Human CHR18: “Stakhanovite” Genes, Missing and uPE1 Proteins in Liver Tissue and HepG2 Cells

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Published: Mar 31, 2021
DOI: https://doi.org/10.18097/BMCRM00144
Keywords:
missing proteins; proteotypic peptides; RNA-seq; Illumina; Oxford Nanopore Technologies; transcriptome; C-HPP; human proteome project

Main Article Content

K.A. Deinichenko
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia; National Research University Higher School of Economics, Moscow Russia
G.S. Krasnov
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia; Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia; Mechnikov Research Institute of Vaccines and Sera, Moscow, Russia
S.P. Radko
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
K.G. Ptitsyn
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
V.V. Shapovalova
Centre for Strategic Planning and Management of Biomedical Health Risks, Federal Medical Biological Agency, Moscow, Russia
O.S. Timoshenko
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
S.A. Khmeleva
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
L.K. Kurbatov
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
Y.Y. Kiseleva
Russian Scientific Center of Roentgenoradiology, Moscow, Russia
E.V. Ilgisonis
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
M.A. Pyatnitskiy
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
E.V. Poverennaya
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
O.I. Kiseleva
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
I.V. Vakhrushev
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
A.V. Tsvetkova
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
I.V. Buromski
Department of Forensic Medicine, Faculty of General Medicine, Pirogov Russian National Research Medical University, Moscow, Russia
S.S. Markin
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
V.G. Zgoda
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia; Skolkovo Institute of Science and Technology, Moscow, Russia
A.I. Archakov
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
A.V. Lisitsa
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
E.A. Ponomarenko
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia

Abstract

Missing (MP) and functionally uncharacterized proteins (uPE1) comprise less than 5% of the total number of proteins encoded by human Chr18 genes. Within half a year, since the January 2020 version of NextProt, the number of entries in the MP+uPE1 datasets changed, mainly due to the achievements of antibody-based proteomics. Assuming that the proteome is closely related to the transcriptome scaffold, quantitative PCR, Illumina HiSeq, and Oxford Nanopore Technology were applied to characterize the liver samples of three male donors in comparison with the HepG2 cell line. The data mining of the Expression Atlas (EMBL-EBI) and the profiling of biopsy samples by using orthogonal methods of transcriptome analysis have shown that in HepG2 cells and the liver, the genes encoding functionally uncharacterized proteins (uPE1) are expressed as low as for the missing proteins (less than 1 copy per cell), except the selected cases of HSBP1L1, TMEM241, C18orf21, and KLHL14. The initial expectation that uPE1 genes might be expressed at higher levels than MP genes, was compromised by severe discrepancies in our semi-quantitative gene expression data and in public databanks. Such discrepancy forced us to revisit the transcriptome of Chr18, the target of the Russian C-HPP Consortium. Tanglegram of highly expressed genes and further correlation analysis have shown the severe dependencies on the mRNA extraction method and the analytical platform. Targeted gene expression analysis by quantitative PCR (qPCR) and high-throughput transcriptome profiling (Illumina HiSeq and ONT MinION) for the same set of samples from normal liver tissue and HepG2 cells revealed the detectable expression of 250+ (92%) protein-coding genes of Chr18 (at least one method). The expression of slightly more than 50% protein-coding genes was detected simultaneously by all three methods. Correlation analysis of the gene expression profiles showed that the grouping of the datasets depended almost equally on both the type of biological material and the experimental method, particularly cDNA/mRNA isolation and library preparation.

Article Details

How to Cite
Deinichenko, K., Krasnov, G., Radko, S., Ptitsyn, K., Shapovalova, V., Timoshenko, O., Khmeleva, S., Kurbatov, L., Kiseleva, Y., Ilgisonis, E., Pyatnitskiy, M., Poverennaya, E., Kiseleva, O., Vakhrushev, I., Tsvetkova, A., Buromski, I., Markin, S., Zgoda, V., Archakov, A., Lisitsa, A., & Ponomarenko, E. (2021). Human CHR18: “Stakhanovite” Genes, Missing and uPE1 Proteins in Liver Tissue and HepG2 Cells. Biomedical Chemistry: Research and Methods, 4(1), e00144. https://doi.org/10.18097/BMCRM00144
Issue
Vol. 4 No. 1 (2021)
Section
EXPERIMENTAL RESEARCH

References

  1. Paik, Y.K., Omenn, G.S., Hancock, W.S., Lane, L., Overall, C.M. (2017) Advances in the Chromosome-Centric Human Proteome Project: Looking to the Future. Expert Review of Proteomics, 1059–1071. DOI
  2. Omenn, G.S., Lane, L., Overall, C.M., Corrales, F.J., Schwenk, J.M., Paik, Y.K., Van Eyk, J.E., Liu, S., Pennington, S., Snyder, M.P., Baker, M.S., Deutsch, E.W. (2019) Progress on Identifying and Characterizing the Human Proteome: 2019 Metrics from the HUPO Human Proteome Project. Journal of Proteome Research, 4098–4107. DOI
  3. Gaudet, P., Argoud-Puy, G., Cusin, I., Duek, P., Evalet, O., Gateau, A., Gleizes, A., Pereira, M., Zahn-Zabal, M., Zwahlen, C., Bairoch, A., Lane, L. (2013) NeXtProt: Organizing Protein Knowledge in the Context of Human Proteome Projects. J. Proteome Res, 12 (1), 293–298. DOI
  4. Archakov, A., Aseev, A., Bykov, V., Grigoriev, A., Govorun, V., Ivanov, V., Khlunov, A., Lisitsa, A., Mazurenko, S., Makarov, A. A., Ponomarenko, E., Sagdeev, R., Skryabin, K. (2011) Gene-Centric View on the Human Proteome Project: The Example of the Russian Roadmap for Chromosome 18. Proteomics , 11 (10), 1853–1856. DOI
  5. Poverennaya, E.V., Ilgisonis, E.V., Ponomarenko, E.A., Kopylov, A.T., Zgoda, V.G., Radko, S.P., Lisitsa, A.V., Archakov, A.I. (2017) Why Are the Correlations between MRNA and Protein Levels so Low among the 275 Predicted Protein-Coding Genes on Human Chromosome 18? J. Proteome Res., 16 (12), 4311–4318. DOI
  6. Zgoda, V.G., Kopylov, A.T., Tikhonova, O.V., Moisa, A.A., Pyndyk, N.V., Farafonova, T.E., Novikova, S.E., Lisitsa, A.V., Ponomarenko, E.A., Poverennaya, E.V., Radko, S.P., Khmeleva, S.A., Kurbatov, L.K., Filimonov, A.D., Bogolyubova, N.A., Ilgisonis, E.V., Chernobrovkin, A.L., Ivanov, A.S., Medvedev, A.E., Mezentsev, Y.V., Moshkovskii, S.A., Naryzhny, S.N., Ilina, E.N., Kostrjukova, E.S., Alexeev, D.G., Tyakht, A.V., Govorun, V.M., Archakov, A.I. (2013) Chromosome 18 Transcriptome Profiling and Targeted Proteome Mapping in Depleted Plasma, Liver Tissue and HepG2 Cells. J. Proteome Res., 12 (1), 123–134. DOI
  7. Ponomarenko, E.A., Kopylov, A.T., Lisitsa, A.V., Radko, S.P., Kiseleva, Y.Y., Kurbatov, L.K., Ptitsyn, K.G., Tikhonova, O.V., Moisa, A.A., Novikova, S.E., Poverennay, E.V., Ilgisonis, E.V., Archakov, A.I. (2014) Chromosome 18 Transcriptoproteome of Liver Tissue and HepG2 Cells and Targeted Proteome Mapping in Depleted Plasma: Update 2013. J. Proteome Res., 13 (1), 183–190.
  8. Radko, S.P., Poverennaya, E.V., Kurbatov, L.K., Ponomarenko, E.A., Lisitsa, A.V., Archakov, A.I. (2019) The “Missing” Proteome: Undetected Proteins, Not-Translated Transcripts, and Untranscribed Genes. J. Proteome Res., 18 (12), 4273–4276. DOI
  9. Segura, V., Medina-Aunon, J. A., Guruceaga, E., Gharbi, S. I., Gonzälez-Tejedo, C., San Chez Del Pino, M.M., Canals, F., Fuentes, M., Ignacio Casal, J., Martínez-Bartolomé, S., Elortza, F., Mato, J. M., Arizmendi, J.M., Abian, J., Oliveira, E., Gil, C., Vivanco, F., Blanco, F., Albar, J.P., Corrales, F.J. (2013) Spanish Human Proteome Project: Dissection of Chromosome 16. J. Proteome Res., 12 (1), 112–122. DOI
  10. Yong-In, K., Jongan, L., Young-Jin, C., Jawon, S., Jisook, P., Soo-Youn, L., Je-Yoel, C. (2015) Proteogenomic Study Beyond Chromosome 9: New Insight Into Expressed Variant Proteome and Transcriptome in Human Lung Adenocarcinoma Tissues. J. Proteome Res., 14 (12), 5007–5016.
  11. Liu, S., Im, H., Bairoch, A., Cristofanilli, M., Chen, R., Deutsch, E. W., Dalton, S., Fenyo, D., Fanayan, S., Gates, C., Gaudet, P., Hincapie, M., Hanash, S., Kim, H., Jeong, S. K., Lundberg, E., Mias, G., Menon, R., Mu, Z., Nice, E., Paik, Y.K., Uhlen, M., Wells, L., Wu, S.L., Yan, F., Zhang, F., Zhang, Y., Snyder, M., Omenn, G.S., Beavis, R. C., Hancock, W.S. (2013) A Chromosome-Centric Human Proteome Project (C-HPP) to Characterize the Sets of Proteins Encoded in Chromosome 17. J Proteome Res, 45–57. DOI
  12. Chang, C., Li, L., Zhang, C., Wu, S., Guo, K., Zi, J., Chen, Z., Jiang, J., Ma, J., Yu, Q., Fan, F., Qin, P., Han, M., Su, N., Chen, T., Wang, K., Zhai, L., Zhang, T., Ying, W., Xu, Z., Zhang, Y., Liu, Y., Liu, X., Zhong, F., Shen, H., Wang, Q., Hou, G., Zhao, H., Li, G., Liu, S., Gu, W., Wang, G., Wang, T., Zhang, G., Qian, X., Li, N., He, Q. Y., Lin, L., Yang, P., Zhu, Y., He, F., Xu, P. (2014) Systematic Analyses of the Transcriptome, Translatome, and Proteome Provide a Global View and Potential Strategy for the C-HPP. J. Proteome Res., 13 (1), 38–49. DOI
  13. Shargunov, A.V., Krasnov, G.S., Ponomarenko, E.A., Lisitsa, A.V., Shurdov, M.A., Zverev, V.V., Archakov, A.I., Blinov, V.M. (2014) Tissue-Specific Alternative Splicing Analysis Reveals the Diversity of Chromosome 18 Transcriptome. J. Proteome Res., 13 (1), 173–182. DOI
  14. Krasnov, G.S., Dmitriev, A.A., Kudryavtseva, A.V., Shargunov, A.V., Karpov, D.S., Uroshlev, L.A., Melnikova, N.V., Blinov, V.M., Poverennaya, E.V., Archakov, A.I., Lisitsa, A.V., Ponomarenko, E.A. (2015) PPLine: An Automated Pipeline for SNP, SAP, and Splice Variant Detection in the Context of Proteogenomics. J. Proteome Res., 14 (9), 3729–3737. DOI
  15. Jeong, S.K., Lee, H.J., Na, K., Cho, J.Y., Lee, M.J., Kwon, J.Y., Kim, H., Park, Y.M., Yoo, J.S., Hancock, W. S., Paik, Y.K. (2013) GenomewidePDB, a Proteomic Database Exploring the Comprehensive Protein Parts List and Transcriptome Landscape in Human Chromosomes. J. Proteome Res., 12 (1), 106–111. DOI
  16. Poverennaya, E.V., Shargunov, A.V., Ponomarenko, E.A., Lisitsa, A.V. (2018) The Gene-Centric Content Management System and Its Application for Cognitive Proteomics. Proteomes, 6 (1). DOI
  17. Tyakht, A.V., Ilina, E.N., Alexeev, D.G., Ischenko, D.S., Gorbachev, A.Y., Semashko, T.A., Larin, A.K., Selezneva, O V., Kostryukova, E.S., Karalkin, P.A., Vakhrushev, I.V., Kurbatov, L.K., Archakov, A.I., Govorun, V.M. (2014) RNA-Seq Gene Expression Profiling of HepG2 Cells: The Influence of Experimental Factors and Comparison with Liver Tissue. BMC Genomics , 15 (1). DOI
  18. Chalmel, F., Rolland, A. D. Linking Transcriptomics and Proteomics in Spermatogenesis. Reproduction, 150 (5), R149–R157. DOI
  19. Fortelny, N., Overall, C. M., Pavlidis, P., Freue, G.V.C. (2017) Can We Predict Protein from MRNA Levels? Nature, 547, E19–E20. DOI
  20. Eraslan, B., Wang, D., Gusic, M., Prokisch, H., Hallström, B.M., Uhlén, M., Asplund, A., Pontén, F., Wieland, T., Hopf, T., Hahne, H., Kuster, B., Gagneur, J. (2019) Quantification and Discovery of Sequence Determinants of Protein‐per‐mRNA Amount in 29 Human Tissues. Mol. Syst. Biol., 15 (2). DOI
  21. De Sousa Abreu, R., Penalva, L.O., Marcotte, E.M., Vogel, C. (2009) Global Signatures of Protein and MRNA Expression Levels. Mol Biosyst, 1512–1526. DOI
  22. Ponomarenko, E.A., Kopylov, A.T., Lisitsa, A.V., Radko, S.P., Kiseleva, Y.Y., Kurbatov, L.K., Ptitsyn, K.G., Tikhonova, O.V., Moisa, A.A., Novikova, S.E., Poverennaya, E.V., Ilgisonis, E.V., Filimonov, A.D., Bogolubova, N.A., Averchuk, V.V., Karalkin, P.A., Vakhrushev, I.V., Yarygin, K.N., Moshkovskii, S.A., Zgoda, V.G., Sokolov, A.S., Mazur, A.M., Prokhortchouck, E.B., Skryabin, K.G., Ilina, E.N., Kostrjukova, E.S., Alexeev, D.G., Tyakht, A.V., Gorbachev, A.Y., Govorun, V.M., Archakov, A.I. (2014) Chromosome 18 Transcriptoproteome of Liver Tissue and HepG2 Cells and Targeted Proteome Mapping in Depleted Plasma: Update 2013. J. Proteome Res., 13 (1), 183–190. DOI
  23. Seki, M., Katsumata, E., Suzuki, A., Sereewattanawoot, S., Sakamoto, Y., Mizushima-Sugano, J., Sugano, S., Kohno, T., Frith C.,M., Tsuchihara, K., Suzuki, Y., Expand, A. (2019) Evaluation and Application of RNA-Seq by MinION. DNA Res ., 26 (1), 55–65.
  24. Poverennaya, E.V., Kopylov, A.T., Ponomarenko, E.A., Ilgisonis, E.V., Zgoda, V.G., Tikhonova, O.V., Novikova, S.E., Farafonova, T.E., Kiseleva, Y.Y., Radko, S.P., Vakhrushev, I.V., Yarygin, K.N., Moshkovskii, S.A., Kiseleva, O.I., Lisitsa, A.V., Sokolov, A.S., Mazur, A.M., Prokhortchouk, E.B., Skryabin, K.G., Kostrjukova, E.S., Tyakht, A.V., Gorbachev, A.Y., Ilina, E.N., Govorun, V.M., Archakov, A.I. (2016) State of the Art of Chromosome 18-Centric HPP in 2016: Transcriptome and Proteome Profiling of Liver Tissue and HepG2 Cells. J. Proteome Res., 15 (11), 4030–4038. DOI
  25. Riedel, G., Rüdrich, U., Fekete-Drimusz, N., Manns, M.P., Vondran, F.W.R., Bock, M. (2014) An Extended ΔCT-Method Facilitating Normalisation with Multiple Reference Genes Suited for Quantitative RT-PCR Analyses of Human Hepatocyte-like Cells. PLoS One, 9 (3). DOI
  26. Wilkening, S., Stahl, F., Bader, A. (2003) Comparison of Primary Human Hepatocytes and Hepatoma Cell Line HepG2 with Regard to Their Biotransformation Properties. Drug Metab. Dispos., 31 (8), 1035–1042. DOI
  27. Wick, R.R., Judd, L. M., Holt, K.E. (2019) Performance of Neural Network Basecalling Tools for Oxford Nanopore Sequencing. Genome Biol., 20 (1). DOI
  28. Li, H. Minimap2: Pairwise Alignment for Nucleotide Sequences. Bioinformatics 2018, 34 (18), 3094–3100. DOI
  29. Patro, R., Duggal, G., Love, M.I., Irizarry, R A., Kingsford, C. (2017) Salmon Provides Fast and Bias-Aware Quantification of Transcript Expression. Nat. Methods, 14 (4), 417–419. DOI
  30. Kulak, N.A., Geyer, P.E., Mann, M. (2017) Loss-Less Nano-Fractionator for High Sensitivity, High Coverage Proteomics. Mol. Cell. Proteomics, 16 (4), 694–705. DOI
  31. Papatheodorou, I., Fonseca, N.A., Keays, M., Tang, Y. A., Barrera, E., Bazant, W., Burke, M., Füllgrabe, A., Fuentes, A.M.P., George, N., Huerta, L., Koskinen, S., Mohammed, S., Geniza, M., Preece, J., Jaiswal, P., Jarnuczak, A.F., Huber, W., Stegle, O., Vizcaino, J. A., Brazma, A., Petryszak, R. (2018) Expression Atlas: Gene and Protein Expression across Multiple Studies and Organisms. Nucleic Acids Res., 46 (D1), D246–D251. DOI
  32. Albert, R., Barabasi, A.L. (2002) Statistical mechanics of complex networks. Rev. Mod. Phys., 74(1), 47. DOI
  33. Poverennaya, E., Kiseleva, O., Ilgisonis, E., Novikova, S., Kopylov, A., Ivanov, Y., Kononikhin, A., Gorshkov, M., Kushlinskii, N., Archakov, Ponomarenko, E. (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 (2), 12. DOI
  34. Misawa, K., Kanazawa, T., Mochizuki, D., Imai, A., Mima, M., Yamada, S., Morita, K., Misawa, Y., Shinmura, K., Mineta, H. (2019) Genes Located on 18q23 Are Epigenetic Markers and Have Prognostic Significance for Patients with Head and Neck Cancer. Cancers, 11 (3). DOI
  35. Chen, K., He, Y., Liu, Y., Yang, X. (2019) Gene Signature Associated with Neuro-Endocrine Activity Predicting Prognosis of Pancreatic Carcinoma. Mol. Genet. Genomic Med., 7 (7). DOI
  36. Rodrigues, R.M., Heymans, A., De Boe, V., Sachinidis, A., Chaudhari, U., Govaere, O., Roskams, T., Vanhaecke, T., Rogiers, V., De Kock, J. (2016) Toxicogenomics-Based Prediction of Acetaminophen-Induced Liver Injury Using Human Hepatic Cell Systems. Toxicol. Lett., 240 (1), 50–59. DOI
  37. Deutsch, E.W., Lane, L., Overall, C. M., Bandeira, N., Baker, M. S., Pineau, C., Moritz, R.L., Corrales, F., Orchard, S., Van Eyk, J.E., Paik, Y.K., Weintraub, S.T., Vandenbrouck, Y., Omenn, G.S. (2019) Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0. J Proteome Res., 2019, 4108–4116. DOI
  38. Tran, J. C., Zamdborg, L., Ahlf, D. R., Lee, J.E., Catherman, A.D., Durbin, K. R., Tipton, J.D., Vellaichamy, A., Kellie, J.F., Li, M., Wu, C., Sweet, S.M.M., Early, B.P., Siuti, N., Leduc, R.D., Compton, P.D., Thomas, P.M., Kelleher, N.L. (2011) Mapping Intact Protein Isoforms in Discovery Mode Using Top-down Proteomics. Nature, 480 (7376), 254–258. DOI
  39. Righetti, P.G., Boschetti, E. (2008) The Proteominer and the Fortyniners: Searching for Gold Nuggets in the Proteomic Arena. Mass Spectrometry Reviews, 596–608. DOI
  40. Dendextend, G.T. (2015) Dendextend: An R Package for Visualizing, Adjusting and Comparing Trees of Hierarchical Clustering. Bioinformatics, 31 (22), 3718–3720. DOI
  41. Dong, H., Ge, X., Shen, Y., Chen, L., Kong, Y., Zhang, H., Man, X., Tang, L., Yuan, H., Wang, H., Zhao, G., Jin, W. (2009) Gene Expression Profile Analysis of Human Hepatocellular Carcinoma Using SAGE and LongSAGE. BMC Med. Genomics, 2. DOI
  42. 49 Shanmugam, A.K., Yocum, A.K., Nesvizhskii, A.I. (2014) Utility of RNA-Seq and GPMDB Protein Observation Frequency for Improving the Sensitivity of Protein Identification by Tandem MS. J. Proteome Res., 13 (9), 4113–4119. DOI
  43. Frith, M. C., Hamada, M., Horton, P. (2010) Parameters for Accurate Genome Alignment. BMC Bioinformatics, 11. DOI
  44. Li, H., Durbin, R. (2010) Fast and Accurate Long-Read Alignment with Burrows-Wheeler Transform. Bioinformatics, 26 (5), 589–595. DOI
  45. Van Delft, J., Gaj, S., Lienhard, M., Albrecht, M. W., Kirpiy, A., Brauers, K., Claessen, S., Lizarraga, D., Lehrach, H., Herwig, R., Kleinjans, J. (2012) Rna-Seq Provides New Insights in the Transcriptome Responses Induced by the Carcinogen Benzo[a]Pyrene. Toxicol. Sci., 130 (2), 427–439. DOI