microcentrifuge tube, 2 mL with lid</td>
		</tr>
		<tr>
			<td>Pure ethanol</td>
			<td>Sigma-Aldrich</td>
			<td>E7023-1L</td>
			<td>200 proof, for molecular biology</td>
		</tr>
		<tr>
			<td>Q-TOF LC/MS</td>
			<td>Agilent Technologies</td>
			<td>G6530B</td>
			<td>6530 Accurate Mass</td>
		</tr>
		<tr>
			<td>Quaternary pump</td>
			<td>Agilent Technologies</td>
			<td>G4204A</td>
			<td>1290 Infinity series</td>
		</tr>
		<tr>
			<td>Syringe filter</td>
			<td>Thermo Scientific</td>
			<td>44514-NN</td>
			<td>17 mm, 0.45 &#956;m, nylon membrane</td>
		</tr>
		<tr>
			<td>Thermostat</td>
			<td>Agilent Technologies</td>
			<td>G1330B</td>
			<td>1290 Infinity series</td>
		</tr>
		<tr>
			<td>Vial</td>
			<td>Agilent Technologies</td>
			<td>8010-0199</td>
			<td>Amber, PFTE red silicone 2 mL with screw top and blue caps</td>
		</tr>
		<tr>
			<td>Vial insert</td>
			<td>Agilent Technologies</td>
			<td>5183-2089</td>
			<td>Vial insert 200 &#956;L for 2mL standard opening, conical</td>
		</tr>
		<tr>
			<td>Water</td>
			<td>Tedia</td>
			<td>WL2212-001</td>
			<td>LC Mass spectrometry</td>
		</tr>
	</tbody>
</table>

semi-targeted ultra-high-performance chromatography coupled to mass spectrometry analysis of phenolic metabolites in plasma of elderly adults

A group of 23 elderly persons was given functional meals (a beverage and a muffin) specially formulated for the prevention of sarcopenia (age-related loss of muscle mass). Plasma samples were taken at the beginning of the intervention and after 30 days of consuming the functional meals. A semi-targeted ultra-high-performance chromatography coupled with tandem mass (UPLC-MS/MS) analysis was carried out to identify phenolic compounds and their metabolites. Plasma proteins were precipitated with ethanol and the samples were concentrated and resuspended in the mobile phase (1:1 acetonitrile: water) before injection into the UPLC-MS/MS instrument. Separation was carried out with a C18 reverse-phase column, and compounds were identified using their experimental mass, isotopic distribution, and fragment pattern. Compounds of interest were compared to those of data banks and the internal semi-targeted library. Preliminary results showed that the major metabolites identified after the intervention were phenylacetic acid, glycitin, 3-hydroxyphenylvaleric acid, and gomisin M2.

The step-by-step process for the identification of phenolic metabolites through the semi-targeted UPLC-MS/MS analysis, in negative mode, of plasma samples is depicted in Figure 2. First, the total ion chromatogram (TIC) from the plasma phenolics extract (obtained after protein precipitation of the total plasma sample) was obtained through the instrument&#39;s qualitative software. Then, the extracted ion chromatogram was used, and the exact mass and fragmentation patter...

Watch this Scientific Journal Video about Semi-Targeted Ultra-High-Performance Chromatography Coupled to Mass Spectrometry Analysis of Phenolic Metabolites in Plasma of Elderly Adults at JoVE.com

Semi-Targeted Ultra-High-Performance Chromatography Coupled to Mass Spectrometry Analysis of Phenolic Metabolites in Plasma of Elderly Adults

The goal of this protocol is to detect phenolic metabolites in plasma using a semi-targeted chromatography-mass spectrometry method.

A group of 23 elderly persons was given functional meals (a beverage and a muffin) specially formulated for the prevention of sarcopenia (age-related loss ...

The goal of this protocol is to detect phenolic metabolites in plasma by a semi-targeted UPLC-MS/MS method. Plasma proteins were precipitated with ethanol and samples concentrated, and re-suspended in the mobile phase, prior to injection in the UPLC equipment. Compounds were identified using a semi targeted library assembled in the lab, using PCDL manager for metabolomics, software and different free online databases.We implemented a nutritional intervention with a muffin and beverage formulate with brosimum alicastrum seed flour. At the end of the intervention, the nutritional and sarcopenic status of the participants were improved, and the total phenolic content of plasma was increased. But we need to identify which phenolic compounds had been absorbed and contributed the positive effect.Identification and quantification of the phenolic compounds that are absorbed after consumption of foods rich in these antioxidants is difficult task but necessary, if one is to demonstrate biological activity of these phytochemicals. The bioavailability of most phenolic components is low, moreover less than 5%of them enter without structural transformation into plasma. Most go through several biotransformation, such as methylation, sulfonation, or glucuronidation.A wide range of studies identified the most common metabolites in biofluids by UPLC-MS and UPLC-MS/MS. However, a large part of the bacteria metabolite is not reported to the lack of databases that contain their complete information. Metabolite identification is complicated by the cost and commercial availability of metabolite standards.Therefore, the best strategy may be untargeted or semi-targeted MS/MS metabolite analysis. This analysis rely on the use of molecular feature information, mass to charge ratio monoisotopic exact mass, isotopic distribution, and fragmentation pattern. To determine the chemical identity and compare it with free available online databases that contain polyphenol metabolites identified in biofluids after consumption of polyphenol rich diets.In the present study, we have developed a semi-targeted UPLC-MS/MS method to analyze the plasma samples of a group of elderly persons involved in a nutritional intervention. Store plasma samples at minus 80 degrees Celsius until analysis. Defrost plasma samples at room temperature for 15 minutes or at 37 Celsius for five minutes.Place 200 microliters of plasma sample in a two millimeter microtube and mix with 1000 microliters of pure ethanol. Vortex plasma sample for 30 seconds. Centrifuge sample at 6, 580 centrifugal force for five minutes.After centrifugation, collect the supernatant with a micropipette or Pasteur pipette and place it in a new microtube. Reserve the supernatant. Mix a pellet with 1000 microliters pure ethanol, vertex for 30 seconds, and then centrifuge at the same conditions.Collect the supernatant and mix with the first supernatant. Remove ethanol from the sample by using pure nitrogen at 135 psi. Keep the needle one centimeter away from the top of the microtube to prevent sample loss and flush until a sample is dry.Resuspend dry samples in 100 microliters of a one is to one mixture of acetonitrile with water. Filter through a 45 micro nylon syringe membrane directly into an HPLC vial micro insert Inject three microliters of sample onto UPLC equipped with a C 18 reverse-phase column. Set the autosampler temperature at 20 Celsius and the column thermostat at 25 Celsius.Inject each sample in triplicate. Use 0.1%formic acid in water as solvent A, and 100%acetonitrile as solvent B.Set the flow rate at 0.4 millimeters per minute and a gradient program as follows, zero to one minute 10%B, one to four minute 30%B, four to six minutes 38%B, six to eight minutes 60%B, eight to 8.5 minutes 60%B, 8.5 to nine minutes 10%B. Set the mass spectrometer in negative mode ionization.Use nitrogen as drying gas at 300 Celsius and flow rate at 13 liters per minute. Set the nebulizer pressure at 30 psi. Set a capillary voltage at 4, 000 volts, fragmentor voltage at 175 volts, and Skimmer voltage at 65 volts.Scan the masses between 100 and 1100 m/z and for the mass spectrometry, scan masses between 50 to 1000 m/z. Set data acquisition into Auto MS/MS mode. Use the following reference mass, 119.036 and 966.0007.Search for phenolic compounds, phenolic metabolites and other compounds of interest in scientific literature. Open the database management software included in the UPLC system. Select file, then select new personal database compound library, PCDL.Then select, create new PCDL. Select the type of PCDL LC/MS metabolomics. Set the name for PCDL.Then select create. In the toolbar, select PCDL and then the allow editing option. Then click the find compounds button.Compounds can be added to the personal library two ways, first by copying them from the instrument's general library. To do this, open the instruments existing database included in the data management so far. Click the button find compounds.In the single search option, enter the compound search criteria to find the compound of interest. In the compound results table, select the compound of interest. To select more than one compound, click the first compound, then hold down the CTRL key, and then click each compound of interest.Then, right click on all the highlighted compounds and select Append to PCDL. In the new window, search and select the specialized personal database file. Mark the box include spectra for compound if present.Click the Append button. In the new dialog box, select Yes to check the new compounds added. Select No to keep searching for more compounds of interest.Second, new compounds can be added manually if they are not available in the instrument's general library. For this open, the specialized personal database. Once opened, select PCDL, and then the allow editing option.Select the edit compounds option. Click the add new button. In the upper section of the window, complete the information of the new compound.Fill in the formula, name, IUPAC name, CAS number, Chemspider ID, and other identifiers. Using information available in the free online libraries, Chemspider, PubChem, And Phenol Explorer to fill in the information of the new compound of interest. Once finished, click the button save as new to save the new compound information in the specialized personal database.Repeat the process with all the compounds of interest to complete the specialized personal database. Use the instrument's qualitative manager software to identify phenolic compounds and phenolic metabolites. Open the sample file.In the Chromatogram panel, select define Chromatograms and extract the total ion chromatogram TIC, extracted ion-chromatogram of MS EIC and EIC of MS/MS. Select the integrate chromatogram option. In the find compounds panel, select find by formula options.In the new window, select formula source and then the database slash library option. Find the personal database previously created and click on open. Select the formula matching option and set a mass match tolerance of five parts per million, ppm.Select the negative ions option and select only the minus H dialog box. In results option, mark the extract EIC, extract cleaned spectrum, extract raw spectrum, and include structured dialog boxes. Select the results filters option.And then mark warn if score is, then set the score match at 80%Then mark do not match if the score is and set the score at 75%Then click on the find compounds by formula to identify compounds of interest in the sample. The step by step process for the identification of phenolic metabolites through the semi-targeted UPLC MS/MS analysis of plasma samples is shown in the next figure. First, the total ion-chromatogram was obtained using a self-created PCDL database that contained 645 phenolic compounds and their metabolites, obtained from free online databases.Next, the extracted ion-chromatogram and fragmentation pattern, or the ions with less than five ppm mass, match tolerance were obtained. Finally, the isotopic distribution of the detected peaks was compared to that of the theoretical compounds. From this analysis, a total of 25 phenolic compounds and metabolites were identified in the plasma samples.To evaluate the effectiveness of the design method in the identification of the absorb phenolic compounds or their metabolites, five samples of the study participants obtained prior and after the 30 day intervention were analyzed. The relative abundance of each compound was calculated by dividing the area under the curve after treatment by AUC before treatment. Table four shows the list of 12 phenol compounds that showed an increase in plasma after the 30 day consumption of the brosimum alicastrum flour containing foods.Phenylacetic acid was only metabolite found consistently in higher concentration after the treatment. Glycitin, a glycosylated isoflavone, and three hydroxyphenylvaleric acid increased in three of the five samples, but decreased in the other two. Gomisin M2, a lignan, was detected in three of the five samples only after the nutritional intervention.The other phenolic compounds and metabolites were found only in one sample and only after the treatment. The method developed present paper, shows that in most parts it was well suited for the objective for which it was created. The sample per treatment was simple and effective.UPLC separation and semi-targeted MS/MS identification of phenolic metabolites were achieved.

semi-targeted-ultra-high-performance-chromatography-coupled-to-mass

Research

JoVE Journal

Biochemistry

3.8K Views.  Universidad Autónoma de Ciudad Juárez. The goal of this protocol is to detect phenolic metabolites in plasma by a semi-targeted UPLC-MS/MS method. Plasma proteins were precipitated with ethanol and samples concentrated, and re-suspended in the mobile phase, prior to injection in the UPLC equipment. Compounds were identified using a semi targeted library assembled in the lab, using PCDL manager for metabolomics, software and different free online databases.We implemented a nutritional intervention with a muffin and beverage ...