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Bioinformatics of Disease Research
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Bioinformatics of Disease Research

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (10 May 2024) | Viewed by 9663

Special Issue Editor

Prof. Dr. Kenta Nakai

E-Mail Website
Guest Editor
The Institute of Medical Sciences, The University of Tokyo, Tokyo 108-8639, Japan
Interests: sequence analysis in molecular biology; bioinformatics in genome analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent technological advances, including that of DNA sequencing, have enabled us to understand most of our diseases in terms of genetic information, which is stored as massive amounts of data. Thus, in modern medical research, computational methods in the analyses of such genetic data are essential. Nevertheless, in my opinion, the value of computational works based on pure public data still tends to be underestimated. It is true that there are works with less novelty, typically just applying existing tools to public data and/or just repeating very similar procedures to another dataset. However, there are also plenty of pure computational works reporting novel/significant biomedical discoveries based on a combination of public data on genomics/epigenomics. In this Special Issue, I would like to invite the submission of the latter kind of work, hoping that this Special Issue will become a showcase of valuable computational works even if they are based on public data only. Of course, we will also welcome manuscripts based on their own wet experiments if they are valuable in terms of the bioinformatics of disease research. We look forward to your submission.

Prof. Dr. Kenta Nakai
Guest Editor

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. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). 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

  • computational approach
  • combination of public data
  • genomics/epigenomics
  • biomarkers in disease
  • multi-omics study
  • application of AI techniques
  • medical informatics

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Published Papers (6 papers)

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Research

13 pages, 593 KiB  
Article
A Heuristic Approach to Analysis of the Genetic Susceptibility Profile in Patients Affected by Airway Allergies
by Domenico Lio, Gabriele Di Lorenzo, Ignazio Brusca, Letizia Scola, Chiara Bellia, Simona La Piana, Maria Barrale, Manuela Bova, Loredana Vaccarino, Giusi Irma Forte and Giovanni Pilato
Genes 2024, 15(8), 1105; https://doi.org/10.3390/genes15081105 - 22 Aug 2024
Viewed by 1014
Abstract
Allergic respiratory diseases such as asthma might be considered multifactorial diseases, having a complex pathogenesis that involves environmental factors and the activation of a large set of immune response pathways and mechanisms. In addition, variations in genetic background seem to play a central [...] Read more.
Allergic respiratory diseases such as asthma might be considered multifactorial diseases, having a complex pathogenesis that involves environmental factors and the activation of a large set of immune response pathways and mechanisms. In addition, variations in genetic background seem to play a central role. The method developed for the analysis of the complexities, as association rule mining, nowadays may be applied to different research areas including genetic and biological complexities such as atopic airway diseases to identify complex genetic or biological markers and enlighten new diagnostic and therapeutic targets. A total of 308 allergic patients and 205 controls were typed for 13 single nucleotide polymorphisms (SNPs) of cytokine and receptors genes involved in type 1 and type 2 inflammatory response (IL-4 rs2243250 C/T, IL-4R rs1801275A/G, IL-6 rs1800795 G/C, IL-10 rs1800872 A/C and rs1800896 A/G, IL-10RB rs2834167A/G, IL-13 rs1800925 C/T, IL-18 rs187238G/C, IFNγ rs 24030561A/T and IFNγR2 rs2834213G/A), the rs2228137C/T of CD23 receptor gene and rs577912C/T and rs564481C/T of Klotho genes, using KASPar SNP genotyping method. Clinical and laboratory data of patients were analyzed by formal statistic tools and by a data-mining technique—market basket analysis—selecting a minimum threshold of 90% of rule confidence. Formal statistical analyses show that IL-6 rs1800795GG, IL-10RB rs2834167G positive genotypes, IL-13 rs1800925CC, CD23 rs2228137TT Klotho rs564481TT, might be risk factors for allergy. Applying the association rule methodology, we identify 10 genotype combination patterns associated with susceptibility to allergies. Together these data necessitate being confirmed in further studies, indicating that the heuristic approach might be a straightforward and useful tool to find predictive and diagnostic molecular patterns that might be also considered potential therapeutic targets in allergy. Full article
(This article belongs to the Special Issue Bioinformatics of Disease Research)
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14 pages, 1637 KiB  
Article
Bioinformatics Study on Site-Specific Variations of Eotaxin-3, a Key Chemokine in Eosinophilic Esophagitis (EoE)
by Deborah Giordano, Antonio d’Acierno, Anna Marabotti, Paola Iovino, Giuseppe Iacomino and Angelo Facchiano
Genes 2024, 15(8), 1073; https://doi.org/10.3390/genes15081073 - 14 Aug 2024
Viewed by 981
Abstract
Eotaxin-3 is a key chemokine with a relevant role in eosinophilic esophagitis, a rare chronic immune/antigen-mediated inflammatory disorder. Eotaxin-3 is a potent activator of eosinophil emergence and migration, which may lead to allergic airway inflammation. We investigated, using bioinformatics tools, the protein structure [...] Read more.
Eotaxin-3 is a key chemokine with a relevant role in eosinophilic esophagitis, a rare chronic immune/antigen-mediated inflammatory disorder. Eotaxin-3 is a potent activator of eosinophil emergence and migration, which may lead to allergic airway inflammation. We investigated, using bioinformatics tools, the protein structure and the possible effects of the known variations reported in public databases. Following a procedure already established, we created a 3D model of the whole protein and modeled the structure of 105 protein variants due to known point mutations. The effects of the amino acid substitution at the level of impact on protein structure, stability, and possibly function were detected by the bioinformatics procedure and described in detail. A web application was implemented to browse the results of the analysis and visualize the 3D models, with the opportunity of downloading the models and analyzing them using their own software. Among 105 amino acid substitutions investigated, the study evidenced in 44 cases at least one change in any of the investigated structural parameters. Other six variations are also relevant, although a structural effect was not detected by our analysis, because they affected amino acids highly conserved, which suggests a possible function role. All these variations should be the object of particular attention, as they may induce a loss of functionality in the protein. Full article
(This article belongs to the Special Issue Bioinformatics of Disease Research)
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17 pages, 2435 KiB  
Article
Combined Analysis of Multi-Study miRNA and mRNA Expression Data Shows Overlap of Selected miRNAs Involved in West Nile Virus Infections
by Franz Leonard Böge, Sergej Ruff, Shamini Hemandhar Kumar, Michael Selle, Stefanie Becker and Klaus Jung
Genes 2024, 15(8), 1030; https://doi.org/10.3390/genes15081030 - 5 Aug 2024
Viewed by 927
Abstract
The emerging zoonotic West Nile virus (WNV) has serious impact on public health. Thus, understanding the molecular basis of WNV infections in mammalian hosts is important to develop improved diagnostic and treatment strategies. In this context, the role of microRNAs (miRNAs) has been [...] Read more.
The emerging zoonotic West Nile virus (WNV) has serious impact on public health. Thus, understanding the molecular basis of WNV infections in mammalian hosts is important to develop improved diagnostic and treatment strategies. In this context, the role of microRNAs (miRNAs) has been analyzed by several studies under different conditions and with different outcomes. A systematic comparison is therefore necessary. Furthermore, additional information from mRNA target expression data has rarely been taken into account to understand miRNA expression profiles under WNV infections. We conducted a meta-analysis of publicly available miRNA expression data from multiple independent studies, and analyzed them in a harmonized way to increase comparability. In addition, we used gene-set tests on mRNA target expression data to further gain evidence about differentially expressed miRNAs. For this purpose, we also studied the use of target information from different databases. We detected a substantial number of miRNA that emerged as differentially expressed from several miRNA datasets, and from the mRNA target data analysis as well. When using mRNA target data, we found that the targetscan databases provided the most useful information. We demonstrated improved miRNA detection through research synthesis of multiple independent miRNA datasets coupled with mRNA target set testing, leading to the discovery of multiple miRNAs which should be taken into account for further research on the molecular mechanism of WNV infections. Full article
(This article belongs to the Special Issue Bioinformatics of Disease Research)
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15 pages, 1361 KiB  
Article
Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression
by Muneeba Jilani, David Degras and Nurit Haspel
Genes 2024, 15(5), 631; https://doi.org/10.3390/genes15050631 - 16 May 2024
Viewed by 1420
Abstract
Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve [...] Read more.
Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan–Meier plots and hazard ratio analysis on the three types of cancer—GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine. Full article
(This article belongs to the Special Issue Bioinformatics of Disease Research)
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18 pages, 3455 KiB  
Article
Deciphering the Immune Microenvironment at the Forefront of Tumor Aggressiveness by Constructing a Regulatory Network with Single-Cell and Spatial Transcriptomic Data
by Kun Xu, Dongshuo Yu, Siwen Zhang, Lanming Chen, Zhenhao Liu and Lu Xie
Genes 2024, 15(1), 100; https://doi.org/10.3390/genes15010100 - 15 Jan 2024
Cited by 1 | Viewed by 3010
Abstract
The heterogeneity and intricate cellular architecture of complex cellular ecosystems play a crucial role in the progression and therapeutic response of cancer. Understanding the regulatory relationships of malignant cells at the invasive front of the tumor microenvironment (TME) is important to explore the [...] Read more.
The heterogeneity and intricate cellular architecture of complex cellular ecosystems play a crucial role in the progression and therapeutic response of cancer. Understanding the regulatory relationships of malignant cells at the invasive front of the tumor microenvironment (TME) is important to explore the heterogeneity of the TME and its role in disease progression. In this study, we inferred malignant cells at the invasion front by analyzing single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data of ER-positive (ER+) breast cancer patients. In addition, we developed a software pipeline for constructing intercellular gene regulatory networks (IGRNs), which help to reduce errors generated by single-cell communication analysis and increase the confidence of selected cell communication signals. Based on the constructed IGRN between malignant cells at the invasive front of the TME and the immune cells of ER+ breast cancer patients, we found that a high expression of the transcription factors FOXA1 and EZH2 played a key role in driving tumor progression. Meanwhile, elevated levels of their downstream target genes (ESR1 and CDKN1A) were associated with poor prognosis of breast cancer patients. This study demonstrates a bioinformatics workflow of combining scRNA-seq and ST data; in addition, the study provides the software pipelines for constructing IGRNs automatically (cIGRN). This strategy will help decipher cancer progression by revealing bidirectional signaling between invasive frontline malignant tumor cells and immune cells, and the selected signaling molecules in the regulatory network may serve as biomarkers for mechanism studies or therapeutic targets. Full article
(This article belongs to the Special Issue Bioinformatics of Disease Research)
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16 pages, 2940 KiB  
Article
The Spherical Evolutionary Multi-Objective (SEMO) Algorithm for Identifying Disease Multi-Locus SNP Interactions
by Fuxiang Ren, Shiyin Li, Zihao Wen, Yidi Liu and Deyu Tang
Genes 2024, 15(1), 11; https://doi.org/10.3390/genes15010011 - 20 Dec 2023
Viewed by 1242
Abstract
Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus [...] Read more.
Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus SNP interactions was proposed. The algorithm uses a spherical search factor and a feedback mechanism of excellent individual history memory to enhance the balance between search and acquisition. Moreover, a multi-objective fitness function based on the decomposition idea was used to evaluate the associations by combining two functions, K2-Score and LR-Score, as an objective function for the algorithm’s evolutionary iterations. The performance evaluation of SEMO was compared with six state-of-the-art algorithms on a simulated dataset. The results showed that SEMO outperforms the comparative methods by detecting SNP interactions quickly and accurately with a shorter average run time. The SEMO algorithm was applied to the Wellcome Trust Case Control Consortium (WTCCC) breast cancer dataset and detected two- and three-point SNP interactions that were significantly associated with breast cancer, confirming the effectiveness of the algorithm. New combinations of SNPs associated with breast cancer were also identified, which will provide a new way to detect SNP interactions quickly and accurately. Full article
(This article belongs to the Special Issue Bioinformatics of Disease Research)
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