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Medical Genetics, Genomics and Bioinformatics—2022

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 30242

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Special Issue Editors

Prof. Dr. Yuriy L. Orlov

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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
Interests: computer genomics; bioinformatics; digital medicine (e-Health); gene expression regulation; ChIP-seq
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Vadim Klimontov

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Laboratory of Endocrinology, Research Institute of Clinical and Experimental Lymphology – Branch of the Federal Research Center Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
Interests: diabetes; obesity; chronic kidney disease; metabolism; biomedicine; e-health
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Marina I. Sekacheva

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Guest Editor

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
Interests: oncology; medicine; medical genetics; genomics

Special Issue Information

Dear Colleagues,

This Special Issue collects papers on medical genomics, human population genetics and computational biology applications in biomedicine, continuing the topic presented earlier in an IJMS series of journal issues: "Medical Genetics, Genomics and Bioinformatics", "Medical Genetics, Genomics and Bioinformatics – 2020" and “Medical Genetics, Genomics and Bioinformatics – 2021".

https://www.mdpi.com/journal/ijms/special_issues/Medical_Genetics_Bioinformatics

https://www.mdpi.com/journal/ijms/special_issues/Medical_Genetics_Bioinformatics_2

https://www.mdpi.com/journal/ijms/special_issues/Medical_Genetics_2021

Based on the readers’ interest in medical genetics and genomics, we will continue to publish in this area based on novel technological approaches, gene networks and metabolic pathways analysis. Here, we focus on bioinformatics and systems biology approaches to medical genetics problems.

Topics of this Special Issue include:

Molecular oncology.
Bioinformatics approaches for medical genomics.
Systems biology and network medicine.
Interdisciplinary research on biomedicine including laboratory animals.
E-health and digital medicine tools.

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 held in Moscow in recent years as well as the international symposium on "Biomedicine, bioinformatics and systems computational biology" held in Novosibirsk, Russia. We welcome novel materials beyond the conference discussion.

Prof. Dr. Yuriy L. Orlov
Prof. Dr. Vadim Klimontov
Prof. Dr. Marina I. Sekacheva
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

medical genetics
oncology
medical genomics
bioinformatics
molecular mechanisms of diseases
systems biology for medicine
e-health
digital medicine

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Published Papers (10 papers)

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Editorial

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4 pages, 223 KiB

Open AccessEditorial

Medical Genetics, Genomics and Bioinformatics—2022

by Vadim V. Klimontov, Konstantin A. Koshechkin, Nina G. Orlova, Marina I. Sekacheva and Yuriy L. Orlov

Int. J. Mol. Sci. 2023, 24(10), 8968; https://doi.org/10.3390/ijms24108968 - 18 May 2023

Cited by 6 | Viewed by 1849

Abstract

The analysis of molecular mechanisms of disease progression challenges the development of bioinformatics tools and omics data integration [...] Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics—2022)

Research

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14 pages, 2474 KiB

Open AccessArticle

Characterization of Continuous Transcriptional Heterogeneity in High-Risk Blastemal-Type Wilms’ Tumors Using Unsupervised Machine Learning

by Yaron Trink, Achia Urbach, Benjamin Dekel, Peter Hohenstein, Jacob Goldberger and Tomer Kalisky

Int. J. Mol. Sci. 2023, 24(4), 3532; https://doi.org/10.3390/ijms24043532 - 9 Feb 2023

Cited by 3 | Viewed by 2052

Abstract

Wilms’ tumors are pediatric malignancies that are thought to arise from faulty kidney development. They contain a wide range of poorly differentiated cell states resembling various distorted developmental stages of the fetal kidney, and as a result, differ between patients in a continuous manner that is not well understood. Here, we used three computational approaches to characterize this continuous heterogeneity in high-risk blastemal-type Wilms’ tumors. Using Pareto task inference, we show that the tumors form a triangle-shaped continuum in latent space that is bounded by three tumor archetypes with “stromal”, “blastemal”, and “epithelial” characteristics, which resemble the un-induced mesenchyme, the cap mesenchyme, and early epithelial structures of the fetal kidney. By fitting a generative probabilistic “grade of membership” model, we show that each tumor can be represented as a unique mixture of three hidden “topics” with blastemal, stromal, and epithelial characteristics. Likewise, cellular deconvolution allows us to represent each tumor in the continuum as a unique combination of fetal kidney-like cell states. These results highlight the relationship between Wilms’ tumors and kidney development, and we anticipate that they will pave the way for more quantitative strategies for tumor stratification and classification. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics—2022)

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12 pages, 806 KiB

Open AccessArticle

In Silico Drug Repurposing in Multiple Sclerosis Using scRNA-Seq Data

by Andrey Shevtsov, Mikhail Raevskiy, Alexey Stupnikov and Yulia Medvedeva

Int. J. Mol. Sci. 2023, 24(2), 985; https://doi.org/10.3390/ijms24020985 - 4 Jan 2023

Cited by 4 | Viewed by 3514

Abstract

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system still lacking a cure. Treatment typically focuses on slowing the progression and managing MS symptoms. Single-cell transcriptomics allows the investigation of the immune system—the key player in MS onset and development—in great detail increasing our understanding of MS mechanisms and stimulating the discovery of the targets for potential therapies. Still, de novo drug development takes decades; however, this can be reduced by drug repositioning. A promising approach is to select potential drugs based on activated or inhibited genes and pathways. In this study, we explored the public single-cell RNA data from an experiment with six patients on single-cell RNA peripheral blood mononuclear cells (PBMC) and cerebrospinal fluid cells (CSF) of patients with MS and idiopathic intracranial hypertension. We demonstrate that AIM2 inflammasome, SMAD2/3 signaling, and complement activation pathways are activated in MS in different CSF and PBMC immune cells. Using genes from top-activated pathways, we detected several promising small molecules to reverse MS immune cells’ transcriptomic signatures, including AG14361, FGIN-1-27, CA-074, ARP 101, Flunisolide, and JAK3 Inhibitor VI. Among these molecules, we also detected an FDA-approved MS drug Mitoxantrone, supporting the reliability of our approach. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics—2022)

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23 pages, 4943 KiB

Open AccessArticle

Multiplex Analysis of Serum Cytokine Profiles in Systemic Lupus Erythematosus and Multiple Sclerosis

by Mark M. Melamud, Evgeny A. Ermakov, Anastasiia S. Boiko, Daria A. Kamaeva, Alexey E. Sizikov, Svetlana A. Ivanova, Natalia M. Baulina, Olga O. Favorova, Georgy A. Nevinsky and Valentina N. Buneva

Int. J. Mol. Sci. 2022, 23(22), 13829; https://doi.org/10.3390/ijms232213829 - 10 Nov 2022

Cited by 24 | Viewed by 3284

Abstract

Changes in cytokine profiles and cytokine networks are known to be a hallmark of autoimmune diseases, including systemic lupus erythematosus (SLE) and multiple sclerosis (MS). However, cytokine profiles research studies are usually based on the analysis of a small number of cytokines and give conflicting results. In this work, we analyzed cytokine profiles of 41 analytes in patients with SLE and MS compared with healthy donors using multiplex immunoassay. The SLE group included treated patients, while the MS patients were drug-free. Levels of 11 cytokines, IL-1b, IL-1RA, IL-6, IL-9, IL-10, IL-15, MCP-1/CCL2, Fractalkine/CX3CL1, MIP-1a/CCL3, MIP-1b/CCL4, and TNFa, were increased, but sCD40L, PDGF-AA, and MDC/CCL22 levels were decreased in SLE patients. Thus, changes in the cytokine profile in SLE have been associated with the dysregulation of interleukins, TNF superfamily members, and chemokines. In the case of MS, levels of 10 cytokines, sCD40L, CCL2, CCL3, CCL22, PDGF-AA, PDGF-AB/BB, EGF, IL-8, TGF-a, and VEGF, decreased significantly compared to the control group. Therefore, cytokine network dysregulation in MS is characterized by abnormal levels of growth factors and chemokines. Cross-disorder analysis of cytokine levels in MS and SLE showed significant differences between 22 cytokines. Protein interaction network analysis showed that all significantly altered cytokines in both SLE and MS are functionally interconnected. Thus, MS and SLE may be associated with impaired functional relationships in the cytokine network. A cytokine correlation networks analysis revealed changes in correlation clusters in SLE and MS. These data expand the understanding of abnormal regulatory interactions in cytokine profiles associated with autoimmune diseases. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics—2022)

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14 pages, 2491 KiB

Open AccessArticle

Selection of Diagnostically Significant Regions of the SLC26A4 Gene Involved in Hearing Loss

by Valeriia Yu. Danilchenko, Marina V. Zytsar, Ekaterina A. Maslova and Olga L. Posukh

Int. J. Mol. Sci. 2022, 23(21), 13453; https://doi.org/10.3390/ijms232113453 - 3 Nov 2022

Cited by 7 | Viewed by 1847

Abstract

Screening pathogenic variants in the SLC26A4 gene is an important part of molecular genetic testing for hearing loss (HL) since they are one of the common causes of hereditary HL in many populations. However, a large size of the SLC26A4 gene (20 coding exons) predetermines the difficulties of its complete mutational analysis, especially in large samples of patients. In addition, the regional or ethno-specific prevalence of SLC26A4 pathogenic variants has not yet been fully elucidated, except variants c.919-2A>G and c.2168A>G (p.His723Arg), which have been proven to be most common in Asian populations. We explored the distribution of currently known pathogenic and likely pathogenic (PLP) variants across the SLC26A4 gene sequence presented in the Deafness Variation Database for the selection of potential diagnostically important parts of this gene. As a result of this bioinformatic analysis, we found that molecular testing ten SLC26A4 exons (4, 6, 10, 11, 13–17 and 19) with flanking intronic regions can provide a diagnostic rate of 61.9% for all PLP variants in the SLC26A4 gene. The primary sequencing of these SLC26A4 regions may be applied as an initial effective diagnostic testing in samples of patients of unknown ethnicity or as a subsequent step after the targeted testing of already-known ethno- or region-specific pathogenic SLC26A4 variants. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics—2022)

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20 pages, 4328 KiB

Open AccessArticle

Prognostic Analysis of Human Pluripotent Stem Cells Based on Their Morphological Portrait and Expression of Pluripotent Markers

by Olga A. Krasnova, Vitaly V. Gursky, Alina S. Chabina, Karina A. Kulakova, Larisa L. Alekseenko, Alexandra V. Panova, Sergey L. Kiselev and Irina E. Neganova

Int. J. Mol. Sci. 2022, 23(21), 12902; https://doi.org/10.3390/ijms232112902 - 26 Oct 2022

Cited by 5 | Viewed by 3208

Abstract

The ability of human pluripotent stem cells for unlimited proliferation and self-renewal promotes their application in the fields of regenerative medicine. The morphological assessment of growing colonies and cells, as a non-invasive method, allows the best clones for further clinical applications to be safely selected. For this purpose, we analyzed seven morphological parameters of both colonies and cells extracted from the phase-contrast images of human embryonic stem cell line H9, control human induced pluripotent stem cell (hiPSC) line AD3, and hiPSC line HPCASRi002-A (CaSR) in various passages during their growth for 120 h. The morphological phenotype of each colony was classified using a visual analysis and associated with its potential for pluripotency and clonality maintenance, thus defining the colony phenotype as the control parameter. Using the analysis of variance for the morphological parameters of each line, we showed that selected parameters carried information about different cell lines and different phenotypes within each line. We demonstrated that a model of classification of colonies and cells by phenotype, built on the selected parameters as predictors, recognized the phenotype with an accuracy of 70–75%. In addition, we performed a qRT-PCR analysis of eleven pluripotency markers genes. By analyzing the variance of their expression in samples from different lines and with different phenotypes, we identified group-specific sets of genes that could be used as the most informative ones for the separation of the best clones. Our results indicated the fundamental possibility of constructing a morphological portrait of a colony informative for the automatic identification of the phenotype and for linking this portrait to the expression of pluripotency markers. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics—2022)

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21 pages, 2824 KiB

Open AccessArticle

Docetaxel Resistance in Castration-Resistant Prostate Cancer: Transcriptomic Determinants and the Effect of Inhibiting Wnt/β-Catenin Signaling by XAV939

by Elena Pudova, Anastasiya Kobelyatskaya, Irina Katunina, Anastasiya Snezhkina, Kirill Nyushko, Maria Fedorova, Vladislav Pavlov, Elizaveta Bulavkina, Alexandra Dalina, Sergey Tkachev, Boris Alekseev, George Krasnov, Vsevolod Volodin and Anna Kudryavtseva

Int. J. Mol. Sci. 2022, 23(21), 12837; https://doi.org/10.3390/ijms232112837 - 25 Oct 2022

Cited by 9 | Viewed by 2132

Abstract

Castration-resistant prostate cancer (CRPC) is a common form of prostate cancer in which docetaxel-based chemotherapy is used as the first line. The present study is devoted to the analysis of transcriptome profiles of tumor cells in the development of resistance to docetaxel as well as to the assessment of the combined effect with the XAV939 tankyrase inhibitor on maintaining the sensitivity of tumor cells to chemotherapy. RNA-Seq was performed for experimental PC3 cell lines as well as for plasma exosome samples from patients with CRPC. We have identified key biological processes and identified a signature based on the expression of 17 mRNA isoforms associated with the development of docetaxel resistance in PC3 cells. Transcripts were found in exosome samples, the increased expression of which was associated with the onset of progression of CRPC during therapy. The suppression of pathways associated with the participation of cellular microtubules has also been shown when cells are treated with docetaxel in the presence of XAV939. These results highlight the importance of further research into XAV939 as a therapeutic agent in the treatment of CRPC; moreover, we have proposed a number of mRNA isoforms with high predictive potential, which can be considered as promising markers of response to docetaxel. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics—2022)

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19 pages, 2399 KiB

Open AccessArticle

Aggregated Genomic Data as Cohort-Specific Allelic Frequencies can Boost Variants and Genes Prioritization in Non-Solved Cases of Inherited Retinal Dystrophies

by Ionut-Florin Iancu, Irene Perea-Romero, Gonzalo Núñez-Moreno, Lorena de la Fuente, Raquel Romero, Almudena Ávila-Fernandez, María José Trujillo-Tiebas, Rosa Riveiro-Álvarez, Berta Almoguera, Inmaculada Martín-Mérida, Marta Del Pozo-Valero, Alejandra Damián-Verde, Marta Cortón, Carmen Ayuso and Pablo Minguez

Int. J. Mol. Sci. 2022, 23(15), 8431; https://doi.org/10.3390/ijms23158431 - 29 Jul 2022

Cited by 4 | Viewed by 2779

Abstract

The introduction of NGS in genetic diagnosis has increased the repertoire of variants and genes involved and the amount of genomic information produced. We built an allelic-frequency (AF) database for a heterogeneous cohort of genetic diseases to explore the aggregated genomic information and boost diagnosis in inherited retinal dystrophies (IRD). We retrospectively selected 5683 index-cases with clinical exome sequencing tests available, 1766 with IRD and the rest with diverse genetic diseases. We calculated a subcohort’s IRD-specific AF and compared it with suitable pseudocontrols. For non-solved IRD cases, we prioritized variants with a significant increment of frequencies, with eight variants that may help to explain the phenotype, and 10/11 of uncertain significance that were reclassified as probably pathogenic according to ACMG. Moreover, we developed a method to highlight genes with more frequent pathogenic variants in IRD cases than in pseudocontrols weighted by the increment of benign variants in the same comparison. We identified 18 genes for further studies that provided new insights in five cases. This resource can also help one to calculate the carrier frequency in IRD genes. A cohort-specific AF database assists with variants and genes prioritization and operates as an engine that provides a new hypothesis in non-solved cases, augmenting the diagnosis rate. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics—2022)

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37 pages, 8245 KiB

Open AccessArticle

Gene Networks of Hyperglycemia, Diabetic Complications, and Human Proteins Targeted by SARS-CoV-2: What Is the Molecular Basis for Comorbidity?

by Olga V. Saik and Vadim V. Klimontov

Int. J. Mol. Sci. 2022, 23(13), 7247; https://doi.org/10.3390/ijms23137247 - 29 Jun 2022

Cited by 7 | Viewed by 3958

Abstract

People with diabetes are more likely to have severe COVID-19 compared to the general population. Moreover, diabetes and COVID-19 demonstrate a certain parallelism in the mechanisms and organ damage. In this work, we applied bioinformatics analysis of associative molecular networks to identify key molecules and pathophysiological processes that determine SARS-CoV-2-induced disorders in patients with diabetes. Using text-mining-based approaches and ANDSystem as a bioinformatics tool, we reconstructed and matched networks related to hyperglycemia, diabetic complications, insulin resistance, and beta cell dysfunction with networks of SARS-CoV-2-targeted proteins. The latter included SARS-CoV-2 entry receptors (ACE2 and DPP4), SARS-CoV-2 entry associated proteases (TMPRSS2, CTSB, and CTSL), and 332 human intracellular proteins interacting with SARS-CoV-2. A number of genes/proteins targeted by SARS-CoV-2 (ACE2, BRD2, COMT, CTSB, CTSL, DNMT1, DPP4, ERP44, F2RL1, GDF15, GPX1, HDAC2, HMOX1, HYOU1, IDE, LOX, NUTF2, PCNT, PLAT, RAB10, RHOA, SCARB1, and SELENOS) were found in the networks of vascular diabetic complications and insulin resistance. According to the Gene Ontology enrichment analysis, the defined molecules are involved in the response to hypoxia, reactive oxygen species metabolism, immune and inflammatory response, regulation of angiogenesis, platelet degranulation, and other processes. The results expand the understanding of the molecular basis of diabetes and COVID-19 comorbidity. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics—2022)

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Review

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15 pages, 2642 KiB

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A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research

by Jeong-An Gim

Int. J. Mol. Sci. 2022, 23(11), 5963; https://doi.org/10.3390/ijms23115963 - 25 May 2022

Cited by 6 | Viewed by 3358

Abstract

Improvements in next-generation sequencing (NGS) technology and computer systems have enabled personalized therapies based on genomic information. Recently, health management strategies using genomics and big data have been developed for application in medicine and public health science. In this review, I first discuss the development of a genomic information management system (GIMS) to maintain a highly detailed health record and detect diseases by collecting the genomic information of one individual over time. Maintaining a health record and detecting abnormal genomic states are important; thus, the development of a GIMS is necessary. Based on the current research status, open public data, and databases, I discuss the possibility of a GIMS for clinical use. I also discuss how the analysis of genomic information as big data can be applied for clinical and research purposes. Tremendous volumes of genomic information are being generated, and the development of methods for the collection, cleansing, storing, indexing, and serving must progress under legal regulation. Genetic information is a type of personal information and is covered under privacy protection; here, I examine the regulations on the use of genetic information in different countries. This review provides useful insights for scientists and clinicians who wish to use genomic information for healthy aging and personalized medicine. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics—2022)

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