Cellular and Molecular Life Sciences

, Volume 70, Issue 19, pp 3709–3722

Paleoproteomic study of the Iceman’s brain tissue

  • Frank Maixner
  • Thorsten Overath
  • Dennis Linke
  • Marek Janko
  • Gea Guerriero
  • Bart H. J. van den Berg
  • Bjoern Stade
  • Petra Leidinger
  • Christina Backes
  • Marta Jaremek
  • Benny Kneissl
  • Benjamin Meder
  • Andre Franke
  • Eduard Egarter-Vigl
  • Eckart Meese
  • Andreas Schwarz
  • Andreas Tholey
  • Albert Zink
  • Andreas Keller
Research Article

DOI: 10.1007/s00018-013-1360-y

Cite this article as:
Maixner, F., Overath, T., Linke, D. et al. Cell. Mol. Life Sci. (2013) 70: 3709. doi:10.1007/s00018-013-1360-y

Abstract

The Tyrolean Iceman, a Copper-age ice mummy, is one of the best-studied human individuals. While the genome of the Iceman has largely been decoded, tissue-specific proteomes have not yet been investigated. We studied the proteome of two distinct brain samples using gel-based and liquid chromatography–mass spectrometry-based proteomics technologies together with a multiple-databases and -search algorithms-driven data-analysis approach. Thereby, we identified a total of 502 different proteins. Of these, 41 proteins are known to be highly abundant in brain tissue and 9 are even specifically expressed in the brain. Furthermore, we found 10 proteins related to blood and coagulation. An enrichment analysis revealed a significant accumulation of proteins related to stress response and wound healing. Together with atomic force microscope scans, indicating clustered blood cells, our data reopens former discussions about a possible injury of the Iceman’s head near the site where the tissue samples have been extracted.

Keywords

Tyrolean IcemanMummyNeolithicPaleoproteomicsMass spectrometryBrain proteomeAncient proteins

Supplementary material

18_2013_1360_MOESM1_ESM.docx (46 mb)
Supplementary material 4 (DOCX 47078 kb)
18_2013_1360_MOESM2_ESM.xlsx (72 kb)
Supplementary material 1 An overview of the proteins identified in-solution and in-gel for both samples, 1024 and 1025, respectively. (XLSX 71 kb)
18_2013_1360_MOESM3_ESM.xlsx (63 kb)
Supplementary material 2 The significant pathways for functional modelling (KEGG and gene ontologies) after adjusting with the Benjamini–Hochberg approach. (XLSX 63 kb)
18_2013_1360_MOESM4_ESM.xlsx (1.9 mb)
Supplementary material 3 A multi-tab table file with information regarding protein identification by the four search engines applied (Mascot, SEQUEST, OMSSA and X!Tandem) either (1) with specification of a protease (Trypsin) or (2) without specification of a protease (“No Enzyme”), respectively. For the in-solution analyses, the identified proteins are listed regarding the number of matches by the four search engines. For the samples analyzed after gel separation, the information about protein identification is linked to the position in the gel, (1) for each of the four search engines (non-merged data) and (2) after merging the data into a form representing a picture of the gel. (XLSX 1966 kb)

Copyright information

© Springer Basel 2013

Authors and Affiliations

  • Frank Maixner
    • 1
  • Thorsten Overath
    • 2
  • Dennis Linke
    • 2
  • Marek Janko
    • 3
  • Gea Guerriero
    • 4
  • Bart H. J. van den Berg
    • 2
  • Bjoern Stade
    • 5
  • Petra Leidinger
    • 6
  • Christina Backes
    • 6
  • Marta Jaremek
    • 7
  • Benny Kneissl
    • 8
  • Benjamin Meder
    • 9
  • Andre Franke
    • 5
  • Eduard Egarter-Vigl
    • 10
  • Eckart Meese
    • 6
  • Andreas Schwarz
    • 11
  • Andreas Tholey
    • 2
  • Albert Zink
    • 1
  • Andreas Keller
    • 6
    • 7
  1. 1.Institute for Mummies and the IcemanEURAC researchBolzanoItaly
  2. 2.Division for Systematic Proteome Research, Institute for Experimental MedicineChristian-Albrechts-Universität KielKielGermany
  3. 3.Center of Smart InterfacesTU DarmstadtDarmstadtGermany
  4. 4.Department Environment and Agro-biotechnologies (EVA)Centre de Recherche Public-Gabriel LippmannBelvauxLuxembourg
  5. 5.Institute of Clinical Molecular BiologyChristian-Albrechts-Universität KielKielGermany
  6. 6.Department of Human GeneticsSaarland UniversityHomburgGermany
  7. 7.Siemens HealthcareErlangenGermany
  8. 8.Software Engineering and BioinformaticsJohannes Gutenberg-University of MainzMainzGermany
  9. 9.Department of Internal Medicine IIIUniversity of HeidelbergHeidelbergGermany
  10. 10.Department of Pathological Anatomy and HistologyGeneral Hospital BolzanoBolzanoItaly
  11. 11.Department of NeurosurgeryGeneral Hospital BolzanoBolzanoItaly