Intelligent Computing Unlocks the Molecular Code of Complex Diseases

Topic Information

Dear Colleagues,

A key aim of post-genomic biomedical research is to systematically unlock the molecular codes of complex diseases. Various molecules and their coordination comprise the basis of life activities, and the perturbations and or disorders of these associations lead to complex diseases. For instance, proteins directly undertake fundamental life activities, chemical compounds regulate metabolism, and multiple non-coding RNA also play a role in cellular development and tumorigenesis. Intelligence computing, represented by artificial intelligence, deep learning, and data analysis, etc., is very promising for accelerating related research. The aim of this Topic is to report on cutting-edge advances in intelligent computing approaches for the discovery of molecular mechanisms and the development of drugs for complex diseases. We are especially interested in research on molecular feature representation, genomics, proteomics data generation or integration, intelligence analysis algorithms for multi-omics data, single-cell RNA sequencing data analysis, drug combination, drug repositioning, evolutionary algorithms, parallel and cloud computing, the prediction of molecular function and structure, and applications of intelligence computing for personalized medicine. Potential topics include, but are not limited to: Sequence analysis; Multi-omics data modeling and analysis; scRNA-seq data analysis; Drug combination and repositioning; Molecular feature learning; ncRNA function annotation; Graph learning and reasoning; Molecular structure and function prediction; Systems biology and computational biology.

Prof. Dr. Zhu-Hong You
Dr. Yangming Li
Dr. Haicheng Yi
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biomolecules biomolecules	4.8	9.4	2011	16.3 Days	CHF 2700
BioTech biotech	2.7	3.7	2012	18.2 Days	CHF 1600
Genes genes	2.8	5.2	2010	16.3 Days	CHF 2600
Non-Coding RNA ncrna	3.6	6.7	2015	21 Days	CHF 1800
Proteomes proteomes	4.0	6.5	2013	34.2 Days	CHF 1800

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Published Papers (4 papers)

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All Biomolecules BioTech Genes ncRNA Proteomes

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13 pages, 2789 KiB  

Open AccessArticle

Towards Decoding Hepatotoxicity of Approved Drugs through Navigation of Multiverse and Consensus Chemical Spaces

by Edgar López-López and José L. Medina-Franco

Biomolecules 2023, 13(1), 176; https://doi.org/10.3390/biom13010176 - 14 Jan 2023

Cited by 6 | Viewed by 2747

Abstract

Drug-induced liver injury (DILI) is the principal reason for failure in developing drug candidates. It is the most common reason to withdraw from the market after a drug has been approved for clinical use. In this context, data from animal models, liver function [...] Read more.

Drug-induced liver injury (DILI) is the principal reason for failure in developing drug candidates. It is the most common reason to withdraw from the market after a drug has been approved for clinical use. In this context, data from animal models, liver function tests, and chemical properties could complement each other to understand DILI events better and prevent them. Since the chemical space concept improves decision-making drug design related to the prediction of structure–property relationships, side effects, and polypharmacology drug activity (uniquely mentioning the most recent advances), it is an attractive approach to combining different phenomena influencing DILI events (e.g., individual “chemical spaces”) and exploring all events simultaneously in an integrated analysis of the DILI-relevant chemical space. However, currently, no systematic methods allow the fusion of a collection of different chemical spaces to collect different types of data on a unique chemical space representation, namely “consensus chemical space.” This study is the first report that implements data fusion to consider different criteria simultaneously to facilitate the analysis of DILI-related events. In particular, the study highlights the importance of analyzing together in vitro and chemical data (e.g., topology, bond order, atom types, presence of rings, ring sizes, and aromaticity of compounds encoded on RDKit fingerprints). These properties could be aimed at improving the understanding of DILI events. Full article

(This article belongs to the Topic Intelligent Computing Unlocks the Molecular Code of Complex Diseases)

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12 pages, 1553 KiB  

Open AccessArticle

Identification of Two Exosomal miRNAs in Circulating Blood of Cancer Patients by Using Integrative Transcriptome and Network Analysis

by Andrés Rincón-Riveros, Josefa Antonia Rodríguez, Victoria E. Villegas and Liliana López-Kleine

Non-Coding RNA 2022, 8(3), 33; https://doi.org/10.3390/ncrna8030033 - 12 May 2022

Viewed by 2800

Abstract

Exosomes carry molecules of great biological and clinical interest, such as miRNAs. The contents of exosomes vary between healthy controls and cancer patients. Therefore, miRNAs and other molecules transported in exosomes are considered a potential source of diagnostic and prognostic biomarkers in cancer. [...] Read more.

Exosomes carry molecules of great biological and clinical interest, such as miRNAs. The contents of exosomes vary between healthy controls and cancer patients. Therefore, miRNAs and other molecules transported in exosomes are considered a potential source of diagnostic and prognostic biomarkers in cancer. Many miRNAs have been detected in recent years. Consequently, a substantial amount of miRNA-related data comparing patients and healthy individuals is available, which contributes to a better understanding of the initiation, development, malignancy, and metastasis of cancer using non-invasive sampling procedures. However, a re-analysis of available ncRNA data is rare. This study used available data about miRNAs in exosomes comparing healthy individuals and cancer patients to identify possible global changes related to the presence of cancer. A robust transcriptomic analysis identified two common miRNAs (miR-495-3p and miR-543) deregulated in five cancer datasets. They had already been implicated in different cancers but not reported in exosomes circulating in blood. The study also examined their target genes and the implications of these genes for functional processes. Full article

(This article belongs to the Topic Intelligent Computing Unlocks the Molecular Code of Complex Diseases)
(This article belongs to the Section Small Non-Coding RNA)

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24 pages, 29819 KiB  

Open AccessFeature PaperArticle

Network Theoretical Approach to Explore Factors Affecting Signal Propagation and Stability in Dementia’s Protein-Protein Interaction Network

by Amit Kumar Lalwani, Kushagra Krishnan, Sali Abubaker Bagabir, Mustfa F. Alkhanani, Atiah H. Almalki, Shafiul Haque, Saurabh Kumar Sharma, R. K. Brojen Singh and Md. Zubbair Malik

Biomolecules 2022, 12(3), 451; https://doi.org/10.3390/biom12030451 - 15 Mar 2022

Cited by 14 | Viewed by 4211

Abstract

Dementia—a syndrome affecting human cognition—is a major public health concern given to its rising prevalence worldwide. Though multiple research studies have analyzed disorders such as Alzheimer’s disease and Frontotemporal dementia using a systems biology approach, a similar approach to dementia syndrome as a [...] Read more.

Dementia—a syndrome affecting human cognition—is a major public health concern given to its rising prevalence worldwide. Though multiple research studies have analyzed disorders such as Alzheimer’s disease and Frontotemporal dementia using a systems biology approach, a similar approach to dementia syndrome as a whole is required. In this study, we try to find the high-impact core regulating processes and factors involved in dementia’s protein–protein interaction network. We also explore various aspects related to its stability and signal propagation. Using gene interaction databases such as STRING and GeneMANIA, a principal dementia network (PDN) consisting of 881 genes and 59,085 interactions was achieved. It was assortative in nature with hierarchical, scale-free topology enriched in various gene ontology (GO) categories and KEGG pathways, such as negative and positive regulation of apoptotic processes, macroautophagy, aging, response to drug, protein binding, etc. Using a clustering algorithm (Louvain method of modularity maximization) iteratively, we found a number of communities at different levels of hierarchy in PDN consisting of 95 “motif-localized hubs”, out of which, 7 were present at deepest level and hence were key regulators (KRs) of PDN (HSP90AA1, HSP90AB1, EGFR, FYN, JUN, CELF2 and CTNNA3). In order to explore aspects of network’s resilience, a knockout (of motif-localized hubs) experiment was carried out. It changed the network’s topology from a hierarchal scale-free topology to scale-free, where independent clusters exhibited greater control. Additionally, network experiments on interaction of druggable genome and motif-localized hubs were carried out where UBC, EGFR, APP, CTNNB1, NTRK1, FN1, HSP90AA1, MDM2, VCP, CTNNA1 and GRB2 were identified as hubs in the resultant network (RN). We finally concluded that stability and resilience of PDN highly relies on motif-localized hubs (especially those present at deeper levels), making them important therapeutic intervention candidates. HSP90AA1, involved in heat shock response (and its master regulator, i.e., HSF1), and EGFR are most important genes in pathology of dementia apart from KRs, given their presence as KRs as well as hubs in RN. Full article

(This article belongs to the Topic Intelligent Computing Unlocks the Molecular Code of Complex Diseases)

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15 pages, 6034 KiB  

Open AccessArticle

RRM2 Alleviates Doxorubicin-Induced Cardiotoxicity through the AKT/mTOR Signaling Pathway

by Yuheng Jiao, Yanyan Li, Jiayan Zhang, Song Zhang, Yafang Zha and Jian Wang

Biomolecules 2022, 12(2), 299; https://doi.org/10.3390/biom12020299 - 12 Feb 2022

Cited by 21 | Viewed by 3898

Abstract

Doxorubicin (DOX) is an effective chemotherapeutic agent that plays an unparalleled role in cancer treatment. However, its serious dose-dependent cardiotoxicity, which eventually contributes to irreversible heart failure, has greatly limited the widespread clinical application of DOX. A previous study has demonstrated that the [...] Read more.

Doxorubicin (DOX) is an effective chemotherapeutic agent that plays an unparalleled role in cancer treatment. However, its serious dose-dependent cardiotoxicity, which eventually contributes to irreversible heart failure, has greatly limited the widespread clinical application of DOX. A previous study has demonstrated that the ribonucleotide reductase M2 subunit (RRM2) exerts salutary effects on promoting proliferation and inhibiting apoptosis and autophagy. However, the specific function of RRM2 in DOX-induced cardiotoxicity is yet to be determined. This study aimed to elucidate the role and potential mechanism of RRM2 on DOX-induced cardiotoxicity by investigating neonatal primary cardiomyocytes and mice treated with DOX. Subsequently, the results indicated that RRM2 expression was significantly reduced in mice hearts and primary cardiomyocytes. Apoptosis and autophagy-related proteins, such as cleaved-Caspase3 (C-Caspase3), LC3B, and beclin1, were distinctly upregulated. Additionally, RRM2 deficiency led to increased autophagy and apoptosis in cells. RRM2 overexpression, on the contrary, alleviated DOX-induced cardiotoxicity in vivo and in vitro. Consistently, DIDOX, an inhibitor of RRM2, attenuated the protective effect of RRM2. Mechanistically, we found that AKT/mTOR inhibitors could reverse the function of RRM2 overexpression on DOX-induced autophagy and apoptosis, which means that RRM2 could have regulated DOX-induced cardiotoxicity through the AKT/mTOR signaling pathway. In conclusion, our experiment established that RRM2 could be a potential treatment in reversing DOX-induced cardiac dysfunction. Full article

(This article belongs to the Topic Intelligent Computing Unlocks the Molecular Code of Complex Diseases)

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