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  1. AU="Kurbatov, Ilya Y"
  2. AU="Guevara-Álvarez, A"
  3. AU="Barker, Darren"
  4. AU="Muschaweckh, Andreas"
  5. AU="Saibi, Lotfi"
  6. AU=Wagener Jeffrey S AU=Wagener Jeffrey S
  7. AU="Christian Tschuschke"
  8. AU="Gong, Hexiang"
  9. AU="Ziembiec, Nancy A"
  10. AU="Hameed, Safraj Shahul"
  11. AU="Elliott, Alison M"
  12. AU="Nakeshbandi, Mohamed"
  13. AU=Sonthalia Nikhil AU=Sonthalia Nikhil
  14. AU="Kohanbash, Gary" AU="Kohanbash, Gary"
  15. AU=Nawrocki Mariusz J.
  16. AU="Jayme Borensztajn"
  17. AU=Schaerf Raymond H M
  18. AU="Chachar, Aijaz Zeeshan Khan"
  19. AU=Forster H V
  20. AU=Yang G-F
  21. AU="Kaplan, Robert C"
  22. AU="Rood, Julian I."
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  24. AU="Jacobsen, Cristiane Cavalcanti"
  25. AU="Anja Kukic"
  26. AU="Zhang, N"
  27. AU="Cunningham, Thomas J"
  28. AU="Wu, Y P"
  29. AU="Sumbul, Sabiha"
  30. AU="Razolli, Daniela S"
  31. AU="Asif, Mohsin"
  32. AU="Choudhary, Diksha"
  33. AU="Liu, Yongmei"
  34. AU=Goldshaid Liat
  35. AU="Sandeep Dhayade"
  36. AU="Ashkin, David"
  37. AU="Yukioka, Hideo"
  38. AU="Giles, M"
  39. AU="Keshavarzian, Tina"
  40. AU="Richard Holtmeier"
  41. AU=Sanborn Keri B
  42. AU=Crestani Larissa AU=Crestani Larissa
  43. AU=Nassiri Amir Ahmad
  44. AU="Keiser, Olivia"
  45. AU="Scholz, Martin"
  46. AU="Vassilis Pigadas" AU="Vassilis Pigadas"
  47. AU="Schlievert, Patrick M"
  48. AU=Egan Katie G
  49. AU="Benzadón, Mariano"
  50. AU="Truong, Thérèse"
  51. AU="de Oliveira, Aline Lima"
  52. AU="Lehto, Sonya G"
  53. AU="Simi Jacob"
  54. AU="Shipman, Michael"
  55. AU=Pons Linda
  56. AU="Notoya, A"
  57. AU="Williams, Olajide A"
  58. AU=Errington Jeff
  59. AU="Tortolani, Christina C"
  60. AU="Patel, Jenil R"
  61. AU="Aires, Rafaela"
  62. AU="Habibelahi, Abbas"
  63. AU="Temes, Javier"
  64. AU="Miwa, Toru"
  65. AU="Jaller, Elvira"
  66. AU="Manso Sanchez, L M"

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  1. Artikel ; Online: Epitranscriptome: Review of Top 25 Most-Studied RNA Modifications.

    Arzumanian, Viktoriia A / Dolgalev, Georgii V / Kurbatov, Ilya Y / Kiseleva, Olga I / Poverennaya, Ekaterina V

    International journal of molecular sciences

    2022  Band 23, Heft 22

    Abstract: The alphabet of building blocks for RNA molecules is much larger than the standard four nucleotides. The diversity is achieved by the post-transcriptional biochemical modification of these nucleotides into distinct chemical entities that are structurally ...

    Abstract The alphabet of building blocks for RNA molecules is much larger than the standard four nucleotides. The diversity is achieved by the post-transcriptional biochemical modification of these nucleotides into distinct chemical entities that are structurally and functionally different from their unmodified counterparts. Some of these modifications are constituent and critical for RNA functions, while others serve as dynamic markings to regulate the fate of specific RNA molecules. Together, these modifications form the epitranscriptome, an essential layer of cellular biochemistry. As of the time of writing this review, more than 300 distinct RNA modifications from all three life domains have been identified. However, only a few of the most well-established modifications are included in most reviews on this topic. To provide a complete overview of the current state of research on the epitranscriptome, we analyzed the extent of the available information for all known RNA modifications. We selected 25 modifications to describe in detail. Summarizing our findings, we describe the current status of research on most RNA modifications and identify further developments in this field.
    Mesh-Begriff(e) RNA Processing, Post-Transcriptional ; RNA/metabolism ; Nucleotides
    Chemische Substanzen RNA (63231-63-0) ; Nucleotides
    Sprache Englisch
    Erscheinungsdatum 2022-11-10
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article ; Review
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms232213851
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Identifying N6-Methyladenosine Sites in HepG2 Cell Lines Using Oxford Nanopore Technology.

    Arzumanian, Viktoriia A / Kurbatov, Ilya Y / Ptitsyn, Konstantin G / Khmeleva, Svetlana A / Kurbatov, Leonid K / Radko, Sergey P / Poverennaya, Ekaterina V

    International journal of molecular sciences

    2023  Band 24, Heft 22

    Abstract: RNA modifications, particularly N6-methyladenosine (m6A), are pivotal regulators of RNA functionality and cellular processes. We analyzed m6A modifications by employing Oxford Nanopore technology and the m6Anet algorithm, focusing on the HepG2 cell line. ...

    Abstract RNA modifications, particularly N6-methyladenosine (m6A), are pivotal regulators of RNA functionality and cellular processes. We analyzed m6A modifications by employing Oxford Nanopore technology and the m6Anet algorithm, focusing on the HepG2 cell line. We identified 3968 potential m6A modification sites in 2851 transcripts, corresponding to 1396 genes. A gene functional analysis revealed the active involvement of m6A-modified genes in ubiquitination, transcription regulation, and protein folding processes, aligning with the known role of m6A modifications in histone ubiquitination in cancer. To ensure data robustness, we assessed reproducibility across technical replicates. This study underscores the importance of evaluating algorithmic reproducibility, especially in supervised learning. Furthermore, we examined correlations between transcriptomic, translatomic, and proteomic levels. A strong transcriptomic-translatomic correlation was observed. In conclusion, our study deepens our understanding of m6A modifications' multifaceted impacts on cellular processes and underscores the importance of addressing reproducibility concerns in analytical approaches.
    Mesh-Begriff(e) Methylation ; Nanopores ; Proteomics ; Reproducibility of Results ; RNA/metabolism ; Adenosine/metabolism ; Cell Line
    Chemische Substanzen RNA (63231-63-0) ; Adenosine (K72T3FS567)
    Sprache Englisch
    Erscheinungsdatum 2023-11-18
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms242216477
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel: The Expectation and Reality of the HepG2 Core Metabolic Profile.

    Kiseleva, Olga I / Kurbatov, Ilya Y / Arzumanian, Viktoriia A / Ilgisonis, Ekaterina V / Zakharov, Svyatoslav V / Poverennaya, Ekaterina V

    Metabolites

    2023  Band 13, Heft 8

    Abstract: To represent the composition of small molecules circulating in HepG2 cells and the formation of the "core" of characteristic metabolites that often attract researchers' attention, we conducted a meta-analysis of 56 datasets obtained through metabolomic ... ...

    Abstract To represent the composition of small molecules circulating in HepG2 cells and the formation of the "core" of characteristic metabolites that often attract researchers' attention, we conducted a meta-analysis of 56 datasets obtained through metabolomic profiling via mass spectrometry and NMR. We highlighted the 288 most commonly studied compounds of diverse chemical nature and analyzed metabolic processes involving these small molecules. Building a complete map of the metabolome of a cell, which encompasses the diversity of possible impacts on it, is a severe challenge for the scientific community, which is faced not only with natural limitations of experimental technologies, but also with the absence of transparent and widely accepted standards for processing and presenting the obtained metabolomic data. Formulating our research design, we aimed to reveal metabolites crucial to the Hepg2 cell line, regardless of all chemical and/or physical impact factors. Unfortunately, the existing paradigm of data policy leads to a streetlight effect. When analyzing and reporting only target metabolites of interest, the community ignores the changes in the metabolomic landscape that hide many molecular secrets.
    Sprache Englisch
    Erscheinungsdatum 2023-08-03
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article ; Review
    ZDB-ID 2662251-8
    ISSN 2218-1989
    ISSN 2218-1989
    DOI 10.3390/metabo13080908
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel: Multiomics Picture of Obesity in Young Adults.

    Kiseleva, Olga I / Pyatnitskiy, Mikhail A / Arzumanian, Viktoriia A / Kurbatov, Ilya Y / Ilinsky, Valery V / Ilgisonis, Ekaterina V / Plotnikova, Oksana A / Sharafetdinov, Khaider K / Tutelyan, Victor A / Nikityuk, Dmitry B / Ponomarenko, Elena A / Poverennaya, Ekaterina V

    Biology

    2024  Band 13, Heft 4

    Abstract: Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes ... ...

    Abstract Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC-MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC-MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m
    Sprache Englisch
    Erscheinungsdatum 2024-04-18
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2661517-4
    ISSN 2079-7737
    ISSN 2079-7737
    DOI 10.3390/biology13040272
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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