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Multi-Omics Approaches to Disentangle Pathomechanisms in Neurological Disease
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Multi-Omics Approaches to Disentangle Pathomechanisms in Neurological Disease

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

Deadline for manuscript submissions: 20 October 2025 | Viewed by 1855

Special Issue Editors

Dr. Francesco Pezzini

E-Mail Website
Guest Editor
Department of Surgery, Dentistry, Paediatrics and Gynaecology, University of Verona, piazzale L. A. Scuro 10, 37134 Verona, Italy
Interests: neurodegeneration; neurobiology; molecular mechanisms; lysosomal storage diseases; in vitro disease models; omics; bioinformatics
Dr. Stefano Doccini

E-Mail Website
Guest Editor
Molecular Medicine, IRCCS Fondazione Stella Maris, via dei Giacinti 2, Calambrone, 56128 Pisa, Italy
Interests: omics; bioinformatics; neurosciences; in vitro disease models; biomarker discovery; bioenergetics; mitochondrial disorders
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to participate in our Special Issue on “Multi-omics Approaches to Disentangle Pathomechanisms in Neurological Disease”, which is being prepared for the International Journal of Molecular Sciences.

Despite significant advancements in recent years, the comprehension of the molecular mechanisms underlying both inherited and acquired neurological diseases still represents a scientific challenge and requires further efforts to be fully achieved. Undeniably, the employment of omics technologies, and especially their integration into the so-called multi-omics approach, has improved our understanding of the molecular basis of diseases affecting the nervous system. Moreover, the possibility of combining datasets from different omics analyses allows us to identify the biomarkers that are suitable for the monitoring of disease progression and eventually develop innovative therapeutical strategies, including the recognition of new molecular candidates for pharmacological targeting.

The aim of this Special Issue is to collect original research articles and review papers dealing with omics technologies and their applications to the investigation of the pathomechanisms underling human disorders affecting the nervous system. Technical papers describing innovative approaches and computational bioinformatic advancements in this field are also highly welcome.

Dr. Francesco Pezzini
Dr. Stefano Doccini
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

  • neurological diseases
  • molecular mechanisms
  • neurodegeneration
  • omics
  • bioinformatics
  • disease biomarkers
  • biochemical pathways
  • diagnostic and therapeutic targets

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Published Papers (3 papers)

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Research

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31 pages, 4404 KB  
Article
Integrative Transcriptomic and Network-Based Analysis of Neuromuscular Diseases
by Federico García-Criado, Lucia Hurtado-García, Elena Rojano, Álvaro Esteban-Martos, Jesús Pérez-García, Pedro Seoane and Juan A. G. Ranea
Int. J. Mol. Sci. 2025, 26(19), 9376; https://doi.org/10.3390/ijms26199376 - 25 Sep 2025
Abstract
Neuromuscular diseases (NMDs) like Duchenne muscular dystrophy (DMD), limb–girdle muscular dystrophy (LGMD), and amyotrophic lateral sclerosis (ALS) are rare, progressive disorders with complex molecular mechanisms. Traditional transcriptomic analyses often struggle to capture systems-level dysregulation, especially given the small sample sizes typical of rare [...] Read more.
Neuromuscular diseases (NMDs) like Duchenne muscular dystrophy (DMD), limb–girdle muscular dystrophy (LGMD), and amyotrophic lateral sclerosis (ALS) are rare, progressive disorders with complex molecular mechanisms. Traditional transcriptomic analyses often struggle to capture systems-level dysregulation, especially given the small sample sizes typical of rare disease studies. Our differential expression analysis of eight public RNA-seq datasets from various cell types in DMD, LGMD, and ALS revealed not only disease-relevant pathways but also unexpected enrichments, such as renal development, suggesting systemic impacts beyond muscle tissue. To address limitations in capturing broader molecular mechanisms, we applied an integrative systems biology approach combining differential expression data, protein–protein interaction (PPI) networks, and network embedding techniques. Comparative functional enrichment revealed shared pathways, including glycosaminoglycan binding in both DMD and FUS-related ALS, implicating extracellular matrix–protein interactions in FUS mutation effects. Mapping DEGs onto the human PPI network and assessing their proximity to causal genes uncovered dysregulated non-coding RNAs, such as PAX8-AS1, SBF2-AS1, and NEAT1, potentially indicating common regulatory roles. We also found candidate genes within disease-proximal clusters, like HS3ST3A1, which may contribute to pathogenesis. Overall, this integrative approach reveals shared transcriptional programs and novel targets, advancing our understanding and potential treatment strategies for NMDs. Full article
(This article belongs to the Special Issue Multi-Omics Approaches to Disentangle Pathomechanisms in Neurological Disease)
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17 pages, 12102 KB  
Article
Multiomics Integration of Parkinson’s Disease Datasets Reveals Unexpected Roles of IRE1 in Its Pathology
by Bianka Alexandra Pasat, Matthieu Moncan, Eleftherios Pilalis, Afshin Samali, Aristotelis Chatziioannou and Adrienne M. Gorman
Int. J. Mol. Sci. 2025, 26(14), 6711; https://doi.org/10.3390/ijms26146711 - 12 Jul 2025
Viewed by 586
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disease. It primarily affects the motor system but is also associated with a range of cognitive impairments that can manifest early in disease progression, indicating its multifaceted nature. In this paper, we performed a [...] Read more.
Parkinson’s disease (PD) is the second most common neurodegenerative disease. It primarily affects the motor system but is also associated with a range of cognitive impairments that can manifest early in disease progression, indicating its multifaceted nature. In this paper, we performed a meta-analysis of transcriptomics and proteomics data using MultiOmicsIntegrator to gain insights into the post-transcriptional modifications and deregulated pathways associated with this disease. Our results reveal differential isoform usage between control and PD patient brain samples that result in enriched alternative splicing events, including an extended UTR length, domain loss, and the upregulation of non-coding isoforms. We found that Inositol-Requiring Enzyme 1 (IRE1) is active in PD samples and examined the role of its downstream signaling through X-box binding mRNA 1 (XBP1) and regulated IRE1-dependent decay (RIDD). We identified several RIDD candidates and showed that the enriched alternative splicing events observed are associated with RIDD. Moreover, in vitro mRNA cleavage assays demonstrated that OSBPL3, C16orf74, and SLC6A1 mRNAs are targets of IRE1 RNAse activity. Finally, a pathway enrichment analysis of both XBP1s and RIDD targets in the PD samples uncovered associations with processes such as immune response, oxidative stress, signal transduction, and cell–cell communication that have previously been linked to PD. These findings highlight a potential regulatory role of IRE in PD. Full article
(This article belongs to the Special Issue Multi-Omics Approaches to Disentangle Pathomechanisms in Neurological Disease)
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Review

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20 pages, 1418 KB  
Review
Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma
by Giovanna Morello, Valentina La Cognata, Maria Guarnaccia, Giulia Gentile and Sebastiano Cavallaro
Int. J. Mol. Sci. 2025, 26(19), 9362; https://doi.org/10.3390/ijms26199362 - 25 Sep 2025
Abstract
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults. It is characterized by a high degree of heterogeneity, meaning that although these tumors may appear morphologically similar, they often exhibit distinct clinical outcomes. By associating specific molecular fingerprints with [...] Read more.
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults. It is characterized by a high degree of heterogeneity, meaning that although these tumors may appear morphologically similar, they often exhibit distinct clinical outcomes. By associating specific molecular fingerprints with different clinical behaviors, high-throughput omics technologies (e.g., genomics, transcriptomics, and epigenomics) have significantly advanced our understanding of GBM, particularly of its extensive heterogeneity, by proposing a molecular classification for the implementation of precision medicine. However, due to the vast volume and complexity of data, the integrative analysis of omics data demands substantial computational power for processing, analyzing and interpreting GBM-related data. Artificial intelligence (AI), which mainly includes machine learning (ML) and deep learning (DL) computational approaches, now presents a unique opportunity to infer valuable biological insights from omics data and enhance the clinical management of GBM. In this review, we explored the potential of integrating multi-omics, imaging radiomics and clinical data with AI to uncover different aspects of GBM (molecular profiling, prognosis, and treatment) and improve its clinical management. Full article
(This article belongs to the Special Issue Multi-Omics Approaches to Disentangle Pathomechanisms in Neurological Disease)
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