For Peer ReviewA 2D DATABASE OF MILK FROM DIFFERENT SPECIES

Journal: PROTEOMICS

Manuscript ID: draft

Manuscript Type: Short Communication

Date Submitted by the Author:

n/a

Complete List of Authors: Roncada, Paola; Italian Experimental Institute L. Spallanzani, proteomic division; University of Milan, Department of Veterinary Clinical Sciences; Fortin, Riccardo; University of Milan, Department of Veterinary Clinical Sciences Carta, Franco; Porto Conte Ricerche, Laboratorio di Proteomica Cuccuru, Maria Antonietta; Italian Experimental Institute L. Spallanzani, proteomic division; Porto Conte Ricerche, Laboratorio di proteomica; Turrini, Franco; University of Turin, Department of Genetics, Biology and Biochemistry Greppi, Gian Franco; University of Milan, Department of Veterinary Clinical Sciences; Lea Biotech srl; ;

Key Words:Two-dimensional difference gel electrophoresis, Matrix-assisted laser desorption/ionization time of flight mass spectrometry, Milk, Databases

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A 2D DATABASE OF MILK FROM DIFFERENT SPECIES

“SHORT COMMUNICATION”

Paola Roncada1,2, Riccardo Fortin2, Franco Carta3, Maria Antonietta Cuccuru1,3 ,Franco Turrini4, Gian Franco Greppi2,5

1 Italian Experimental Institute “L. Spallanzani”, Milan, Italy; 2 Department of Veterinary Clinical Sciences, University of Milan, Italy; 3 Porto Conte Ricerche, Alghero, (SS); 4 Department of Genetics, Biology and Biochemistry, University of Turin, Italy; 5 Lea Biotech srl, Milan, Italy Running title: 2D database of milk Correspondig author: Dr. Paola Roncada Istituto Sperimentale Italiano “Lazzaro Spallanzani”, proteomic division, viale Forlanini, 23 - 20134 Milano FAX: +39 02 76111108; e-mail: roncadap@unimi.it Standard abbreviations: 2-DE = two dimensional electrophoresis. Keywords: Two-dimensional gel electrophoresis; Matrix-assisted laser desorption/ionization time of flight mass spectrometry; Milk; Databases.

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ABSTRACT Milk is an excellent protein food that provides essential amino acids, calcium, phosphate, casein, lipids and organic nitrogen for humans and animals of all ages. Caseins account for 80% of the total protein in bovine milk, and exist primarily as calcium phosphate stabilized micelle complexes. Caseins are a heterogeneous family of proteins predominated by αs1-, αs2-, β-, and κ-caseins. Individual casein proteins are small molecules with a molecular mass of 20 to 25 kDa, and primary amino acid sequences that are high in proline content. Milk contains numerous minor proteins found mainly in the whey and milk fat globule membrane fractions. These minor proteins do not have significant functional properties like casein and whey fractions, but many have been identified as having physiological effects. The minor proteins include enzymes, metal-binding proteins, enzyme inhibitors, vitamin binding proteins, and numerous growth factors. Several minor dairy proteins have been included as bioactive ingredients in nutraceutical products. Aim of this study is the construction of a two dimensional database of milk of different species: human, cow, water buffalo, horse, donkey, goat and sheep. The identification of proteins has been done by means of MALDI TOF mass spectrometry followed by peptide mass fingerprint.

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Although in the last few years mass spectrometry approach is becoming more and more powerful and popular in analysing complex proteomes as a stand alone technique, high resolution 2-D electrophoresis (2-DE) using IPGs coupled with MS (MALDI-TOF MS for peptide mass fingerprinting or tandem MS for peptide sequencing) is still considered the cornerstone in modern proteomics. These techniques are able to give an output of a tremendous mass of data, especially when used for comparison of many biological entities (i.e. clinical discovery of novel biomarkers, comparative proteomic studies, etc). The most promising aspect of this type of “data mining” is the efforts currently undergoing to build Proteome Database Systems (PDS) which aspire to become a comprehensive, organic and web accessible way to submit and keep track of 2-DE and MS data. An example of PDS is the Proteome Database System for Microbial Research accessible through the world wide web at http://www.mpiib-berlin.mpg.de/2D-PAGE [1, 2]. Milk is the dietary basis for the newborn growth and development until weaning age is reached. Indeed one of the most important features which characterize each mammalian species is the administration of specific nutrients to the newborn through milk with a specific composition in order to satisfy specific needs. Milk from different species vary from each other for salt, carbohydrates and lipids contents, but most notably for protein quality and concentration. Up to date, no inter-species proteomic evaluation has been performed on milk proteins. Milk from animal origin is a major human nutrient worldwide; it is well known that proteolysis of caseins from human and ruminants’ milk (especially bovine) is source of a wide variety of potentially immunologically active peptides [3, 4, 5] which gives rise to the necessity of producing infant formulas of very controlled protein composition. Moreover, milk has been identified as a premier candidate as biological matrix for prognosis or diagnosis of several pathologies of the mammary gland like cancer in humans, mastitis [6, 7] and mycotoxicosis in animals. All this heterogeneity of aspects of interest concerning “milk science” leads to the necessity of developing a powerful tool suitable for discerning “at a glance” the diversity in protein composition of milk from different species or formulas and from different physio-pathological states. Nowadays this tool is surely the offspring of Proteomics and Bioinformatics. Aim of this work is the presentation of the building up of a PDS for milk proteins. Main goal of this project is the construction of a species-based comparative approach to describe the proteome of milk through 2-DE and eventually MS analysis. Typically proteomic analysis on milk proteins may be performed on whole defatted milk for studying casein composition [8, 9, 10, 11, 12, 13, 14] or on whey [15, 16, 17] or milk fat globules (MFGs) membrane proteins [18, 19, 20] for studying less abundant protein species. In order to start the building up of this milk database, 2-DE has been used

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for the separation of whole milk proteins from human, cow, water buffalo, horse, donkey, goat and sheep. Sample preparation and 2-DE All fresh milk were obtained from healthy individuals, collected at an appropriate time in order to have mature milk from every species and frozen at -20°C until used. Samples were centrifuged at 3000g for 15 minutes at 4°C and the fat layer was then carefully removed. 120 µl (~1200-1500 µgof protein for micropreparative gels) and 50 µl (~500-650 µg of protein for analytical gels) of human, horse and donkey milk and 90 µl (~1200-1500 µg of proteins for micropreparative gels) and 35 µl (~500-650 µg of proteins for analytical gels) of cow, water buffalo, sheep and goat milk were diluted up to 300 µl using a solution containing 7M urea, 2M thiourea, 4% CHAPS, 1% DTT and 1,6% Ampholine pH 4-6.5. After a short vortexing, cup-loading or ingel-rehydration method loading was performed overnight for a total of 20 h at 20°C using home made 11cm pH 4-7L IPG strips. After full incorporation of samples, IPG strips were transferred to a Manifold equipped IPGphor II (Amersham Biosciences); IEF was performed at 20°C and the protocol comprised a series of “step” increases in voltage as follows: 50V for 2h, 100V for 2h, 500V for 2h, 1000V for 3h, 3000V for 3h, 4000V for 3h, 5000V for 3h, 6000V for 2h and 8000V held constant until 65’000 V/h in total. After first dimension, IPG strips were equilibrated twice for 15 minutes under gentle agitation with a solution containing 6M urea, 2% SDS, 50 mM Tris-HCl pH 8,8 and 30% glycerol; 1% DTT was added to the first equilibration and 2.5% iodoacetamide and a trace of bromophenol blue to the second one. Second dimension was performed using homemade 10-15% acrylamide gradient vertical SDS slab gels (dimensions 170 x 160 x 1.5 mm) on a Ruby SE-600 electrophoresis system (Amersham Biosciences). IPG strips were put on top of SDS gels (poured up to 1 cm from the top of the plates) and sealed with 1.5 ml of a solution containing 0.5% low melting point agarose diluted in hot SDS running buffer (25 mM Tris-HCl pH 8.3, 192 mM glycine, 0.1% SDS). 10-220 kDa molecular weight markers from Invitrogen (15 µl on paper pads) were applied at one end of IPG strips. Second dimension protocol was: 15 mA/gel for 20 minutes, then 100 mA total until the bromophenol blue front line came out of the gels. Micropreparative gels were stained with colloidal Coomassie Brilliant Blue G-250 [21], while analytical gels were stained with silver nitrate as previously described [22]. Peptide preparation and MS analysis

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Coomassie blue-stained spots were excised from 2-DE gels and proteins were digested with trypsin. Pieces of gel were destained with several washes with 5 mM NH4HCO3/Acetonitrile (ACN) (50/50); and successively dried with pure ACN. The gel slices were rehydrated for 45 min at 4°C in 20 µl of a digestion buffer containing 10 ng/µl of trypsin and 5 mM NH4HCO3 [23]. Excess protease solution was removed and the volume adjusted with 5 mM NH4HCO3 to cover the gel slices. Digestion was allowed to proceed overnight at 37°C. The mass spectrometer used in this work was a Tofspec SE (Micromass, Manchester, UK) equipped with a delayed extraction unit. The laser wavelength was 337 nm and accelerating voltage was 20kV [24]. Peptide spectra were obtained in reflectron mode in the range 800-4000 Da. The peptide solution was loaded onto the MALDI target plate by mixing 1.5 µl of each solution with the same volume of a matrix solution, prepared dissolving 10 mg/ml alpha-cyano-4-hydroxycinnamic acid solution in 40% acetonitrile-0.1% trifluoroacetic acid (vol/vol) and allowed to dry. External calibration was done by using the fragment ions from the standard peptides, adrenocorticotropic hormone 18-39 and angiotensin I. Each mass spectrum was generated by accumulating data from 100-120 laser shots [25]. Database searches were done with the peptide masses against the SWISSPROT or the nonredundant NCBI database using the search programs Peptident [26] and ProFound [27] respectively. The following parameters were used in the searches: taxa Homo sapiens, Bos Taurus or other mammalian, protein molecular mass range from 10 to 100 kDa, trypsin digest, monoisotopic peptide masses, one or two missed cleavage by trypsin and a mass deviation of 500 ppm allowed in the SWISS-PROT or NCBI databases searches. Chemical modifications such as carbamidomethylation of cysteine were taken into consideration for the queries. In particular, to improve the quality of identification for bovine proteins, we found that Peptident is more suitable to identify secreted mature proteins as previously described [8]. Milk Proteome Database architecture Main software compositionThe real backbone of the entire software is Data Base containing all experiment informations. These informations are stored both manually or automatically from captured gel images and spots detection (see fig. 1). A Fuzzy / Neural Network structure can easily match captured informations between themselves or between standard, already stored, data. Data BaseThe Milk Proteome DataBase is implemented in SQL-SERVER software. Maximun informations number is codified and the values are stored in auxiliary tables, so user can immediately match

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different background situations of every particular test. Some tables are used to solve the one-to-many connections. So the entire DB structure (see fig. 2) is arranged in three kind of tables:

• Main tables • One-to-many connection tables • Decoding tables

Fuzzy and Neural NetworksFuzzy procedure structure. Data manipulation and Data matching are developed in FuzzyWorld software. This software is an application of SmallTalk Language and lets users work into his numerical data, translating crisp format in fuzzy format. All stored data have the function of learning the procedure, creating rules that will be used in new data evaluation. Inserting in the DB also standard data, procedure can match new data with standard data and already stored data having same or similar characteristics. From Fuzzy procedure to Data Base and vice versa. From Fuzzy procedure forms, users can open DB environment and directly analyze inserted data. Users can also implement with new data his DB, in order to give to Fuzzy procedure more informations for learning, and so be able to create new, most accurate, rules. Fuzzy procedure in spots detection Fuzzy criteria and methods are also used in spot detection (see next par.) as a powerful tool in defining spot boundary and morphological structure and in deciding whether a spot is interesting or not. Image captureA special software developed in SmallTalk language can capture the 2D gel images. Gels have to be scanned in .bmp format and the software can auto-detect all image spots. Initial setting The procedure starts storing in a file all informations about image characteristics: scale, minimum and maximum isoelectric points, markers for molecular weight, and so forth. All these informations have been stored in a DB table containing data about experiment characteristics. Automatic spot detection The spot detection begin outlining spot external boundary and, from this indications, an algorithm computes the spot centre. The procedure then can compute isoelectric point and molecular weight,

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area, angle and volume for every single spot. For spot detection, commercially available softwares like, for example, PDQuest (Bio-Rad), ImageMaster 2D Platinum (Amersham Biosciences) or freeware (TopSpot available at http://www.mpiib-berlin.mpg.de/2D-PAGE/download.html) are suitable for submission of data to the milk 2D database. Manual correction Users, pointing on the detected spots from captured images, can disable all non-wished spots, or activate particular detection for non-automatic detected spots. Data storage All data concerning detected spots are stored in the DB “Spot” table, relating to image captured:

• X and Y position of spot centre • Isoelectrical point • Molecular weight • Angle • Area of spot in pixels • Volume of spot (i.e. area per colour intensity)

Building up of the database Instruction of the Fuzzy and Neural Network Database started from 2-DE maps of whole milk from different species following the protocols described above (see fig. 3). Automated gel matching of 2-DE from whole milk of not closely related species has been found almost impossible due to the high variability in proteomic patterns. To explain better this issue, it may be noticed that phylogenetically related species (like horse and donkey, cow and water buffalo, goat and sheep) share a high degree of homology in milk protein patterns, while milk from different species (like human and bovine) are very different, as shown in figure 4. To overcome this difficulty of interpretation, we started a comparative study of milk using MS as a robust method of investigation. In table 1 are reported the identified milk proteins respectively of human and bovine whole milk. This kind of approach (2-DE and MALDI-TOF MS and eventually tandem MS for unclear or unidentified peptide mass fingerprints) has been chosen for starting instruction of the Fuzzy and Neural Network Database for what concerns whole milk; this will be extended also to 2-DE of MFGs, milk sera and casein fraction: this will be the starting point of a holistic characterization of milk proteins in regard to animal and human health and milk quality.

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CONCLUDING REMARKS The authors strongly encourage groups willing to submit their own 2D maps for the building up of the Milk Proteome DataBase to use, if possible, the very same 2-DE protocol described in Materials and Methods for whole milk (protocols for MFGs, milk sera and casein fraction will be published in a future paper). This will greatly lower inter-laboratory experimental variability and will enhance starting training of the Fuzzy procedure. ACKNOWLEDGEMENTS We would like to thank Alejandro F. Reimondo and Alfredo D’Angelo for the precious cooperation in the construction of the Data Base. This work was supported by FIRB APROLAT - Analisi proteomica del latte (MIUR); national coordinator Prof. Gian Franco Greppi.

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FIGURE AND TABLE LEGENDS Figure 1: Schematic overview of the informatics approach for milk protein research Figure 2: Schematic structure of the 2D Milk Proteome DataBase Figure 3: 2-DE of whole milk from different species. First dimension: IPG 4-7L; second

dimension SDS-PAGE 10-15%. Figure 4: 2-DE of human and cow whole milk. First dimension: IPG 4-7L; second dimension

SDS-PAGE 10-15%. Numbers of spots refer to identified proteins by MALDI-TOF MS reported in table 1.

Table 1: Identifications of proteins from fig. 4 by MALDI-TOF MS.

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REFERENCES [1] Pleissner K.P., Schmelzer P., Wehrl W., Jungblut P.R., Proteomics 2004, 4, 2987-2990. [2] Pleissner K.P., Eifert T., Buettner S., Schmidt F. et al., Proteomics 2004, 4, 1305-1313. [3] Ferranti P., Traisci M.V., Picariello G., Nasi A. et al., J. Dairy Res. 2004, 71, 74-87. [4] Bargeman G., Houwing J., Recio I., Koops G.H. et al., Biotechnol. Bioeng. 2002, 80, 599-609. [5] Goldfarb M., Proteomics 2001, 1, 721-725. [6] Chen P.W., Chen W.C., Mao F.C., J. Vet. Med. Sci. 2004, 66, 345-350. [7] Hogarth C.J., Fitzpatrick J.L., Nolan A.M., Young F.J. et al., Proteomics 2004, 4, 2094-2100. [8] Holland J.W., Deeth H.C., Alewood P.F., Proteomics 2004, 4, 743-752. [9] Mamone G., Caira S., Garro G., Nicolai A. et al., Electrophoresis 2003, 24, 2824-2837. [10] Miralles B., Leaver J., Ramos M., Amigo L., J. Chromatogr. A. 2003, 1007, 47-53. [11] Roncada P., Gaviraghi A., Liberatori S., Canas B. et al., Proteomics 2002, 2, 723-726. [12] Egito A.S., Miclo L., Lopez C., Adam A. et al., J. Dairy Sci. 2002, 85, 697-706. [13] Goldfarb M.F., Adv. Exp. Med. Biol. 2001, 501, 535-539. [14] Galvani M., Hamdan M., Righetti P.G., Rapid. Commun. Mass. Spectrom. 2001, 15, 258-264. [15] Yamada M., Murakami K., Wallingford J.C., Yuki Y., Electrophoresis 2002, 23, 1153-1160 [16] Miralles B., Bartolome B., Amigo L., Ramos M., J. Dairy Sci. 2000, 83, 2759-2765. [17] Herrouin M., Molle D., Fauquant J., Ballestra F. et al., J. Protein. Chem. 2000, 19, 105-115. [18] Fortunato D., Giuffrida M.G., Cavaletto M., Garoffo L.P. et al., Proteomics 2003, 3, 897-905. [19] Charlwood J, Hanrahan S, Tyldesley R, Langridge J. et al., Anal. Biochem. 2002, 301, 314-324 [20] Quaranta S., Giuffrida M.G., Cavaletto M., Giunta C. et al., Electrophoresis 2001, 22, 1810-1818. [21] Rabilloud T., Charmont S., Proteome Research: Two-Dimensional Gel Electrophoresis and Identification

Methods. Springer. 2000, 109-110. [22] Murakami K., Lagarde M., Yuki Y., Electrophoresis 1998, 19, 2521-2527. [23] Wang J.L., Wan J.H., Luo L., Xiong S.X. et al., Acta Biochim. Biophys. Sin. 2000, 32, 373-378. [24] Hamdan H., Righetti P.G., Mass Spectrom. Rev. 2002, 21, 287. [25] Fountoulakis M., Langen H., Anal. Biochem. 1997, 250, 153-156. [26] http://au.expasy.org/tools/peptident.html [27] http://129.85.19.192/profound_bin/WebProFound.exe?FORM=1

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Schematic overview of the informatics approach for milk protein research 282x211mm (72 x 72 DPI)

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Schematic structure of the 2D Milk Proteome DataBase 309x209mm (72 x 72 DPI)

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2-DE of whole milk from different species. First dimension: IPG 4-7L; second dimension SDS-PAGE 10-15%

243x282mm (72 x 72 DPI)

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2-DE of human and cow whole milk. First dimension: IPG 4-7L; second dimension SDS-PAGE 10-15%. Numbers of spots refer to identified proteins by MALDI-TOF MS reported in

table 1. 397x226mm (72 x 72 DPI)

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Protein NCBI /

Swissprot Acc. No.

pIcalc.

MW (kDa) Calc.

Spot number

(see fig. 4)

Matched Peptides

Sequence Coverage

(%)

HUMAN MILK

Poly-Ig receptor gi|514366 5.38 76.44 1 24 40

Serum Albumin gi|4389275 5.69 67.99 2 27 62 gi|4389275 5.69 67.99 3 19 52

Immunoglobulin alpha-1 heavy chain constant region gi|184749 6.06 38.41 4 14 51

Adipophilin fragment gi|34577059 6.34 48.27 5 20 57

Beta-casein gi|288098 5.52 25.29 6 5 29 gi|288098 5.52 25.29 7 5 29

Immunoglobulin kappa light chain VLJ region gi|21669355 6.81 29.16 8 5 33

Alpha S1-casein gi|1359714 5.17 20.88 9 8 34

Fatty acid-binding protein gi|227994 7.00 14.77 10 10 68

BOVINE MILK

Alpha-S1 casein P02662 4.98 24.57 11 7 37

Alpha-S2 casein P02663 8.54 26.17 12 6 41

Beta-lactoglobulin P02754 5.02 20.27 13 6 65

Alpha-lactalbumin P00711 4.93 16.69 14 7 42

Kappa casein P02668 5.93 18.97 15 11 41 P02668 5.93 18.97 16 11 41

Serum albumin P02769 5.82 71.24 17 24 47

Lactoferrin P24627 8.69 80.00 18 12 27

S100 calcium-binding protein P79342 5.50 11.23 19 3 24

Xanthine dehydrogenase gi|1620375 7.97 148.87 20 22 17

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