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Dr. Gustavo de Souza

Director of Bioinformatics

Dr. de Souza is the Head of Proteomics at DNA GTx who brings advanced proteomics perspective, supporting the integration of protein-level data with broader research and discovery efforts. His advisory role helps ensure cohesive, high-confidence scientific outcomes.

  1. Pereira D.C.B.G., Cavalcante J.V.F., Cavalcanti L.F., Falcao R.M., de Souza J.E.S., Dalmolin R.J.S., Rego T.G., Mangul S., de Souza G.A., Terramate P. & Lima J.P.M.S. CaRinDB: An integrated database of common cancer mutations and residue interaction network parameters. Bioinformatics Advances 6,
    https://academic.oup.com/bioinformaticsadvances/article/6/1/vbaf313/8440752
  2. Gonçalves A.C.A., Nunes T.M.D., Parize E., Gerhardt E.C.M., de Souza G.A., Scholl J., Forchhammer K. & Huergo, L.F. The activity of the ribonucleotide monophosphatase UmpH is controlled by interaction with the GlnK signaling protein in Escherichia coli. J Biol Chem 300, 107931, 2024.
    https://www.sciencedirect.com/science/article/pii/S0021925824024335
  3. Machado K.C.T., Fiuza T.S., de Souza S.J. & de Souza G.A. Detecting predicted cancer-testis antigens in proteomics datasets of healthy and tumoral samples. IEEE Access 12, 150930-150939, 2024.
    https://ieeexplore.ieee.org/abstract/document/10709354
  4. Dahlberg D., Holm S., Sagen E.M.L., Michelsen A.E., Stensland M.E., de Souza G.A., Muller E.G., Connely J.P., Revheim M-E., Halvorsen B. & Hassel B. Bacterial brain abscesses expand despite effective antibiotic treatment: a process powered by osmosis due to neutrophil cell death. Neurosurgery 94, 1079-1087, 2024.
    https://journals.lww.com/neurosurgery/fulltext/2024/05000/bacterial_brain_abscesses_expand_despite_effective.22.aspx
  5. Dahlberg D., Holm S., Sagen E.M.L., Michelsen A.E., Stensland M.E., de Souza G.A., Halvorsen B. & Hassel B. Neutrophil cell death (NETosis) drives bacterial brain abscesses growth. Brain and Spine 3, 102317, 2023.
    https://www.sciencedirect.com/science/article/pii/S2772529423006057
  6. Falcao R.M., Kokaraki G., de Wispelaere W., Amant F., de Souza G.A., de Souza J.E.S., Carlson J.W. & Petta T.B. The expression of the immunoproteasome subunit PSMB9 is related to distinct molecular subtypes of uterine leiomyosarcoma. Cancers 14, 5007, 2022.
    https://www.mdpi.com/2072-6694/14/20/5007
  7. da Cunha L.M., Terramate P., Fiuza T.S., da Silva V.L., Kroll J.E., de Souza S.J. & de Souza G.A. dbPepVar: a novel cancer proteogenomics database. IEEE Access 10, 90982-90994, 2022.
    https://ieeexplore.ieee.org/abstract/document/9867999
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  8. Dahlberg D., Rummel J., Distante S., de Souza G.A., Stensland M.E., Mariussen E., Rootwelt H., Voie O. & Hassel B. Glioblastoma microenvironment contains multiple hormonal and non-hormonal growth-stimulating factors. Fluids Barriers CNS 19, 45, 2022.
    https://link.springer.com/article/10.1186/s12987-022-00333-z
  9. Gravina F., Degaut F.L., Gerhardt E.C.M., Pedrosa F.O., Souza E.M., de Souza G.A. & Huergo L.F. The protein-protein interaction network of the Escherichia coli EIIANtr regulatory protein reveals a role in cell motility and metabolic control. Res Microbiol 172, 103882, 2021.
    https://www.sciencedirect.com/science/article/pii/S0923250821000966
  10. Ramberg H., Richardson E., de Souza G.A., Rakaee M., Stensland M.E., Braadland P.R., Nygard S., Ogren O., Guldvik I.J., Berge V., Svindland A., Tasken K.A. & Andersen S. Proteomic analyses identify major vault protein as a prognostic biomarker for fatal prostate cancer. Carcinogenesis 42, 685-693, 2021.
    https://academic.oup.com/carcin/article/42/5/685/6145758
  11. Thome C.H., Ferreira G.A., Pereira-Martins D.A., dos Santos G.A., Almeida-Silveira D.R., Weinhauser I., de Souza G.A., Houtsma R., Schuringa J.J., Rego E.M. & Faça V.M. The expression of NTAL and its protein interactors is associated with clinical outcomes in acute myeloid leucemia. Mol Cell Proteomics 20, 2021.
    https://www.mcponline.org/article/S1535-9476(21)00064-5/fulltext
  12. Gerhardt E.C.M., Parize E., Gravina F., Pontes F.L.D., Santos A.R.S., Araujo G.A.T., Goedert A.C., Urbanski A.H., Steffens M.B.R., Chubatsu L.S., Pedrosa F.O., Souza E.M., Forchhammer K., Ganusova E., Alexandre G., de Souza G.A. & Huergo L.F. The protein-protein interaction network reveals a novel role of the signal transduction protein PII in the control of c-di-GMP homeostasis in Azospirillum brasilense. Msystems 5, 817-820, 2020.
    https://journals.asm.org/doi/full/10.1128/msystems.00817-20
  13. Fiuza T.S., Lima J.P.M.S. & de Souza G.A. EpitoCore: mining conserved epitope vaccine candidates in the core proteome of multiple bacteria strains. Front Immunol 11, 816, 2020.
    https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2020.00816/full
  14. Santos A.R.S., Gerhardt E.C.M., Parize E., Pedrosa F.O., Steffens M.B.R., Chubatsu L.S., Souza E.M., Passaglia L.M.P., Sant’anna F.H., de Souza G.A., Huergo L.F. & Forchhammer K. NAD+ biosynthesis in bacteria is controlled by global carbon/nitrogen levels via PII signaling. J Biol Chem 295, 6165-6176, 2020.
    https://www.jbc.org/article/S0021-9258(17)48243-3/fulltext
  15. Kukolj C., Pedrosa F.O., de Souza G.A., Summer L.W., Lei Z., Sumner B., do Amaral F.P., Juexin W., Trupti J., Huergo L.F., Monteiro R.A., Valdameri G., Stacey G. & de Souza E.M. Proteomic and metabolomic analysis of Azospirillum brasilense ntrC mutante under high and low nitrogen conditions. J Prot Res 19, 92-105, 2019.
    https://pubs.acs.org/doi/abs/10.1021/acs.jproteome.9b00397
  16. Tomescu-Baciu A., Johansen J.N., Holmoy T., Greiff V., Stensland M.E., de Souza G.A., Vartdal F. & Lossius A. Persistence of intrathecal oligoclonal B cells and IgG in multiple sclerosis. J Immunol 333, 576966, 2019.
    https://www.sciencedirect.com/science/article/abs/pii/S0165572819301894
  17. Machado K.C.T., Fortuin S., Tomazella G.G., Fonseca A.F., Warren R.M., Wiker H.G., de Souza S.J. & de Souza G.A. On the impact of the pangenome and annotation discrepancies while building protein sequence databases for bacterial proteogenomics. Front Microbiology 10, 1410, 2019.
    https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2019.01410/full
  18. Mathiesen S.B., Lunde M., Aronsen J.M., Romaine A., Kaupang A., Martinsen M., de Souza G.A., Nyman T.A., Sjaastad I., Christensen G. & Carlson C.R. The cardiac syndecan-4 interactome reveals a role for syndecan-4 in nluclear translocation of muscle LIM protein (MLP). J Biol Chem 294, 8717-8731, 2019.
    https://www.sciencedirect.com/science/article/pii/S002192582036395X
  19. Sikorski K., Mehta A., Inngjerdingen M., Thakor F., Kling S., Kalina T., Nyman T.A., Stensland M.E., Zhou W., de Souza G.A., Holden L., Stuchly J., Templin M. & Lund-Johansen F. A high-throughput pipeline for validation of antibodies. Nature Methods 15, 909-912, 2018.
    https://www.nature.com/articles/s41592-018-0179-8
  20. Tutturen A.E.V., Dorum S., Clancy T., Reims H.M., Christophersen A., Lundin K.E.A., Sollid L.M., de Souza G.A. & Stamnaes J. Characterization of the small intestinal lesion in celiac disease by label-free quantitative mass spectrometry. The Am Journal of Pathology 188, 1563-1579, 2018.
    https://www.sciencedirect.com/science/article/pii/S0002944017309173
  21. Kostas M., Haugsten E.M., Zhen Y., Sorensen V., Szybowska P., Fiorito E., Lorenz S., Jones N., de Souza G.A., Wiedlocha A. & Wesche J. Protein tyrosine phosphatase receptor type G (PTPRG) controls fibroblast growth factor receptor (FGFR) 1 activity and influences sensitivity to FGFR kinase inhibitors. Mol Cell Proteomics 17, 850-870, 2018.
    https://www.mcponline.org/article/S1535-9476(20)32463-4/fulltext
  22. Gravina F., Sanchuki H.S., Rodrigues T.E., Gerhardt E.C.M., Pedrosa F.O., Souza E.M., Valdameri G., de Souza G.A. & Huergo L.F. Proteome analysis of an Escherichia coli ptsN-null strain under different nitrogen regimes. J Proteomics 174, 28-35, 2018.
    https://www.sciencedirect.com/science/article/abs/pii/S1874391917304293
  23. Shao W., Pedrioli P.G.A., Wolski W., Scurtescu C., Schmid E., Vizcaino J.A., Courcelles M., Schuster H., Kowalewski D., Marino F., Arlehamn C.S.L., Vaughan K., Peters B., Sette A., Ottenhoff T.H.M., Meijgaarden K.E., Nieuwenhuizen N., Kaufmann S.H.E., Schlapbach R., Castle J.C., Nesvizhskii A.I., Nielsen M., Deutsch E.W., Campbell D.S., Moritz R.L., Zubarev R.A., Ytterberg A.J., Purcell A.W., Marcilla M., Paradela A., Wang Q., Costello C.E., Ternette N., van Veelen P.A., van Els C.A.C.M., Heck A.J.R., de Souza G.A., Sollid L.S., Admon A., Stevanovic S., Rammensee H.G., Thibault P., Perreault C., Bassani-Stenberg M., Aebersold R. & Caron E. The SysteMHC atlas project. Nucleic Acids Res 46, D1237-D1243, 2018.
    https://academic.oup.com/nar/article/46/D1/D1237/4056238
  24. da Silva V.L., Fonseca A.F., da Silva T.E., Coelho A.C., Kroll J.E., de Souza J.E.S., Stranski B., de Souza G.A. & de Souza S.J. Genome-wide identification of cancer/testis genes and their association with prognosis in a pan-cancer analysis. Oncotarget 8, 92966-92977, 2017.
    https://pmc.ncbi.nlm.nih.gov/articles/PMC5696236/
  25. Hassel B., de Souza G.A., Stensland M.E., Ivanovic J., Voie O. & Dahlberg D. The proteome of pus from human brain abscesses: host-derived neurotoxic proteins and the cell type diversity of the CNS pus. J Neurosurgery 1, 829-837, 2017.
    https://thejns.org/view/journals/j-neurosurg/129/3/article-p829.xml
  26. Iversen R., Snir O., Stensland M., Kroll J.E., Steinsbo O., Korponay-Szabo I.R., Lundin K.E.A., de Souza G.A. & Sollid L.M. Strong clonal relatedness between serum and gut IgA despite different plasma cell origins. Cell Reports 20, 2357-2367, 2017.
    https://www.cell.com/cell-reports/fulltext/S2211-1247(17)31143-9
  27. Havfer T.L., Wanichawan P., Manfra O., de Souza G.A., Lunde M., Martinsen M., Louch W.E., Sejersted O.M. & Carlson C.R. Mapping the in vitro interactome of cardiac sodium (Na+) – calcium (Ca2+) exchanger 1 (NCX1). Proteomics 17, 2017.
    https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.201600417
  28. Kroll J.E., da Silva V.L., de Souza S.J. & de Souza G.A. A tool for integrating genetic and mass spectrometry-based peptide data: Proteogenomics Viewer. Bioessays 39, 2017.
    https://onlinelibrary.wiley.com/doi/abs/10.1002/bies.201700015
  29. Tortelli Jr T.C., de Godoy L.M.F., de souza G.A., Bonatto D., Otake A.H., de Freitas Saito R., Rosa J.C., Greene L.J. & Chammas R. Accumulation of prohibitin is a common cellular response to different stressing stimuli and protects melanoma cells from ER stress and chemotherapy-induced cell death. Oncotarget 8, 43114-43129, 2017.
    https://pmc.ncbi.nlm.nih.gov/articles/PMC5522132/
  30. Sanchuki H.B., Gravina F., Rodrigues T.E., Gerhardt E.C., Pedrosa F.O., Souza E.M., Raittz R.T., Valdameri G., de Souza G.A. & Huergo, L.F. Dynamics of the Escherichia coli proteome in response to nitrogen starvation and entry into the stationary phase. Biochim Biophys Acta 1865, 344-352, 2017.
    https://www.sciencedirect.com/science/article/abs/pii/S1570963916302564
  31. Rowczenio D., Stensland M., de Souza G.A., Strom E.H., Gilbertson J.A., Taylor G., Rendell N., Minogue S., Efebera Y.A., Lachmann H.J., Wechalekar A.D., Hawkins P.N., Heimdal K.R., Selvig K., Laegreid I.K., Gilmore J.D & Wien T.N. Renal amyloidosis associated with five novel variants in the fibrinogen A-alpha chain protein. Kidney Int Rep 2, 461-469, 2017.
    https://www.sciencedirect.com/science/article/pii/S246802491630170X
  32. Paiva A.L.S., Oliveira J.T.A., de Souza G.A. & Vasconcelos I.M. Label-free proteomics reveal that Cowpea Severe Mosaic Virus trasiently suppresses the host leaf protein accumulation during the compatible interaction with cowpea (Vigna unguiculata [L] Walp.). J Prot Res 15, 4208-4220, 2016.
    https://pubs.acs.org/doi/abs/10.1021/acs.jproteome.6b00211
  33. Haugsten E.M., Sorensen V., Bosakova M.K., de Souza G.A., Krekci P., Wiedlocha A. & Wesche J. Proximity labeling reveals molecular determinants of FGFR4 endosomal transport. J Prot Res 15, 3841-3855, 2016.
    https://pubs.acs.org/doi/abs/10.1021/acs.jproteome.6b00652
  34. Vasilevska J., de Souza G.A., Stensland M., Skrastina D., Zhulenvovs D., Paplausks R., Kurena B., Kozlovska T. & Zajakina A. Comparative protein profiling of B16 mouse melanoma cells susceptible and non-susceptible to alphavirus infection: effect of the tumor microenvironment. Cancer Biol & Ther 10, 1035-1050, 2016.
    https://www.tandfonline.com/doi/full/10.1080/15384047.2016.1219813
  35. Karlsen T., de Souza G.A., Odegaard B., Engebretsen L. & Brinchmann J. microRNA-140 inhibits inflammation and stimulates chondrogenesis in a model of interleukin-1 beta-induced osteoarthrites. Mol Ther Nucleic Acids 5, e373, 2016.
    https://www.cell.com/molecular-therapy-family/nucleic-acids/fulltext/S2162-2531(17)30084-7
  36. Lund-Johansen F., Carrillo D.R., Mehta A., Sikorski K., Inngjerdingen M., Kalina T., Roysland K., de Souza G.A., Bradbury A., Lacrevisse Q. & Stuchly A. MetaMass, a tool for meta-analysis of subcellular proteomics data. Nature Methods 13, 837-840, 2016.
    https://www.nature.com/articles/nmeth.3967
  37. Dorum S., Steinsbo O., Bergseng E., Arntzen M.O., de Souza G.A. & Sollid L.M. Gluten-specific antibodies of celiac disease gut plasma cells recognize long proteolytic fragments that typically harbor T-cell epitopes. Sci Rep 6, 25565, 2016.
    https://www.nature.com/articles/srep25565
  38. Riaz T., Sollid L.M., Olsen I. & de Souza G.A. Quantitative Proteomics of gut-derived Th1 and Th1/Th17 clones reveal the presence of CD28+ NKG2D- Th1 cytotoxic CD4+ T cells. Mol Cell Proteomics 15, 1007-1016, 2016.
    https://www.sciencedirect.com/science/article/pii/S1535947620336574
  39. Kalayou S., Granum C., Berntsen H.F., Groseth P.K., Verhaegen S., Conolly S., Brandt I., de Souza G.A. & Ropstad, E. Label-free based quantitative proteomics analysis of primary neonatal porcine Leydig cells exposed to the persistent contaminant 3-methylsulfonyl-DDE. J Proteomics 137, 68-82, 2016.
    https://www.sciencedirect.com/science/article/abs/pii/S1874391915302062
  40. Camassa L.M.A., Lunde L.K., Hoddevik E.H., Stensland M., Boldt H.B., de Souza G.A., Ottersen O.P. & Amiry-Moghaddam M. Mechanisms underlying AQP4 accumulation in astrocyte endfeet. GLIA 63, 2073-2091, 2015.
    https://onlinelibrary.wiley.com/doi/abs/10.1002/glia.22878
  41. Johansen J.N., Vartdal F., Desmarais C., Tutturen A.E.V., de Souza G.A., Lossius A. & Holmøy T. Intrathecal BCR transcriptome in multiple sclerosis versus other neuroinflammation: Equally diverse and compartmentalized, but more mutated, biased and overlapping with the proteome. Clin Immunol 160, 211-225, 2015.
    https://www.sciencedirect.com/science/article/abs/pii/S152166161500203X
  42. Fortuin S., Tomazella G.G., Nagayaryan N., Sampson S., van Pittius N.G., Soares N.C., Wiker H.G., Warren R.M. & de Souza G.A. Phosphoproteomic analysis of a clinical Mycobacterium tuberculosis Beijing isolate: Expanding the mycobacterial phosphoproteome catalogue. Front Microbiology 6, 1-17, 2015.
    https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2015.00006/full
  43. Bergseng E., Dorum S., Arntzen M.Ø., Nielsen M., Nygard S., Buus S., de Souza G.A. & Sollid L.M. Different binding motifs of the celiac disease associated HLA molecules DQ2.5, DQ2.2 and DQ7.5 revealed by relative quantitative proteomics of endogenous peptide repertoires. Immunogenetics 67, 73-84, 2015.
    https://link.springer.com/article/10.1007/s00251-014-0819-9
  44. Kroll J.E., de Souza S.J. & de Souza G.A. Identification of rare alternative splicing events in MS/MS data reveals a significant fraction of alternative translation initiation sites. PeerJ e673, 2014.
    https://peerj.com/articles/673/
  45. Dorum S., Bodd M., Fallang L-E., Bergseng E., Stamnaes J., de Souza G.A. & Sollid L.M. HLA-DQ molecules as affinity matrix for identification of gluten T-cell epitopes. J Immunol 193, 4497-4506, 2014.
    https://academic.oup.com/jimmunol/article-abstract/193/9/4497/7966450
  46. Ostevik L., Gunnes G., de Souza G.A., Wien T.N. & Sorby R. Nasal and ocular amyloidosis in a 15 year-old horse. Acta Vet Scand 56,
    https://link.springer.com/article/10.1186/s13028-014-0050-6
  47. Ostevik L., de Souza G.A., Wien T.N., Gunnes G. & Sorby R. Characterization of amyloid in equine recurrent uveitis as AA amyloid. J Comp Pathol 151, 228-233,
    https://www.sciencedirect.com/science/article/abs/pii/S0021997514000632
  48. Tutturen A.E.V., Fleckenstein B. & de Souza, G.A. Assessing the citrulinome in rheumatoid arthritis synovial fluid with and without enrichment of citrullinated peptides. J Proteom Res. 13, 2867-2873, 2014.
    https://pubs.acs.org/doi/abs/10.1021/pr500030x
  49. Rodrigues T., Gerhardt E., Oliveira M., Chubatsu L., Pedrosa F., Souza E., de Souza G.A., Muller-Santos M. & Huergo L.F. Search for novel targets of the PII signal transduction protein in Bacteria identifies the BCCP component of acetyl-CoA carboxylase as a PII binding partner. Mol Microbiol 91, 751-761, 2014.
    https://onlinelibrary.wiley.com/doi/full/10.1111/mmi.12493
  50. Koehler C., Arntzen M.Ø., de Souza G.A. & Thiede B. An approach for triplex-isobaric peptide termini labeling (Triplex-IPTL). Anal Chem 85, 2478-2485, 2013.
    https://pubs.acs.org/doi/abs/10.1021/ac3035508

  51. Tomazella G.G., Risberg K., Mylvaganam H., Lindemann P.C., Thiede B., de Souza G.A. & Wiker H.G. Proteomic analysis of a multi-resistant clinical Escherichia coli isolate of unknown genomic background. J Proteomics 75, 1830-1837, 2012.
    https://www.sciencedirect.com/science/article/abs/pii/S1874391911007056
  52. de Souza G.A., Leversen N.A., Målen H. & Wiker H.G. Bacterial proteins with cleaved or uncleaved signal peptides of the general secretory pathway. J Proteomics 75, 502-510, 2011.
    https://www.sciencedirect.com/science/article/pii/S1874391911004313
  53. de Souza G.A. & Wiker H.G. A proteomic view of mycobacteria. Proteomics 11, 3118-3127, 2011.
    https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.201100043
  54. Wiker H.G., Tomazella G.G. & de Souza G.A. A quantitative view on leprae antigens by proteomics. Review, J Proteomics74, 1711-1719, 2011.
    https://www.sciencedirect.com/science/article/abs/pii/S1874391911000212
  55. Målen H., de Souza G.A., Pathak S., Søfteland T. & Wiker H. Comparison of membrane proteins of Mycobacterium tuberculosis H37Rv and H37Ra strains. BMC Microbiology 11, 18, 2011.
    https://link.springer.com/article/10.1186/1471-2180-11-18
  56. de Souza G.A., Arntzen M.Ø., Fortuin S., Schürch A.C., Målen H., McEvoy C.R.E., van Soolingen D., Thiede B., Warren R.M. & Wiker H.G. Proteogenomic analysis of polymorphisms and gene annotation divergences in prokaryotes using a clustered mass spectrometry-friendly database. Mol Cell Proteomics 10, M110.002527, 2011.
    https://www.mcponline.org/article/S1535-9476(20)31374-8/fulltext
  57. de Souza G.A., Fortuin S., Pando R.H., McEnvoy C.R.E., Koehler C.J., Thiede B., Warren R.M. & Wiker H.G. Using a label-free proteomic method to identify differentially abundant proteins in closely related hypo- and hyper-virulent clinical Mycobacterium tuberculosis Mol Cell Proteomics 9, 2414-2423, 2010.
    https://www.sciencedirect.com/science/article/pii/S153594762034500X
  58. de Souza G.A., Arntzen M.O. & Wiker H.G. MSMSpdbb: providing protein databases of closely related organisms to improve proteomic characterization of prokaryotic microbes. Bioinformatics 26, 698-699, 2010.
    https://academic.oup.com/bioinformatics/article/26/5/698/212275
  59. Tomazella G.G., da Silva I., Thome C.H., Greene L.J., Koehler C.J., Thiede B., Wiker H.G. & de Souza G.A. Analysis of detergent-insoluble and whole cell lysate fractions of resting neutrophils using high-resolution mass spectrometry. J Proteome Res 9, 2030-2036, 2010.
    https://pubs.acs.org/doi/abs/10.1021/pr1000253
  60. Målen H., Pathak S., Søfteland T., de Souza G.A. & Wiker H.G. Definition of novel cell envelope associated proteins in Triton X-114 extracts of Mycobacterium tuberculosis BMC Microbiology 10, 132, 2010.
    https://link.springer.com/article/10.1186/1471-2180-10-132
  61. de Souza G.A. & Wiker H.G. Proteomic approaches to study immunity in infection. Invited as part of book chapter in Methods in Microbiology 37, Eds Dietrich Kabelitz and Stefan Kaufmann. Elsevier, 101-114, 2010.
    https://www.sciencedirect.com/science/chapter/bookseries/abs/pii/S058095171037005X
  62. Carpenter J.E., Jackson W., de Souza G.A., Haarr L. & Grose C. Insulin degrading enzyme binds to the nonglycosylated precursor of varicella zoster virus gE protein found in the endoplasmic reticulum. J Virology 84, 847-855, 2010.
    https://journals.asm.org/doi/full/10.1128/jvi.01801-09
  63. de Souza G.A., Søfteland T., Koehler C.J., Thiede B. & Wiker H.G. Validating divergent ORF annotation of the Mycobacterium leprae genome through a full translation dataset and peptide identification by tandem mass spectrometry. Proteomics 9, 3233-3243, 2009.
    https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.200800955
  64. Tomazella G.G., da Silva I., Laure H.J., Rosa J.C., Chammas R., Wiker H.G., de Souza G.A. & Greene, L.J. Proteomic analysis of total cellular proteins of human neutrophils. Proteome Sci 7, 32, 2009.
    https://link.springer.com/article/10.1186/1477-5956-7-32
  65. Pathak S., de Souza G.A., Salte T., Wiker H.G. & Åsjo B. HIV induces both a down-regulation of IRAK-4 that impairs TLR signalling and an up-regulation of the antibiotic peptide dermicidin in monocytic cells. Scand J Immunol 70, 264-276, 2009.
    https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-3083.2009.02299.x
  66. de Souza G.A. & Wiker H.G. The impact of proteomic advances on bacterial gene annotation. Review, Current Proteomics 6, 84-92, 2009.
    https://www.benthamdirect.com/content/journals/cp/10.2174/157016409788680947
  67. Leversen N.A., de Souza G.A., Målen H., Prasad S., Jonassen I. & Wiker H.G. Evaluation of signal peptide prediction algorithms for identification of mycobacterial signal peptides using sequence data from proteomics methods. Microbiology-SRG 155, 2375-2383, 2009.
    https://www.microbiologyresearch.org/content/journal/micro/10.1099/mic.0.025270-0

  68. Schenk S., Schoenhals G.J., de Souza G.A. & Mann M. A high confidence manually validated human blood plasma protein reference set. BMC Med Genomics 1, 41, 2008.
    https://link.springer.com/article/10.1186/1755-8794-1-41
  69. de Souza G.A., Målen H., Søfteland T., Sælesminde G., Prasad S., Jonassen I. & Wiker H.G. High accuracy mass spectrometry analysis as a tool to verify and improve gene annotation using Mycobacterium tuberculosis as an example. BMC Genomics 9, 316, 2008.
    https://link.springer.com/article/10.1186/1471-2164-9-316
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