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Systems Bioinformatics: How Networks and Systems Biology Boost Precision/Personalized Medicine

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 16842

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Special Issue Editor

Prof. Dr. George M. Spyrou

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Guest Editor

1. The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus
2. Cyprus School of Molecular Medicine, P.O. Box 23462, 1683 Nicosia, Cyprus
Interests: systems bioinformatics; network-based analysis and integration; computational methods for biomarker discovery and drug repurposing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Systems bioinformatics combine network-based bioinformatics approaches with systems biology to provide answers to problems that deal with multilevel and multiscale data handling and integration, discovery of patterns of biomarkers, analysis of systemic effects, revealing connected communities of molecular pathways related to a disease, analysis of synergistic effects on a molecular level, bridging the gap between molecular and phenotypic findings, monitoring of network differentiation among biological states, and many others. This is a framework in which systems approaches are applied to -omics data, setting the level of resolution and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system’s individual components. Systems bioinformatics is a modern view of what computer science, physics, mathematics, and other sciences can do for biology and medicine, generating new knowledge, methods, tools as well as more demanding questions for precision and personalized medicine.

Therefore, authors are invited to submit original research and review articles which address the progress and current standing of network-based and other systems bioinformatics approaches for precision/personalized medicine.

Prof. George M. Spyrou
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. 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

Systems bioinformatics
Network-based integration
Network analysis
Network-based differentiation
Network medicine
Computational tools for precision/personalized medicine
Network-based diagnostics and therapeutics

Published Papers (3 papers)

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Research

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25 pages, 5362 KiB

Open AccessArticle

Investigating the Transition of Pre-Symptomatic to Symptomatic Huntington’s Disease Status Based on Omics Data

by Christiana C. Christodoulou, Margarita Zachariou, Marios Tomazou, Evangelos Karatzas, Christiana A. Demetriou, Eleni Zamba-Papanicolaou and George M. Spyrou

Int. J. Mol. Sci. 2020, 21(19), 7414; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21197414 - 08 Oct 2020

Cited by 18 | Viewed by 3570

Abstract

Huntington’s disease is a rare neurodegenerative disease caused by a cytosine–adenine–guanine (CAG) trinucleotide expansion in the Huntingtin (HTT) gene. Although Huntington’s disease (HD) is well studied, the pathophysiological mechanisms, genes and metabolites involved in HD remain poorly understood. Systems bioinformatics can reveal synergistic relationships among different omics levels and enables the integration of biological data. It allows for the overall understanding of biological mechanisms, pathways, genes and metabolites involved in HD. The purpose of this study was to identify the differentially expressed genes (DEGs), pathways and metabolites as well as observe how these biological terms differ between the pre-symptomatic and symptomatic HD stages. A publicly available dataset from the Gene Expression Omnibus (GEO) was analyzed to obtain the DEGs for each HD stage, and gene co-expression networks were obtained for each HD stage. Network rewiring, highlights the nodes that change most their connectivity with their neighbors and infers their possible implication in the transition between different states. The CACNA1I gene was the mostly highly rewired node among pre-symptomatic and symptomatic HD network. Furthermore, we identified AF198444 to be common between the rewired genes and DEGs of symptomatic HD. CNTN6, DEK, LTN1, MST4, ZFYVE16, CEP135, DCAKD, MAP4K3, NUPL1 and RBM15 between the DEGs of pre-symptomatic and DEGs of symptomatic HD and CACNA1I, DNAJB14, EPS8L3, HSDL2, SNRPD3, SOX12, ACLY, ATF2, BAG5, ERBB4, FOCAD, GRAMD1C, LIN7C, MIR22, MTHFR, NABP1, NRG2, OTC, PRAMEF12, SLC30A10, STAG2 and Y16709 between the rewired genes and DEGs of pre-symptomatic HD. The proteins encoded by these genes are involved in various biological pathways such as phosphatidylinositol-4,5-bisphosphate 3-kinase activity, cAMP response element-binding protein binding, protein tyrosine kinase activity, voltage-gated calcium channel activity, ubiquitin protein ligase activity, adenosine triphosphate (ATP) binding, and protein serine/threonine kinase. Additionally, prominent molecular pathways for each HD stage were then obtained, and metabolites related to each pathway for both disease stages were identified. The transforming growth factor beta (TGF-β) signaling (pre-symptomatic and symptomatic stages of the disease), calcium (Ca²⁺) signaling (pre-symptomatic), dopaminergic synapse pathway (symptomatic HD patients) and Hippo signaling (pre-symptomatic) pathways were identified. The in silico metabolites we identified include Ca²⁺, inositol 1,4,5-trisphosphate, sphingosine 1-phosphate, dopamine, homovanillate and L-tyrosine. The genes, pathways and metabolites identified for each HD stage can provide a better understanding of the mechanisms that become altered in each disease stage. Our results can guide the development of therapies that may target the altered genes and metabolites of the perturbed pathways, leading to an improvement in clinical symptoms and hopefully a delay in the age of onset. Full article

(This article belongs to the Special Issue Systems Bioinformatics: How Networks and Systems Biology Boost Precision/Personalized Medicine)

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19 pages, 3209 KiB

Open AccessArticle

Analyzing Gene Expression Profiles from Ataxia and Spasticity Phenotypes to Reveal Spastic Ataxia Related Pathways

by Andrea C. Kakouri, Christina Votsi, Marios Tomazou, George Minadakis, Evangelos Karatzas, Kyproula Christodoulou and George M. Spyrou

Int. J. Mol. Sci. 2020, 21(18), 6722; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21186722 - 14 Sep 2020

Cited by 5 | Viewed by 2467

Abstract

Spastic ataxia (SA) is a group of rare neurodegenerative diseases, characterized by mixed features of generalized ataxia and spasticity. The pathogenetic mechanisms that drive the development of the majority of these diseases remain unclear, although a number of studies have highlighted the involvement of mitochondrial and lipid metabolism, as well as calcium signaling. Our group has previously published the GBA2 c.1780G > C (p.Asp594His) missense variant as the cause of spastic ataxia in a Cypriot consanguineous family, and more recently the biochemical characterization of this variant in patients’ lymphoblastoid cell lines. GBA2 is a crucial enzyme of sphingolipid metabolism. However, it is unknown if GBA2 has additional functions and therefore additional pathways may be involved in the disease development. The current study introduces bioinformatics approaches to better understand the pathogenetic mechanisms of the disease. We analyzed publicly available human gene expression datasets of diseases presented with ‘ataxia’ or ‘spasticity’ in their clinical phenotype and we performed pathway analysis in order to: (a) search for candidate perturbed pathways of SA; and (b) evaluate the role of sphingolipid signaling pathway and sphingolipid metabolism in the disease development, through the identification of differentially expressed genes in patients compared to controls. Our results demonstrate consistent differential expression of genes that participate in the sphingolipid pathways and highlight alterations in the pathway level that might be associated with the disease phenotype. Through enrichment analysis, we discuss additional pathways that are connected to sphingolipid pathways, such as PI3K-Akt signaling, MAPK signaling, calcium signaling, and lipid and carbohydrate metabolism as the most enriched for ataxia and spasticity phenotypes. Full article

(This article belongs to the Special Issue Systems Bioinformatics: How Networks and Systems Biology Boost Precision/Personalized Medicine)

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Review

Jump to: Research

37 pages, 5861 KiB

Open AccessReview

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

by Efstathios Iason Vlachavas, Jonas Bohn, Frank Ückert and Sylvia Nürnberg

Int. J. Mol. Sci. 2021, 22(6), 2822; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22062822 - 10 Mar 2021

Cited by 9 | Viewed by 9716

Abstract

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings. Full article

(This article belongs to the Special Issue Systems Bioinformatics: How Networks and Systems Biology Boost Precision/Personalized Medicine)

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