Analytical and Bioanalytical Chemistry

, Volume 410, Issue 12, pp 2949–2959 | Cite as

Analysis of hard protein corona composition on selective iron oxide nanoparticles by MALDI-TOF mass spectrometry: identification and amplification of a hidden mastitis biomarker in milk proteome

  • Massimiliano Magro
  • Mattia Zaccarin
  • Giovanni Miotto
  • Laura Da Dalt
  • Davide Baratella
  • Piero Fariselli
  • Gianfranco Gabai
  • Fabio Vianello
Research Paper

Abstract

Surface active maghemite nanoparticles (SAMNs) are able to recognize and bind selected proteins in complex biological systems, forming a hard protein corona. Upon a 5-min incubation in bovine whey from mastitis-affected cows, a significant enrichment of a single peptide characterized by a molecular weight at 4338 Da originated from the proteolysis of aS1-casein was observed. Notably, among the large number of macromolecules in bovine milk, the detection of this specific peptide can hardly be accomplished by conventional analytical techniques. The selective formation of a stable binding between the peptide and SAMNs is due to the stability gained by adsorption-induced surface restructuration of the nanomaterial. We attributed the surface recognition properties of SAMNs to the chelation of iron(III) sites on their surface by sterically compatible carboxylic groups of the peptide. The specific peptide recognition by SAMNs allows its easy determination by MALDI-TOF mass spectrometry, and a threshold value of its normalized peak intensity was identified by a logistic regression approach and suggested for the rapid diagnosis of the pathology. Thus, the present report proposes the analysis of hard protein corona on nanomaterials as a perspective for developing fast analytical procedures for the diagnosis of mastitis in cows. Moreover, the huge simplification of proteome complexity by exploiting the selectivity derived by the peculiar SAMN surface topography, due to the iron(III) distribution pattern, could be of general interest, leading to competitive applications in food science and in biomedicine, allowing the rapid determination of hidden biomarkers by a cutting edge diagnostic strategy.

Graphical abstract

The topography of iron(III) sites on surface active maghemite nanoparticles (SAMNs) allows the recognition of sterically compatible carboxylic groups on proteins and peptides in complex biological matrixes. The analysis of hard protein corona on SAMNs led to the determination of a biomarker for cow mastitis in milk by MALDI-TOF mass spectrometry.

Keywords

Biomarker Magnetic nanoparticles MALDI-TOF Milk Protein corona 

Notes

Acknowledgements

This work was supported by the University of Padua (Italy), grant PRAT 2015 (progetti di Ateneo) n. CPDA159850, “Assegni di Ricerca Junior” 2014 n. CPDR148959, and by the CARIPARO Foundation.

Compliance with ethical standards

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. According to the Directive 2010/63/EU of the European Parliament and the D.L. 26/2014 of the Italian Government, no ethical approval is needed for carrying out experimental research on milk samples coming from cows under physiological lactation.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

216_2018_976_MOESM1_ESM.pdf (728 kb)
ESM 1 (PDF 727 kb)

References

  1. 1.
    Rotello VM. Interfacing inorganic nanoparticles with biology. Bioconjug Chem. 2017;28:1–2.CrossRefGoogle Scholar
  2. 2.
    Forest V, Pourchez J. The nanoparticle protein corona: the myth of average. Nano Today. 2016;11:700–3.CrossRefGoogle Scholar
  3. 3.
    O’Brien J, Shea KJ. Tuning the protein corona of hydrogel nanoparticles: the synthesis of abiotic protein and peptide affinity reagents. Acc Chem Res. 2016;49:1200–10.CrossRefGoogle Scholar
  4. 4.
    Corbo C, Molinaro R, Tabatabaei M, Farokhzad OC, Mahmoudi M. Personalized protein corona on nanoparticles and its clinical implications. Biomater Sci. 2017;5:378–87.CrossRefGoogle Scholar
  5. 5.
    Caputo D, Papi M, Coppola R, Palchetti S, Digiacomo L, Caracciolo G, et al. A protein corona-enabled blood test for early cancer detection. Nano. 2017;9:349–54.Google Scholar
  6. 6.
    Magro M, Domeneghetti S, Baratella D, Jakubec P, Salviulo G, Bonaiuto E, et al. Colloidal surface active maghemite nanoparticles for biologically safe CrVI remediation: from core-shell nanostructures to pilot plant development. Chem Eur J. 2016;22:14219–26.CrossRefGoogle Scholar
  7. 7.
    Cmiel V, Skopalik J, Polakova K, Solar J, Havrdova M, Milde D, et al. Rhodamine bound maghemite as a long-term dual imaging nanoprobe of adipose tissue-derived mesenchymal stromal cells. Eur Biophys J. 2016;46:433–44.CrossRefGoogle Scholar
  8. 8.
    Skopalik J, Polakova K, Havrdova M, Justan I, Magro M, Milde D, et al. Mesenchymal stromal cell labeling by new uncoated superparamagnetic maghemite nanoparticles in comparison with commercial Resovist—an initial in vitro study. Int J Nanomedicine. 2014;9:5355–72.CrossRefGoogle Scholar
  9. 9.
    Venerando R, Miotto G, Magro M, Dallan M, Baratella D, Bonaiuto E, et al. Magnetic nanoparticles with covalently bound self-assembled protein corona for advanced biomedical applications. J Phys Chem C. 2013;117:20320–31.CrossRefGoogle Scholar
  10. 10.
    Miotto G, Magro M, Terzo M, Zaccarin M, Da Dalt L, Bonaiuto E, et al. Protein corona as a proteome fingerprint: the example of hidden biomarkers for cow mastitis. Colloids Surf B Biointerfaces. 2016;140:40–9.CrossRefGoogle Scholar
  11. 11.
    Celi P, Gabai G. Oxidant/antioxidant balance in animal nutrition and health: the role of protein oxidation. Front Vet Sci. 2015;2:48.CrossRefGoogle Scholar
  12. 12.
    Gomes F, Henriques M. Control of bovine mastitis: old and recent therapeutic approaches. Curr Microbiol. 2016;72:377–82.CrossRefGoogle Scholar
  13. 13.
    Thomas FC, Waterston M, Hastie P, Parkin T, Haining H, Eckersall PD. The major acute phase proteins of bovine milk in a commercial dairy herd. BMC Vet Res. 2015;11:207.CrossRefGoogle Scholar
  14. 14.
    Akerstedt M, Persson Waller K, Sternesjo A. Haptoglobin and serum amyloid A in relation to the somatic cell count in quarter, cow composite and bulk tank milk samples. J Dairy Res. 2007;74:198–203.CrossRefGoogle Scholar
  15. 15.
    Heikkila AM, Nousiainen JI, Pyorala S. Costs of clinical mastitis with special reference to premature culling. J Dairy Sci. 2012;95:139–50.CrossRefGoogle Scholar
  16. 16.
    Verma A, Ambatipudi K. Challenges and opportunities of bovine milk analysis by mass spectrometry. Clin Proteom. 2016;13:8.CrossRefGoogle Scholar
  17. 17.
    Boehmer JL. Proteomic analyses of host and pathogen responses during bovine mastitis. J Mammary Gland Biol Neoplasia. 2011;16:323–38.CrossRefGoogle Scholar
  18. 18.
    Thomas FC, Mullen W, Tassi R, Ramirez-Torres A, Mudaliar M, McNelly TN, et al. Mastitomics, the integrated omics of bovine milk in an experimental model of Streptococcus uberis mastitis: 1. High abundance proteins, acute phase proteins and peptidomics. Mol BioSyst. 2016;12:2735–47.CrossRefGoogle Scholar
  19. 19.
    Mudaliar M, Tassi R, Thomas FC, McNeilly TN, Weidt SK, McLaughlin M, et al. Mastitomics, the integrated omics of bovine milk in an experimental model of Streptococcus uberis mastitis: 2. Label-free relative quantitative proteomics. Mol BioSyst. 2016;12:2748–61.CrossRefGoogle Scholar
  20. 20.
    Thomas FC, Mudaliar M, Tassi R, McNeilly TN, Burchmore R, Burgess K, et al. Mastitomics, the integrated omics of bovine milk in an experimental model of Streptococcus uberis mastitis: 3. Untargeted metabolomics. Mol BioSyst. 2016;12:2762–9.CrossRefGoogle Scholar
  21. 21.
    Nissen A, Bendixen E, Ingvartsen KL, Rontved CM. In-depth analysis of low abundant proteins in bovine colostrum using different fractionation techniques. Proteomics. 2012;12:2866–78.CrossRefGoogle Scholar
  22. 22.
    Tacoma R, Fields J, Ebenstein DB, Lam YW, Greenwood SL. Characterization of the bovine milk proteome in early-lactation Holstein and Jersey breeds of dairy cows. J Proteome. 2016;130:200–10.CrossRefGoogle Scholar
  23. 23.
    Bislev SL, Kusebauch U, Codrea MC, Beynon RJ, Harman VM, Rontved CM, et al. Quantotypic properties of QconCAT peptides targeting bovine host response to Streptococcus uberis. J Proteome Res. 2012;11:1832–43.CrossRefGoogle Scholar
  24. 24.
    Roncada P, Piras C, Soggiu A, Turk R, Urbani A, Bonizzi L. Farm animal milk proteomics. J Proteome. 2012;75:4259–74.CrossRefGoogle Scholar
  25. 25.
    Nissen A, Bendixen E, Ingvartsen KL, Rontved CM. Expanding the bovine milk proteome through extensive fractionation. J Dairy Sci. 2013;96:7854–66.CrossRefGoogle Scholar
  26. 26.
    Magro M, Valle G, Russo U, Nodari L, Vianello F. Maghemite nanoparticles and method for preparing thereof. US Patent 8,980,218 17 March 2015.Google Scholar
  27. 27.
    Magro M, Sinigaglia G, Nodari L, Tucek J, Polakova K, Marusak Z, et al. Charge binding of rhodamine derivative to OH- stabilized nanomaghemite: universal nanocarrier for construction of magnetofluorescent biosensors. Acta Biomater. 2012;8:2068–76.CrossRefGoogle Scholar
  28. 28.
    Magro M, Campos R, Baratella D, Lima G, Hola K, Divoky C, et al. A magnetically drivable nanovehicle for curcumin with antioxidant capacity and MRI relaxation properties. Chem Eur J. 2014;20:11913–20.CrossRefGoogle Scholar
  29. 29.
    Magro M, Baratella D, Jakubec P, Zoppellaro G, Tucek J, Aparicio C, et al. Triggering mechanism for DNA electrical conductivity: reversible electron transfer between DNA and iron oxide nanoparticles. Adv Funct Mater. 2015;25:1822–31.CrossRefGoogle Scholar
  30. 30.
    Sinigaglia G, Magro M, Miotto G, Cardillo S, Agostinelli E, Zboril R, et al. Catalytically active bovine serum amine oxidase bound to fluorescent and magnetically drivable nanoparticles. Int J Nanomedicine. 2012;7:2249–59.Google Scholar
  31. 31.
    Neuhoff V, Stamm R, Eibl H. Clear background and highly sensitive protein staining with Coomassie Blue dyes in polyacrylamide gels: a systematic analysis. Electrophoresis. 1985;6:427–48.CrossRefGoogle Scholar
  32. 32.
    Magro M, Campos R, Baratella D, Ferreira MI, Bonaiuto E, Corraducci V, et al. Magnetic purification of curcumin from Curcuma longa rhizome by novel naked maghemite nanoparticles. J Agric Food Chem. 2015;63:912–20.CrossRefGoogle Scholar
  33. 33.
    Magro M, Fasolato L, Bonaiuto E, Andreani NA, Baratella D, Corraducci V, et al. Enlightening mineral iron sensing in Pseudomonas fluorescens by surface active maghemite nanoparticles: involvement of the OprF porin. BBA Gen Subj. 1860;2016:2202–10.Google Scholar
  34. 34.
    Magro M, Faralli A, Baratella D, Bertipaglia I, Giannetti S, Salviulo G, et al. Avidin functionalized maghemite nanoparticles and their application for recombinant human biotinyl-SERCA purification. Langmuir. 2012;28:15392–401.CrossRefGoogle Scholar
  35. 35.
    Magro M, Baratella D, Pianca N, Toninello A, Grancara S, Zboril R, et al. Electrochemical determination of hydrogen peroxide production by isolated mitochondria: a novel nanocomposite carbon-maghemite nanoparticle electrode. Sensors Actuators B Chem. 2013;176:315–22.CrossRefGoogle Scholar
  36. 36.
    Bonaiuto E, Magro M, Baratella D, Jakubec P, et al. Ternary hybrid γ-Fe2O3/CrVI/amine oxidase nanostructure for electrochemical sensing: application for polyamine detection in tumor tissue. Chem Eur J. 2016;22:1–8.CrossRefGoogle Scholar
  37. 37.
    Urbanova V, Magro M, Gedanken A, Baratella D, Jakubec P, Sconcerle E, et al. Nanocrystalline iron oxides, composites, and related materials as a platform for electrochemical, magnetic, and chemical biosensors. Chem Mater. 2014;26:6653–73.CrossRefGoogle Scholar
  38. 38.
    Baratella D, Magro M, Sinigaglia G, Zboril R, Salviulo G, Vianello F. A glucose biosensor based on surface active maghemite nanoparticles. Biosens Bioelectron. 2013;45:13–8.CrossRefGoogle Scholar
  39. 39.
    Magro M, Bonaiuto E, Baratella D, de Almeida RJ, Jakubec P, Corraducci V, et al. Electrocatalytic nanostructured ferric tannates: characterization and application of a polyphenol nanosensor. ChemPhysChem. 2016;17:3196–203.CrossRefGoogle Scholar
  40. 40.
    Cedervall T, Lynch I, Lindman S, Berggard T, Thulin E, Nilsson H, et al. Understanding the nanoparticle-protein corona using methods to quantify exchange rates and affinities of proteins for nanoparticles. Proc Natl Acad Sci U S A. 2007;104:2050–5.CrossRefGoogle Scholar
  41. 41.
    Magro M, Baratella D, Salviulo G, Polakova K, Zoppellaro G, Tucek J, et al. Core-shell hybrid nanomaterial based on prussian blue and surface active maghemite nanoparticles as stable electrocatalyst. Biosens Bioelectron. 2014;52:159–65.CrossRefGoogle Scholar
  42. 42.
    Meltretter J, Schmidt A, Humeny A, Becker CM, Pischetsrieder M. Analysis of the peptide profile of milk and its changes during thermal treatment and storage. J Agric Food Chem. 2008;56:2899–906.CrossRefGoogle Scholar
  43. 43.
    Baum F, Fedorova M, Ebner J, Hoffmann R, Pischetsrieder M. Analysis of the endogenous peptide profile of milk: identification of 248 mainly casein-derived peptides. J Proteome Res. 2013;12:5447–62.CrossRefGoogle Scholar
  44. 44.
    Liu Y, Eichler J, Pischetsrieder M. Virtual screening of a milk peptide database for the identification of food-derived antimicrobial peptides. Mol Nutr Food Res. 2015;59:2243–54.CrossRefGoogle Scholar
  45. 45.
    Lahov E, Regelson W. Antibacterial and immunostimulating casein-derived substances from milk: casecidin, isracidin peptides. Food Chem Toxicol. 1996;34:131–45.CrossRefGoogle Scholar
  46. 46.
    Clare DA, Swaisgood HE. Bioactive milk peptides: a prospectus. J Dairy Sci. 2000;83:1187–95.CrossRefGoogle Scholar
  47. 47.
    Mansor R, Mullen W, Albalat A, Zerefos P, Mischak H, Barrett DC, et al. A peptidomic approach to biomarker discovery for bovine mastitis. J Proteome. 2013;85:89–98.CrossRefGoogle Scholar
  48. 48.
    Magro M, Esteves Moritz D, Bonaiuto E, Baratella D, Terzo M, Jakubec P, et al. Citrinin mycotoxin recognition and removal by naked magnetic nanoparticles. Food Chem. 2016;203:505–12.CrossRefGoogle Scholar
  49. 49.
    Rodriguez JA, Fernández-García M. Synthesis, properties, and applications of oxide nanomaterials. 1st ed. Hoboken: Wiley; 2007.CrossRefGoogle Scholar
  50. 50.
    Chen LX, Liu T, Thurnauer MC, Csencsits R, Rajh T. Fe2O3 nanoparticle structures investigated by x-ray absorption near-edge structure, surface modifications, and model calculations. J Phys Chem B. 2002;106:8539–46.CrossRefGoogle Scholar
  51. 51.
    Ueda EKM, Gout PW, Morganti L. Current and prospective applications of metal ion–protein binding. J Chromatogr A. 2003;988:1–23.CrossRefGoogle Scholar
  52. 52.
    Marks DS, Hopf TA, Sander C. Protein structure prediction from sequence variation. Nat Biotechnol. 2012;30:1072–80.CrossRefGoogle Scholar
  53. 53.
    Sormanni P, Camilloni C, Fariselli P, Vendruscolo M. The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins. J Mol Biol. 2015;427:982–96.CrossRefGoogle Scholar
  54. 54.
    Xu D, Zhang Y. Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins. 2012;80:1715–35.CrossRefGoogle Scholar
  55. 55.
    Reißer S, Strandberg E, Steinbrecher T, Ulrich AS. 3D hydrophobic moment vectors as a tool to characterize the surface polarity of amphiphilic peptides. Biophys J. 2014;106:2385–94.CrossRefGoogle Scholar
  56. 56.
    Ruegg PL, Pantoja JCF. Understanding and using somatic cell counts to improve milk quality. Irish J Agric Food Res. 2013;52:101–17.Google Scholar
  57. 57.
    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–30.Google Scholar
  58. 58.
    Chou TC, Hsu W, Wang CH, Chen YJ, Fang JM. Rapid and specific influenza virus detection by functionalized magnetic nanoparticles and mass spectrometry. J Nanotechnol. 2011;9:52.Google Scholar
  59. 59.
    Strimbu K, Tavel JA. What are biomarkers? Curr Opin HIV AIDS. 2010;5:463–6.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Massimiliano Magro
    • 1
    • 2
  • Mattia Zaccarin
    • 3
    • 4
  • Giovanni Miotto
    • 3
    • 4
  • Laura Da Dalt
    • 1
  • Davide Baratella
    • 1
  • Piero Fariselli
    • 1
  • Gianfranco Gabai
    • 1
  • Fabio Vianello
    • 1
    • 2
  1. 1.Department of Comparative Biomedicine and Food ScienceUniversity of PaduaLegnaroItaly
  2. 2.Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry and Experimental PhysicsPalacky UniversityOlomoucCzech Republic
  3. 3.Department of Molecular MedicineUniversity of PaduaPaduaItaly
  4. 4.Proteomics FacilityAzienda Ospedaliera di Padova and University of PaduaPaduaItaly

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