Sensoproteomics: A New Approach for the Identification of Taste

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Chemistry and Biology of Aroma and Taste

Sensoproteomics: A New Approach for the Identification of Taste-Active Peptides in Fermented Foods Karin Sebald, Andreas Dunkel, Johannes Schäfer, Jorg Hinrichs, and Thomas Hofmann J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b04479 • Publication Date (Web): 05 Oct 2018 Downloaded from http://pubs.acs.org on October 6, 2018

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Journal of Agricultural and Food Chemistry

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Sensoproteomics: A New Approach for the Identification

2

of Taste-Active Peptides in Fermented Foods

3 4

Karin Sebald†, Andreas Dunkel†,‡, Johannes Schäfer§, Jörg Hinrichs§,

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and Thomas Hofmann†,‡,$,*

6 7 8 †

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Chair for Food Chemistry and Molecular Sensory Science, Technical University of

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Munich, Lise-Meitner-Straße 34, D-85354 Freising, Germany, ‡

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Leibniz-Institute for Food Systems Biology at the Technical University of Munich,

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Lise-Meitner Str. 34, D-85354 Freising, Germany, §

13

Institute of Food Science and Biotechnology, Department of Soft Matter Science

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and Dairy Technology, University of Hohenheim, Garbenstr. 21, D-70599 Stuttgart,

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Germany, $

16

Bavarian Center for Biomolecular Mass Spectrometry, Technical University of

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Munich, Gregor-Mendel-Straße 4, D-85354 Freising, Germany.

18 19 20 21 22

*

23

PHONE

+49-8161-712902

24

FAX

+49-8161-712949

25

E-MAIL

To whom correspondence should be addressed

[email protected]

26

1 ACS Paragon Plus Environment


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ABSTRACT

28 29

Aiming at the identification of the key bitter peptides in fermented foods, a new

30

approach, coined “sensoproteomics” was developed and applied to fresh cheese

31

samples differing in bitter taste intensity. By means of MPLC-fractionation of the

32

water-soluble cheese extracts in combination with taste dilution analysis, complex

33

fractions with intense bitter taste were located and, then, screened by UPLC-

34

MS/MS for the entire repertoire of ~1600 candidate peptides, extracted from a

35

literature meta-analysis on dairy products, by using a total of 120 selected reaction

36

monitoring (SRM) methods computed in silico. A total of 340 out of the 1,600

37

peptides were found in the cheese samples, amongst which 17 peptides were

38

identified as candidate bitter peptides by considering only peptides that were

39

located in the bitter tasting MPLC fractions (signal to noise ratio: ≥ 10) with a fold-

40

change of ≥ 3 when comparing the less bitter to the more bitter cheese sample, and

41

that were validated by comparison with the synthetic reference peptides. While

42

EIVPNS[phos]VEQK (αs1-CN70-78) and INTIASGEPT (κ–CN122-131) did not exhibit

43

any bitter taste up to 2,000 µmol/L, 15 of the 17 target peptides showed bitter taste

44

thresholds

45

(IQKEDVPS, αs1-CN81-88). Finally, quantitative peptide analysis, followed by

46

calculation of dose-over-threshold factors revealed a primary contribution of

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MAPKHKEMPFPKYPVEPF (β–CN102-119) and ARHPHPHLSFM (κ-CN96-106) to the

48

perceived bitter taste of the fresh cheese samples. Finally, the evolution of the bitter

49

peptides throughout two different fresh cheese manufacturing processes were

50

quantitatively recorded.

ranging

from

30

(ARHPHPHLSFM,

κ-CN96-106)

51


to

690 µmol/L

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Keywords: sensoproteomics, fresh cheese, bitter peptides, taste dilution analysis

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54

INTRODUCTION

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Due to their alluring flavor, fermented protein-rich foods, such as, e.g. cheese,

57

yoghurt, cocoa, cured ham, soy sauce, fish sauce, and soy paste, as well as

58

enzymatic protein hydrolysates are highly appreciated by consumers all over the

59

world. Whereas multiple studies published in the past 40 years have been

60

performed to decipher the combinatorial codes of volatile food odorants,1 the

61

knowledge on the non-volatile key peptides which, upon enzymatic protein

62

digestion, contribute to the typical taste profile of fermented foods, is still rather

63

fragmentary.

64

Activity-guided fractionation combined with analytical sensory tools like the

65

taste dilution analysis (TDA),2 revealed the identification of some bitter-tasting

66

peptides in Gouda cheese,3 Emmentaler cheese,4 Cheddar,5 Camembert,6

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Ragusano cheese,7 and whey protein hydrolysate,8 as well as umami-enhancing

68

peptides in cured fish,9 cured ham,10 and wheat gluten hydrolysate, respectively.11

69

Furthermore, mouthfullness (kokumi) enhancing γ-glutamyl peptides in Gouda

70

cheese,12 Parmesan cheese13 and other blue-mold cheeses,14 as well as salt-

71

enhancing arginyl-peptides have been recently reported as natural taste modulators

72

in fish sauce15 and enzymatically hydrolysed proteins.16 Since fermented, protein-

73

rich foods comprise an enormous complexity and multiplicity of peptides released

74

upon protein digestion, the iterative fractionation and purification approach, followed

75

by sequencing of the taste-active target peptides is rather challenging and

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laborious, and hampers the straightforward characterization of the taste-active

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peptidome of fermented foods in due time.


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As an alternative to the sensory-directed identification approach, purely

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analytical approaches based on high-end mass spectrometric sequencing have

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been developed and revealed a vast number of peptides in fermented foods, e.g. a

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total number of ~1600 unique peptide sequences, comprising between three and 63

82

amino acid moieties, have been reported in dairy products like milk,17 kefir18 and

83

yoghurt, respectively.19 (Figure 1). However, which of these peptides are taste-

84

active? To answer this question, combining targeted proteomics tools with sensory

85

analytics may be a promising strategy to identify those key molecules among the

86

bulk of literature reported peptides that give the highest taste contribution to

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fermented foods. Taste-active crude fractions isolated from fermented food samples

88

varying in their overall taste intensity may be comparatively analysed by means of

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targeted proteomics techniques to map the differences in their peptide composition.

90

To predict MS/MS parameters of pre-defined peptides, new software tools are able

91

to provide the transitions and corresponding MS/MS parameters for each target

92

peptide in silico.20,

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selected reaction monitoring (SRM) methods, the most reliable and intense

94

transitions may be used to uniquely identify the target peptides using the

95

corresponding synthesized peptides as references. The SRM assays developed by

96

following this strategy then hold promise to enable the sensitive and robust

97

quantitative mapping of target peptides in corresponding taste-active crude fractions

98

comparatively isolated from fermented food samples varying in the taste intensity.

21

After screening the taste-active, crude food fractions with

99

This new approach, coined “sensoproteomics”, should be applied on the

100

bitter-tasting key peptides in fermented fresh cheese as an example. The state of

101

the art to produce the so-called fermented-concentrated fresh cheese involves the

102

gelation of milk via starter cluture mediated acidificatiton and renneting, followed by



103

the separation of the acid, e.g. by stirring the gel and ultrafiltration, in order to obtain

104

the fresh cheese (FC) as a highly concentrated microgel suspension.22 In order to

105

avoid the production of acid whey, which is mostly discarded today, and obtain the

106

re-usable sweet whey22, in an alternative process the standardized milk is

107

concentrated up to a desired protein content by means of ultrafiltration, followed by

108

fermentation. This process, however, results in an alternative fresh cheese (aFC)

109

exhibiting an increased bitterness which, in addition to the increased concentration

110

of soluble calcium ions, is assumed to be caused by bitter peptides.24,25

111

The objective of the present study was, therefore, to apply the new

112

“sensoproteomics” approach to the identification of taste-active key peptides in two

113

fresh cheese samples differing in bitter taste intensity. The peptides should then be

114

quantitated by LC-MS/MS in process samples to monitor the evolution of the key

115

bitter peptides during fresh cheese processing.

116 117 118

MATERIALS AND METHODS

119 120

Chemicals. HPLC-grade solvents and formic acid were obtained from Merck

121

(Darmstadt, Germany), acetonitrile used for MS analysis was purchased from

122

Honeywell Burdick & Jackson (Seelze, Germany), and synthetic reference peptides

123

were purchased from Peptides and Elephants (Potsdam, Germany). Deionized

124

water used for chromatography was prepared by means of a Milli-Q water A5

125

system (Millipore, Molsheim, France). A fat-free fresh cheese with 9% protein

126

content (FC9) was prepared by fermentation followed by ultrafiltration, and two

127

bitter-tasting fat-free fresh cheeses with 9 (aFC9) and 12% protein (aFC12),


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respectively, were prepared by means of an alternative process involving

129

ultrafiltration of the standardized milk, followed by fermentation as reported

130

recently.23

131

Sequential Solvent Extraction. Samples (80 g each) of the fresh cheese

132

sample aFC12 was extracted with an aliquot (200 mL) of aqueous formic acid (0.1%

133

in water), homogenized for 5 min by means of an Ultra-Turrax T 25 basic (IKA

134

Labortechnik, Germany), and centrifuged at 10,000 rpm for 30 min at 4 °C (Avanti J-

135

E, Beckman-Coulter, Krefeld, Germany). The upper liquid layer (pH 4.6) was

136

separated from the protein pellet, which was re-extracted with aqueous formic acid

137

(0.1% in water) as detailed above. The aqueous layers were pooled, filtered (113 A

138

filter, 110 mm, Carl Roth, Karlsruhe, Germany), and lyophilized to afford the water-

139

soluble extract (WSE; yield: 6.4%), which was kept at -20 °C until further analysis.

140

The protein pellet was re-extracted twice with methanol (200 mL) and, after

141

filtration, the solvent was separated in vacuum at 35 °C, followed by freeze-drying.

142

The methanolic extract (ME; yield: 0.5%) was stored at -20 °C until further use.

143

Ultrafiltration. An aliquot (5 g each) of WSE obtained from samples aFC12

144

was dissolved in water (300 mL) and separated by means of ultrafiltration using a

145

400 mL filtration unit (Amicon, Merck Millipore, Darmstadt, Germany), equipped with

146

a 1 kDa MW cutoff membrane (Ultracell Ultrafiltration Discs, Amicon Bioseparations,

147

EMD Millipore Corporations, Billerica, USA) and operated with nitrogen (0.3 MPa).

148

The high-molecular weight fraction (HMW; > 1 kDa) obtained was taken up in water

149

(50 mL) and the ultrafiltration procedure was repeated twice to remove low

150

molecular weight compounds (LMW; ≤ 1 kDa). The HMW fraction (yield: 12%) and

151

the pooled LMW fraction (yield: 88%) were lyophilized and stored at -20 °C until

152

further use.



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Medium-Performance Liquid Chromatography (MPLC). For preparative

154

separation, a MPLC system (Büchi Labortechnik AG, Flawil, Switzerland) was

155

equipped with two C-605 type pumps, a C-620 type communication module, an

156

injection unit (Büchi), a Sedex 90 type evaporative light scattering detector (LT-

157

ELSD, SEDERE, Alfortville Cedex, France) with nebulizer, a split (39/1, v/v) and a

158

fraction collector (C-660, Büchi, Flawil, Switzerland). Data acquisition was

159

performed with the software Sepacore Control (Version 1.2, Büchi, Flawil,

160

Switzerland). For analysis of the HMW-fraction, an aliquot was dissolved in water

161

(15 mL); the solution was adjusted to pH 4.6 with aqueous formic acid (1% in water)

162

and after filtration (0.45 µm) injected manually into the MPLC system (Büchi

163

Labortechnik AG, Flawil, Swiss) using a 250 x 40 mm i.d. polypropylene cartridge

164

filled with 25-40 µm LiChroprep RP-18 material (Merck, Darmstadt, Germany) for

165

separation. Chromatography was performed with a gradient of water and methanol,

166

both containing 1% formic acid, at a flow rate of 40 mL/min using the following

167

gradient: 0 min, 20% B; 1 min, 20% B; 3 min, 45% B; 22 min, 80% B; 23 min, 100%

168

B; 28 min, 100% B; 29 min, 20% B; 37 min, 20% B. Fractions were collected in

169

1 min steps and recombined to six fractions, namely M1-M6, freed from solvent

170

under vacuum, followed by lyophilization and obtained in yields of 29, 18, 8, 30, 8

171

and 7%, respectively. Collected fractions were stored at -20 °C until further use.

172

Analytical Sensory Experiments. General Conditions, Panel Training. A total

173

of 15 panelists, who had no history of known taste disorders and who had given the

174

informed consent to participate in the present sensory tests, were trained in weekly

175

training sessions for at least two years using solutions of purified reference

176

compounds

177

methodologies.3,15 The sensory sessions were performed at 22-25 °C in an air-

in

order

to

become

familiar

with


the

taste

language

and

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conditioned room with separated booth while the panelists wore nose clips to

179

prevent cross-modal interactions with odorants. To minimize the uptake of any toxic

180

compound, test samples were lyophilized twice prior to use and sensory analyses

181

were performed by using the sip-and spit method, which means the test solutions

182

were not swallowed but expectorated.

183

Taste Profile Analysis. The cheese samples FC9, aFC9, and aFC12 were

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presented to 15 trained panelists to evaluate the taste qualities “bitter” and “sour” on

185

an intensity scale from 0 (not detectable) to 5 (strongly detectable). The lyophilized

186

fractions WSE, HMW, and LMW, respectively, were dissolved in bottled water in

187

their “natural” cheese concentrations, that means in amounts as they were isolated

188

from aCC12 and the natural pH-value of the cheese samples (pH 4.6) with trace

189

amounts of 1% aqueous formic acid. The solutions were then presented to 15

190

trained panelists to evaluate the taste qualities “bitter” and “sour” on an intensity

191

scale from 0 (not detectable) to 5 (strongly detectable).

192

Taste Dilution Analysis (TDA). The lyophilized MPLC-fractions were taken up

193

in bottled water (20 mL), adjusted to pH 4.6 with a 1% aqueous solution of formic

194

acid and diluted stepwise 1:1 with water (pH 4.6). The series of dilutions were

195

randomly presented to the panelists in a two-alternative forced choice (2-AFC) test

196

in order of ascending concentrations. The panelists were asked to determine the

197

dilution step at which a difference between sample and blank water could be

198

detected. The so-called taste dilution (TD) factor3,24,25 was determined for each

199

fraction by the sensory panel in two separate independent sessions and were

200

averaged. The TD-factors between individuals and separate sessions did not differ

201

by more than plus/minus one dilution step.



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Determination of Human Taste Recognition Thresholds. The trained panelists

203

determined the taste threshold concentrations of purified synthetic peptides (>98%,

204

LC-MS) in bottled water adjusted to pH 4.6 with trace amounts of aqueous formic

205

acid (1% in water) using a three-alternative forced choice (3-AFC) test with

206

ascending stimulus concentrations as reported earlier.3

207

Ultra-Performance

Liquid

Chromatography

-

Time-Of-Flight

Mass

208

Spectrometry (UPLC-ToF-MS). UPLC-ToF-MS measurements were acquired on a

209

Sciex TripleTOF 6600 mass spectrometer (Sciex, Darmstadt, Germany) connected

210

to a Shimadzu Nexera X2 system (Shimadzu, Kyoto, Japan) operating in the

211

positive electrospray ionization mode. Instrumentation control and data acquisition

212

were performed with AnalystTF software (v 1.7.1; Sciex, Darmstadt, Germany). Ion

213

spay voltage was set at 5500 eV, source temperature was 550 °C, nebulizing gas

214

(0.38 MPa), heating gas (0.45 MPa) and nitrogen served as curtain gas (0.24 MPa),

215

to effectively desolvate ions. Chromatography was performed on a 150 x 2 mm, 1.7

216

µm, Kinetex C18 column (Phenomenex, Aschaffenburg, Germany) equipped with a

217

guard column of the same type with a gradient of 1% aqueous formic acid and

218

acetonitrile containing 1% formic acid at a flow rate of 0.3 ml/min with the following

219

gradient: 0 min, 5% B; 0.5 min, 5% B; 14 min; 40% B, 15 min, 100% B; 16 min,

220

100% B; 17 min, 5% B; 20 min, 5% B. Column oven was tempered at 40 °C and the

221

injection volume was 10 µL. The TOF MS scan was performed from m/z 100 to

222

2000 with an accumulation time of 250 ms.

223

Targeted Proteomics. Liquid Chromatography-Mass Spectrometry (LC-

224

MS/MS). All targeted proteomics LC-MS/MS measurements were acquired on a

225

5500 QTrap LC-MS/MS system (Sciex, Darmstadt, Germany) connected to a

226

Shimadzu Nexera X2 system (Shimadzu, Kyoto, Japan) operating in the positive


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electrospray ionization mode. Ion spay voltage was set at 5,500 eV, source

228

temperature was 400 °C, nebulizing gas (0.38 MPa), heating gas (0.45 MPa) and

229

nitrogen served as curtain gas (0.24 MPa), to effectively desolvate the ions.

230

Chromatography was performed on a 150 x 2 mm, 1.7 µm, Kinetex C8 column

231

(Phenomenex, Aschaffenburg, Germany) equipped with a guard column of the

232

same type with a gradient of 0.1% aqueous formic acid and acetonitrile containing

233

0.1% formic acid at a flow rate of 0.1 ml/min with the following gradient: 0 min, 1%

234

B; 10 min; 40% B, 13 min, 100% B; 15 min, 100% B; 17 min, 1% B; 20 min, 1% B.

235

Column oven was tempered at 40 °C and the injection volume was 1 µL of each

236

sample. Using nitrogen in the collision cell, peptide fragmentation was performed by

237

using calculated peptide specific collision energies as described previously.26

238

Instrumentation control and data acquisition were performed with Sciex Analyst

239

software (v 1.6.2).

240

In Silico Development of Selected Reaction Monitoring (SRM) Methods. The

241

targeted proteomics software Skyline (64-bit 3.5.0.9319) was used to create and

242

optimize selected reaction monitoring (SRM) methods21 of approximately 1600

243

unique peptides ranging from five to 25 amino acids in length resulting from a

244

literature meta-analysis on dairy peptides from bovine caseins (Figure 1).3, 5, 17-19, 27-

245

52

246

precursor ions, with b- and y-product ions with m/z higher than precursor m/z. A

247

total of 120 multiple SRM methods were created with a maximum of 300 transitions

248

per method and a dwell time of 10 ms and exported by Skyline. Q1 and Q3

249

resolution was set to unit (0.7). The used SRM methods are given in the Supporting

250

Information. The predicted SRM methods were used for a screening in the WSE-

251

fraction isolated from cheese samples tCC9 and aCC9. To achieve this, the

About 34.300 mass transitions were calculated for doubly and triply charged



252

lyophilized WSE was re-dissolved in water in “natural” concentrations and

253

membrane filtered (0.45 µm). After mass spectrometry analysis, the raw files were

254

imported into Skyline and chromatographic data from each peptide were manually

255

analyzed to assess the signal quality. Peptides with ambiguous or nonexistent

256

peaks were excluded. After refinement of the transition list, the five best transitions

257

for each peptide were selected based on the intensity and the collision energy (CE)

258

was optimized26, followed by the export of optimized SRM methods.

259

Identification of Candidate Bitter Peptides in Fresh Cheese Samples. Using

260

the optimized SRM methods, the WSE of cheese FC9 and aFC9, respectively, and

261

the bitter-tasting MPLC fractions M2 to M4 were screened in both cheeses in order

262

to locate and identify candidate bitter peptides. To achieve this, the lyophilized

263

extracts and fractions (M2 – M4) were re-dissolved in water in “natural”

264

concentrations, membrane filtered (0.45 µm) and, then, used for LC-MS/MS

265

analysis.

266

Quantification of Candidate Bitter Peptides in Fresh Cheese Samples. A

267

portion (1 g) of fresh cheese or intermediates of cheese manufacturing was placed

268

in a centrifuge tube (CK68_15ml, Precellys, France) and an aliquot (5 mL) of 0.1%

269

aqueous formic acid was added. After homogenization (Precellys, Bertin

270

Instruments, Montigny-le-Bretonneux, France) at 6,000 rpm (3 x 20 s), the slurry

271

was centrifuged (4,000 rpm, 5 min) and the supernatant transferred into a

272

volumetric flask (10 mL). An aliquot (5 mL) of 0.1 % aqueous formic acid was added

273

to the protein pellet (residue) and extracted as detailed above. The supernatant was

274

transferred into the volumetric flask (10 mL) and made up to 10 mL with 0.1%

275

aqueous formic acid, followed by membrane-filtration (0.45 µm) and LC-MS/MS

276

analysis.


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LC-MS/MS analysis was done on a 5500 QTrap LC-MS/MS system (Sciex,

278

Darmstadt, Germany) connected to a Shimadzu Nexera X2 system (Shimadzu,

279

Kyoto, Japan) operating in the positive electrospray ionization mode. Ion spay

280

voltage was set at 5500 eV, source temperature was 400 °C, nebulizing gas (0.38

281

MPa), heating gas (0.45 MPa) and nitrogen served as curtain gas (0.24 MPa), to

282

effectively desolvate ions. The MS/MS parameters are given as Supporting

283

Information. An aliquot (1 µL) of the prepared sample was injected into the LC-

284

MS/MS system connected to a 150 x 2.0 mm i.d., 1.7 µm, Kinetex C8 column

285

(Phenomenex, Aschaffenburg, Germany) equipped with a guard column of the

286

same type. Eluent A consisted of 1% formic acid in water and eluent B was 1%

287

formic acid in acetonitrile. Using a flow rate of 0.25 ml/min, chromatography was

288

performed starting with 5% B, 7 min 40% B, 8 min 100% B, 11 min 100% B, 12 min

289

5% B, 15 min 5% B. Peptides were analyzed in the ESI positive ionization mode

290

using specific mass transitions and MS/MS parameter. Quantitation was performed

291

using an external standard calibration with standard solutions ranging from 0.001 to

292

10 mg/L (eight-point calibration). System control and data acquisition were

293

performed using Analyst 1.6.2 (Sciex, Darmstadt, Germany).

294

Quantitative 1H NMR Spectroscopy. Synthesized reference peptides were

295

dissolved in D2O (5.0 mmol/L). An aliquot (600 µL) was transferred into 178×5 mm

296

inner diameter NMR tubes (USC tubes, Bruker, Rheinstetten) and analysed by

297

means of a 400 MHz Avance III NMR spectrometer (Bruker, Rheinstetten,

298

Germany). Instrument calibration and data processing were performed as detailed

299

earlier.53 The specific proton resonance signal at 7.10 ppm (d, 2H) of the external

300

standard L-tyrosine (6.68 mmol/L) was used for external calibration.

301



302 303 304

RESULTS AND DISCUSSION

305 306

Aiming at the identification of candidate bitter peptides in fresh cheese,

307

samples of a state-of-the-art, fat-free fresh cheese (FC9) and a fresh cheese

308

produced by an alternative process (aFC9),23 both with a protein content of 9%,

309

were sensorially evaluated by a trained panellists, who were asked to rate the

310

individual intensities of the taste qualities sour and bitter on a linear scale between

311

0 (not detectable) and 5 (strongly detectable). The alternatively produced cheese

312

aFC9 showed an increased bitter taste rated with an intensity of 2.0, while sample

313

FC9 was judged with a score of 1.6 (Table 1). In order to answer the question as to

314

whether an increased protein content in alternatively produced cheese would lead

315

to increased levels of candidate bitter peptides, an additional cheese (aFC12) was

316

prepared with a protein content of 12%. Sensory evaluation of aFC12 demonstrated

317

an increased bitterness score of 2.6 when compared to the cheese sample aFC9

318

(2.0). Regarding the perceived sourness, no significant difference could be

319

observed, as all three evaluated fresh cheeses showed comparable intensities

320

ranging between 2.3 and 2.7.

321

As previous studies on dairy products revealed the bitter peptides to be water-

322

soluble,3, 6, 54 the most bitter-tasting sample aFC12 was sequentially extracted with

323

water containing 0.1% formic acid, followed by methanol to afford the water-soluble

324

extract (WSE) and the methanolic extract (ME) after freeze-drying. Both extracts

325

were taken up in “natural” concentrations and adjusted to the pH value of the fresh

326

cheese (pH 4.6) with trace amounts of formic acid prior to taste profile analysis 14 ACS Paragon Plus Environment

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(Table 1). Bitterness of the WSE fraction was rated with an intensity of 2.1, whereas

328

the ME isolate showed only a low bitter taste intensity judged with a score of 0.6. As

329

the remaining protein pellet did not show any taste activity (data not shown), it was

330

concluded that the key bitter compounds are present in the WSE fraction.

331

Ultrafiltration of the Bitter-Tasting Water-Soluble Extract (WSE). To sort

332

out the bitter peptides from the less bitter or tasteless compounds, the WSE was

333

separated into a low-molecular weight fraction (LMW) and a high-molecular weight

334

fraction (HMW) by means of ultrafiltration with a 1 kDa cutoff and the LMW and

335

HMW fraction, respectively, each in “natural concentration” in aqueous solutions

336

(pH 4.6), were sensorially evaluated (Table 1). The panelists reported the HMW

337

fraction to show by far the high bitter taste intensity, judged with a score of 3.1,

338

while the LMW fraction showed only marginal bitter taste (0.6). To locate potential

339

bitter peptides candidates imparting the bitter taste of fresh cheese, the HMW

340

fraction was further subfractionated by means of MPLC, followed by taste dilution

341

analysis (TDA).

342

MPLC-TDA of the Bitter-Tasting HMW-Fraction. MPLC fractionation of the

343

HMW fraction on RP-18 material revealed six subfractions, namely M1-M6, which

344

were freed from solvent under vacuum, lyophilized twice, dissolved in bottled water

345

in their “natural” concentrations and, then, used for TDA. The bitter tasting MPLC-

346

fractions M2, M3 and M4, obtained in yields of 18, 8 and 30%, respectively, were

347

evaluated with the highest taste dilution (TD)-factors of 8, 2 and 16 (Figure 2A). To

348

gain a first impression of the complexity of these bitter MPLC fractions, a C8-UPLC-

349

ToF-MS screening was performed. As the analysis of all three fractions showed

350

several hundreds of peptide signals (Figure 2B-2D), it was concluded that a further

351

activity-guided fractionation to resolve the subfractions M2 to M4 into the individual



352

taste-active peptides is too time and laboratory intensive, if at all possible.

353

Therefore, a new approach, coined “sensoproteomics”, was developed to identify

354

the candidate bitter peptides by integrating the activity-based sensomics approach

355

with a targeted quantitative proteomics approach (Figure 3).

356

Identification of Candidate Bitter Peptides. To capture all candidate

357

peptides already identified in literature by high-end mass spectrometric sequencing

358

and subsequent database research, a literature meta-analysis on dairy peptides

359

from bovine caseins was performed.3, 5,

360

peptide sequences were reported in dairy products like milk17, kefir18 and yoghurt19

361

(Figure 1A). These candidate peptides comprise between three and 63 amino acid

362

moieties, with most peptides showing five to 14 amino acids residues (Figure 1B).

363

Using the proteomics software Skyline21, suitable MS/MS parameters were

364

generated in silico for the UPLC-MS/MS analysis of these 1,600 candidate peptides,

365

followed by the creation of 120 selected reaction monitoring (SRM) methods with a

366

maximum of 300 transitions per sample injection (see supporting information).

367

Aimed at identifying those candidate peptides, which are located in the bitter-tasting

368

fresh cheese, the WSE was screened by using the SRM methods generated in

369

silico. Target peptides that could not be unequivocally detected by LC-MS/MS in

370

WSE were sort out (Figure 4A), whereas peptides showing unambiguous mass

371

transition chromatograms with perfectly aligned retention times for doubly and triply

372

charged precursor ions were included into the list of candidate peptides (Figure

373

4B). To ensure high confidence for specific detection of the targeted analytes, the

374

five best mass transitions for each of the 340 peptides were selected based on

375

SRM signal intensity and the collision energies (CE) for each mass transition was

376

optimized by means of the Skyline software.26

17-19, 27-52

A total number of ~1,600 unique


Page 16 of 46

Page 17 of 46


377

In order to identify the key bitter peptides, the optimized SRM methods were

378

then used for comparative UPLC-MS/MS analysis of the cheese samples FC9 and

379

aFC9. To achieve this, the water-soluble extracts (WSE) isolated from FC9 and

380

aFC9 were injected in quintuplicates for each method resulting in 1,200 runs: 120

381

SRM methods x 2 samples x 5 injections. In order to reduce complexity and focus

382

on the most important among the 340 candidate bitter peptides, the peptide data

383

were filtered using the following three selection criteria (Figure 3): first, only

384

peptides showing a fold-change of ≥ 3 when comparing the less bitter sample tCC9

385

to the more intense bitter tasting sample aCC9 were further considered; this

386

reduced the target peptides from 340 to 33. Second, only peptides with a signal to

387

noise ratio of ≥ 10 were considered in order to enable reliable quantitation; this

388

reduced the number of target peptides from 33 to 26. Finally, only those peptides

389

were considered that eluted in the MPLC-fractions M2 to M4, as those fractions

390

have been demonstrated to exhibit the highest bitter taste activities; following this

391

filtering approach, a total of 23 peptides were selected as candidate bitter peptides

392

(Figure 5).

393

To verify these peptides, the 23 candidate peptides were synthesized, purified,

394

and their retention times (UPLC) and SRM mass transitions (MS/MS) compared to

395

the data obtained by the UPLC-MS/MS screening of the cheese samples FC9 and

396

aFC9, respectively. Following this approach led to the unequivocal identification of

397

17 candidate peptides in fresh cheese (Table 2). The bitter-tasting fraction M2

398

contained 12 peptides, namely FFSDKIAK, YQQKPVAL, ARHPHPHLSFM,

399

AIPPKKNQDKTEIPTINTIASGEPT,

400

EIVPNS[phos]VEQK,

401

KVLPVPQKAVPYPQ,

INTIASGEPT,

IQKEDVPS, and

VLPVPQ,

VFGKEKVNEL,

DIKQM,

MAPKHKEMPFPKYPVEPF, respectively.


The

peptides


Page 18 of 46

402

TQTPVVVPPFLQPE and LHLPLP were identified in fraction M3, whereas the three

403

peptides LHLPLPLL, HLPLPLLQ, and VAPFPEVFGKE are found in fraction M4.

404

Upon comparing FC9 and aFC9, the highest fold-changes were found for the

405

κ-casein-derived peptides ARHPHPHLSFM and AIPPKKNQDKTEIPTINTIASGEPT,

406

which were 20.5 and 11.9 times present in higher concentrations in the more

407

intensely bitter tasting cheese aFC9 (Table 2). This cheese sample also contained

408

the other peptides at three- to six-fold increased levels.

409

Grouping of the 1,600 peptides screened by in the cheese samples in

410

alignment with the structures of αs1-, αs2-, β- and κ-casein revealed the seven

411

peptides MAPKHKEMPFPKYPVEPF (β–CN102-119), KVLPVPQKAVPYPQ (β-CN169-

412

182),

413

CN133-138), LHLPLPLL (β–CN133-140), and HLPLPLLQ (β–CN134-141) to be released

414

from β-casein, while κ-casein functioned as precursor for the five peptides

415

FFSDKIAK (κ–CN17-24), YQQKPVAL (κ–CN43-50), ARHPHPHLSFM (κ-CN96-106),

416

AIPPKKNQDKTEIPTINTIASGEPT (κ–CN107-131), and INTIASGEPT (κ–CN122-131),

417

respectively

418

VFGKEKVNEL (αs1-CN31-40), DIKQM (αs1-CN56-60), EIVPNS[phos]VEQK (αs1-CN70-

419

78),

420

whereas, interestingly, no candidate peptides were found to be released from αs2-

421

casein (Figure 6).

and VLPVPQ (β–CN170-175), TQTPVVVPPFLQPE (β–CN78-91), LHLPLP (β–

(Figure

6).

The

five

peptides

VAPFPEVFGKE

(αs1-CN25-35),

and IQKEDVPS (αs1-CN81-88) originate from the sequence of αs1-casein

422

Sequence alignment of the peptides with the structures of αs1-, β- and κ-casein

423

and the potential cleavage sites of the endogenous milk enzymes plasmin,

424

cathepsins B, D, and G, as well as the coagulant enzymes chymosin and

425

lactococcal proteinase are depicted in Figure 7. The sequence of the identified

426

peptide VAPFPEVFGKE (αs1-CN25-35) indicates the action of chymosine55 or


Page 19 of 46


427

cathepsin D,56 reported to cleave αs1-casein after aa25 (Figure 7A). The C-terminus

428

of VFGKEKVNEL (αs1-CN31-40), EIVPNS[phos]VEQK (αs1-CN70-78), and IQKEDVPS

429

(αs1-CN81-88), respectively, is in alignment with the cleavage of αs1-casein after aa40

430

by chymosin,55 after aa78 by or plasmin,57 and after aa88 by lactococcal

431

proteinase.55 The N-terminus of the peptides ARHPHPHLSFM (κ-CN96-106),

432

AIPPKKNQDKTEIPTINTIASGEPT (κ-CN107-131), MAPKHKEMPFPKYPVEPF (β-

433

CN102-119), LHLPLP (β-CN133-138), LHLPLPLL (β-CN133-140) and KVLPVQKAVPYPQ

434

(β-CN169-182), could be formed during the action of lactococcal proteinase.55, 58

435

It is interesting to notice that most of identified peptides were released from the

436

same sequence domains of ß-casein. The fact that the central part of β-casein is a

437

sensitive substrate for proteases during ripening of several cow cheeses is well

438

documented in the literature.59 For example, three peptides, namely LHLPLP (β–

439

CN133-138), LHLPLPLL (β–CN133-140), and (HLPLPLLQ (β–CN134-141) originate from

440

the region aa(133-141) differing in deletion of one or more amino acid residues at

441

both termini of the peptide sequence (Figure 7B). This indicates the activity of

442

bacterial amino peptidases and carboxy peptidases cleaving amino acids from the

443

individual N- or C-terminus of peptides. Although it is known from literature that

444

bitter peptides are generated mainly from breakdown of αs1- and ß-casein, to the

445

best of our knowledge κ-casein has been identified in the present study for the first

446

time as a source for the generation of bitter peptides (Figure 7C). In order to

447

elucidate the taste contribution of the peptides identified, the human bitter

448

recognition thresholds were determined in the following.

449

Sensory Analysis of Bitter Peptides. Prior to sensory analysis, the purity of

450

the synthesized reference peptides were checked by RP-HPLC, UPLC-ToF-MS and

451

quantitative 1H-NMR53 to be above 98%. In order to determine their bitter taste



452

threshold concentration, the target peptides were evaluated in aqueous solutions

453

(pH 4.6) by means of the three-alternative forced-choice test in ascending stimulus

454

concentrations up to a maximum concentration of 2000 µmol/L. The bitter taste

455

thresholds determined ranged from 30 (ARHPHPHLSFM) to 690 µmol/L found for

456

IQKEDVPS (Table 2). Among all peptides, the κ-casein-derived ARHPHPHLSFM

457

(κ-CN96-106; 30 µmol/L), the αs1-casein-derived peptides VFGKEKVNEL (αs1-CN31-40;

458

110 µmol/L) and DIKQM (αs1-CN56-60; 60 µmol/L) and the β-casein-derived peptides

459

MAPKHKEMPFPKYPVEPF (β–CN102-119; 90 µmol/L) and LHLPLPLL (β–CN133-140;

460

110 µmol/L) were found with rather low bitter taste thresholds. In contrast, the

461

peptides EIVPNS[phos]VEQK (αs1-CN70-78) and INTIASGEPT (κ–CN122-131) did not

462

exhibit any bitter taste up to the tested concentration of 2000 µmol/L.

463

Quantitation and Dose/Activity Considerations of Bitter Peptides in Fresh

464

Cheese. To investigate the sensory contribution of the identified peptides to the

465

bitter taste of fresh cheese, first, the peptides were quantitated by means of

466

UHPLC-MS/MS-MRM using external calibration with the purified reference peptides.

467

The concentration of the target peptides ranged from 0.2 (IQKEDVPS in tCC9) up to

468

86.8 µmol/kg determined for MAPKHKEMPFPKYPVEPF in aCC12 with the

469

peptides MAPKHKEMPFPKYPVEPF, TQTPVVVPPFLQPE, ARHPHPHLSFM, and

470

VFGKEKVNEL found at high levels (6.6 to 86.8 µmol/kg) in all three cheese

471

samples (Table 3). Comparison of FC9 with the alternatively produced cheese

472

aFC9 revealed increased concentrations of all candidate bitter peptides in the latter

473

sample, e.g. AIPPKKNQDKTEIPTINTIASGEPT, VLPVPQ, INTIASGEPT were 6.7,

474

4.9, and 4.3 times increased in sample aFC9. Next to the manufacturing process,

475

also the protein content of the cheese affected the concentrations of the bitter

476

peptides. The generation of some peptides was favoured with increased protein


Page 20 of 46

Page 21 of 46


477

content

of

the

cheese,

e.g.

MAPKHKEMPFPKYPVEPF

(β–CN102-119),

478

AIPPKKNQDKTEIPTINTIASGEPT (κ–CN107-131), INTIASGEPT (κ–CN122-131), and

479

KVLPVPQKAVPYPQ (β-CN169-182) increased ~3 times and DIKQM (αs1-CN56-60),

480

ARHPHPHLSFM (κ–CN96-106), IQKEDVPS (αs1-CN81-88) and YQQKPVAL (κ–CN43-

481

50)

482

some other peptides were only marginally affected or even decreased slightly with

483

increasing protein content, such as, LHLPLP (β–CN133-138), VFGKEKVNEL (αs1-

484

CN31-40), EIVPNS[phos]VEQK (αs1-CN70-78), VLPVPQ (β–CN170-175) and HLPLPLLQ

485

(β–CN134-141), respectively.

~2 times when raising the protein content from 9 to 12% (Table 3). In contrary,

486

The dose-over-threshold (DoT) factor was then calculated for each peptide as

487

the ratio of the concentration of a target peptide in fresh cheese and its bitter taste

488

threshold concentration54, 60 (Table 3). A primary bitter taste contribution was found

489

for MAPKHKEMPFPKYPVEPF and ARHPHPHLSFM with DoT-factors of 0.1 and

490

0.2 in FC9, 0.3 and 0.5 in aFC9, and 1.0 and 1.0 on aFC12, respectively. These

491

data are well in line with the perceived increase in bitter taste from samples FC9

492

over aFC9 to aFC12 (Table 1). In comparison, the peptides VFGKEKVNEL, DIKQM

493

and TQTPVVVPPFLQPE were found in samples aFC9 and aFC12 with DoT-factors

494

of 0.1. As bitter compounds of the same chemical class were reported to co-activate

495

the same bitter taste receptors and mixtures of such agonists found to additively

496

increase the perceived bitterness61, such sub-threshold peptides (0.1≤ DoT≤1.0)

497

may contribute to the overall bitter taste perception of the cheese samples aFC9

498

and aFC12, respectively.

499

Sensomics

Mapping

of

Bitter

Peptides

during

Fresh

Cheese

500

Manufacturing. In order to gain a first insight into which manufacturing step is

501

responsible for the formation of the identified peptides, samples were drawn from



Page 22 of 46

502

standardized fat-free milk and each intermediary product throughout processing of

503

FC9 and aFC9. For the process currently used in industry (FC9), the standardized

504

milk is thermally treated, the caseins are precipitated by adding rennet and starter

505

cultures, followed by separation of the acidic whey by microfiltration (Figure 8A).

506

The alternative process (aFC9) uses the standardized milk which is first

507

concentrated up to a desired protein content by means of microfiltration to afford the

508

sweet whey and the retentate which is then fermented.23 The target peptides were

509

then quantitatively analyzed by LC-MS/MS-MRM and the data summarized in a

510

heatmap, in which the quantitative data in the respective column are scaled to

511

visualized differences within the manufacturing steps (Figure 8B). In the milk

512

samples, only two peptides, namely IQKEDVPS and HLPLPLLQ, were found in very

513

low concentrations of 0.02 µmol/kg. Independent on the manufacturing process,

514

one hour after the addition of rennet the bitter peptide generation seem not to be

515

affected. In comparison, the addition of Lactococcus lactis ssp. as starter culture

516

initiated the spontaneous generation of bitter peptides, e.g. KVLPVPQKAVPYPQ (in

517

FC9) and VLPVPQ (in aFC9) were released in concentrations of 5.5 and 21.1

518

µmol/kg, respectively. While KVLPVPQKAVPYPQ in the FC9 process then

519

decreased to 2.9 µmol/kg with increasing fermentation time to 11 h, the peptides

520

MAPKHKEMPFPKYPVEPF,

521

EIVPNS[phos]VEQK increased from 3.5, 0.3, 0.1, and 0.01 after adding the starter

522

cultures to 6.9, 0.9, 0.6, and 0.1 µmol/kg, respectively, after 11 h of fermentation. In

523

the aFC9 process, fermentation induced a drastic increase of all target peptides,

524

some of which decreased again after running through a maximum after 2 h

525

(KVLPVPQKAVPYPQ), 11 h (VLPVPQ), 13 h (LHLPLP, YQQKPVAL). Taking all

526

these data into account, the different manufacturing processes as well as the

LHLPLPLL,

VAPFPEVFGKE,


and

Page 23 of 46


527

addition of the starter culture were concluded to have the highest impact on bitter

528

peptide generation.

529

The new “sensoproteomics” approach presented here enabled the

530

successful identification of bitter-tasting key peptides in fresh cheese without the

531

laboratory intensive activity-guided fractionation aimed at resolving extracts into the

532

individual taste-active peptides prior to identification. The combination of crude

533

MPLC-fractionation combined with the taste dilution analysis, followed by UPLC-

534

MS/MS-based screening of the most taste active subfractions for the entire

535

repertoire of literature-known candidate peptides using in silico computed SRM

536

methods, the verification of the target peptides by synthetic references, and, finally,

537

taste threshold analysis, accurate quantitation and dose/activity considerations

538

open a new avenue towards an accelerated identification of taste-active peptides in

539

fermented foods and to follow the evolution of these peptides throughout food

540

manufacturing processes.

541 542

ACKNOWLEDGMENT

543

This IGF Project of the FEI is supported via AiF within the program for

544

promoting the Industrial Collective Research (IGF) of the German Ministry of

545

Economic Affairs and Energy (BMWi), based on a resolution of the German

546

Parliament. Furthermore, we thank R. Hammerl for the acquisition of the

547

quantitative NMR data.

548 549 550

SUPPORTING INFORMATION AVAILABLE



551

MS parameter, peptide data and concentrations of peptides in intermediates

552

throughout the fresh cheese manufacturing process are available free of charge via

553

the Internet at http://pubs.acs.org.

554 555 556


Page 24 of 46

Page 25 of 46


557

LITERATURE CITED

558

(1) Dunkel, A.; Steinhaus, M.; Kotthoff, M.; Nowak, B.; Krautwurst, D.; Schieberle,

559

P.; Hofmann, T., Nature’s chemical signatures in human olfaction: a foodborne

560

perspective for future biotechnology. Angew. Chem. Int. Edit. 2014, 53, 7124-7143.

561

(2) Frank, O.; Ottinger, H.; Hofmann, T., Characterization of an intense bitter-tasting

562

1 H, 4 H-quinolizinium-7-olate by application of the taste dilution analysis, a novel

563

bioassay for the screening and identification of taste-active compounds in foods. J.

564

Agric. Food Chem. 2001, 49, 231-238.

565

(3) Toelstede, S.; Hofmann, T., Sensomics mapping and identification of the key

566

bitter metabolites in Gouda cheese. J. Agric. Food Chem. 2008, 56, 2795-2804.

567

(4) Warmke, R.; Belitz, H.-D.; Grosch, W., Evaluation of taste compounds of Swiss

568

cheese (Emmentaler). Z. Lebensm. Unters. For. 1996, 203, 230-235.

569

(5) Singh, T. K.; Fox, P. F.; Healy, Á., Isolation and identification of further peptides

570

in the diafiltration retentate of the water-soluble fraction of Cheddar cheese. J.

571

Dairy Res. 1997, 64, 433-443.

572

(6) Engel, E.; Septier, C.; Leconte, N.; Salles, C.; Le Quere, J.-L., Determination of

573

taste-active compounds of a bitter Camembert cheese by omission tests. J. Dairy

574

Res. 2001, 68, 675-688.

575

(7) Fallico, V.; McSweeney, P. L. H.; Horne, J.; Pediliggieri, C.; Hannon, J. A.;

576

Carpino, S.; Licitra, G., Evaluation of bitterness in Ragusano cheese. J. Dairy Sci.

577

2005, 88, 1288-1300.

578

(8) Liu, X.; Jiang, D.; Peterson, D. G., Identification of bitter peptides in whey protein

579

hydrolysate. J. Agric. Food Chem. 2013, 62, 5719-5725.



580

(9) Noguchi, M.; Arai, S.; Yamashita, M.; Kato, H.; Fujimaki, M., Isolation and

581

identification of acidic oligopeptides occurring in a flavor potentiating fraction from a

582

fish protein hydrolysate. J. Agric. Food Chem. 1975, 23, 49-53.

583

(10) Dang, Y.; Gao, X.; Ma, F.; Wu, X., Comparison of umami taste peptides in

584

water-soluble extractions of Jinhua and Parma hams. LWT-Food Sci. Technol.

585

2015, 60, 1179-1186.

586

(11) Schlichtherle-Cerny, H.; Amadò, R., Analysis of taste-active compounds in an

587

enzymatic hydrolysate of deamidated wheat gluten. J. Agric. Food Chem. 2002, 50,

588

1515-1522.

589

(12) Toelstede, S.; Dunkel, A.; Hofmann, T., A series of kokumi peptides impart the

590

long-lasting mouthfulness of matured Gouda cheese. J. Agric. Food Chem. 2009,

591

57, 1440-1448.

592

(13) Hillmann, H.; Behr, J. r.; Ehrmann, M. A.; Vogel, R. F.; Hofmann, T., Formation

593

of kokumi-enhancing γ-glutamyl dipeptides in Parmesan cheese by means of γ-

594

glutamyltransferase activity and stable isotope double-labeling studies. J. Agric.

595

Food Chem. 2016, 64, 1784-1793.

596

(14) Toelstede, S.; Hofmann, T., Kokumi-active glutamyl peptides in cheeses and

597

their biogeneration by Penicillium roquefortii. J. Agric. Food Chem. 2009, 57, 3738-

598

3748.

599

(15) Schindler, A.; Dunkel, A.; Stähler, F.; Backes, M.; Ley, J.; Meyerhof, W.;

600

Hofmann, T., Discovery of salt taste enhancing arginyl dipeptides in protein digests

601

and fermented fish sauces by means of a sensomics approach. J. Agric. Food

602

Chem. 2011, 59, 12578-12588.


Page 26 of 46

Page 27 of 46


603

(16) Harth, L.; Krah, U.; Linke, D.; Dunkel, A.; Hofmann, T.; Berger, R. G., Salt taste

604

enhancing l-arginyl dipeptides from casein and lysozyme released by peptidases of

605

basidiomycota. J. Agric. Food Chem. 2016, 66, 2344-2353.

606

(17) Baum, F.; Fedorova, M.; Ebner, J.; Hoffmann, R.; Pischetsrieder, M., Analysis

607

of the endogenous peptide profile of milk: identification of 248 mainly casein-derived

608

peptides. J. Proteome Res. 2013, 12, 5447-5462.

609

(18) Ebner, J.; Arslan, A. A.; Fedorova, M.; Hoffmann, R.; Küçükçetin, A.;

610

Pischetsrieder, M., Peptide profiling of bovine kefir reveals 236 unique peptides

611

released from caseins during its production by starter culture or kefir grains. J.

612

Proteomics 2015, 117, 41-57.

613

(19) Jin, Y.; Yu, Y.; Qi, Y.; Wang, F.; Yan, J.; Zou, H., Peptide profiling and the

614

bioactivity character of yogurt in the simulated gastrointestinal digestion. J.

615

Proteomics 2016, 141, 24-46.

616

(20) Lange, V.; Picotti, P.; Domon, B.; Aebersold, R., Selected reaction monitoring

617

for quantitative proteomics: a tutorial. Mol.Sys. Biol. 2008, 4, 222.

618

(21) MacLean, B.; Tomazela, D. M.; Shulman, N.; Chambers, M.; Finney, G. L.;

619

Frewen, B.; Kern, R.; Tabb, D. L.; Liebler, D. C.; MacCoss, M. J., Skyline: an open

620

source

621

experiments. Bioinformatics 2010, 26, 966-968.

622

(22) Siso, M. G., The biotechnological utilization of cheese whey: a review.

623

Bioresource Technol. 1996, 57, 1-11.

624

(23) Schäfer, J.; Hofmann, T.; Hinrichs, J., Processing und bitterer Geschmack von

625

Frischkäse

626

Mikrofiltration. DMW - Die Milchwirtschaft 2015, 6, 722-726.

document

aus

editor

for

gesäuerter

creating

and

Magermilch

analyzing

und

targeted

proteomics

Magermilchkonzentrat


durch


627

(24) Scharbert, S.; Holzmann, N.; Hofmann, T., Identification of the astringent taste

628

compounds in black tea infusions by combining instrumental analysis and human

629

bioresponse. J. Agric. Food Chem. 2004, 52, 3498-3508.

630

(25) Stark, T.; Bareuther, S.; Hofmann, T., Sensory-guided decomposition of

631

roasted cocoa nibs (Theobroma cacao) and structure determination of taste-active

632

polyphenols. J. Agric. Food Chem. 2005, 53, 5407-5418.

633

(26) MacLean, B.; Tomazela, D. M.; Abbatiello, S. E.; Zhang, S.; Whiteaker, J. R.;

634

Paulovich, A. G.; Carr, S. A.; MacCoss, M. J., Effect of collision energy optimization

635

on the measurement of peptides by selected reaction monitoring (SRM) mass

636

spectrometry. Anal. Chem. 2010, 82, 10116.

637

(27) Korhonen, H.; Pihlanto, A., Bioactive peptides: production and functionality. Int.

638

Dairy J. 2006, 16, 945-960.

639

(28) Silva, S. V.; Malcata, F. X., Caseins as source of bioactive peptides. Int. Dairy

640

J. 2005, 15, 1-15.

641

(29) Donkor, O. N.; Henriksson, A.; Singh, T.; Vasiljevic, T.; Shah, N. P., ACE-

642

inhibitory activity of probiotic yoghurt. Int. Dairy J. 2007, 17, 1321-1331.

643

(30) Schieber, A.; Brückner, H., Characterization of oligo-and polypeptides isolated

644

from yoghurt. Eur. Food Res. Technol. 2000, 210, 310-313.

645

(31) Sadat, L.; Cakir-Kiefer, C.; N’Negue, M.-A.; Gaillard, J.-L.; Girardet, J.-M.;

646

Miclo, L., Isolation and identification of antioxidative peptides from bovine α-

647

lactalbumin. Int. Dairy J. 2011, 21, 214-221.

648

(32) FitzGerald, R. J.; Meisel, H., Milk protein-derived peptide inhibitors of

649

angiotensin-I-converting enzyme. British Journal of Nutrition 2000, 84, 33-37.


Page 28 of 46

Page 29 of 46


650

(33) Farvin, K. S.; Baron, C. P.; Nielsen, N. S.; Otte, J.; Jacobsen, C., Antioxidant

651

activity of yoghurt peptides: Part 2–Characterisation of peptide fractions. Food

652

Chem. 2010, 123, 1090-1097.

653

(34) Pihlanto-Leppälä, A.; Rokka, T.; Korhonen, H., Angiotensin I converting

654

enzyme inhibitory peptides derived from bovine milk proteins. Int. Dairy J. 1998, 8,

655

325-331.

656

(35) Capriotti, A. L.; Cavaliere, C.; Piovesana, S.; Samperi, R.; Laganà, A., Recent

657

trends in the analysis of bioactive peptides in milk and dairy products. Anal. Bioanal.

658

Chem. 2016, 408, 2677-2685.

659

(36) Saito, T., Antihypertensive peptides derived from bovine casein and whey

660

proteins. In Bioactive components of milk, Springer: 2008; pp 295-317.

661

(37) Alli, I.; Okoniewska, M.; Gibbs, B.; Konishi, Y., Identification of peptides in

662

Cheddar cheese by electrospray ionization mass spectrometry. Int. Dairy J. 1998, 8,

663

643-649.

664

(38) Abubakar, A.; Saito, T.; Kitazawa, H.; Kawai, Y.; Itoh, T., Structural analysis of

665

new antihypertensive peptides derived from cheese whey protein by proteinase K

666

digestion. J. Dairy Sci. 1998, 81, 3131-3138.

667

(39) Saito, T.; Nakamura, T.; Kitazawa, H.; Kawai, Y.; Itoh, T., Isolation and

668

structural analysis of antihypertensive peptides that exist naturally in Gouda cheese.

669

J. Dairy Sci. 2000, 83, 1434-1440.

670

(40) Haileselassie, S.; Lee, B.; Gibbs, B., Purification and identification of potentially

671

bioactive peptides from enzyme-modified cheese. J. Dairy Sci. 1999, 82, 1612-

672

1617.

673

(41) Lee, K.; Warthesen, J., Mobile Phases in Reverse‐Phase HPLC for the

674

Determination of Bitter Peptides in Cheese. J. Food Sci. 1996, 61, 291-294.



675

(42) Meisel, H.; Bockelmann, W., Bioactive peptides encrypted in milk proteins:

676

proteolytic activation and thropho-functional properties. A. Van Leeuw. J. Microb.

677

1999, 76, 207-215.

678

(43) Michaelidou, A.; Alichanidis, E.; Urlaub, H.; Polychroniadou, A.; Zerfiridis, G.

679

K., Isolation and identification of some major water-soluble peptides in Feta cheese.

680

J. Dairy Sci. 1998, 81, 3109-3116.

681

(44) Pihlanto-Leppälä, A., Bioactive peptides derived from bovine whey proteins:

682

opioid and ace-inhibitory peptides. Trends Food Sci. Technol. 2000, 11, 347-356.

683

(45) Fitzgerald, R. J.; Murray, B. A., Bioactive peptides and lactic fermentations. Int.

684

J. Dairy Technol. 2006, 59, 118-125.

685

(46) Rizzello, C.; Losito, I.; Gobbetti, M.; Carbonara, T.; De Bari, M.; Zambonin, P.,

686

Antibacterial activities of peptides from the water-soluble extracts of Italian cheese

687

varieties. J. Dairy Sci. 2005, 88, 2348-2360.

688

(47) Gómez-Ruiz, J. Á.; Ramos, M.; Recio, I., Identification and formation of

689

angiotensin-converting enzyme-inhibitory peptides in Manchego cheese by high-

690

performance liquid chromatography–tandem mass spectrometry. J. Chromatogr., A

691

2004, 1054, 269-277.

692

(48) Gouldsworthy, A. M.; Leaver, J.; Banks, J. M., Application of a mass

693

spectrometry sequencing technique for identifying peptides present in Cheddar

694

cheese. Int. Dairy J. 1996, 6, 781-790.

695

(49) Silva, R. A.; Bezerra, V. S.; Pimentel, M. d. C. B.; Porto, A. L. F.; Cavalcanti, M.

696

T. H.; José Luiz Filho, L., Proteomic and peptidomic profiling of Brazilian artisanal

697

‘Coalho’cheese. J. Sci. Food Agric. 2016.


Page 30 of 46

Page 31 of 46


698

(50) Gagnaire, V.; Mollé, D.; Herrouin, M.; Léonil, J., Peptides identified during

699

Emmental cheese ripening: origin and proteolytic systems involved. J. Agric. Food

700

Chem. 2001, 49, 4402-4413.

701

(51) Hernández-Ledesma, B.; Amigo, L.; Ramos, M.; Recio, I., Application of high-

702

performance liquid chromatography–tandem mass spectrometry to the identification

703

of biologically active peptides produced by milk fermentation and simulated

704

gastrointestinal digestion. J. Chromatogr., A 2004, 1049, 107-114.

705

(52) Hernández‐Ledesma, B.; Miralles, B.; Amigo, L.; Ramos, M.; Recio, I.,

706

Identification of antioxidant and ACE‐inhibitory peptides in fermented milk. J. Sci.

707

Food Agric. 2005, 85, 1041-1048.

708

(53) Frank, O.; Kreissl, J. K.; Daschner, A.; Hofmann, T., Accurate Determination of

709

Reference Materials and Natural Isolates by Means of Quantitative 1H NMR

710

Spectroscopy. J. Agric. Food Chem. 2014, 62, 2506-2515.

711

(54) Hillmann, H.; Hofmann, T., Quantitation of key tastants and re-engineering the

712

taste of Parmesan cheese. J. Agric. Food Chem. 2016, 64, 1794-1805.

713

(55) Sousa, M.; Ardö, Y.; McSweeney, P., Advances in the study of proteolysis

714

during cheese ripening. Int. Dairy J. 2001, 11, 327-345.

715

(56) Hurley, M.; Larsen, L.; Kelly, A.; McSweeney, P., The milk acid proteinase

716

cathepsin D: a review. Int. Dairy J. 2000, 10, 673-681.

717

(57) Le Bars, D.; Gripon, J., Hydrolysis of αs1-casein by bovine plasmin. Le Lait

718

1993, 73, 337-344.

719

(58) Kunji, E. R.; Mierau, I.; Hagting, A.; Poolman, B.; Konings, W. N., The

720

proteotytic systems of lactic acid bacteria. A. Van Leeuw. J. Microb. 1996, 70, 187-

721

221.



722

(59) Ferranti, P.; Itolli, E.; Barone, F.; Malorni, A.; Garro, G.; Laezza, P.; Chianese,

723

L.; Migliaccio, F.; Stingo, V.; Addeo, F., Combined high resolution chromatographic

724

techniques (FPLC and HPLC) and mass spectrometry-based identification of

725

peptides and proteins in Grana Padano cheese. Le lait 1997, 77, 683-697.

726

(60) Toelstede, S.; Hofmann, T., Quantitative studies and taste re-engineering

727

experiments toward the decoding of the nonvolatile sensometabolome of Gouda

728

cheese. J. Agric. Food Chem. 2008, 56, 5299-5307.

729

(61) Intelmann, D.; Batram, C.; Kuhn, C.; Haseleu, G.; Meyerhof, W.; Hofmann, T.,

730

Three TAS2R bitter taste receptors mediate the psychophysical responses to bitter

731

compounds of hops (Humulus lupulus L.) and beer. Chemosens. Percept. 2009, 2,

732

118-132.

733

(62) Considine, T.; Geary, S.; Kelly, A.; McSweeney, P., Proteolytic specificity of

734

cathepsin G on bovine α s1-and β-caseins. Food Chem. 2002, 76, 59-67.

735

(63) Considine, T.; Healy, A.; Kelly, A.; McSweeney, P., Hydrolysis of bovine

736

caseins by cathepsin B, a cysteine proteinase indigenous to milk. Int. Dairy J. 2004,

737

14, 117-124.

738 739


Page 32 of 46

Page 33 of 46

740


Figure Captions

741 742

Figure 1. Literature meta-analysis on cleavage peptides from bovine caseins with

743

(A) number of peptides previously identified (taken from literature

744

references) and (B) the distribution of amino acid residues of unique

745

peptides.

746 747

Figure 2. (A) RP 18-MPLC-ELSD chromatogram of the high-molecular weight

748

(HMW)-fraction isolated from fresh cheese sample FC12 and UPLC-

749

TOF-MS analysis of the bitter-tasting MPLC fractions M2 (B), M3 (C),

750

and M4 (D).

751 752 753

Figure 3. Experimental workflow of the “sensoproteomics” approach used for the identification of bitter peptides in fresh cheese.

754 755

Figure 4. Mass transition chromatograms of doubly and triply charged ions for (A)

756

FALPQYLKTVYQHQ as an example for a candidate peptide not existing

757

in fresh cheese and (B) for ARHPHPHLSFM as an example for a

758

candidate peptide detected in fresh cheese.

759 760

Figure 5. Molecular weight to fold-change diagram showing the 340 candidate

761

peptides detected in the water-soluble extract (WSE) of the fresh cheese

762

sample aFC9 (black points). For further analysis, 23 peptides (red points)

763

were considered that were located in the bitter tasting MPLC fractions 33 ACS Paragon Plus Environment


764

with a signal to noise ratio of ≥ 10 and with a fold-change of ≥ 3 between

765

samples FC9 and aFC9.

766 767

Figure 6. Phylogenetic tree of peptide sequences identified in literature with

768

highlighted bitter peptides generated from bovine proteins αs1- (red), β-

769

(green) and κ-casein (purple) in fresh cheese. No bitter peptides were

770

found to originate from αs2-casein (blue).

771 772

Figure 7. Amino acid sequence of bovine αs1- (A), β- (B) and κ-(C)-casein and

773

assignment of the identified bitter peptides (black arrows) as well as

774

candidate cleavage sites of the endogenous milk enzymes plasmin

775

(pentagon), cathepsin B, G and D (triangles), and the rennet enzyme

776

chymosin (square) and the lactococcus lactis ssp. protease (cycle)

777

(adapted from ref 17, 55-58, 62, 63).

778 779

Figure 8. (A) Process flow for the processing of fresh cheese using the currently

780

used fermentation-concentration (left) and the alternative concentration-

781

fermentation process (right), and (B) scaled quantitative mapping of the

782

evolution of bitter peptides throughout the processes. The dendrogram is

783

based on an agglomerative linkage algorithm.

784


Page 34 of 46

Page 35 of 46


785

Table 1. Taste Profile of Fresh cheese, Water-Soluble Extract (WSE), Methanolic

786

Extract (ME), and the Low-Molecular Weight (LMW) and High-Molecular Weight

787

Fraction (HMW) Prepared from WSE

intensity2 of the taste qualities sample

1

bitter

sour

FC9

1.6 ± 0.4

2.4 ± 0.4

aFC9

2.0 ± 0.5

2.7 ± 0.4

aFC12

2.6 ± 0.4

2.3 ± 0.3

WSE

2.1 ± 0.5

3.6 ± 0.5

ME

0.6 ± 0.3

0.4 ± 0.2

LMW

0.6 ± 0.1

3.4 ± 0.3

HMW

3.1 ± 0.4

1.0 ± 0.2

fresh cheese

extracts made from aFC12

788

1

789

content prepared by the fermentation-concentration process (FC9), two fat-free

790

fresh cheeses with 9 (aFC9) and 12% protein (aFC12), both made by the alternative

791

concentration-fermentation process, and the water soluble extract (WSE), methanol

792

extract (ME), the low-molecular weight fraction (LMW; ≤ 1 kDa), and the high-

Samples used for sensory analysis were a fat-free fresh cheese with 9% protein

793

molecular weight fraction (HMW; > 1 kDa) isolated from the cheese sample aFC12.

794

2

795

is given as the mean of triplicates.

Intensity was rated on a scale from 0 (not detectable) to 5 (strongly detectable) and

796 797 798 799



Page 36 of 46

800

Table 2. Molecular Weight, Casein Sequence Alignment, and Bitter Taste

801

Thresholds (BTC) of Peptides Identified in Fresh Cheese sequence

MW

alignment2

[Da]

VAPFPEVFGKE

αs1-CN (25-35)

1218.64

4.7

570

VFGKEKVNEL

αs1-CN (31-40)

1161.65

2.9

110

DIKQM

αs1-CN (56-60)

633.32

5

60

EIVPNS[phos]VEQK

αs1-CN (70-78)

1221.57

4

< 1200

IQKEDVPS

αs1-CN (81-88)

914.48

3.1

690

MAPKHKEMPFPKYPVEPF

β–CN (102-119)

2172.10

3.4

90

LHLPLP

β–CN (133-138)

688.43

3.5

340

LHLPLPLL

β–CN (133-140)

914.60

3

110

HLPLPLLQ

β–CN (134-141)

929.58

3.7

440

VLPVPQ

β–CN (170-175)

651.40

4

310

TQTPVVVPPFLQPE

β–CN (78-91)

1550.84

3

280

KVLPVPQKAVPYPQ

β-CN (169-182)

1562.93

3.1

140

AIPPKKNQDKTEIPTINTIASGEPT

κ–CN (107-131)

2663.43

11.9

290

INTIASGEPT

κ–CN (122-131)

1001.51

3

< 1500

FFSDKIAK

κ–CN (17-24)

954.52

5.8

570

YQQKPVAL

κ–CN (43-50)

945.54

5.3

500

ARHPHPHLSFM

κ-CN (96-106)

1328.67

20.5

30

peptides sequence1

BTC

FC3

[µmol/L]4

802

1

Sequences given in one-letter code. 2αs1-CN variant B; β-CN variant A2, κ-CN

803

variant A. 3Fold-change of a peptide is calculated by the quotient of the area in the

804

bitter-tasting (aFC9) and less bitter-tasting fresh cheese (FC9). 4Taste threshold

805

concentrations were determined by means of a 3-AFC-test in bottled water (pH 4.6).

806 36 ACS Paragon Plus Environment

Page 37 of 46


807

Table 3. Bitter Taste Thresholds, Concentrations and Dose-over-Threshold (DoT)

808

Factors of Candidate Marker Peptides in the Investigated Fresh Cheese Samples conc. [µmol/kg]2 (DoT-factor3) in

1

peptides sequence

FC9

aFC9

aFC12

AIPPKKNQDKTEIPTINTIASGEPT

1.3 (

Sensoproteomics: A New Approach for the Identification of Taste

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