The molecular landscape of premature aging diseases defined by multilayer network exploration

Premature Aging (PA) diseases are rare genetic disorders that mimic some aspects of physiological aging at an early age. Various causative genes of PA diseases have been identified in recent years, providing insights into some dysfunctional cellular functions. However, the identification of PA genes also revealed significant genetic heterogeneity and highlighted the gaps in our understanding of PA molecular mechanisms. Furthermore, many patients remain undiagnosed. Overall, the current lack of knowledge about PA diseases hinders the development of effective diagnosis and therapies and poses significant challenges to improving patient care. Here, we present a network-based approach to systematically unravel the cellular functions disrupted in PA diseases. Leveraging a novel community identification algorithm, we delved into a vast multilayer network of biological interactions to extract the communities of 67 PA diseases from their 132 associated genes. We found that these communities can be grouped into six distinct clusters, each reflecting specific cellular functions: DNA repair, cell cycle, transcription regulation, inflammation, cell communication, and vesicle-mediated transport. We propose that these clusters collectively represent the landscape of the molecular mechanisms that are perturbed in PA diseases, providing a framework for better understanding their pathogenesis. Intriguingly, most clusters also exhibited a significant enrichment in genes associated with physiological aging, suggesting a potential overlap between the molecular underpinnings of PA diseases and natural aging. ### Competing Interest Statement AV is employee and shareholder of F. Hoffmann-La Roche Ltd. NL is shareholder of ProGeLife and its subsidiary Calysens. NL is employee (Chief Scientist Rare Disease) at the insternational research institute Servier. ### Funding Statement This work received support from: The French National Research Agency (ANR-21-CE45-000101) The Association Francaise contre les Myopathies (AFM) The Excellence Initiative of Aix-Marseille University A*Midex (a French Investissements d’Avenir programme) Institute MarMaRa AMX-19-IET-007 The European Union’s Horizon 2020 research and innovation programme under the EJP RD COFUND-EJP No 825575. GPT-4 was used to improve syntax and readability. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used (or will use) ONLY openly available human data that were originally located at: - Orphanet: - HuRI: - APID: - Reactome: - Human Protein Atlas: - CORUM: - hu.MAP: I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data and codes produced are available online at: Supplementary Materials: https://zenodo.org/records/10400905 Networks: https://www.ndexbio.org/index.html#/search?searchType=All&searchString=cecile. beust&searchTermExpansion=false Community identification in multilayer networks: itRWR Python Package https://github.com/anthbapt/ itRWR Complete analysis workflow: Community identification, clustering, and enrichment analyses https://github.com/CecileBeust/PA_Communities.git