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Proteomic Approach to Investigating Expression, Localization, and Functions of the SOWAHD Gene Protein Product during Granulocytic Differentiation

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Abstract

Cataloging human proteins and evaluation of their expression, cellular localization, functions, and potential medical significance are important tasks for the global proteomic community. At present, localization and functions of protein products for almost half of protein-coding genes remain unknown or poorly understood. Investigation of organelle proteomes is a promising approach to uncovering localization and functions of human proteins. Nuclear proteome is of particular interest because many nuclear proteins, e.g., transcription factors, regulate functions that determine cell fate. Meta-analysis of the nuclear proteome, or nucleome, of HL-60 cells treated with all-trans-retinoic acid (ATRA) has shown that the functions and localization of a protein product of the SOWAHD gene are poorly understood. Also, there is no comprehensive information on the SOWAHD gene expression at the protein level. In HL-60 cells, the number of mRNA transcripts of the SOWAHD gene was determined as 6.4 ± 0.7 transcripts per million molecules. Using targeted mass spectrometry, the content of the SOWAHD protein was measured as 0.27 to 1.25 fmol/μg total protein. The half-life for the protein product of the SOWAHD gene determined using stable isotope pulse-chase labeling was ~19 h. Proteomic profiling of the nuclear fraction of HL-60 cells showed that the content of the SOWAHD protein increased during the ATRA-induced granulocytic differentiation, reached the peak value at 9 h after ATRA addition, and then decreased. Nuclear location and involvement of the SOWAHD protein in the ATRA-induced granulocytic differentiation have been demonstrated for the first time.

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Abbreviations

ATRA:

all-trans retinoic acid

FBS:

fetal bovine serum

GO:

Gene Ontology knowledgebase

HL-60 cells:

acute myeloid leukemia cells

SILAC:

stable isotope labeling with amino acids in cell culture

SIS peptide:

stable isotope labeled standard peptide

SOWAHD:

ankyrin repeat domain-containing protein 58

SRM:

selected reaction monitoring

TMT:

tandem mass tag (isobaric label)

References

  1. Consortium UniProt (2023) UniProt: The Universal Protein Knowledgebase in 2023, Nucleic Acids Res., 51, D523-D531, https://doi.org/10.1093/nar/gkac1052.

    Article  CAS  Google Scholar 

  2. Lane, L., Argoud-Puy, G., Britan, A., Cusin, I., Duek, P. D., Evalet, O., Gateau, A., Gaudet, P., Gleizes, A., Masselot, A., et al. (2012) NeXtProt: a knowledge platform for human proteins, Nucleic Acids Res., 40, D76-D83, https://doi.org/10.1093/nar/gkr1179.

    Article  CAS  PubMed  Google Scholar 

  3. Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015) Proteomics. Tissue-based map of the human proteome, Science, 347, 1260419, https://doi.org/10.1126/science.1260419.

    Article  CAS  PubMed  Google Scholar 

  4. Kim, M. S., Pinto, S. M., Getnet, D., Nirujogi, R. S., Manda, S. S., Chaerkady, R., Madugundu, A. K., Kelkar, D. S., Isserlin, R., Jain, S., et al. (2014) A draft map of the human proteome, Nature, 509, 575-581, https://doi.org/10.1038/nature13302.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Adhikari, S., Nice, E. C., Deutsch, E. W., Lane, L., Omenn, G. S., Pennington, S. R., Paik, Y. K., Overall, C. M., Corrales, F. J., Cristea, I. M., et al. (2020) A high-stringency blueprint of the human proteome, Nat. Commun., 11, 5301, https://doi.org/10.1038/s41467-020-19045-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Salzberg, S. L. (2018) Open questions: how many genes do we have? BMC Biol., 16, 94, https://doi.org/10.1186/s12915-018-0564-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kopylov, A. T., Ponomarenko, E. A., Ilgisonis, E. V., Pyatnitskiy, M. A., Lisitsa, A. V., Poverennaya, E. V., Kiseleva, O. I., Farafonova, T. E., Tikhonova, O. V., Zavialova, M. G., et al. (2019) 200+ protein concentrations in healthy human blood plasma: targeted quantitative SRM SIS screening of chromosomes 18, 13, Y, and the mitochondrial chromosome encoded proteome, J. Proteome Res., 18, 120-129, https://doi.org/10.1021/acs.jproteome.8b00391.

    Article  CAS  PubMed  Google Scholar 

  8. Poverennaya, E., Kiseleva, O., Ilgisonis, E., Novikova, S., Kopylov, A., Ivanov, Y., Kononikhin, A., Gorshkov, M., Kushlinskii, N., Archakov, A., et al. (2020) Is it possible to find needles in a haystack? Meta-analysis of 1000+ MS/MS files provided by the Russian proteomic consortium for mining missing proteins, Proteomes, 8, 12, https://doi.org/10.3390/PROTEOMES8020012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Paik, Y. K., Overall, C. M., Corrales, F., Deutsch, E. W., Lane, L., and Omenn, G. S. (2018) toward completion of the human proteome parts list: progress uncovering proteins that are missing or have unknown function and developing analytical methods, J. Proteome Res., 17, 4023-4030, https://doi.org/10.1021/acs.jproteome.8b00885.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Thul, P. J., and Lindskog, C. (2018) The human protein atlas: a spatial map of the human proteome, Protein Sci., 27, 233-244, https://doi.org/10.1002/pro.3307.

    Article  CAS  PubMed  Google Scholar 

  11. Van Bortle, K., and Corces, V. G. (2012) Nuclear organization and genome function, Annu. Rev. Cell Dev. Biol., 28, 163-187, https://doi.org/10.1146/annurev-cellbio-101011-155824.

    Article  CAS  PubMed  Google Scholar 

  12. Vakhrushev, I. V., Novikova, S. E., Tsvetkova, A. V., Karalkin, P. A., Pyatnitskii, M. A., Zgoda, V. G., and Yarygin, K. N. (2018) Proteomic profiling of HL-60 cells during ATRA-induced differentiation, Bull. Exp. Biol. Med., 165, 530-543, https://doi.org/10.1007/s10517-018-4210-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Novikova, S., Tolstova, T., Kurbatov, L., Farafonova, T., Tikhonova, O., Soloveva, N., Rusanov, A., Archakov, A., and Zgoda, V. (2022) Nuclear proteomics of induced leukemia cell differentiation, Cells, 11, 3221, https://doi.org/10.3390/cells11203221.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zheng, P. Z., Wang, K. K., Zhang, Q. Y., Huang, Q. H., Du, Y. Z., Zhang, Q. H., Xiao, D. K., Shen, S. H., Imbeaud, S., Eveno, E., et al. (2005) Systems analysis of transcriptome and proteome in retinoic acid/arsenic trioxide-induced cell differentiation apoptosis of promyelocytic leukemia, Proc. Natl. Acad. Sci. USA, 102, 7653-7658, https://doi.org/10.1073/pnas.0502825102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang, W.-J., Tang, W., and Qiu, Z.-Y. (2009) Comparative proteomics analysis on differentiation of human promyelocytic leukemia HL-60 cells into granulocyte and monocyte lineages, Chinese J. Cancer, 28, 117-121.

    Google Scholar 

  16. Novikova, S., Tikhonova, O., Kurbatov, L., Farafonova, T., Vakhrushev, I., Lupatov, A., Yarygin, K., and Zgoda, V. (2021) Omics technologies to decipher regulatory networks in granulocytic cell differentiation, Biomolecules, 11, 907, https://doi.org/10.3390/biom11060907.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jian, P., Li, Z. W., Fang, T. Y., Jian, W., Zhuan, Z., Mei, L. X., Yan, W. S., and Jian, N. (2011) Retinoic acid induces HL-60 cell differentiation via the upregulation of MiR-663, J. Hematol. Oncol., 4, 20, https://doi.org/10.1186/1756-8722-4-20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Schwanhäusser, B., Gossen, M., Dittmar, G., and Selbach, M. (2009) Global analysis of cellular protein translation by pulsed SILAC, Proteomics, 9, 205-209, https://doi.org/10.1002/pmic.200800275.

    Article  CAS  PubMed  Google Scholar 

  19. Claydon, A. J., and Beynon, R. (2012) Proteome dynamics: revisiting turnover with a global perspective, Mol. Cell. Proteomics, 11, 1551-1565, https://doi.org/10.1074/mcp.O112.022186.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ross, A. B., Langer, J. D., and Jovanovic, M. (2021) Proteome turnover in the spotlight: approaches, applications, and perspectives, Mol. Cell. Proteomics, 20, 100016, https://doi.org/10.1074/mcp.R120.002190.

    Article  CAS  PubMed  Google Scholar 

  21. Holman, S. W., Hammond, D. E., Simpson, D. M., Waters, J., Hurst, J. L., and Beynon, R. J. (2016) Protein turnover measurement using selected reaction monitoring-mass spectrometry (SRM-MS), Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 374, 20150362, https://doi.org/10.1098/rsta.2015.0362.

    Article  CAS  Google Scholar 

  22. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., and Kingsford, C. (2017) Salmon provides fast and bias-aware quantification of transcript expression, Nat. Methods, 14, 417-419, https://doi.org/10.1038/nmeth.4197.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Novikova, S. E., Vakhrushev, I. V., Tsvetkova, A. V., Shushkova, N. A., Farafonova, T. E., Yarygin, K. N., and Zgoda, V. G. (2019) Proteomics of transcription factors: identification of pool of HL-60 cell line-specific regulatory proteins [in Russian], Biomed. Khim., 65, 294-305, https://doi.org/10.18097/PBMC20196504294.

    Article  CAS  PubMed  Google Scholar 

  24. Wiśniewski, J. R., Zougman, A., Nagaraj, N., and Mann, M. (2009) Universal sample preparation method for proteome analysis, Nat. Methods, 6, 359-362, https://doi.org/10.1038/nmeth.1322.

    Article  CAS  PubMed  Google Scholar 

  25. Mohammad, N. S., Nazli, R., Zafar, H., and Fatima, S. (2022) Effects of lipid based multiple micronutrients supplement on the birth outcome of underweight pre-eclamptic women: a randomized clinical trial, Pak. J. Med. Sci., 38, 219-226, https://doi.org/10.12669/pjms.38.1.4396.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Liu, Y., Mi, Y., Mueller, T., Kreibich, S., Williams, E. G., Van Drogen, A., Borel, C., Frank, M., Germain, P.-L., Bludau, I., et al. (2019) Multi-omic measurements of heterogeneity in HeLa cells across laboratories, Nat. Biotechnol., 37, 314-322, https://doi.org/10.1038/s41587-019-0037-y.

    Article  CAS  PubMed  Google Scholar 

  27. Zhu, Q., Wang, J., Zhang, Q., Wang, F., Fang, L., Song, B., Xie, C., and Liu, J. (2020) Methylation-driven Genes PMPCAP1, SOWAHC and ZNF454 as potential prognostic biomarkers in lung squamous cell carcinoma, Mol. Med. Rep., 21, 1285-1295, https://doi.org/10.3892/mmr.2020.10933.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ruan, B., Feng, X., Chen, X., Dong, Z., Wang, Q., Xu, K., Tian, J., Liu, J., Chen, Z., Shi, W., et al. (2020) Identification of a set of genes improving survival prediction in kidney renal clear cell carcinoma through integrative reanalysis of transcriptomic data, Dis. Markers, 2020, 8824717, https://doi.org/10.1155/2020/8824717.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Mori, Y., Yokota, H., Hoshino, I., Iwatate, Y., Wakamatsu, K., Uno, T., and Suyari, H. (2021) Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer, Sci. Rep., 11, 16521, https://doi.org/10.1038/s41598-021-95969-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Gillespie, M., Jassal, B., Stephan, R., Milacic, M., Rothfels, K., Senff-Ribeiro, A., Griss, J., Sevilla, C., Matthews, L., Gong, C., et al. (2022) The reactome pathway knowledgebase, Nucleic Acids Res., 50, D687-D692, https://doi.org/10.1093/nar/gkab1028.

    Article  CAS  PubMed  Google Scholar 

  31. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M. (2016) KEGG as a reference resource for gene and protein annotation, Nucleic Acids Res., 44, D457-D462, https://doi.org/10.1093/nar/gkv1070.

    Article  CAS  PubMed  Google Scholar 

  32. Han, H., Shim, H., Shin, D., Shim, J. E., Ko, Y., Shin, J., Kim, H., Cho, A., Kim, E., Lee, T., et al. (2015) TRRUST: a reference database of human transcriptional regulatory interactions, Sci. Rep., 5, 11432, https://doi.org/10.1038/srep11432.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lin, Y., Mehta, S., Küçük-McGinty, H., Turner, J. P., Vidovic, D., Forlin, M., Koleti, A., Nguyen, D.-T., Jensen, L. J., Guha, R., et al. (2017) Drug target ontology to classify and integrate drug discovery data, J. Biomed. Semantics, 8, 50, https://doi.org/10.1186/s13326-017-0161-x.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Mathieson, T., Franken, H., Kosinski, J., Kurzawa, N., Zinn, N., Sweetman, G., Poeckel, D., Ratnu, V. S., Schramm, M., Becher, I., et al. (2018) Systematic analysis of protein turnover in primary cells, Nat. Commun., 9, 689, https://doi.org/10.1038/s41467-018-03106-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gomez, G., Lee, J. H., Veldman, M. B., Lu, J., Xiao, X., and Lin, S. (2012) Identification of vascular and hematopoietic genes downstream of etsrp by deep sequencing in zebrafish, PLoS One, 7, e31658, https://doi.org/10.1371/journal.pone.0031658.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., Gable, A. L., Fang, T., Doncheva, N. T., Pyysalo, S., et al. (2023) The STRING database in 2023: protein-protein association networks and functional Enrichment analyses for any sequenced genome of interest, Nucleic Acids Res., 51, D638-D646, https://doi.org/10.1093/nar/gkac1000.

    Article  CAS  PubMed  Google Scholar 

  37. Kang, Y., Xie, H., and Zhao, C. (2019) Ankrd45 is a novel ankyrin repeat protein required for cell proliferation, Genes (Basel), 10, 462, https://doi.org/10.3390/genes10060462.

    Article  CAS  PubMed  Google Scholar 

  38. Kumar, A., and Balbach, J. (2021) Folding and stability of ankyrin repeats control biological protein function, Biomolecules, 11, 840, https://doi.org/10.3390/biom11060840.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

We thank Prof. A. E. Medvedev (Institute of Biomedical Chemistry, Moscow, Russia) for helpful discussion and critical reading of the manuscript. The study was performed using equipment of the “Human Proteome” Core Facility at the Institute of Biomedical Chemistry.

Funding

This work was supported by the Russian Science Foundation (project 21-74-20122).

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Authors and Affiliations

  1. Institute of Biomedical Chemistry, 119121, Moscow, Russia

    Svetlana E. Novikova, Tatyana V. Tolstova, Natalya A. Soloveva, Tatyana E. Farafonova, Olga V. Tikhonova, Leonid K. Kurbatov, Aleksandr L. Rusanov & Victor G. Zgoda

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  1. Svetlana E. Novikova
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  2. Tatyana V. Tolstova
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  3. Natalya A. Soloveva
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  4. Tatyana E. Farafonova
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  5. Olga V. Tikhonova
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  6. Leonid K. Kurbatov
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  7. Aleksandr L. Rusanov
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  8. Victor G. Zgoda
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Contributions

S.E.N., A.L.R., and V.G.Z. developed the concept and managed the study; S.E.N., T.V.T., N.A.S., T.E.F., O.V.T., and L.K.K. conducted experiments; S.E.N., O.V.T., L.K.K., A.L.R., and V.G.Z. discussed the study results; S.E.N., wrote the text of the article; V.G.Z. edited the manuscript.

Corresponding authors

Correspondence to Svetlana E. Novikova, Natalya A. Soloveva or Victor G. Zgoda.

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The authors declare no conflict of interest. This article does not contain description of studies involving humans or animals performed by any of the authors.

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Novikova, S.E., Tolstova, T.V., Soloveva, N.A. et al. Proteomic Approach to Investigating Expression, Localization, and Functions of the SOWAHD Gene Protein Product during Granulocytic Differentiation. Biochemistry Moscow 88, 1668–1682 (2023). https://doi.org/10.1134/S000629792310019X

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  • DOI: https://doi.org/10.1134/S000629792310019X

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