Elena A. Ponomarenko

Dr. Elena A. Ponomarenko


Research Focus & Metrics

Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 December 2012 in Journal of Proteome Research
Verified
Journal Article
Closed access
VZVictor G. Zgoda
Victor G. Zgoda
AKArthur Kopylov
Arthur Kopylov
OTOlga V. Tikhonova
Olga V. Tikhonova
AMAlexander A. Moisa
Alexander A. Moisa
NPNadezhda V. Pyndyk
Nadezhda V. Pyndyk
AAAlexander Archakov
Alexander Archakov

13 December 2013 in Journal of Proteome Research
Verified
Journal Article
Closed access
EPElena A. Ponomarenko
Elena A. Ponomarenko
AKArthur Kopylov
Arthur Kopylov
ALAndrey V. Lisitsa
Andrey V. Lisitsa
SRSergey P. Radko
Sergey P. Radko
YKYana Yu. Kiseleva
Yana Yu. Kiseleva
AAAlexander Archakov
Alexander Archakov

10 October 2015 in Electrophoresis
Verified
Journal Article
Open access
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Stanislav NaryzhnyStanislav Naryzhny
Stanislav Naryzhny
VZVictor Zgoda
Victor Zgoda
MMMaria A. Maynskova
Maria A. Maynskova
SNSvetlana Novikova
Svetlana Novikova
NRNatalia Leonidovna Ronzhina
Natalia Leonidovna Ronzhina
AAAlexander Archakov
Alexander Archakov
Virtual and experimental 2DE coupled with ESI LC‐MS/MS was introduced to obtain better representation of the information about human proteome. The proteins from HEPG2 cells and human blood plasma were run by 2DE. After staining and protein spot identification by MALDI‐TOF MS, the protein maps were generated. The experimental physicochemical parameters (pI/Mw) of the proteoforms further detected by ESI LC‐MS/MS in these spots were obtained. Next, the theoretical pI and Mw of identified proteins were calculated using program Compute pI/Mw (http://web.expasy.org/compute_pi/pi_tool‐doc.html). Accordingly, the relationship between theoretical and experimental parameters was analyzed, and the correlation plots were built. Additionally, virtual/experimental information about different protein species/proteoforms from the same genes was extracted. As it was revealed from the plots, the major proteoforms detected in HepG2 cell line have pI/Mw parameters similar to theoretical values. In opposite, the minor protein species have mainly very different from theoretical pI and Mw parameters. A similar situation was observed in plasma in much higher degree. It means that minor protein species are heavily modified in cell and even more in plasma proteome.

Verified
Journal Article
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Petr G. LokhovPetr G. Lokhov
Petr G. Lokhov
Elena BalashovaElena Balashova
Elena Balashova
OTOxana Trifonova
Oxana Trifonova
Dmitry MaslovDmitry Maslov
Dmitry Maslov
EPElena A. Ponomarenko
Elena A. Ponomarenko
AAAlexander Archakov
Alexander Archakov
Scientists currently use only a small portion of the information contained in the blood metabolome. The identification of metabolites is a huge challenge because only highly abundant and well-separated compounds can be easily identified in complex samples. However, new approaches that enhance the identification of compounds have emerged; among them, the identification of compounds based on their involvement in a particular biological context is a recent development. In this work, this approach was first applied to identify metabolites in complex samples and, together with metabolite set enrichment analysis, was used for the evaluation of blood plasma from obese patients. The proposed approach was found to provide a statistically sound overview of the biochemical pathways, thus presenting additional information on obesity. Obesity progression was demonstrated to be accompanied by marked alterations in steroidogenesis, androstenedione metabolism, and androgen and estrogen metabolism. The findings of this study suggest that the workflow used for blood analysis is sufficient to demonstrate obesity at the biochemical pathway level as well as to monitor the response to treatment. This workflow is also expected to be suitable for studying other metabolic diseases.

18 February 2022 in Molecules
Verified
Journal Article
Open access
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YIYuri D. Ivanov
Yuri D. Ivanov
ATAmir Taldaev
Amir Taldaev
ALAndrey Valerievich Lisitsa
Andrey Valerievich Lisitsa
EPElena A. Ponomarenko
Elena A. Ponomarenko
AAAlexander Archakov
Alexander Archakov
The three-dimensional structure of monomers and homodimers of CYP102A1/WT (wild-type) proteins and their A83F and A83I mutant forms was predicted using the AlphaFold2 (AF2) and AlphaFold Multimer (AFMultimer) programs, which were compared with the rate constants of hydroxylation reactions of these enzyme forms to determine the efficiency of intra- and interprotein electron transport in the CYP102A1 hydroxylase system. The electron transfer rate constants (ket), which determine the rate of indole hydroxylation by the CYP102A1 system, were calculated based on the distances (R) between donor-acceptor prosthetic groups (PG) FAD→FMN→HEME of these proteins using factor β, which describes an exponential decay from R the speed of electron transport (ET) according to the tunnelling mechanism. It was shown that the structure of monomers in the homodimer, calculated using the AlpfaFold Multimer program, is in good agreement with the experimental structures of globular domains (HEME-, FMN-, and FAD-domains) in CYP102A1/WT obtained by X-ray structural analysis, and the structure of isolated monomers predicted in AF2 does not coincide with the structure of monomers in the homodimer, although a high level of similarity in individual domains remains. The structures of monomers and homodimers of A83F and A83I mutants were also calculated, and their structures were compared with the wild-type protein. Significant differences in the structure of all isolated monomers with respect to the structures of monomers in homodimers were also found for them, and at the same time, insignificant differences were revealed for all homodimers. Comparative analysis for CYP102A1/WT between the calculated intra- and interprotein distances FAD→FMN→HEME and the rate constants of hydroxylation in these proteins showed that the distance between prosthetic groups both in the monomer and in the dimer allows the implementation of electron transfer between PGs, which is consistent with experimental literature data about kcat. For the mutant form of monomer A83I, an increase in the distance between PGs was obtained, which can restrict electron transportation compared to WT; however, for the dimer of this protein, a decrease in the distance between PGs was observed compared to the WT form, which can lead to an increase in the electron transfer rate constant and, accordingly, kcat. For the monomer and homodimer of the A83F mutant, the calculations showed an increase in the distance between the PGs compared to the WT form, which should have led to a decrease in the electron transfer rate, but at the same time, for the homodimer, the approach of the aromatic group F262 with heme can speed up transportation for this form and, accordingly, the rate of hydroxylation.

4 November 2021 in Biology
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Journal Article
Open access
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MPMikhail A Pyatnitskiy
Mikhail A Pyatnitskiy
VAViktoriia Armenovna Arzumanian
Viktoriia Armenovna Arzumanian
SRSergey P. Radko
Sergey P. Radko
KPKonstantin Ptitsyn
Konstantin Ptitsyn
IVIgor Vakhrushev
Igor Vakhrushev
EPElena A. Ponomarenko
Elena A. Ponomarenko
Long-read direct RNA sequencing developed by Oxford Nanopore Technologies (ONT) is quickly gaining popularity for transcriptome studies, while fast turnaround time and low cost make it an attractive instrument for clinical applications. There is a growing interest to utilize transcriptome data to unravel activated biological processes responsible for disease progression and response to therapies. This trend is of particular interest for precision medicine which aims at single-patient analysis. Here we evaluated whether gene abundances measured by MinION direct RNA sequencing are suited to produce robust estimates of pathway activation for single sample scoring methods. We performed multiple RNA-seq analyses for a single sample that originated from the HepG2 cell line, namely five ONT replicates, and three replicates using Illumina NovaSeq. Two pathway scoring methods were employed—ssGSEA and singscore. We estimated the ONT performance in terms of detected protein-coding genes and average pairwise correlation between pathway activation scores using an exhaustive computational scheme for all combinations of replicates. In brief, we found that at least two ONT replicates are required to obtain reproducible pathway scores for both algorithms. We hope that our findings may be of interest to researchers planning their ONT direct RNA-seq experiments.