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Ponomarenko EA, Krasnov GS, Kiseleva OI, Kryukova PA, Arzumanian VA, Dolgalev GV, Ilgisonis EV, Lisitsa AV, Poverennaya EV. Workability of mRNA Sequencing for Predicting Protein Abundance. Genes (Basel) 2023; 14:2065. [PMID: 38003008 PMCID: PMC10671741 DOI: 10.3390/genes14112065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell's state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of "transcript-protein" pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome-proteome levels for different tissues does not exceed 0.3-0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction.
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Affiliation(s)
| | - George S. Krasnov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia;
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2
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Ilgisonis EV, Ponomarenko EA, Tarbeeva SN, Lisitsa AV, Zgoda VG, Radko SP, Archakov AI. Gene-centric coverage of the human liver transcriptome: QPCR, Illumina, and Oxford Nanopore RNA-Seq. Front Mol Biosci 2022; 9:944639. [PMID: 36545510 PMCID: PMC9760921 DOI: 10.3389/fmolb.2022.944639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 11/10/2022] [Indexed: 12/07/2022] Open
Abstract
It has been shown that the best coverage of the HepG2 cell line transcriptome encoded by genes of a single chromosome, chromosome 18, is achieved by a combination of two sequencing platforms, Illumina RNA-Seq and Oxford Nanopore Technologies (ONT), using cut-off levels of FPKM > 0 and TPM > 0, respectively. In this study, we investigated the extent to which the combination of these transcriptomic analysis methods makes it possible to achieve a high coverage of the transcriptome encoded by the genes of other human chromosomes. A comparative analysis of transcriptome coverage for various types of biological material was carried out, and the HepG2 cell line transcriptome was compared with the transcriptome of liver tissue cells. In addition, the contribution of variability in the coverage of expressed genes in human transcriptomes to the creation of a draft human transcriptome was evaluated. For human liver tissues, ONT makes an extremely insignificant contribution to the overall coverage of the transcriptome. Thus, to ensure maximum coverage of the liver tissue transcriptome, it is sufficient to apply only one technology: Illumina RNA-Seq (FPKM > 0).
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3
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Murzina SA, Voronin VP, Churova MV, Ruokolainen TR, Shulgina NS, Provotorov DS, Tikhonova OV, Nemova NN. The Effects of Low-Level Helium-Neon (He-Ne) Laser Irradiation on Lipids and Fatty Acids, and the Activity of Energetic Metabolism Enzymes and Proteome in the Blastula Stage and Underyearlings of the Atlantic Salmon Salmo salar: A Novel Approach in Salmonid Restoration Procedures in the North. Biomolecules 2022; 12:133. [PMID: 35053280 PMCID: PMC8774099 DOI: 10.3390/biom12010133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/05/2022] [Accepted: 01/12/2022] [Indexed: 11/28/2022] Open
Abstract
The effect of He-Ne laser irradiation on fishery parameters as well as on biochemical state, including the lipids and fatty acids, the activity of energy metabolism enzymes and the proteome in the blastula stage and in underyearlings of wild Atlantic salmon after irradiation at the cleavage stage/early blastula (considered as the stages when the cell has a high potential for differentiation) was studied. Low mortality rates of eggs were determined during embryogenesis, as well as increased weight gain and lower morality rates of underyearlings in the experimental group. This is confirmed by changes in a number of interrelated indicators of lipid metabolism: a decrease in total lipids content, including diacylglycerols, triacylglycerols, cholesterol esters, and the phospholipids content remained unchanged. The embryos in the blastula stage (experimental group) had higher aerobic capacity and an increase in pentose phosphate pathway activity. The proteome profiles of eggs in the blastula stage were 131 proteins, of which 48 were significantly identified. The major protein was found to be phosvitin. The proteomes of underyearlings were represented by 2018 proteins, of which 49 were unique for the control and 39 for the experimental group. He-Ne laser irradiation had a strong effect on the contents of histone proteins.
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Affiliation(s)
- Svetlana A Murzina
- Institute of Biology of the Karelian Research Centre of the Russian Academy of Sciences (IB KarRC RAS), 11 Pushkinskaya Street, 185910 Petrozavodsk, Russia
| | - Viktor P Voronin
- Institute of Biology of the Karelian Research Centre of the Russian Academy of Sciences (IB KarRC RAS), 11 Pushkinskaya Street, 185910 Petrozavodsk, Russia
| | - Maria V Churova
- Institute of Biology of the Karelian Research Centre of the Russian Academy of Sciences (IB KarRC RAS), 11 Pushkinskaya Street, 185910 Petrozavodsk, Russia
| | - Tatiana R Ruokolainen
- Institute of Biology of the Karelian Research Centre of the Russian Academy of Sciences (IB KarRC RAS), 11 Pushkinskaya Street, 185910 Petrozavodsk, Russia
| | - Natalia S Shulgina
- Institute of Biology of the Karelian Research Centre of the Russian Academy of Sciences (IB KarRC RAS), 11 Pushkinskaya Street, 185910 Petrozavodsk, Russia
| | - Dmitriy S Provotorov
- Institute of Biology of the Karelian Research Centre of the Russian Academy of Sciences (IB KarRC RAS), 11 Pushkinskaya Street, 185910 Petrozavodsk, Russia
| | - Olga V Tikhonova
- Institute of Biomedical Chemistry (IBMC), 10 Pogodinskaya Street, 119121 Moscow, Russia
| | - Nina N Nemova
- Institute of Biology of the Karelian Research Centre of the Russian Academy of Sciences (IB KarRC RAS), 11 Pushkinskaya Street, 185910 Petrozavodsk, Russia
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4
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Timoshenko OS, Khmeleva SA, Poverennaya EV, Kiseleva YY, Kurbatov LK, Radko SP, Buromski IV, Markin SS, Lisitsa AV, Archakov AI, Ponomarenko EA. [PCR analysis of the expression of chromosome 18 genes in human liver tissue: interindividual variability]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2021; 67:418-426. [PMID: 34730555 DOI: 10.18097/pbmc20216705418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Using human chromosome 18 (Ch18) genes as an example, a PCR analysis of the interindividual variability of gene expression in liver tissue was performed. Although the quantitative profiles of the Ch18 transcriptome, expressed in the number of cDNA copies per single cell, showed a high degree of correlation between donors (Pearson correlation coefficients ranged from 0.963 to 0.966), the expression of the significant number of genes (from 13% to 19%, depending on the method of experimental data normalization) varied by more than 4-fold when comparing donors pairwise. At the same time, the proportion of differentially expressed genes increased with a decrease in the level of their expression. It is shown that the higher quantitative variability of low-abundance transcripts is mainly not technical, but biological. Bioinformatic analysis of the interindividual variability of the differential expression of chromosome 18 genes in human liver tissue did not reveal any statistically significant groups of genes related to certain biological processes that indicated a rather transient nature of the interindividual variability of their expression, probably reflecting the response of cells of an individual to specific external stimuli.
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Affiliation(s)
| | - S A Khmeleva
- Institute of Biomedical Chemistry, Moscow, Russia
| | | | - Y Y Kiseleva
- Russian Scientific Center of Roentgenoradiology, Moscow, Russia
| | - L K Kurbatov
- Institute of Biomedical Chemistry, Moscow, Russia
| | - S P Radko
- Institute of Biomedical Chemistry, Moscow, Russia
| | - I V Buromski
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - S S Markin
- Institute of Biomedical Chemistry, Moscow, Russia
| | - A V Lisitsa
- Institute of Biomedical Chemistry, Moscow, Russia
| | - A I Archakov
- Institute of Biomedical Chemistry, Moscow, Russia
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5
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Novikova S, Tikhonova O, Kurbatov L, Farafonova T, Vakhrushev I, Lupatov A, Yarygin K, Zgoda V. Omics Technologies to Decipher Regulatory Networks in Granulocytic Cell Differentiation. Biomolecules 2021; 11:907. [PMID: 34207065 PMCID: PMC8233756 DOI: 10.3390/biom11060907] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/10/2021] [Accepted: 06/15/2021] [Indexed: 01/01/2023] Open
Abstract
Induced granulocytic differentiation of human leukemic cells under all-trans-retinoid acid (ATRA) treatment underlies differentiation therapy of acute myeloid leukemia. Knowing the regulation of this process it is possible to identify potential targets for antileukemic drugs and develop novel approaches to differentiation therapy. In this study, we have performed transcriptomic and proteomic profiling to reveal up- and down-regulated transcripts and proteins during time-course experiments. Using data on differentially expressed transcripts and proteins we have applied upstream regulator search and obtained transcriptome- and proteome-based regulatory networks of induced granulocytic differentiation that cover both up-regulated (HIC1, NFKBIA, and CASP9) and down-regulated (PARP1, VDR, and RXRA) elements. To verify the designed network we measured HIC1 and PARP1 protein abundance during granulocytic differentiation by selected reaction monitoring (SRM) using stable isotopically labeled peptide standards. We also revealed that transcription factor CEBPB and LYN kinase were involved in differentiation onset, and evaluated their protein levels by SRM technique. Obtained results indicate that the omics data reflect involvement of the DNA repair system and the MAPK kinase cascade as well as show the balance between the processes of the cell survival and apoptosis in a p53-independent manner. The differentially expressed transcripts and proteins, predicted transcriptional factors, and key molecules such as HIC1, CEBPB, LYN, and PARP1 may be considered as potential targets for differentiation therapy of acute myeloid leukemia.
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Affiliation(s)
| | | | | | | | | | | | | | - Victor Zgoda
- Orekhovich Institute of Biomedical Chemistry, Pogodinskaya 10, 119121 Moscow, Russia; (S.N.); (O.T.); (L.K.); (T.F.); (I.V.); (A.L.); (K.Y.)
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6
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Ilgisonis E, Vavilov N, Ponomarenko E, Lisitsa A, Poverennaya E, Zgoda V, Radko S, Archakov A. Genome of the Single Human Chromosome 18 as a "Gold Standard" for Its Transcriptome. Front Genet 2021; 12:674534. [PMID: 34194472 PMCID: PMC8238407 DOI: 10.3389/fgene.2021.674534] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/17/2021] [Indexed: 01/29/2023] Open
Abstract
The cutoff level applied in sequencing analysis varies according to the sequencing technology, sample type, and study purpose, which can largely affect the coverage and reliability of the data obtained. In this study, we aimed to determine the optimal combination of parameters for reliable RNA transcriptome data analysis. Toward this end, we compared the results obtained from different transcriptome analysis platforms (quantitative polymerase chain reaction, Illumina RNASeq, and Oxford Nanopore Technologies MinION) for the transcriptome encoded by human chromosome 18 (Chr 18) using the same sample types (HepG2 cells and liver tissue). A total of 275 protein-coding genes encoded by Chr 18 was taken as the gene set for evaluation. The combination of Illumina RNASeq and MinION nanopore technologies enabled the detection of at least one transcript for each protein-coding gene encoded by Chr 18. This combination also reduced the probability of false-positive detection of low-copy transcripts due to the simultaneous confirmation of the presence of a transcript by the two fundamentally different technologies: short reads essential for reliable detection (Illumina RNASeq) and long-read sequencing data (MinION). The combination of these technologies achieved complete coverage of all 275 protein-coding genes on Chr 18, identifying transcripts with non-zero expression levels. This approach can improve distinguishing the biological and technical reasons for the absence of mRNA detection for a given gene in transcriptomics.
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Affiliation(s)
| | | | | | | | | | - Victor Zgoda
- Institute of Biomedical Chemistry, Moscow, Russia
| | - Sergey Radko
- Institute of Biomedical Chemistry, Moscow, Russia
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7
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Krasnov G, Shkrigunov T, Radko S, Ptitsyn K, Shapovalova V, Timoshenko O, Khmeleva S, Kurbatov L, Kiseleva Y, Ilgisonis E, Kiseleva O, Vakhrushev I, Tsvetkova A, Buromski I, Markin S, Archakov A, Lisitsa A, Ponomarenko E. Human Chr18 transcriptome dataset combined from the Illumina HiSeq, ONT MinION, and qPCR data. Data Brief 2021; 36:107130. [PMID: 34095379 PMCID: PMC8166769 DOI: 10.1016/j.dib.2021.107130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/26/2021] [Accepted: 05/03/2021] [Indexed: 11/01/2022] Open
Abstract
The chromosome-centric dataset was created by applying several technologies of transcriptome profiling. The described dataset is available at NCBI repository (BioProject ID PRJNA635536). The dataset referred to the same type of tissue, cell lines, transcriptome sequencing technologies, and was accomplished in a period of 8 years (the first data were obtained in 2013 while the last ones - in 2020). The high-throughput sequencing technologies were employed along with the quantitative PCR (qPCR) approach, for data generation using the gene expression level assessment. qPCR was performed for a limited group of genes, encoded on human chromosome 18, for the Russian part of the Chromosome-Centric Human Proteome Project. The data of high-throughput sequencing are provided as Excel spreadsheets, where the data on FPKM and TMP values were evaluated for the whole transcriptome with both Illumina HiSeq and Oxford Nanopore Technologies MinION sequencing.
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Affiliation(s)
- George Krasnov
- Institute of Biomedical Chemistry, Moscow, Russia.,Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.,Mechnikov Research Institute of Vaccines and Sera, Moscow, Russia
| | | | - Sergey Radko
- Institute of Biomedical Chemistry, Moscow, Russia
| | | | - Valeriya Shapovalova
- Centre for Strategic Planning and Management of Biomedical Health Risks, Federal Medical Biological Agency, Moscow, Russia
| | | | | | | | - Yana Kiseleva
- Russian Scientific Center of Roentgen Radiology, Moscow, Russia
| | | | | | | | | | - Ivan Buromski
- Department of Forensic Medicine, Faculty of General Medicine, Pirogov Russian National Research Medical University, Moscow, Russia
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8
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Radko S, Ptitsyn K, Novikova S, Kiseleva Y, Moysa A, Kurbatov L, Mannanova M, Zgoda V, Ponomarenko E, Lisitsa A, Archakov A. Evaluation of Aptamers as Affinity Reagents for an Enhancement of SRM-Based Detection of Low-Abundance Proteins in Blood Plasma. Biomedicines 2020; 8:E133. [PMID: 32456365 PMCID: PMC7277749 DOI: 10.3390/biomedicines8050133] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 05/18/2020] [Accepted: 05/22/2020] [Indexed: 12/12/2022] Open
Abstract
Selected reaction monitoring (SRM) is a mass spectrometric technique characterized by the exceptionally high selectivity and sensitivity of protein detection. However, even with this technique, the quantitative detection of low- and ultralow-abundance proteins in blood plasma, which is of great importance for the search and verification of novel protein disease markers, is a challenging task due to the immense dynamic range of protein abundance levels. One approach used to overcome this problem is the immunoaffinity enrichment of target proteins for SRM analysis, employing monoclonal antibodies. Aptamers appear as a promising alternative to antibodies for affinity enrichment. Here, using recombinant protein SMAD4 as a model target added at known concentrations to human blood plasma and SRM as a detection method, we investigated a relationship between the initial amount of the target protein and its amount in the fraction enriched with SMAD4 by an anti-SMAD4 DNA-aptamer immobilized on magnetic beads. It was found that the aptamer-based enrichment provided a 30-fold increase in the sensitivity of SRM detection of SMAD4. These results indicate that the aptamer-based affinity enrichment of target proteins can be successfully employed to improve quantitative detection of low-abundance proteins by SRM in undepleted human blood plasma.
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Affiliation(s)
- Sergey Radko
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (K.P.); (S.N.); (A.M.); (L.K.); (M.M.); (V.Z.); (E.P.); (A.L.); (A.A.)
| | - Konstantin Ptitsyn
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (K.P.); (S.N.); (A.M.); (L.K.); (M.M.); (V.Z.); (E.P.); (A.L.); (A.A.)
| | - Svetlana Novikova
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (K.P.); (S.N.); (A.M.); (L.K.); (M.M.); (V.Z.); (E.P.); (A.L.); (A.A.)
| | - Yana Kiseleva
- Russian Scientific Center of Roentgenoradiology, Moscow 117485, Russia;
| | - Alexander Moysa
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (K.P.); (S.N.); (A.M.); (L.K.); (M.M.); (V.Z.); (E.P.); (A.L.); (A.A.)
| | - Leonid Kurbatov
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (K.P.); (S.N.); (A.M.); (L.K.); (M.M.); (V.Z.); (E.P.); (A.L.); (A.A.)
| | - Maria Mannanova
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (K.P.); (S.N.); (A.M.); (L.K.); (M.M.); (V.Z.); (E.P.); (A.L.); (A.A.)
| | - Victor Zgoda
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (K.P.); (S.N.); (A.M.); (L.K.); (M.M.); (V.Z.); (E.P.); (A.L.); (A.A.)
| | - Elena Ponomarenko
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (K.P.); (S.N.); (A.M.); (L.K.); (M.M.); (V.Z.); (E.P.); (A.L.); (A.A.)
| | - Andrey Lisitsa
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (K.P.); (S.N.); (A.M.); (L.K.); (M.M.); (V.Z.); (E.P.); (A.L.); (A.A.)
| | - Alexander Archakov
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (K.P.); (S.N.); (A.M.); (L.K.); (M.M.); (V.Z.); (E.P.); (A.L.); (A.A.)
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9
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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 2020; 8:proteomes8020012. [PMID: 32456206 PMCID: PMC7356824 DOI: 10.3390/proteomes8020012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 05/18/2020] [Accepted: 05/19/2020] [Indexed: 12/04/2022] Open
Abstract
Despite direct or indirect efforts of the proteomic community, the fraction of blind spots on the protein map is still significant. Almost 11% of human genes encode missing proteins; the existence of which proteins is still in doubt. Apparently, proteomics has reached a stage when more attention and curiosity need to be exerted in the identification of every novel protein in order to expand the unusual types of biomaterials and/or conditions. It seems that we have exhausted the current conventional approaches to the discovery of missing proteins and may need to investigate alternatives. Here, we present an approach to deciphering missing proteins based on the use of non-standard methodological solutions and encompassing diverse MS/MS data, obtained for rare types of biological samples by members of the Russian Proteomic community in the last five years. These data were re-analyzed in a uniform manner by three search engines, which are part of the SearchGUI package. The study resulted in the identification of two missing and five uncertain proteins detected with two peptides. Moreover, 149 proteins were detected with a single proteotypic peptide. Finally, we analyzed the gene expression levels to suggest feasible targets for further validation of missing and uncertain protein observations, which will fully meet the requirements of the international consortium. The MS data are available on the ProteomeXchange platform (PXD014300).
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10
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Archakov AI, Aseev AL, Bykov VA, Grigoriev AI, Govorun VM, Ilgisonis EV, Ivanov YD, Ivanov VT, Kiseleva OI, Kopylov AT, Lisitsa AV, Mazurenko SN, Makarov AA, Naryzhny SN, Pleshakova TO, Ponomarenko EA, Poverennaya EV, Pyatnitskii MA, Sagdeev RZ, Skryabin KG, Zgoda VG. Challenges of the Human Proteome Project: 10-Year Experience of the Russian Consortium. J Proteome Res 2019; 18:4206-4214. [PMID: 31599598 DOI: 10.1021/acs.jproteome.9b00358] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
This manuscript collects all the efforts of the Russian Consortium, bottlenecks revealed in the course of the C-HPP realization, and ways of their overcoming. One of the main bottlenecks in the C-HPP is the insufficient sensitivity of proteomic technologies, hampering the detection of low- and ultralow-copy number proteins forming the "dark part" of the human proteome. In the frame of MP-Challenge, to increase proteome coverage we suggest an experimental workflow based on a combination of shotgun technology and selected reaction monitoring with two-dimensional alkaline fractionation. Further, to detect proteins that cannot be identified by such technologies, nanotechnologies such as combined atomic force microscopy with molecular fishing and/or nanowire detection may be useful. These technologies provide a powerful tool for single molecule analysis, by analogy with nanopore sequencing during genome analysis. To systematically analyze the functional features of some proteins (CP50 Challenge), we created a mathematical model that predicts the number of proteins differing in amino acid sequence: proteoforms. According to our data, we should expect about 100 000 different proteoforms in the liver tissue and a little more in the HepG2 cell line. The variety of proteins forming the whole human proteome significantly exceeds these results due to post-translational modifications (PTMs). As PTMs determine the functional specificity of the protein, we propose using a combination of gene-centric transcriptome-proteomic analysis with preliminary fractionation by two-dimensional electrophoresis to identify chemically modified proteoforms. Despite the complexity of the proposed solutions, such integrative approaches could be fruitful for MP50 and CP50 Challenges in the framework of the C-HPP.
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Affiliation(s)
| | | | | | | | - Vadim M Govorun
- Federal Research and Clinical Center of Physical-Chemical Medicine , Moscow 119435 , Russia
| | | | - Yuri D Ivanov
- Institute of Biomedical Chemistry , Moscow 119435 , Russia
| | - Vadim T Ivanov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Moscow 117997 , Russia
| | | | | | | | - Sergey N Mazurenko
- Joint Institute for Nuclear Research , Dubna, Moscow region 141980 , Russia
| | | | | | | | | | | | | | - Renad Z Sagdeev
- International Tomography Center , Novosibirsk 630090 , Russia
| | - Konstantin G Skryabin
- The Federal Research Centre "Fundamentals of Biotechnology" , Moscow 119071 , Russia
| | - Victor G Zgoda
- Institute of Biomedical Chemistry , Moscow 119435 , Russia
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11
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Radko SP, Poverennaya EV, Kurbatov LK, Ponomarenko EA, Lisitsa AV, Archakov AI. The "Missing" Proteome: Undetected Proteins, Not-Translated Transcripts, and Untranscribed Genes. J Proteome Res 2019; 18:4273-4276. [PMID: 31621326 DOI: 10.1021/acs.jproteome.9b00383] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Chromosome-centric Human Proteome Project aims at characterizing the expression of proteins encoded in each chromosome at the tissue, cell, and subcellular levels. The proteomic profiling of a particular tissue or cell line commonly results in a substantial portion of proteins that are not observed (the "missing" proteome). The concurrent transcriptome profiling of the analyzed tissue/cells samples may help define the set of untranscribed genes in a given type of tissue or cell, thus narrowing the size of the "missing" proteome and allowing us to focus on defining the reasons behind undetected proteins, namely, whether they are technical (insufficient sensitivity of protein detection) or biological (correspond to not-translated transcripts). We believe that the quantitative polymerase chain reaction (qPCR) can provide an efficient approach to studying low-abundant transcripts related to undetected proteins due to its high sensitivity and the possibility of ensuring the specificity of detection via the simple Sanger sequencing of PCR products. Here we illustrated the feasibility of such an approach on a set of low-abundant transcripts. Although inapplicable to the analysis of whole transcriptome, qPCR can successfully be utilized to profile a limited cohort of transcripts encoded on a particular chromosome, as we previously demonstrated for human chromosome 18.
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Affiliation(s)
- Sergey P Radko
- Institute of Biomedical Chemistry , 119121 Moscow , Russia
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12
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Novikova SE, Vakhrushev IV, Tsvetkova AV, Shushkova NA, Farafonova TE, Yarygin KN, Zgoda VG. [Proteomics of transcription factors: identification of pool of HL-60 cell line-specific regulatory proteins]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2019; 65:294-305. [PMID: 31436170 DOI: 10.18097/pbmc20196504294] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
HL-60 promyelocytic cells are a widely used as a model for studying induced granulocytic differentiation. Investigation of proteins of the nuclear fraction, particularly transcription factors, is necessary for a better understanding of molecular mechanisms of cell maturation. Mass spectrometry is a powerful tool for analyzing a proteome due to its high sensitivity, specificity and performance. In this paper, using the selected reaction monitoring (SRM) method, we have assessed the levels of RBPJ, STAT1, CEBPB, CASP3, VAV1, PRKDC, PARP1 and UBC9 nuclear proteins isolated using hypertonic buffer, detergents (sodium dodecyl sulfate (SDS), sodium deoxycholate (DOC) and fissionable detergent ProteaseMAX™) and using centrifugation in a sucrose density gradient. The minimum and maximum protein content was 1.13±0.28 and 14.34±1.63 fmol/mkg of total protein for the transcription factor RBPJ and ubiquitin-protein ligase type I UBC9, respectively. According to the results of shotgun mass spectrometric analysis of nuclear fractions, 2356 proteins were identified, of which 106 proteins were annotated as transcription factors. 37 transcription factors were uniquely identified in the fraction obtained by centrifugation in a sucrose density gradient, while only 9 and 8 transcription factors were uniquely identified in the nuclear fractions obtained using hypertonic buffer and detergents, respectively. The transcription factors identified in the HL-60 cell line represent regulatory molecules; their directed profiling under the influence of differentiation inducers, will shed light on the mechanism of granulocyte maturation.
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Affiliation(s)
- S E Novikova
- Institute of Biomedical Chemistry, Moscow, Russia
| | | | | | | | | | - K N Yarygin
- Institute of Biomedical Chemistry, Moscow, Russia
| | - V G Zgoda
- Institute of Biomedical Chemistry, Moscow, Russia
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13
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Kontostathi G, Makridakis M, Zoidakis J, Vlahou A. Applications of multiple reaction monitoring targeted proteomics assays in human plasma. Expert Rev Mol Diagn 2019; 19:499-515. [PMID: 31057016 DOI: 10.1080/14737159.2019.1615448] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Introduction: Multiple (or selected) reaction monitoring-mass spectrometry (MRM/SRM) is a targeted proteomic method that can be used for relative and absolute quantification. Multiple reports exist supporting the potential of the approach in proteomic biomarker validation. Areas covered: To get an overview of the applications of MRM in protein quantification in plasma, a search in MedLine/PubMed was performed using the keywords: 'MRM/SRM plasma proteomic/proteomics/proteome'. The retrieved studies were further filtered to focus on disease biomarkers and the main results are summarized. Expert opinion: MRM is increasingly employed for the quantification of both well-established but also newly discovered putative biomarkers and occasionally their post-translationally modified forms in plasma. Fractionation is regularly required for the detection of low abundance proteins. Standardized procedures to facilitate assay establishment and marker quantification have been proposed and, in few cases, implemented. Nevertheless, in most cases, absolute quantification is not performed. To advance, multiple technical issues including the regular use of standard labeled peptides and appropriate quality controls to monitor assay performance should be considered. Additionally, clinical aspects involving careful study design to address biomarker clinical use should also be considered.
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Affiliation(s)
- Georgia Kontostathi
- a Biotechnology Division , Biomedical Research Foundation, Academy of Athens (BRFAA) , Athens , Greece
| | - Manousos Makridakis
- a Biotechnology Division , Biomedical Research Foundation, Academy of Athens (BRFAA) , Athens , Greece
| | - Jerome Zoidakis
- a Biotechnology Division , Biomedical Research Foundation, Academy of Athens (BRFAA) , Athens , Greece
| | - Antonia Vlahou
- a Biotechnology Division , Biomedical Research Foundation, Academy of Athens (BRFAA) , Athens , Greece
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14
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Ptitsyn KG, Novikova SE, Kiseleva YY, Moysa AA, Kurbatov LK, Farafonova TE, Radko SP, Zgoda VG, Archakov AI. [Use of DNA-aptamers for enrichment of low abundant proteins in cellular extracts for quntitative detection by selected reaction monitoring]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2019; 64:5-9. [PMID: 29460828 DOI: 10.18097/pbmc20186401005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The relationship between the amount of a target protein in a complex biological sample and its amount measured by selected reaction monitoring (SRM) mass spectrometry upon the affinity enrichment of target protein with aptamers immobilized on a solid phase was studied. Human thrombin added in known concentrations to cellular extracts derived from bacterial cells was used as model target protein. It has been demonstrated that the affinity enrichment of thrombin in cellular extracts by means of the thrombin-binding aptamer immobilized on the surface of magnetic microbeads results in an approximately 10-fold increase of the concentration of target protein and a 100-fold decrease of the low limit of a target protein concentration range where its quantitative detection by SRM is possible without an interference from other peptides present in a tryptic digest.
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Affiliation(s)
- K G Ptitsyn
- Institute of Biomedical Chemistry, Moscow, Russia
| | - S E Novikova
- Institute of Biomedical Chemistry, Moscow, Russia
| | - Y Y Kiseleva
- Russian Scientific Center of Roentgenoradiology, Moscow, Russia
| | - A A Moysa
- Institute of Biomedical Chemistry, Moscow, Russia
| | - L K Kurbatov
- Institute of Biomedical Chemistry, Moscow, Russia
| | | | - S P Radko
- Institute of Biomedical Chemistry, Moscow, Russia
| | - V G Zgoda
- Institute of Biomedical Chemistry, Moscow, Russia
| | - A I Archakov
- Institute of Biomedical Chemistry, Moscow, Russia
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15
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Ershov PV, Mezentsev YV, Yablokov EO, Kaluzhskiy LA, Florinskaya AV, Gnedenko OV, Zgoda VG, Vakhrushev IV, Raeva OS, Yarygin KN, Gilep AA, Usanov SA, Medvedev AE, Ivanov AS. Direct Molecular Fishing of Protein Partners for Proteins Encoded by Genes of Human Chromosome 18 in HepG2 Cell Lysate. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2019. [DOI: 10.1134/s1068162019010059] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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16
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Kopylov AT, Ponomarenko EA, Ilgisonis EV, Pyatnitskiy MA, Lisitsa AV, Poverennaya EV, Kiseleva OI, Farafonova TE, Tikhonova OV, Zavialova MG, Novikova SE, Moshkovskii SA, Radko SP, Morukov BV, Grigoriev AI, Paik YK, Salekdeh GH, Urbani A, Zgoda VG, Archakov AI. 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 2018; 18:120-129. [PMID: 30480452 DOI: 10.1021/acs.jproteome.8b00391] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This work continues the series of the quantitative measurements of the proteins encoded by different chromosomes in the blood plasma of a healthy person. Selected Reaction Monitoring with Stable Isotope-labeled peptide Standards (SRM SIS) and a gene-centric approach, which is the basis for the implementation of the international Chromosome-centric Human Proteome Project (C-HPP), were applied for the quantitative measurement of proteins in human blood plasma. Analyses were carried out in the frame of C-HPP for each protein-coding gene of the four human chromosomes: 18, 13, Y, and mitochondrial. Concentrations of proteins encoded by 667 genes were measured in 54 blood plasma samples of the volunteers, whose health conditions were consistent with requirements for astronauts. The gene list included 276, 329, 47, and 15 genes of chromosomes 18, 13, Y, and the mitochondrial chromosome, respectively. This paper does not make claims about the detection of missing proteins. Only 205 proteins (30.7%) were detected in the samples. Of them, 84, 106, 10, and 5 belonged to chromosomes 18, 13, and Y and the mitochondrial chromosome, respectively. Each detected protein was found in at least one of the samples analyzed. The SRM SIS raw data are available in the ProteomeXchange repository (PXD004374, PASS01192).
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Sergey A Moshkovskii
- Institute of Biomedical Chemistry , Moscow 119435 , Russia.,Pirogov Russian National Research Medical University , Moscow 117997 , Russia
| | - Sergey P Radko
- Institute of Biomedical Chemistry , Moscow 119435 , Russia
| | - Boris V Morukov
- Institute of Medico-Biological Problems , Moscow 123007 , Russia
| | | | - Young-Ki Paik
- Yonsei Proteome Research Center , Yonsei University , Seoul 03722 , Korea
| | - Ghasem Hosseini Salekdeh
- Department of Molecular Systems Biology, Cell Science Research Center , Royan Institute for Stem Cell Biology and Technology, ACECR , Tehran , Iran.,Department of Molecular Sciences , Macquarie University , Sydney , New South Wales 2109 , Australia.,Department of Systems Biology , Agricultural Biotechnology Research Institute of Iran , Karaj , Iran
| | - Andrea Urbani
- Area of Diagnostic Laboratories , Fondazione Policlinico Gemelli-IRCCS , Rome 00168 , Italy.,Institute of Biochemistry and Clinical Biochemistry , Catholic University of the Sacred Heart , Rome 00168 , Italy
| | - Victor G Zgoda
- Institute of Biomedical Chemistry , Moscow 119435 , Russia
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17
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Manual method of visually identifying candidate signals for a targeted peptide. J Chromatogr B Analyt Technol Biomed Life Sci 2018; 1083:258-270. [PMID: 29554522 DOI: 10.1016/j.jchromb.2018.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 01/05/2018] [Accepted: 01/15/2018] [Indexed: 11/20/2022]
Abstract
The purpose of this study is to improve peptide signal identification in groups of extracted ion chromatograms (XICs) obtained with the liquid chromatography-selected reaction monitoring (LC-SRM) technique and a triple quadrupole mass spectrometer (QqQ) operating in one of the supported multiple reaction monitoring (MRM) modes. The imperfection of quadrupole mass analyzers causes ion interference, which impedes the identification of peptide signals as chromatographic peak groups in relevant retention time intervals. To investigate this problem in depth, the QqQ conversion of the eluate into XIC groups was considered as the consecutive transformations of the particles' abundances as the corresponding functions of retention time. In this study, the hypothesis that, during this conversion, the same chromatographic profile should be preserved as an implicit sign in each chromatographic peak of the signal was confirmed for peptides. To examine chromatographic profiles, continuous transformations of XIC groups were derived and implemented in srm2prot Express software (s2pe, http://msr.ibmc.msk.ru/s2pe). Because of ion interference, several peptide-like signals may appear in one XIC group. Therefore, these signals must be considered candidates for a targeted peptide's signal and should be resolved after identification. The theoretical investigation of intensity functions as XICs that are not distorted by noise produced three rules for Identifying Candidate Signals for a targeted Peptide (ICSP, http://msr.ibmc.msk.ru/ICSP) that constitute the proposed manual visual method. We theoretically and experimentally compared this method with the conventional semiempirical intuitive technique and found that the former significantly streamlines peptide signal identification and avoids typical errors.
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18
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Poverennaya EV, Shargunov AV, Ponomarenko EA, Lisitsa AV. The Gene-Centric Content Management System and Its Application for Cognitive Proteomics. Proteomes 2018; 6:proteomes6010012. [PMID: 29473895 PMCID: PMC5874771 DOI: 10.3390/proteomes6010012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 02/07/2018] [Accepted: 02/20/2018] [Indexed: 12/28/2022] Open
Abstract
The Human Proteome Project is moving into the next phase of creating and/or reconsidering the functional annotations of proteins using the chromosome-centric paradigm. This challenge cannot be solved exclusively using automated means, but rather requires human intelligence for interpreting the combined data. To foster the integration between human cognition and post-genome array a number of specific tools were recently developed, among them CAPER, GenomewidePDB, and The Proteome Browser (TPB). For the purpose of tackling the task of protein functional annotating the Gene-Centric Content Management System (GenoCMS) was expanded with new features. The goal was to enable bioinformaticans to develop self-made applications and to position these applets within the generalized informational canvas supported by GenoCMS. We report the results of GenoCMS-enabled integration of the concordant informational flows in the chromosome-centric framework of the human chromosome 18 project. The workflow described in the article can be scaled to other human chromosomes, and also supplemented with new tracks created by the user. The GenoCMS is an example of a project-oriented informational system, which are important for public data sharing.
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Affiliation(s)
| | | | | | - Andrey V Lisitsa
- Orekhovich Institute of Biomedical Chemistry, Moscow 119191, Russia.
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19
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Manes NP, Nita-Lazar A. Application of targeted mass spectrometry in bottom-up proteomics for systems biology research. J Proteomics 2018; 189:75-90. [PMID: 29452276 DOI: 10.1016/j.jprot.2018.02.008] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 01/25/2018] [Accepted: 02/07/2018] [Indexed: 02/08/2023]
Abstract
The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).
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Affiliation(s)
- Nathan P Manes
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Aleksandra Nita-Lazar
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
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20
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Poverennaya EV, Ilgisonis EV, Ponomarenko EA, Kopylov AT, Zgoda VG, Radko SP, Lisitsa AV, Archakov AI. 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 2017; 16:4311-4318. [PMID: 28956606 DOI: 10.1021/acs.jproteome.7b00348] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In this work targeted (selected reaction monitoring, SRM, PASSEL: PASS00697) and panoramic (shotgun LC-MS/MS, PRIDE: PXD00244) mass-spectrometric methods as well as transcriptomic analysis of the same samples using RNA-Seq and PCR methods (SRA experiment IDs: SRX341198, SRX267708, SRX395473, SRX390071) were applied for quantification of chromosome 18 encoded transcripts and proteins in human liver and HepG2 cells. The obtained data was used for the estimation of quantitative mRNA-protein ratios for the 275 genes of the selected chromosome in the selected tissues. The impact of methodological limitations of existing analytical proteomic methods on gene-specific mRNA-protein ratios and possible ways of overcoming these limitations for detection of missing proteins are also discussed.
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Affiliation(s)
| | | | | | | | - Victor G Zgoda
- Institute of Biomedical Chemistry RAS , 119121 Moscow, Russia
| | - Sergey P Radko
- Institute of Biomedical Chemistry RAS , 119121 Moscow, Russia
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21
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Novikova SE, Kurbatov LK, Zavialova MG, Zgoda VG, Archakov AI. [Omics technologies in diagnostics of lung adenocarcinoma]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2017; 63:181-210. [PMID: 28781253 DOI: 10.18097/pbmc20176303181] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
To date lung adenocarcinoma (LAC) is the most common type of lung cancer. Numerous studies on LAC biology resulted in identification of crucial mutations in protooncogenes and activating neoplastic transformation pathways. Therapeutic approaches that significantly increase the survival rate of patients with LAC of different etiology have been developed and introduced into clinical practice. However, the main problem in the treatment of LAC is early diagnosis, taking into account both factors and mechanisms responsible in tumor initiation and progression. Identification of a wide biomarker repertoire with high specificity and reliability of detection appears to be a solution to this problem. In this context, proteins with differential expression in normal and pathological condition, suitable for detection in biological fluids are the most promising biomarkers. In this review we have analyzed literature data on studies aimed at search of LAC biomarkers. The major attention has been paid to protein biomarkers as the most promising and convenient subject of clinical diagnosis. The review also summarizes existing knowledge on posttranslational modifications, splice variants, isoforms, as well as model systems and transcriptome changes in LAC.
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Affiliation(s)
- S E Novikova
- Institute of Biomedical Chemistry, Moscow, Russia
| | - L K Kurbatov
- Institute of Biomedical Chemistry, Moscow, Russia
| | | | - V G Zgoda
- Institute of Biomedical Chemistry, Moscow, Russia
| | - A I Archakov
- Institute of Biomedical Chemistry, Moscow, Russia
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22
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Poverennaya EV, Kopylov AT, Ponomarenko EA, Ilgisonis EV, Zgoda VG, Tikhonova OV, Novikova SE, Farafonova TE, Kiseleva YY, Radko SP, Vakhrushev IV, Yarygin KN, Moshkovskii SA, Kiseleva OI, Lisitsa AV, Sokolov AS, Mazur AM, Prokhortchouk EB, Skryabin KG, Kostrjukova ES, Tyakht AV, Gorbachev AY, Ilina EN, Govorun VM, Archakov AI. State of the Art of Chromosome 18-Centric HPP in 2016: Transcriptome and Proteome Profiling of Liver Tissue and HepG2 Cells. J Proteome Res 2016; 15:4030-4038. [PMID: 27527821 DOI: 10.1021/acs.jproteome.6b00380] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
A gene-centric approach was applied for a large-scale study of expression products of a single chromosome. Transcriptome profiling of liver tissue and HepG2 cell line was independently performed using two RNA-Seq platforms (SOLiD and Illumina) and also by Droplet Digital PCR (ddPCR) and quantitative RT-PCR. Proteome profiling was performed using shotgun LC-MS/MS as well as selected reaction monitoring with stable isotope-labeled standards (SRM/SIS) for liver tissue and HepG2 cells. On the basis of SRM/SIS measurements, protein copy numbers were estimated for the Chromosome 18 (Chr 18) encoded proteins in the selected types of biological material. These values were compared with expression levels of corresponding mRNA. As a result, we obtained information about 158 and 142 transcripts for HepG2 cell line and liver tissue, respectively. SRM/SIS measurements and shotgun LC-MS/MS allowed us to detect 91 Chr 18-encoded proteins in total, while an intersection between the HepG2 cell line and liver tissue proteomes was ∼66%. In total, there were 16 proteins specifically observed in HepG2 cell line, while 15 proteins were found solely in the liver tissue. Comparison between proteome and transcriptome revealed a poor correlation (R2 ≈ 0.1) between corresponding mRNA and protein expression levels. The SRM and shotgun data sets (obtained during 2015-2016) are available in PASSEL (PASS00697) and ProteomeExchange/PRIDE (PXD004407). All measurements were also uploaded into the in-house Chr 18 Knowledgebase at http://kb18.ru/protein/matrix/416126 .
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Affiliation(s)
| | - Arthur T Kopylov
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | - Elena A Ponomarenko
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | | | - Victor G Zgoda
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | - Olga V Tikhonova
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | - Svetlana E Novikova
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | - Tatyana E Farafonova
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | - Yana Yu Kiseleva
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | - Sergey P Radko
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | - Igor V Vakhrushev
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | - Konstantin N Yarygin
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | - Sergei A Moshkovskii
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia.,Pirogov Russian National Research Medical University , Ostrovitianov Str. 1, Moscow 117997, Russia
| | - Olga I Kiseleva
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | - Andrey V Lisitsa
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
| | - Alexey S Sokolov
- Center "Bioengineering" Russian Academy of Sciences , Prospect 60-let Oktyabrya, 7, Build.1, Moscow 119071, Russia
| | - Alexander M Mazur
- Center "Bioengineering" Russian Academy of Sciences , Prospect 60-let Oktyabrya, 7, Build.1, Moscow 119071, Russia
| | - Egor B Prokhortchouk
- Center "Bioengineering" Russian Academy of Sciences , Prospect 60-let Oktyabrya, 7, Build.1, Moscow 119071, Russia
| | - Konstantin G Skryabin
- Center "Bioengineering" Russian Academy of Sciences , Prospect 60-let Oktyabrya, 7, Build.1, Moscow 119071, Russia
| | - Elena S Kostrjukova
- Scientific Research Institute of Physical-Chemical Medicine , Malaya Pirogovskaya, 1a, Moscow 119435, Russia
| | - Alexander V Tyakht
- Scientific Research Institute of Physical-Chemical Medicine , Malaya Pirogovskaya, 1a, Moscow 119435, Russia
| | - Alexey Yu Gorbachev
- Scientific Research Institute of Physical-Chemical Medicine , Malaya Pirogovskaya, 1a, Moscow 119435, Russia
| | - Elena N Ilina
- Scientific Research Institute of Physical-Chemical Medicine , Malaya Pirogovskaya, 1a, Moscow 119435, Russia
| | - Vadim M Govorun
- Scientific Research Institute of Physical-Chemical Medicine , Malaya Pirogovskaya, 1a, Moscow 119435, Russia
| | - Alexander I Archakov
- Institute of Biomedical Chemistry , Pogodinskaya Street, 10, Moscow 119121, Russia
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23
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Ilgisonis EV, Kopylov AT, Zgoda VG. Dataset of target mass spectromic proteome profiling for human chromosome 18. Data Brief 2016; 8:1365-9. [PMID: 27595127 PMCID: PMC4995477 DOI: 10.1016/j.dib.2016.07.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Revised: 06/30/2016] [Accepted: 07/19/2016] [Indexed: 10/28/2022] Open
Abstract
Proteome profiling is a type of quantitative analysis that reveals level of protein expression in the sample. Proteome profiling by using selected reaction monitoring is an approach for the Chromosome-centric Human Proteome Project (C-HPP). Here we describe dataset generated in the course of the pilot phase of Russian part of C-HPP, which was focused on human Chr 18 proteins. Proteome profiling was performed using stable isotope-labeled standards (SRM/SIS) for plasma, liver tissue and HepG2 cells. Dataset includes both positive and negative results of protein detection. These data were partly discussed in recent publications, "Chromosome 18 Transcriptome Profiling and Targeted Proteome Mapping in Depleted Plasma, Liver Tissue and HepG2 Cells" [1] and "Chromosome 18 transcriptoproteome of liver tissue and HepG2 Cells and targeted proteome mapping in depleted plasma: Update 2013" [2], supporting the accompanying publication "State of the Chromosome 18-centric HPP in 2016: Transcriptome and Proteome Profiling of Liver Tissue and HepG2 Cells" [3], and are deposited at the ProteomeXchange via the PASSEL repository with the dataset identifier PASSEL: PASS00697 for liver and HepG2 cell line.
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Affiliation(s)
| | | | - Victor G Zgoda
- Orekhovich Institute of Biomedical Chemistry, Moscow, Russia
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24
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Ponomarenko EA, Poverennaya EV, Ilgisonis EV, Pyatnitskiy MA, Kopylov AT, Zgoda VG, Lisitsa AV, Archakov AI. The Size of the Human Proteome: The Width and Depth. Int J Anal Chem 2016; 2016:7436849. [PMID: 27298622 PMCID: PMC4889822 DOI: 10.1155/2016/7436849] [Citation(s) in RCA: 280] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Revised: 04/11/2016] [Accepted: 04/19/2016] [Indexed: 01/01/2023] Open
Abstract
This work discusses bioinformatics and experimental approaches to explore the human proteome, a constellation of proteins expressed in different tissues and organs. As the human proteome is not a static entity, it seems necessary to estimate the number of different protein species (proteoforms) and measure the number of copies of the same protein in a specific tissue. Here, meta-analysis of neXtProt knowledge base is proposed for theoretical prediction of the number of different proteoforms that arise from alternative splicing (AS), single amino acid polymorphisms (SAPs), and posttranslational modifications (PTMs). Three possible cases are considered: (1) PTMs and SAPs appear exclusively in the canonical sequences of proteins, but not in splice variants; (2) PTMs and SAPs can occur in both proteins encoded by canonical sequences and in splice variants; (3) all modification types (AS, SAP, and PTM) occur as independent events. Experimental validation of proteoforms is limited by the analytical sensitivity of proteomic technology. A bell-shaped distribution histogram was generated for proteins encoded by a single chromosome, with the estimation of copy numbers in plasma, liver, and HepG2 cell line. The proposed metabioinformatics approaches can be used for estimation of the number of different proteoforms for any group of protein-coding genes.
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25
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Ivanov AS, Ershov PV, Molnar AA, Mezentsev YV, Kaluzhskiy LA, Yablokov EO, Florinskaya AV, Gnedenko OV, Medvedev AE, Kozin SA, Mitkevich VA, Makarov AA, Gilep AA, Luschik AY, Gaidukevich IV, Usanov SA. Direct molecular fishing in molecular partners investigation in protein–protein and protein–peptide interactions. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2016. [DOI: 10.1134/s1068162016010052] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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26
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Locard-Paulet M, Pible O, Gonzalez de Peredo A, Alpha-Bazin B, Almunia C, Burlet-Schiltz O, Armengaud J. Clinical implications of recent advances in proteogenomics. Expert Rev Proteomics 2016; 13:185-99. [DOI: 10.1586/14789450.2016.1132169] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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27
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Kliuchnikova A, Kuznetsova K, Moshkovskii S. ADAR-mediated messenger RNA editing: analysis at the proteome level. ACTA ACUST UNITED AC 2016; 62:510-519. [DOI: 10.18097/pbmc20166205510] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Post-transcriptional RNA editing by RNA specific adenosine deaminases (ADAR) was discovered more than two decades ago. It provides additional regulation of animal and human transcriptome. In most cases, it occurs in nervous tissue, where, as a result of the reaction, adenosine is converted to inosine in particular sites of RNA. In case of messenger RNA, during translation, inosine is recognized as guanine leading to amino acid substitutions. Those substitutions are shown to affect substantially the function of proteins, e.g. subunits of the glutamate receptor. Nevertheless, most of the works on RNA editing use analysis of nucleic acids, even those which deal with a coding RNA. In this review, we propose the use of shotgun proteomics based on high resolution liquid chromatography and mass spectrometry for investigation of the effects of RNA editing at the protein level. Recently developed methods of big data processing allow combining the results of various omics techniques, being referred to as proteogenomics. The proposed proteogenomic approach for the analysis of RNA editing at the protein level will directly conduct a qualitative and quantitative analysis of protein edited sequences in the scale of whole proteome.
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Affiliation(s)
| | | | - S.A. Moshkovskii
- Institute of Biomedical Chemistry, Moscow, Russia; Pirogov Russian National Research Medical University, Moscow, Russia
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28
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Naryzhny SN, Maynskova MA, Zgoda VG, Ronzhina NL, Kleyst OA, Vakhrushev IV, Archakov AI. Virtual-Experimental 2DE Approach in Chromosome-Centric Human Proteome Project. J Proteome Res 2015; 15:525-30. [PMID: 26667816 DOI: 10.1021/acs.jproteome.5b00871] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
To obtain more information about human proteome, especially about proteoforms (protein species) coded by 18th chromosome, we separated proteins from human cancer cell line (HepG2) by two-dimensional gel electrophoresis (2DE). Initially, proteins in major spots were identified by MALDI-MS peptide mass fingerprinting. According to parameters (pI/Mw) of identified proteins the gel was calibrated. Using this calibrated gel, a virtual 2D map of proteoforms coded by Chromosome 18 was constructed. Next, the produced gel was divided into 96 sections with determined coordinates. Each section was cut, shredded, and treated by trypsin according to mass-spectrometry protocol. After protein identification by shotgun mass spectrometry using ESI LC-MS/MS, a list of 20 462 proteoforms (product of 3774 genes) was generated. Among them, 165 proteoforms are representing 39 genes of 18th chromosome. The 3D graphs showing the distribution of different proteoforms from the same gene in 2D map were generated. This is a first step in creation of 2DE-based knowledge database of proteins coded by 18th chromosome.
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Affiliation(s)
- Stanislav N Naryzhny
- Institute of Biomedical Chemistry of Russian Academy of Medical Sciences , Pogodinskaya 10, Moscow 119121, Russia.,Petersburg Nuclear Physics Institute, National Research Center "Kurchatov Institute" , Leningrad Region, Gatchina 188300, Russia
| | - Maria A Maynskova
- Institute of Biomedical Chemistry of Russian Academy of Medical Sciences , Pogodinskaya 10, Moscow 119121, Russia
| | - Victor G Zgoda
- Institute of Biomedical Chemistry of Russian Academy of Medical Sciences , Pogodinskaya 10, Moscow 119121, Russia
| | - Natalia L Ronzhina
- Petersburg Nuclear Physics Institute, National Research Center "Kurchatov Institute" , Leningrad Region, Gatchina 188300, Russia
| | - Olga A Kleyst
- Petersburg Nuclear Physics Institute, National Research Center "Kurchatov Institute" , Leningrad Region, Gatchina 188300, Russia
| | - Igor V Vakhrushev
- Institute of Biomedical Chemistry of Russian Academy of Medical Sciences , Pogodinskaya 10, Moscow 119121, Russia
| | - Alexander I Archakov
- Institute of Biomedical Chemistry of Russian Academy of Medical Sciences , Pogodinskaya 10, Moscow 119121, Russia
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29
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Zhou L, Li Q, Wang J, Huang C, Nice EC. Oncoproteomics: Trials and tribulations. Proteomics Clin Appl 2015; 10:516-31. [PMID: 26518147 DOI: 10.1002/prca.201500081] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2015] [Revised: 09/19/2015] [Accepted: 10/27/2015] [Indexed: 02/05/2023]
Affiliation(s)
- Li Zhou
- State Key Laboratory of Biotherapy and Cancer Center; West China Hospital; Sichuan University, and Collaborative Innovation Center for Biotherapy; Chengdu P. R. China
- Department of Neurology; The Affiliated Hospital of Hainan Medical College; Haikou Hainan P. R. China
| | - Qifu Li
- Department of Neurology; The Affiliated Hospital of Hainan Medical College; Haikou Hainan P. R. China
| | - Jiandong Wang
- Department of Biomedical; Chengdu Medical College; Chengdu Sichuan Province P. R. China
| | - Canhua Huang
- State Key Laboratory of Biotherapy and Cancer Center; West China Hospital; Sichuan University, and Collaborative Innovation Center for Biotherapy; Chengdu P. R. China
| | - Edouard C. Nice
- State Key Laboratory of Biotherapy and Cancer Center; West China Hospital; Sichuan University, and Collaborative Innovation Center for Biotherapy; Chengdu P. R. China
- Department of Biochemistry and Molecular Biology; Monash University; Clayton Australia
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30
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Moskaleva N, Moysa A, Novikova S, Tikhonova O, Zgoda V, Archakov A. Spaceflight Effects on Cytochrome P450 Content in Mouse Liver. PLoS One 2015; 10:e0142374. [PMID: 26561010 PMCID: PMC4641588 DOI: 10.1371/journal.pone.0142374] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Accepted: 10/21/2015] [Indexed: 12/30/2022] Open
Abstract
Hard conditions of long-term manned spaceflight can affect functions of many biological systems including a system of drug metabolism. The cytochrome P450 (CYP) superfamily plays a key role in the drug metabolism. In this study we examined the hepatic content of some P450 isoforms in mice exposed to 30 days of space flight and microgravity. The CYP content was established by the mass-spectrometric method of selected reaction monitoring (SRM). Significant changes in the CYP2C29, CYP2E1 and CYP1A2 contents were detected in mice of the flight group compared to the ground control group. Within seven days after landing and corresponding recovery period changes in the content of CYP2C29 and CYP1A2 returned to the control level, while the CYP2E1 level remained elevated. The induction of enzyme observed in the mice in the conditions of the spaceflight could lead to an accelerated biotransformation and change in efficiency of pharmacological agents, metabolizing by corresponding CYP isoforms. Such possibility of an individual pharmacological response to medication during long-term spaceflights and early period of postflight adaptation should be taken into account in space medicine.
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Affiliation(s)
| | | | | | | | - Victor Zgoda
- Institute of Biomedical Chemistry, Moscow, Russia
- * E-mail:
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31
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Naryzhny SN, Zgoda VG, Maynskova MA, Novikova SE, Ronzhina NL, Vakhrushev IV, Khryapova EV, Lisitsa AV, Tikhonova OV, Ponomarenko EA, Archakov AI. Combination of virtual and experimental 2DE together with ESI LC-MS/MS gives a clearer view about proteomes of human cells and plasma. Electrophoresis 2015; 37:302-9. [DOI: 10.1002/elps.201500382] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Revised: 09/17/2015] [Accepted: 09/28/2015] [Indexed: 11/07/2022]
Affiliation(s)
- Stanislav N. Naryzhny
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences; Moscow Russia
- B.P. Konstantinov Petersburg Nuclear Physics Institute; National Research Center "Kurchatov Institute,"; Gatchina, Leningrad district Russia
| | - Victor G. Zgoda
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences; Moscow Russia
| | - Maria A. Maynskova
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences; Moscow Russia
| | - Svetlana E. Novikova
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences; Moscow Russia
| | - Natalia L. Ronzhina
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences; Moscow Russia
| | - Igor V. Vakhrushev
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences; Moscow Russia
| | - Elena V. Khryapova
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences; Moscow Russia
| | - Andrey V. Lisitsa
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences; Moscow Russia
| | - Olga V. Tikhonova
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences; Moscow Russia
| | - Elena A. Ponomarenko
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences; Moscow Russia
| | - Alexander I. Archakov
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences; Moscow Russia
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32
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Krasnov GS, Dmitriev AA, Kudryavtseva AV, Shargunov AV, Karpov DS, Uroshlev LA, Melnikova NV, Blinov VM, Poverennaya EV, Archakov AI, Lisitsa AV, Ponomarenko EA. PPLine: An Automated Pipeline for SNP, SAP, and Splice Variant Detection in the Context of Proteogenomics. J Proteome Res 2015; 14:3729-37. [DOI: 10.1021/acs.jproteome.5b00490] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- George Sergeevich Krasnov
- Engelhardt
Institute of Molecular Biology, Russian Academy of Sciences, Moscow, 111991 Russia
- Orekhovich
Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, 119121 Russia
- Mechnikov Research Institute of Vaccines and Sera, Moscow, 105064 Russia
| | | | - Anna Viktorovna Kudryavtseva
- Engelhardt
Institute of Molecular Biology, Russian Academy of Sciences, Moscow, 111991 Russia
- Herzen
Moscow Cancer Research Institute, Ministry of Healthcare of the Russian Federation, Moscow, 125284 Russia
| | - Alexander Valerievich Shargunov
- Orekhovich
Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, 119121 Russia
- Mechnikov Research Institute of Vaccines and Sera, Moscow, 105064 Russia
| | - Dmitry Sergeevich Karpov
- Engelhardt
Institute of Molecular Biology, Russian Academy of Sciences, Moscow, 111991 Russia
- Orekhovich
Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, 119121 Russia
| | | | | | - Vladimir Mikhailovich Blinov
- Orekhovich
Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, 119121 Russia
- Mechnikov Research Institute of Vaccines and Sera, Moscow, 105064 Russia
| | | | | | - Andrey Valerievich Lisitsa
- Orekhovich
Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow, 119121 Russia
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33
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Horvatovich P, Lundberg EK, Chen YJ, Sung TY, He F, Nice EC, Goode RJ, Yu S, Ranganathan S, Baker MS, Domont GB, Velasquez E, Li D, Liu S, Wang Q, He QY, Menon R, Guan Y, Corrales FJ, Segura V, Casal JI, Pascual-Montano A, Albar JP, Fuentes M, Gonzalez-Gonzalez M, Diez P, Ibarrola N, Degano RM, Mohammed Y, Borchers CH, Urbani A, Soggiu A, Yamamoto T, Salekdeh GH, Archakov A, Ponomarenko E, Lisitsa A, Lichti CF, Mostovenko E, Kroes RA, Rezeli M, Végvári Á, Fehniger TE, Bischoff R, Vizcaíno JA, Deutsch EW, Lane L, Nilsson CL, Marko-Varga G, Omenn GS, Jeong SK, Lim JS, Paik YK, Hancock WS. Quest for Missing Proteins: Update 2015 on Chromosome-Centric Human Proteome Project. J Proteome Res 2015; 14:3415-31. [DOI: 10.1021/pr5013009] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Péter Horvatovich
- Analytical
Biochemistry, Department of Pharmacy, University of Groningen, A. Deusinglaan
1, 9713 AV Groningen, The Netherlands
| | - Emma K. Lundberg
- Science
for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Yu-Ju Chen
- Institute
of Chemistry, Academia Sinica, 128 Academia Road Sec. 2, Taipei 115, Taiwan
| | - Ting-Yi Sung
- Institute
of Information Science, Academia Sinica, 128 Academia Road Sec. 2, Taipei 115, Taiwan
| | - Fuchu He
- The State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, No. 27 Taiping Road, Haidian District, Beijing 100850, China
| | - Edouard C. Nice
- Department
of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Robert J. Goode
- Department
of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Simon Yu
- Department
of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Shoba Ranganathan
- Department
of Chemistry and Biomolecular Sciences and ARC Centre of Excellence
in Bioinformatics, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Mark S. Baker
- Australian
School of Advanced Medicine, Macquarie University, Sydney, NSW 2109, Australia
| | - Gilberto B. Domont
- Proteomics Unit, Institute of Chemistry, Federal University of Rio de Janeiro, Cidade Universitária, Av Athos da Silveira Ramos 149, CT-A542, 21941-909 Rio de Janeriro, Rj, Brazil
| | - Erika Velasquez
- Proteomics Unit, Institute of Chemistry, Federal University of Rio de Janeiro, Cidade Universitária, Av Athos da Silveira Ramos 149, CT-A542, 21941-909 Rio de Janeriro, Rj, Brazil
| | - Dong Li
- The State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, No. 27 Taiping Road, Haidian District, Beijing 100850, China
| | - Siqi Liu
- Beijing Institute of Genomics and BGI Shenzhen, No. 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- BGI Shenzhen, Beishan Road, Yantian District, Shenzhen, 518083, China
| | - Quanhui Wang
- Beijing Institute of Genomics and BGI Shenzhen, No. 1 Beichen West Road, Chaoyang District, Beijing 100101, China
| | - Qing-Yu He
- Key Laboratory of Functional Protein
Research of Guangdong
Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Rajasree Menon
- Department of Computational Medicine & Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
| | - Yuanfang Guan
- Departments of Computational Medicine & Bioinformatics and Computer Sciences, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
| | - Fernando J. Corrales
- ProteoRed-ISCIII,
Biomolecular and Bioinformatics Resources Platform (PRB2), Spanish
Consortium of C-HPP (Chr-16), CIMA, University of Navarra, 31008 Pamplona, Spain
- Chr16 SpHPP Consortium, CIMA, University of Navarra, 31008 Pamplona, Spain
| | - Victor Segura
- ProteoRed-ISCIII,
Biomolecular and Bioinformatics Resources Platform (PRB2), Spanish
Consortium of C-HPP (Chr-16), CIMA, University of Navarra, 31008 Pamplona, Spain
- Chr16 SpHPP Consortium, CIMA, University of Navarra, 31008 Pamplona, Spain
| | - J. Ignacio Casal
- Department
of Cellular and Molecular Medicine, Centro de Investigaciones Biológicas (CIB-CSIC), 28040 Madrid, Spain
| | | | - Juan P. Albar
- Centro Nacional de Biotecnologia (CNB-CSIC), Cantoblanco, 28049 Madrid, Spain
| | - Manuel Fuentes
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Maria Gonzalez-Gonzalez
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Paula Diez
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Nieves Ibarrola
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Rosa M. Degano
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Yassene Mohammed
- University of Victoria-Genome British Columbia Proteomics
Centre, Vancouver Island
Technology Park, #3101−4464 Markham Street, Victoria, British Columbia V8Z 7X8, Canada
- Center
for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Christoph H. Borchers
- University of Victoria-Genome British Columbia Proteomics
Centre, Vancouver Island
Technology Park, #3101−4464 Markham Street, Victoria, British Columbia V8Z 7X8, Canada
| | - Andrea Urbani
- Proteomics
and Metabonomic, Laboratory, Fondazione Santa Lucia, Rome, Italy
- Department
of Experimental Medicine and Surgery, University of Rome “Tor Vergata”, Rome, Italy
| | - Alessio Soggiu
- Department
of Veterinary Science and Public Health (DIVET), University of Milano, via Celoria 10, 20133 Milano, Italy
| | - Tadashi Yamamoto
- Institute
of Nephrology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Ghasem Hosseini Salekdeh
- Department of Molecular Systems Biology at Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran, Karaj, Iran
| | | | | | - Andrey Lisitsa
- Orechovich Institute of Biomedical Chemistry, Moscow, Russia
| | - Cheryl F. Lichti
- Department
of Pharmacology and Toxicology, The University of Texas Medical Branch, Galveston, Texas 77555-0617, United States
| | - Ekaterina Mostovenko
- Department
of Pharmacology and Toxicology, The University of Texas Medical Branch, Galveston, Texas 77555-0617, United States
| | - Roger A. Kroes
- Falk Center for Molecular Therapeutics, Department of Biomedical Engineering, Northwestern University, 1801 Maple Ave., Suite 4300, Evanston, Illinois 60201, United States
| | - Melinda Rezeli
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Ákos Végvári
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Thomas E. Fehniger
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Rainer Bischoff
- Analytical
Biochemistry, Department of Pharmacy, University of Groningen, A. Deusinglaan
1, 9713 AV Groningen, The Netherlands
| | - Juan Antonio Vizcaíno
- European Molecular
Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, CB10 1SD, Hinxton, Cambridge, United Kingdom
| | - Eric W. Deutsch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States
| | - Lydie Lane
- SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
- Department
of Human Protein Science, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Carol L. Nilsson
- Department
of Pharmacology and Toxicology, The University of Texas Medical Branch, Galveston, Texas 77555-0617, United States
| | - György Marko-Varga
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Gilbert S. Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics and School of Public Health, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
| | - Seul-Ki Jeong
- Departments of Integrated Omics for Biomedical Science & Biochemistry, College of Life Science and Technology, Yonsei Proteome Research Center, Yonsei University, Seoul, 120-749, Korea
| | - Jong-Sun Lim
- Departments of Integrated Omics for Biomedical Science & Biochemistry, College of Life Science and Technology, Yonsei Proteome Research Center, Yonsei University, Seoul, 120-749, Korea
| | - Young-Ki Paik
- Departments of Integrated Omics for Biomedical Science & Biochemistry, College of Life Science and Technology, Yonsei Proteome Research Center, Yonsei University, Seoul, 120-749, Korea
| | - William S. Hancock
- The
Barnett Institute of Chemical and Biological Analysis, Northeastern University, 140 The Fenway, Boston, Massachusetts 02115, United States
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34
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Tabas-Madrid D, Alves-Cruzeiro J, Segura V, Guruceaga E, Vialas V, Prieto G, García C, Corrales FJ, Albar JP, Pascual-Montano A. Proteogenomics Dashboard for the Human Proteome Project. J Proteome Res 2015; 14:3738-49. [DOI: 10.1021/acs.jproteome.5b00466] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Daniel Tabas-Madrid
- ProteoRed-ISCIII, National Center for Biotechnology-CSIC (CNB), C/Darwin 3, Madrid 28049, Spain
| | - Joao Alves-Cruzeiro
- ProteoRed-ISCIII, National Center for Biotechnology-CSIC (CNB), C/Darwin 3, Madrid 28049, Spain
| | - Victor Segura
- ProteoRed-ISCIII,
Center for Applied Medical Research (CIMA), University of Navarra, Avda. Pío XII, 55, Pamplona E-31008, Spain
| | - Elizabeth Guruceaga
- ProteoRed-ISCIII,
Center for Applied Medical Research (CIMA), University of Navarra, Avda. Pío XII, 55, Pamplona E-31008, Spain
| | - Vital Vialas
- ProteoRed-ISCIII, National Center for Biotechnology-CSIC (CNB), C/Darwin 3, Madrid 28049, Spain
| | - Gorka Prieto
- Department
of Communication Engineering E.T.S. Ingenierı́a de Bilbao, University of the Basque Country (UPV/EHU), Alda. Urquijo, s/n, Bilbao 48013, Spain
| | - Carlos García
- Computer
Science Faculty, Complutense University of Madrid (UCM), C/ Jose
Garcı́á Santesmases 9, Madrid 28040, Spain
| | - Fernando J. Corrales
- ProteoRed-ISCIII,
Center for Applied Medical Research (CIMA), University of Navarra, Avda. Pío XII, 55, Pamplona E-31008, Spain
| | - Juan Pablo Albar
- ProteoRed-ISCIII, National Center for Biotechnology-CSIC (CNB), C/Darwin 3, Madrid 28049, Spain
| | - Alberto Pascual-Montano
- ProteoRed-ISCIII, National Center for Biotechnology-CSIC (CNB), C/Darwin 3, Madrid 28049, Spain
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35
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Yang S, Zhang X, Diao L, Guo F, Wang D, Liu Z, Li H, Zheng J, Pan J, Nice EC, Li D, He F. CAPER 3.0: A Scalable Cloud-Based System for Data-Intensive Analysis of Chromosome-Centric Human Proteome Project Data Sets. J Proteome Res 2015; 14:3720-8. [PMID: 25794139 DOI: 10.1021/pr501335w] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The Chromosome-centric Human Proteome Project (C-HPP) aims to catalog genome-encoded proteins using a chromosome-by-chromosome strategy. As the C-HPP proceeds, the increasing requirement for data-intensive analysis of the MS/MS data poses a challenge to the proteomic community, especially small laboratories lacking computational infrastructure. To address this challenge, we have updated the previous CAPER browser into a higher version, CAPER 3.0, which is a scalable cloud-based system for data-intensive analysis of C-HPP data sets. CAPER 3.0 uses cloud computing technology to facilitate MS/MS-based peptide identification. In particular, it can use both public and private cloud, facilitating the analysis of C-HPP data sets. CAPER 3.0 provides a graphical user interface (GUI) to help users transfer data, configure jobs, track progress, and visualize the results comprehensively. These features enable users without programming expertise to easily conduct data-intensive analysis using CAPER 3.0. Here, we illustrate the usage of CAPER 3.0 with four specific mass spectral data-intensive problems: detecting novel peptides, identifying single amino acid variants (SAVs) derived from known missense mutations, identifying sample-specific SAVs, and identifying exon-skipping events. CAPER 3.0 is available at http://prodigy.bprc.ac.cn/caper3.
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Affiliation(s)
- Shuai Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine , Beijing 100850, China.,National Center for Protein Sciences Beijing , Beijing 102206, China
| | - Xinlei Zhang
- Beijing Genestone Technology Ltd. , Beijing 100085, China
| | - Lihong Diao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine , Beijing 100850, China.,National Center for Protein Sciences Beijing , Beijing 102206, China
| | - Feifei Guo
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine , Beijing 100850, China.,National Center for Protein Sciences Beijing , Beijing 102206, China.,Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College , Beijing 100005, China
| | - Dan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine , Beijing 100850, China.,National Center for Protein Sciences Beijing , Beijing 102206, China
| | - Zhongyang Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine , Beijing 100850, China.,National Center for Protein Sciences Beijing , Beijing 102206, China
| | - Honglei Li
- Beijing Genestone Technology Ltd. , Beijing 100085, China
| | - Junjie Zheng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine , Beijing 100850, China.,National Center for Protein Sciences Beijing , Beijing 102206, China
| | - Jingshan Pan
- Shandong Computer Science Center (National Supercomputer Center in Jinan) , Shandong 250101, China
| | - Edouard C Nice
- Department of Biochemistry and Molecular Biology, Monash University , Clayton, Victoria 3800, Australia
| | - Dong Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine , Beijing 100850, China.,National Center for Protein Sciences Beijing , Beijing 102206, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine , Beijing 100850, China.,National Center for Protein Sciences Beijing , Beijing 102206, China
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36
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Archakov A, Lisitsa A, Ponomarenko E, Zgoda V. Recent advances in proteomic profiling of human blood: clinical scope. Expert Rev Proteomics 2015; 12:111-3. [DOI: 10.1586/14789450.2015.1018895] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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37
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Ponomarenko E, Zgoda V, Kopylov A, Poverennaya E, Ilgisonis E, Lisitsa A, Archakov A. The Russian part of the human proteome project:first results and prospects. ACTA ACUST UNITED AC 2015; 61:169-75. [DOI: 10.18097/pbmc20156102169] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The article summarizes the achievements of the pilot phase (2010-2014) of the Russian part of the international project “Human Proteome” and identifies the directions for further work on the study of the human chromosome 18 proteome in the framework of the project main phase (2015-2022). The pilot phase of the project was focused on the detection of at least one protein for each chromosome 18 protein-coding gene in three types of the biological material. The application of mass spectrometric detection of proteins by the methods of multiple reactions monitoring (MRM) and gene-centric approach made it possible to detect 95% of master forms of proteins, for 60% of which the quantitative assessment of the protein content was obtained in at least one type of the biological material. The task of the main phase of the project is to measure the proteome size of healthy individuals, taking into account the modified protein forms, providing for both the bioinformatics prediction of the quantity of proteins types and the selective experimental measurement of single proteoforms. Since the ranges of protein concentrations corresponding to the normal physiological state have not been identified, the work of the main phase of the project is focused on the study of clinically healthy individuals. The absence of these data complicates significantly the interpretation of the patients’ blood proteomic profiles and prevents creating diagnostic tests. In the long term prospect, implementation of the project envisages development of a diagnostic test system based on multiple reactions monitoring (MRM) for quantitative measurement of the protein forms associated with some diseases. Development of such test systems will allow predicting the extent of risk of different diseases, diagnosing a disease at its early stage and monitoring the effectiveness of the treatment.
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Affiliation(s)
| | - V.G. Zgoda
- Institute of Biomedical Chemistry, Moscow, Russia
| | - A.T. Kopylov
- Institute of Biomedical Chemistry, Moscow, Russia
| | | | | | - A.V. Lisitsa
- Institute of Biomedical Chemistry, Moscow, Russia
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38
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Tyakht AV, Ilina EN, Alexeev DG, Ischenko DS, Gorbachev AY, Semashko TA, Larin AK, Selezneva OV, Kostryukova ES, Karalkin PA, Vakhrushev IV, Kurbatov LK, Archakov AI, Govorun VM. RNA-Seq gene expression profiling of HepG2 cells: the influence of experimental factors and comparison with liver tissue. BMC Genomics 2014; 15:1108. [PMID: 25511409 PMCID: PMC4378340 DOI: 10.1186/1471-2164-15-1108] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 12/11/2014] [Indexed: 12/12/2022] Open
Abstract
Background Human hepatoma HepG2 cells are used as an in vitro model of the human liver. High-throughput transcriptomic sequencing is an advanced approach for assessing the functional state of a tissue or cell type. However, the influence of experimental factors, such as the sample preparation method and inter-laboratory variation, on the transcriptomic profile has not been evaluated. Results The whole-transcriptome sequencing of HepG2 cells was performed using the SOLiD platform and validated using droplet digital PCR. The gene expression profile was compared to the results obtained with the same sequencing method in another laboratory and using another sample preparation method. We also compared the transcriptomic profile HepG2 cells with that of liver tissue. Comparison of the gene expression profiles between the HepG2 cell line and liver tissue revealed the highest variation, followed by HepG2 cells submitted to two different sample preparation protocols. The lowest variation was observed between HepG2 cells prepared by two different laboratories using the same protocol. The enrichment analysis of the genes that were differentially expressed between HepG2 cells and liver tissue mainly revealed the cancer-associated gene signature of HepG2 cells and the activation of the response to chemical stimuli in the liver tissue. The HepG2 transcriptome obtained with the SOLiD platform was highly correlated with the published transcriptome obtained with the Illumina and Helicos platforms, with moderate correspondence to microarrays. Conclusions In the present study, we assessed the influence of experimental factors on the HepG2 transcriptome and identified differences in gene expression between the HepG2 cell line and liver cells. These findings will facilitate robust experimental design in the fields of pharmacology and toxicology. Our results were supported by a comparative analysis with previous HepG2 gene expression studies. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-1108) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexander V Tyakht
- Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya 1a, Moscow 119435, Russia.
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39
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Ivanov AS, Medvedev A, Ershov P, Molnar A, Mezentsev Y, Yablokov E, Kaluzhsky L, Gnedenko O, Buneeva O, Haidukevich I, Sergeev G, Lushchyk A, Yantsevich A, Medvedeva M, Kozin S, Popov I, Novikova S, Zgoda V, Gilep A, Usanov S, Lisitsa A, Archakov A. Protein interactomics based on direct molecular fishing on paramagnetic particles: practical realization and further SPR validation. Proteomics 2014; 14:2261-74. [PMID: 25044858 DOI: 10.1002/pmic.201400117] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 05/27/2014] [Accepted: 07/10/2014] [Indexed: 01/13/2023]
Abstract
There is increasing evidence that proteins function in the cell as integrated stable or temporally formed protein complexes, interactomes. Previously, using model systems we demonstrated applicability of direct molecular fishing on paramagnetic particles for protein interactomics (Ershov et al. Proteomics, 2012, 12, 3295). In the present study, we have used a combination of affinity-based molecular fishing and subsequent MS for investigation of human liver proteins involved in interactions with immobilized microsomal cytochrome b5 (CYB5A), and also transthyretin and BSA as alternative affinity ligands (baits). The LC-MS/MS identification of prey proteins fished on these baits revealed three sets of proteins: 98, 120, and 220, respectively. Comparison analysis of these sets revealed only three proteins common for all the baits. In the case of paired analysis, the number of common proteins varied from 2 to 9. The binding capacity of some identified proteins has been validated by a SPR-based biosensor. All the investigated proteins effectively interacted with the immobilized CYB5A (Kd values ranged from 0.07 to 1.1 μM). Results of this study suggest that direct molecular fishing is applicable for analysis of protein-protein interactions (PPI) under normal and pathological conditions, in which altered PPIs are especially important.
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Affiliation(s)
- Alexis S Ivanov
- Orechovich Institute of Biomedical Chemistry, Moscow, Russia
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40
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Sobolev BN, Veselovsky AV, Poroikov VV. Prediction of protein post-translational modifications: main trends and methods. RUSSIAN CHEMICAL REVIEWS 2014. [DOI: 10.1070/rc2014v083n02abeh004377] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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41
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Lane L, Bairoch A, Beavis RC, Deutsch EW, Gaudet P, Lundberg E, Omenn GS. Metrics for the Human Proteome Project 2013-2014 and strategies for finding missing proteins. J Proteome Res 2013; 13:15-20. [PMID: 24364385 DOI: 10.1021/pr401144x] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
One year ago the Human Proteome Project (HPP) leadership designated the baseline metrics for the Human Proteome Project to be based on neXtProt with a total of 13,664 proteins validated at protein evidence level 1 (PE1) by mass spectrometry, antibody-capture, Edman sequencing, or 3D structures. Corresponding chromosome-specific data were provided from PeptideAtlas, GPMdb, and Human Protein Atlas. This year, the neXtProt total is 15,646 and the other resources, which are inputs to neXtProt, have high-quality identifications and additional annotations for 14,012 in PeptideAtlas, 14,869 in GPMdb, and 10,976 in HPA. We propose to remove 638 genes from the denominator that are "uncertain" or "dubious" in Ensembl, UniProt/SwissProt, and neXtProt. That leaves 3844 "missing proteins", currently having no or inadequate documentation, to be found from a new denominator of 19,490 protein-coding genes. We present those tabulations and web links and discuss current strategies to find the missing proteins.
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Affiliation(s)
- Lydie Lane
- SIB-Swiss Institute of Bioinformatics , CMU - Rue Michel-Servet 1, 1211 Geneva, Switzerland
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42
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Ponomarenko EA, Kopylov AT, Lisitsa AV, Radko SP, Kiseleva YY, Kurbatov LK, Ptitsyn KG, Tikhonova OV, Moisa AA, Novikova SE, Poverennaya EV, Ilgisonis EV, Filimonov AD, Bogolubova NA, Averchuk VV, Karalkin PA, Vakhrushev IV, Yarygin KN, Moshkovskii SA, Zgoda VG, Sokolov AS, Mazur AM, Prokhortchouck EB, Skryabin KG, Ilina EN, Kostrjukova ES, Alexeev DG, Tyakht AV, Gorbachev AY, Govorun VM, Archakov AI. Chromosome 18 transcriptoproteome of liver tissue and HepG2 cells and targeted proteome mapping in depleted plasma: update 2013. J Proteome Res 2013; 13:183-90. [PMID: 24328317 DOI: 10.1021/pr400883x] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
We report the results obtained in 2012-2013 by the Russian Consortium for the Chromosome-centric Human Proteome Project (C-HPP). The main scope of this work was the transcriptome profiling of genes on human chromosome 18 (Chr 18), as well as their encoded proteome, from three types of biomaterials: liver tissue, the hepatocellular carcinoma-derived cell line HepG2, and blood plasma. The transcriptome profiling for liver tissue was independently performed using two RNaseq platforms (SOLiD and Illumina) and also by droplet digital PCR (ddPCR) and quantitative RT-PCR. The proteome profiling of Chr 18 was accomplished by quantitatively measuring protein copy numbers in the three types of biomaterial (the lowest protein concentration measured was 10(-13) M) using selected reaction monitoring (SRM). In total, protein copy numbers were estimated for 228 master proteins, including quantitative data on 164 proteins in plasma, 171 in the HepG2 cell line, and 186 in liver tissue. Most proteins were present in plasma at 10(8) copies/μL, while the median abundance was 10(4) and 10(5) protein copies per cell in HepG2 cells and liver tissue, respectively. In summary, for liver tissue and HepG2 cells a "transcriptoproteome" was produced that reflects the relationship between transcript and protein copy numbers of the genes on Chr 18. The quantitative data acquired by RNaseq, PCR, and SRM were uploaded into the "Update_2013" data set of our knowledgebase (www.kb18.ru) and investigated for linear correlations.
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Affiliation(s)
- Elena A Ponomarenko
- Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences , 10 Pogodinskaya Street, Moscow 119121, Russia
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43
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Wang D, Liu Z, Guo F, Diao L, Li Y, Zhang X, Huang Z, Li D, He F. CAPER 2.0: An Interactive, Configurable, and Extensible Workflow-Based Platform to Analyze Data Sets from the Chromosome-centric Human Proteome Project. J Proteome Res 2013; 13:99-106. [DOI: 10.1021/pr400795c] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Dan Wang
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, 33 Life Science Park Road, Beijing 100850, China
- National Center for Protein Sciences Beijing, 33 Life Science Park Road, Beijing 102206, China
- National Engineering Research Center for Protein Drugs, 33 Life Science Park Road, Beijing 100850, China
| | - Zhongyang Liu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, 33 Life Science Park Road, Beijing 100850, China
- National Center for Protein Sciences Beijing, 33 Life Science Park Road, Beijing 102206, China
- National Engineering Research Center for Protein Drugs, 33 Life Science Park Road, Beijing 100850, China
| | - Feifei Guo
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, 33 Life Science Park Road, Beijing 100850, China
- National Center for Protein Sciences Beijing, 33 Life Science Park Road, Beijing 102206, China
- National Engineering Research Center for Protein Drugs, 33 Life Science Park Road, Beijing 100850, China
- Institute
of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, 5 Dong Dan San Tiao, Beijing 100005, China
| | - Lihong Diao
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, 33 Life Science Park Road, Beijing 100850, China
- National Center for Protein Sciences Beijing, 33 Life Science Park Road, Beijing 102206, China
- National Engineering Research Center for Protein Drugs, 33 Life Science Park Road, Beijing 100850, China
| | - Yang Li
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, 33 Life Science Park Road, Beijing 100850, China
- National Center for Protein Sciences Beijing, 33 Life Science Park Road, Beijing 102206, China
- National Engineering Research Center for Protein Drugs, 33 Life Science Park Road, Beijing 100850, China
| | - Xinlei Zhang
- Beijing Genestone Technology, Ltd., F21-103, FengLinLvZhou, Kexueyuan Nanli, Datun Road, Beijing 100085, China
| | - Zechi Huang
- Beijing Genestone Technology, Ltd., F21-103, FengLinLvZhou, Kexueyuan Nanli, Datun Road, Beijing 100085, China
| | - Dong Li
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, 33 Life Science Park Road, Beijing 100850, China
- National Center for Protein Sciences Beijing, 33 Life Science Park Road, Beijing 102206, China
- National Engineering Research Center for Protein Drugs, 33 Life Science Park Road, Beijing 100850, China
| | - Fuchu He
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, 33 Life Science Park Road, Beijing 100850, China
- National Center for Protein Sciences Beijing, 33 Life Science Park Road, Beijing 102206, China
- National Engineering Research Center for Protein Drugs, 33 Life Science Park Road, Beijing 100850, China
- Institute
of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, 5 Dong Dan San Tiao, Beijing 100005, China
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Poverennaya EV, Bogolubova NA, Bylko NN, Ponomarenko EA, Lisitsa AV, Archakov AI. Gene-centric content management system. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1844:77-81. [PMID: 23994227 DOI: 10.1016/j.bbapap.2013.08.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2012] [Revised: 08/07/2013] [Accepted: 08/13/2013] [Indexed: 01/17/2023]
Abstract
The Human Proteome Project (HPP) was started two years ago and the international consortia have elaborated a number of informational resources to harbor the HPP data. Selected informational resources are currently used to elaborate the HPP baseline metrics, which were introduced to estimate future contribution of HPP to the knowledge domain. We developed a Web-based tool Gene-centric Content Management System (GenoCMS) for comparing public resources to proprietary results by using the representation of proteins as color-coded catalog. Within our CMS, the features of protein-coding genes are uploaded from the public domain and then appended by additional features derived from original experimental workflows. We describe the heat-map/traffic light representation of our proteomic experiments as the background of data taken from NeXtProt, MS/MS repositories, the Human Protein Atlas and the RNAseqAtlas. The system presented at www.kb18.ru comprises a collaborative knowledge base for annotating the gene sets and disseminating these annotations through the Web. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
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Affiliation(s)
- Ekaterina V Poverennaya
- Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences (RAMS), Russia.
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45
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Kolesanova EF, Sanzhakov MA, Kharybin ON. Development of the schedule for multiple parallel "difficult" Peptide synthesis on pins. INTERNATIONAL JOURNAL OF PEPTIDES 2013; 2013:197317. [PMID: 24027588 PMCID: PMC3762146 DOI: 10.1155/2013/197317] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Accepted: 07/09/2013] [Indexed: 12/20/2022]
Abstract
Unified schedule for multiple parallel solid-phase synthesis of so-called "difficult" peptides on polypropylene pins was developed. Increase in the efficiency of 9-fluorenyl(methoxycarbonyl) N-terminal amino-protecting group removal was shown to have a greater influence on the accuracy of the "difficult" peptide synthesis than the use of more efficient amino acid coupling reagents such as aminium salts. Hence the unified schedule for multiple parallel solid-phase synthesis of "difficult" peptides included the procedure for N-terminal amino group deprotection modified by applying a more efficient reagent for the deprotection and the standard procedure of amino acid coupling by carbodiimide method with an additional coupling using aminium salts, if necessary. Amino acid coupling with the help of carbodiimide allows to follow the completeness of the coupling via the bromophenol blue indication, thus providing the accuracy of the synthesis and preventing an overexpenditure of expensive reagents. About 100 biotinylated hepatitis C virus envelope protein fragments, most of which represented "difficult" peptides, were successfully obtained by synthesis on pins with the help of the developed unified schedule.
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Affiliation(s)
- Ekaterina F. Kolesanova
- Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, 10 Pogodinskaya Ulica, Moscow 119121, Russia
| | - Maxim A. Sanzhakov
- Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, 10 Pogodinskaya Ulica, Moscow 119121, Russia
| | - Oleg N. Kharybin
- Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, 10 Pogodinskaya Ulica, Moscow 119121, Russia
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