Next Article in Journal
Rational Design of Bifunctional Microporous Organic Polymers Containing Anthracene and Triphenylamine Units for Energy Storage and Biological Applications
Previous Article in Journal
Modelling Microglial Innate Immune Memory In Vitro: Understanding the Role of Aerobic Glycolysis in Innate Immune Memory
Previous Article in Special Issue
Characterization of Continuous Transcriptional Heterogeneity in High-Risk Blastemal-Type Wilms’ Tumors Using Unsupervised Machine Learning
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Medical Genetics, Genomics and Bioinformatics—2022

by
Vadim V. Klimontov
1,
Konstantin A. Koshechkin
2,
Nina G. Orlova
3,
Marina I. Sekacheva
4 and
Yuriy L. Orlov
1,2,5,6,*
1
Research Institute of Clinical and Experimental Lymphology—Branch of the Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (RICEL—Branch of IC&G SB RAS), 630060 Novosibirsk, Russia
2
The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
3
Department of Mathematics, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia
4
Institute of Personalized Oncology, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
5
Institute of Life Sciences and Biomedicine, Far Eastern Federal University, 690922 Vladivostok, Russia
6
Agrarian and Technological Institute, Peoples’ Friendship University of Russia, 117198 Moscow, Russia
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(10), 8968; https://doi.org/10.3390/ijms24108968
Submission received: 2 May 2023 / Accepted: 12 May 2023 / Published: 18 May 2023
(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics—2022)
The analysis of molecular mechanisms of disease progression challenges the development of bioinformatics tools and omics data integration. We have presented an earlier series of journal special issues: “Medical Genetics, Genomics and Bioinformatics”,Medical Genetics, Genomics and Bioinformatics–2020” and “Medical Genetics, Genomics and Bioinformatics–2021” [1]. This special journal issue “Medical Genetics, Genomics and Bioinformatics—2022” (https://www.mdpi.com/journal/ijms/special_issues/Medical_Genetics_2022, accessed on 12 May 2023) collected papers on medical genomics, human population genetics and computational biology applications in biomedicine, continuing the topic of medical genetics and genomics. Here, we focused on bioinformatics and systems biology approaches to medical genetics problems, molecular oncology and bioinformatics approaches for medical genomics.
The papers on bioinformatics applications were originally discussed at the “Bioinformatics of Genome Regulation and Structure/Systems Biology” (BGRS/SB) multiconference 2022 and its biomedical symposia in Novosibirsk, Russia (https://bgrssb.icgbio.ru/2022/, accessed on 12 May 2023). The current collection continues the series of post-conference journal special issues presenting the highlights from the set of meetings on genetics and systems biology highlighting recent trends in cancer genomics [2,3]. BGRS/SB discussions of systems biology and genomics achievements were published in thematic journal issues [3].
Molecular mechanisms of human disease progression [1,2] are being studied using omics technologies. We should acknowledge the current, parallel IJMS special journal issue, Bioinformatics of Gene Regulations and Structure (https://www.mdpi.com/journal/ijms/special_issues/Bioinformatics_Gene, accessed on 12 May 2023) and the new IJMS journal issue New Sights into Bioinformatics of Gene Regulations and Structure (https://www.mdpi.com/journal/ijms/special_issues/MVA479KFR7, accessed on 12 May 2023). The recent special issue on computational genomics in Life (https://www.mdpi.com/journal/life/special_issues/computational_genomics_life, accessed on 12 May 2023) and Genes journal also has works on medical bioinformatics [4] organized as a follow-up of BGRS/SB and special journal issue series.
This issue Medical Genetics, Genomics and Bioinformatics—2022 presents recent studies on medical genomics. It contains eight research manuscripts and one review, each concerning a bioinformatics solution for the analysis of the molecular mechanisms underlying disease progression.
Jeong-An Gim [5] presented a review of Big Data analysis using a genomic information management system. The analysis of genomic information as Big Data can be applied for clinical and research purposes [6]. Tremendous volumes of genomic information are being generated, and the development of methods for its collection, cleansing, storing, indexing and serving must progress under legal regulation. The improvement of technology has enabled precision or personalized therapy based on genomic information, such as genotype, gene expression and DNA methylation patterns, solving challenging problems for application in the clinic.
Almost all the research papers in this special issue deal with NGS data to study disease mechanisms and find candidate genes. Ionut-Florin Iancu [7] analyzed aggregated genomic data as cohort-specific allelic frequencies for inherited retinal dystrophies (IRD). The authors built an allelic-frequency database for a heterogeneous cohort of genetic diseases to explore the aggregated genomic information in IRD in retrospective analysis with available clinical exome sequencing tests [8].
Elena Pudova et al. [9] analyzed transcriptomic data in prostate cancer. The authors performed RNA-Seq for experimental PC3 cell lines as well as for plasma exosome samples from patients with castration-resistant prostate cancer. The work by the same authors’ group on gene expression changes in prostate cancer following bioinformatic analysis [10] was published in the Research Topic on “Bioinformatics of Genome Expression” in the Frontiers in genetics special journal issue (https://www.frontiersin.org/research-topics/40408/bioinformatics-of-genome-regulation-and-systems-biology-volume-iii, accessed on 12 May 2023). Recent work by the same team on lymphatic dissemination in prostate cancer complemented this study [11]. Overall, E. Pudova and colleagues [11] have shown transcriptomic profiling of the experimental prostate cancer cell line, in the process of acquiring resistance to docetaxel, using the RNA-Seq approach.
Yaron Trink and co-authors studied heterogeneity in Wilms’ tumor pediatric malignancy related to faulty kidney development [12]. Wilms’ tumors are highly heterogeneous and contain varying proportions of cells [13]. Using a dataset of microarray gene expression measurements and an unsupervised machine learning algorithm, Y. Trink and colleagues have developed a computational classification model for Wilms’ tumors.
The topic of cell classification is continued by Olga Krasnova and colleagues [14] presenting regenerative medicine application on human pluripotent stem cells lines (embryonic stem cell line H9 and control human-induced pluripotent stem cells [15]). The authors completed a morphological assessment of growing colonies and cells that allow the best clones to be safely selected for further clinical applications. The morphological phenotype of each colony was classified using a visual analysis and associated with its potential for pluripotency and clonality maintenance. The authors have shown the fundamental possibility of constructing a morphological portrait of a colony that is informative for the automatic identification of the phenotype [14].
Mark Melamud et al. [16] studied cytokine profiles in autoimmune diseases, including systemic lupus erythematosus and multiple sclerosis. The authors reconstructed and analyzed the protein interaction network expanding the understanding of abnormal regulatory interactions in cytokine profiles associated with autoimmune diseases [17]. Autoimmune diseases were the aim of the next study by Andrey Shevtsov et al. [18]. Using single cell RNA-seq data for an in silico drug, the authors detected several promising small molecules to reverse the transcriptomic signatures of multiple sclerosis immune cells. Among these molecules, A. Shevtsov and colleagues also detected an FDA-approved multiple sclerosis drug, Mitoxantrone, supporting the reliability of the approach [18]. The single cell sequencing analysis is on trend for current cancer studies. In the parallel IJMS special issue, Bioinformatics of Gene Regulations and Structure–2022 (https://www.mdpi.com/journal/ijms/special_issues/Bioinformatics_Gene, accessed on 12 May 2023) the same authors’ group presented a new tool for scRNA-seq imputation via integration with single-cell ATAC-seq that increases the power of the analysis [19].
Valeriia Danilchenko and colleagues [20] studied mutation in genes causing hearing loss. The authors analyzed known pathogenic variants across the SLC26A4 gene sequence presented in the Deafness Variation Database (https://deafnessvariationdatabase.org/, accessed on 12 May 2023) for the selection of potential diagnostically important parts of this gene. Initial diagnostic testing for hearing loss was suggested. Studying genetic variants in populations is an important approach applied earlier for diabetes [21]. Olga Saik and Vadim Klimontov [22] considered the challenging problem of COVID-19 complications and comorbidity including diabetes. Using text-mining-based approaches and the ANDSystem [23,24] as a bioinformatics tool, the authors reconstructed and matched networks related to hyperglycemia, diabetic complications, insulin resistance and beta cell dysfunction with networks of SARS-CoV-2-targeted proteins. The results expand the understanding of the molecular basis of diabetes and COVID-19 comorbidity [25,26].
Overall, the current special issue on bioinformatics confirmed research interests in medical genomics and bioinformatics studies [1,2]. We note here the new MDPI IJMS special issue New Sights into Bioinformatics of Gene Regulations and Structure topic at (https://www.mdpi.com/journal/ijms/special_issues/MVA479KFR7, accessed on 12 May 2023) to continue the paper selection on bioinformatics and genomics in human diseases, as well as the research topic at Frontiers in genetics (https://www.frontiersin.org/research-topics/53085/high-throughput-sequencing-based-investigation-of-chronic-disease-markers-and-mechanisms---volume-ii, accessed on 12 May 2023).

Funding

The study was carried out at the expense of the state funding of the RICEL–Branch of IC&G SB RAS (V.K.). The publication was prepared with the support of the RUDN University Strategic Academic Leadership Program (Y.O.).

Acknowledgments

The authors are grateful to all the reviewers and guest editors who helped review and validate the thematic special issues on bioinformatics.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Orlov, Y.L.; Anashkina, A.A.; Klimontov, V.V.; Baranova, A.V. Medical Genetics, Genomics and Bioinformatics Aid in Understanding Molecular Mechanisms of Human Diseases. Int. J. Mol. Sci. 2021, 22, 9962. [Google Scholar] [CrossRef] [PubMed]
  2. Anashkina, A.A.; Leberfarb, E.Y.; Orlov, Y.L. Recent Trends in Cancer Genomics and Bioinformatics Tools Development. Int. J. Mol. Sci. 2021, 22, 12146. [Google Scholar] [CrossRef] [PubMed]
  3. Orlov, Y.L.; Baranova, A.V.; Hofestädt, R.; Kolchanov, N.A. Computational genomics at BGRS\SB-2016: Introductory note. BMC Genom. 2016, 17 (Suppl. 14), 996. [Google Scholar] [CrossRef] [PubMed]
  4. Voropaeva, E.N.; Pospelova, T.I.; Orlov, Y.L.; Churkina, M.I.; Berezina, O.V.; Gurazheva, A.A.; Ageeva, T.A.; Seregina, O.B.; Maksimov, V.N. The Methylation of the p53 Targets the Genes MIR-203, MIR-129-2, MIR-34A and MIR-34B/C in the Tumor Tissue of Diffuse Large B-Cell Lymphoma. Genes 2022, 13, 1401. [Google Scholar] [CrossRef] [PubMed]
  5. Gim, J.A. Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research. Int. J. Mol. Sci. 2022, 23, 5963. [Google Scholar] [CrossRef]
  6. Pramanik, P.K.D.; Pal, S.; Mukhopadhyay, M. Healthcare big data: A comprehensive overview. In Research Anthology on Big Data Analytics, Architectures, and Applications; Information Resources Management Association, Ed.; IGI Global: Hershey, PA, USA, 2022; pp. 119–147. [Google Scholar] [CrossRef]
  7. Iancu, I.; Perea-Romero, I.; Núñez-Moreno, G.; de la Fuente, L.; Romero, R.; Ávila-Fernandez, A.; Trujillo-Tiebas, M.; Riveiro-Álvarez, R.; Almoguera, B.; Martín-Mérida, I.; et al. Aggregated Genomic Data as Cohort-Specific Allelic Frequencies can Boost Variants and Genes Prioritization in Non-Solved Cases of Inherited Retinal Dystrophies. Int. J. Mol. Sci. 2022, 23, 8431. [Google Scholar] [CrossRef]
  8. Sanchez-Navarro, I.; Da Silva, L.R.J.R.J.; Blanco-Kelly, F.; Zurita, O.; Sanchez-Bolivar, N.; Villaverde, C.; Lopez-Molina, M.I.I.; Garcia-Sandoval, B.; Tahsin-Swafiri, S.; Minguez, P.; et al. Combining targeted panel-based resequencing and copy-number variation analysis for the diagnosis of inherited syndromic retinopathies and associated ciliopathies. Sci. Rep. 2018, 8, 5285. [Google Scholar] [CrossRef]
  9. Pudova, E.; Kobelyatskaya, A.; Katunina, I.; Snezhkina, A.; Nyushko, K.; Fedorova, M.; Pavlov, V.; Bulavkina, E.; Dalina, A.; Tkachev, S.; et al. Docetaxel Resistance in Castration-Resistant Prostate Cancer: Transcriptomic Determinants and the Effect of Inhibiting Wnt/beta-Catenin Signaling by XAV939. Int. J. Mol. Sci. 2022, 23, 12837. [Google Scholar] [CrossRef]
  10. Pudova, E.A.; Krasnov, G.S.; Kobelyatskaya, A.A.; Savvateeva, M.V.; Fedorova, M.S.; Pavlov, V.S.; Nyushko, K.M.; Kaprin, A.D.; Alekseev, B.Y.; Trofimov, D.Y.; et al. Gene Expression Changes and Associated Pathways Involved in the Progression of Prostate Cancer Advanced Stages. Front. Genet. 2021, 11, 613162. [Google Scholar] [CrossRef]
  11. Pudova, E.A.; Kobelyatskaya, A.A.; Katunina, I.V.; Snezhkina, A.V.; Fedorova, M.S.; Pavlov, V.S.; Bakhtogarimov, I.R.; Lantsova, M.S.; Kokin, S.P.; Nyushko, K.M.; et al. Lymphatic Dissemination in Prostate Cancer: Features of the Transcriptomic Profile and Prognostic Models. Int. J. Mol. Sci. 2023, 24, 2418. [Google Scholar] [CrossRef]
  12. Trink, Y.; Urbach, A.; Dekel, B.; Hohenstein, P.; Goldberger, J.; Kalisky, T. Characterization of Continuous Transcriptional Heterogeneity in High-Risk Blastemal-Type Wilms’ Tumors Using Unsupervised Machine Learning. Int. J. Mol. Sci. 2023, 24, 3532. [Google Scholar] [CrossRef] [PubMed]
  13. Hohenstein, P.; Pritchard-Jones, K.; Charlton, J. The yin and yang of kidney development and Wilms’ tumors. Genes Dev. 2015, 29, 467–482. [Google Scholar] [CrossRef] [PubMed]
  14. Krasnova, O.; Gursky, V.; Chabina, A.; Kulakova, K.; Alekseenko, L.; Panova, A.; Kiselev, S.; Neganova, I. Prognostic Analysis of Human Pluripotent Stem Cells Based on Their Morphological Portrait and Expression of Pluripotent Markers. Int. J. Mol. Sci. 2022, 23, 12902. [Google Scholar] [CrossRef] [PubMed]
  15. Rowe, R.G.; Daley, G.Q. Induced pluripotent stem cells in disease modelling and drug discovery. Nat. Rev. Genet. 2019, 20, 377–388. [Google Scholar] [CrossRef] [PubMed]
  16. Melamud, M.; Ermakov, E.; Boiko, A.; Kamaeva, D.; Sizikov, A.; Ivanova, S.; Baulina, N.; Favorova, O.; Nevinsky, G.; Buneva, V. Multiplex Analysis of Serum Cytokine Profiles in Systemic Lupus Erythematosus and Multiple Sclerosis. Int. J. Mol. Sci. 2022, 23, 13829. [Google Scholar] [CrossRef]
  17. Melamud, M.M.; Ermakov, E.A.; Boiko, A.S.; Parshukova, D.A.; Sizikov, A.E.; Ivanova, S.A.; Nevinsky, G.A.; Buneva, V.N. Serum cytokine levels of systemic lupus erythematosus patients in the presence of concomitant cardiovascular diseases. Endocr. Metab. Immune Disord. Drug Targets 2022, 22, 852–861. [Google Scholar]
  18. Shevtsov, A.; Raevskiy, M.; Stupnikov, A.; Medvedeva, Y. In Silico Drug Repurposing in Multiple Sclerosis Using scRNA-Seq Data. Int. J. Mol. Sci. 2023, 24, 985. [Google Scholar] [CrossRef]
  19. Raevskiy, M.; Yanvarev, V.; Jung, S.; Del Sol, A.; Medvedeva, Y.A. Epi-Impute: Single-Cell RNA-seq Imputation via Integration with Single-Cell ATAC-seq. Int. J. Mol. Sci. 2023, 24, 6229. [Google Scholar] [CrossRef]
  20. Danilchenko, V.; Zytsar, M.; Maslova, E.; Posukh, O. Selection of Diagnostically Significant Regions of the SLC26A4 Gene Involved in Hearing Loss. Int. J. Mol. Sci. 2022, 23, 13453. [Google Scholar] [CrossRef]
  21. Bai, H.; Liu, H.; Suyalatu, S.; Guo, X.; Chu, S.; Chen, Y.; Lan, T.; Borjigin, B.; Orlov, Y.L.; Posukh, O.L.; et al. Association analysis of genetic variants with type 2 diabetes in a Mongolian population in China. J. Diabetes Res. 2015, 2015, 613236. [Google Scholar] [CrossRef]
  22. Saik, O.; Klimontov, V. Gene Networks of Hyperglycemia, Diabetic Complications, and Human Proteins Targeted by SARS-CoV-2: What Is the Molecular Basis for Comorbidity? Int. J. Mol. Sci. 2022, 23, 7247. [Google Scholar] [CrossRef] [PubMed]
  23. Ivanisenko, T.; Demenkov, P.; Kolchanov, N.; Ivanisenko, V. The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition. Int. J. Mol. Sci. 2022, 23, 14934. [Google Scholar] [CrossRef] [PubMed]
  24. Ivanisenko, V.A.; Saik, O.V.; Ivanisenko, N.V.; Tiys, E.S.; Ivanisenko, T.V.; Demenkov, P.S.; Kolchanov, N.A. ANDSystem: An Associative Network Discovery System for automated literature mining in the field of biology. BMC Syst. Biol. 2015, 9 (Suppl. S2), S2. [Google Scholar] [CrossRef]
  25. Gerasimov, A.; Lebedev, G.; Lebedev, M.; Semenycheva, I. COVID-19 Dynamics: A Heterogeneous Model. Front Public Health 2021, 8, 558368. [Google Scholar] [CrossRef] [PubMed]
  26. Lebedev, G.; Fartushnyi, E.; Fartushnyi, I.; Shaderkin, I.; Klimenko, H.; Kozhin, P.; Koshechkin, K.; Ryabkov, I.; Tarasov, V.; Morozov, E. Fomina I. Technology of Supporting Medical Decision-Making Using Evidence-Based Medicine and Artificial Intelligence. Procedia Comput. Sci. 2020, 176, 1703–1712. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Klimontov, V.V.; Koshechkin, K.A.; Orlova, N.G.; Sekacheva, M.I.; Orlov, Y.L. Medical Genetics, Genomics and Bioinformatics—2022. Int. J. Mol. Sci. 2023, 24, 8968. https://doi.org/10.3390/ijms24108968

AMA Style

Klimontov VV, Koshechkin KA, Orlova NG, Sekacheva MI, Orlov YL. Medical Genetics, Genomics and Bioinformatics—2022. International Journal of Molecular Sciences. 2023; 24(10):8968. https://doi.org/10.3390/ijms24108968

Chicago/Turabian Style

Klimontov, Vadim V., Konstantin A. Koshechkin, Nina G. Orlova, Marina I. Sekacheva, and Yuriy L. Orlov. 2023. "Medical Genetics, Genomics and Bioinformatics—2022" International Journal of Molecular Sciences 24, no. 10: 8968. https://doi.org/10.3390/ijms24108968

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop