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Biomedicines
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Integrative Genomic Analysis, Big Data Processing and Programming, and Pharmaco-Informatics...
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Integrative Genomic Analysis, Big Data Processing and Programming, and Pharmaco-Informatics in Biomedicine

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Drug Discovery, Development and Delivery".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 7495

Special Issue Editor

Dr. Bashir Lawal

E-Mail Website
Guest Editor
Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15213, USA
Interests: therapeutic target; integrative genomic analysis; big data processing and programming; cancer genetics and immunobiology; translational research

Special Issue Information

Dear Colleagues,

The development of high-throughput omics and deep sequencing technologies has generated a plethora of data in the public and private omics domains. However, laboratory analysis of these data has failed to meet the urgent clinical needs in cancer, diabetes, cardiovascular diseases, and other chronic diseases. Therefore, innovative computational strategies are needed to address urgent clinical problems.  The application of multi-disciplinary strategies that include bioinformatics, genetics, molecular and cell biology, and translational studies would accelerate the proper detection of genetic alterations and determine appropriate disease targets based on next-generation sequencing and genome profiling technologies. This Special Issue welcomes multi-disciplinary original research articles and review papers on integrative genomic analysis, computational genomics, big data processing and programming, cancer genetics, immunobiology, immune checkpoint response prediction, drug target identification and validation, network pharmacology, pharmacogenomics, system biology, and protein–ligand interactions, as well as translational research.

Dr. Bashir Lawal
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. Biomedicines 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 genomics
  • big data processing and programming
  • cancer genetics
  • immunobiology
  • immune checkpoint response prediction
  • drug target identification and validation
  • network pharmacology
  • pharmacogenomics
  • system biology
  • protein–ligand interaction

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Published Papers (3 papers)

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Research

20 pages, 7728 KiB  
Article
Unveiling the Potential of Migrasomes: A Machine-Learning-Driven Signature for Diagnosing Acute Myocardial Infarction
by Yihao Zhu, Yuxi Chen, Jiajin Xu and Yao Zu
Biomedicines 2024, 12(7), 1626; https://doi.org/10.3390/biomedicines12071626 - 22 Jul 2024
Cited by 4 | Viewed by 2505
Abstract
Background: Recent studies have demonstrated that the migrasome, a newly functional extracellular vesicle, is potentially significant in the occurrence, progression, and diagnosis of cardiovascular diseases. Nonetheless, its diagnostic significance and biological mechanism in acute myocardial infarction (AMI) have yet to be fully explored. [...] Read more.
Background: Recent studies have demonstrated that the migrasome, a newly functional extracellular vesicle, is potentially significant in the occurrence, progression, and diagnosis of cardiovascular diseases. Nonetheless, its diagnostic significance and biological mechanism in acute myocardial infarction (AMI) have yet to be fully explored. Methods: To remedy this gap, we employed an integrative machine learning (ML) framework composed of 113 ML combinations within five independent AMI cohorts to establish a predictive migrasome-related signature (MS). To further elucidate the biological mechanism underlying MS, we implemented single-cell RNA sequencing (scRNA-seq) of cardiac Cd45+ cells from AMI-induced mice. Ultimately, we conducted mendelian randomization (MR) and molecular docking to unveil the therapeutic effectiveness of MS. Results: MS demonstrated robust predictive performance and superior generalization, driven by the optimal combination of Stepglm and Lasso, on the expression of nine migrasome genes (BMP1, ITGB1, NDST1, TSPAN1, TSPAN18, TSPAN2, TSPAN4, TSPAN7, TSPAN9, and WNT8A). Notably, ITGB1 was found to be predominantly expressed in cardiac macrophages in AMI-induced mice, mechanically regulating macrophage transformation between anti-inflammatory and pro-inflammatory. Furthermore, we showed a positive causality between genetic predisposition towards ITGB1 expression and AMI risk, positioning it as a causative gene. Finally, we showed that ginsenoside Rh1, which interacts closely with ITGB1, could represent a novel therapeutic approach for repressing ITGB1. Conclusions: Our MS has implications in forecasting and curving AMI to inform future diagnostic and therapeutic strategies for AMI. Full article
(This article belongs to the Special Issue Integrative Genomic Analysis, Big Data Processing and Programming, and Pharmaco-Informatics in Biomedicine)
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11 pages, 491 KiB  
Article
Assessment of Substrate Status of Drugs Metabolized by Polymorphic Cytochrome P450 (CYP) 2 Enzymes: An Analysis of a Large-Scale Dataset
by Jakob Sommer, Justyna Wozniak, Judith Schmitt, Jana Koch, Julia C. Stingl and Katja S. Just
Biomedicines 2024, 12(1), 161; https://doi.org/10.3390/biomedicines12010161 - 12 Jan 2024
Cited by 3 | Viewed by 1807
Abstract
Background: The analysis of substrates of polymorphic cytochrome P450 (CYP) enzymes is important information to enable drug–drug interactions (DDIs) analysis and the relevance of pharmacogenetics in this context in large datasets. Our aim was to compare different approaches to assess the substrate properties [...] Read more.
Background: The analysis of substrates of polymorphic cytochrome P450 (CYP) enzymes is important information to enable drug–drug interactions (DDIs) analysis and the relevance of pharmacogenetics in this context in large datasets. Our aim was to compare different approaches to assess the substrate properties of drugs for certain polymorphic CYP2 enzymes. Methods: A standardized manual method and an automatic method were developed and compared to assess the substrate properties for the metabolism of drugs by CYP2D6, 2C9, and 2C19. The automatic method used a matching approach to three freely available resources. We applied the manual and automatic methods to a large real-world dataset deriving from a prospective multicenter study collecting adverse drug reactions in emergency departments in Germany (ADRED). Results: In total, 23,878 medication entries relating to 895 different drugs were analyzed in the real-world dataset. The manual method was able to assess 12.2% (n = 109) of drugs, and the automatic method between 12.1% (n = 109) and 88.9% (n = 796), depending on the resource used. The CYP substrate classifications demonstrated moderate to almost perfect agreements for CYP2D6 and CYP2C19 (Cohen’s Kappa (κ) 0.48–0.90) and fair to moderate agreements for CYP2C9 (κ 0.20–0.48). Conclusion: A closer look at different classifications between methods revealed that both methods are prone to error in different ways. While the automated method excels in time efficiency, completeness, and actuality, the manual method might be better able to identify CYP2 substrates with clinical relevance. Full article
(This article belongs to the Special Issue Integrative Genomic Analysis, Big Data Processing and Programming, and Pharmaco-Informatics in Biomedicine)
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23 pages, 9630 KiB  
Article
In Silico Evaluation of HN-N07 Small Molecule as an Inhibitor of Angiogenesis and Lymphangiogenesis Oncogenic Signatures in Non-Small Cell Lung Cancer
by Lung-Ching Chen, Ntlotlang Mokgautsi, Yu-Cheng Kuo, Alexander T. H. Wu and Hsu-Shan Huang
Biomedicines 2023, 11(7), 2011; https://doi.org/10.3390/biomedicines11072011 - 17 Jul 2023
Cited by 3 | Viewed by 2256
Abstract
Tumor angiogenesis and lymphangiogenesis pathways have been identified as important therapeutic targets in non-small cell lung cancer (NSCLC). Bevacizumab, which is a monoclonal antibody, was the initial inhibitor of angiogenesis and lymphangiogenesis that received approval for use in the treatment of advanced non-small [...] Read more.
Tumor angiogenesis and lymphangiogenesis pathways have been identified as important therapeutic targets in non-small cell lung cancer (NSCLC). Bevacizumab, which is a monoclonal antibody, was the initial inhibitor of angiogenesis and lymphangiogenesis that received approval for use in the treatment of advanced non-small cell lung cancer (NSCLC) in combination with chemotherapy. Despite its usage, patients may still develop resistance to the treatment, which can be attributed to various histological subtypes and the initiation of treatment at advanced stages of cancer. Due to their better specificity, selectivity, and safety compared to chemotherapy, small molecules have been approved for treating advanced NSCLC. Based on the development of multiple small-molecule antiangiogenic drugs either in house and abroad or in other laboratories to treat NSCLC, we used a quinoline-derived small molecule—HN-N07—as a potential target drug for NSCLC. Accordingly, we used computational simulation tools and evaluated the drug-likeness properties of HN-N07. Moreover, we identified target genes, resulting in the discovery of the target BIRC5/HIF1A/FLT4 pro-angiogenic genes. Furthermore, we used in silico molecular docking analysis to determine whether HN-N07 could potentially inhibit BIRC5/HIF1A/FLT4. Interestingly, the results of docking HN-N07 with the BIRC5, FLT4, and HIF1A oncogenes revealed unique binding affinities, which were significantly higher than those of standard inhibitors. In summary, these results indicate that HN-N07 shows promise as a potential inhibitor of oncogenic signaling pathways in NSCLC. Ongoing studies that involve in vitro experiments and in vivo investigations using tumor-bearing mice are in progress, aiming to evaluate the therapeutic effectiveness of the HN-N07 small molecule. Full article
(This article belongs to the Special Issue Integrative Genomic Analysis, Big Data Processing and Programming, and Pharmaco-Informatics in Biomedicine)
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