https://www.um.edu.mt/library/oar/handle/123456789/814 Mon, 06 Apr 2026 10:46:44 GMT 2026-04-06T10:46:44Z https://www.um.edu.mt/library/oar/handle/123456789/144527 Title: Epitranscriptomics in atherosclerosis : unraveling RNA modifications, editing and splicing and their implications in vascular disease Authors: Stopa, Victoria; Dafou, Dimitra; Karagianni, Korina; Yaël Nossent, A.; Farrugia, Rosienne; Devaux, Yvan; Sopic, Miron Abstract: Atherosclerosis remains a leading cause of morbidity and mortality worldwide, driven by complex molecular mechanisms involving gene regulation and post-transcriptional processes. Emerging evidence highlights the critical role of epitranscriptomics, the study of chemical modifications occurring on RNA molecules, in atherosclerosis development. Epitranscriptomics provides a new layer of regulation in vascular health, influencing cellular functions in endothelial cells, smooth muscle cells, and macrophages, thereby shedding light on the pathogenesis of atherosclerosis and presenting new opportunities for novel therapeutic targets. This review provides a comprehensive overview of the epitranscriptomic landscape, focusing on key RNA modifications such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), pseudouridine (Ψ), RNA editing mechanisms including A-to-I and C-to-U editing and RNA isoforms. The functional implications of these modifications in RNA stability, alternative splicing, and microRNA biology are discussed, with a focus on their roles in inflammatory signaling, lipid metabolism, and vascular cell adaptation within atherosclerotic plaques. We also highlight how these modifications influence the generation of RNA isoforms, potentially altering cellular phenotypes and contributing to disease progression. Despite the promise of epitranscriptomics, significant challenges remain, including the technical limitations in detecting RNA modifications in complex tissues and the need for deeper mechanistic insights into their causal roles in atherosclerotic pathogenesis. Integrating epitranscriptomics with other omics approaches, such as genomics, proteomics, and metabolomics, holds the potential to provide a more holistic understanding of the disease. Wed, 01 Jan 2025 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/144527 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/144526 Title: Relevance of pre-analytical factors in multiomics : toward a standardized blood processing protocol Authors: Trindade, Fábio; Sopić, Miron; Davies, Michael J.; Tsatsanis, Christos; Pinet, Florence; Ferreira, Helena Beatriz; Munjas, Jelena; Mayilyan, Karine R.; Formosa, Melissa Marie; Attard, Ritienne; Farrugia, Rosienne; Vitorino, Rui; Khatib, Soliman; Bezzina Wettinger, Stephanie; Novella, Susana; Kosek, Vít; Sohrabi, Yahya; Magni, Paolo; Devaux, Yvan; de Gonzalo-Calvo, David; Mardal, Marie Abstract: To implement multiomic studies successfully, there is a need to overcome challenges in steps ranging from study design to data integration. As blood is the preferred matrix for sampling in such studies, we review how pre-analytical factors affect genomics, transcriptomics, proteomics, and metabolomics and propose a harmonized blood processing protocol. Plasma is preferred, as clotting of serum may cause contamination from lysed cells. Transcriptomics is highly sensitive to platelet contamination, making platelet-poor plasma ideal. Processing delays and room-temperature storage compromise the stability of several analytes classes. To ensure comparability, the Standard PREanalytical Code (SPREC) should document all phases of sample handling. We recommend collecting blood in K2EDTA tubes and separating plasma via two centrifugations (1600×g and 16,000×g, 10 min at 4 °C). Samples should be checked for hemolysis, icterus, and lipemia and then stored at −80 °C [SPREC: PL2.PED.A1.C.J.A.D]. Following this standardized protocol or documenting deviations from it can improve multiomic reproducibility. Wed, 01 Jan 2025 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/144526 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/144525 Title: Multiomics in atherosclerotic cardiovascular disease Authors: Tybjærg Nordestgaard, Liv; Wolford, Brooke N.; de Gonzalo-Calvo, David; Sopić, Miron; Devaux, Yvan; Matic, Ljubica; Bezzina Wettinger, Stephanie; Schmid, Johannes A.; Amigó, Núria; Masana, Lluís; Catapano, Alberico L.; Kardassis, Dimitris; Magni, Paolo Abstract: Background and aims: Atherosclerotic cardiovascular disease (ASCVD) is among the leading causes of death worldwide, and technological advances have made it possible to expand the repertoire of biomarkers used in diagnostics and treatment of ASCVD. These include different omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics). We introduce the various layers of omic data and how they can be used in diagnostics and treatment of ASCVD. Further, we discuss future possibilities of combining multiomic data with machine learning (ML) and artificial intelligence (AI) to develop algorithms for facilitating precision medicine. Methods: we reviewed the current literature on omic data in ASCVD and its integration with ML/AI. Results: Genomics has been used to generate polygenic risk scores (PRS), which have shown promising results in risk prediction of ASCVD. Key epigenetic changes implicated in atherosclerosis include deoxyribonucleic acid (DNA) methylation. Transcriptomics has been used to identify transcripts, including micro ribonucleic acid (miRNAs), implicated in atherosclerosis progression. Proteomic risk scores have shown independent predictive information and outperformed clinical risk models, and within the metabolomics field, lipidomics has emerged as a promising predictive tool. The combination of multiomic data analysis with ML and AI methods has already demonstrated potential in the development of clinical models. Conclusions: A major effort is necessary to bring omic data and technologies to the clinical field. Further support will be offered by the generation of clinically applicable/approved AI/ML algorithms able to translate large datasets into valuable information for accurate precision medicine approaches. Wed, 01 Jan 2025 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/144525 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/144524 Title: Current status and challenges of multi-omics research using animal models of atherosclerosis Authors: Mitić, Tijana; Georgescu, Adriana; Alexandru-Moise, Nicoleta; Davies, Michael J.; Vindis, Cecile; Novella, Susana; Gerdts, Eva; Kararigas, Georgios; Bezzina Wettinger, Stephanie; Formosa, Melissa M.; Kwak, Brenda R.; Molica, Filippo; Amigo, Nuria; Caporali, Andrea; de la Cuesta, Fernando; Fernando Hall, Ignacio; Chroni, Angeliki; Martelli, Fabio; Schmid, Johannes A.; Magni, Paolo; Kardassis, Dimitris Abstract: Atherosclerosis is an underlying cause of cardiovascular diseases (CVD) which account for most deaths worldwide. Use of diverse preclinical models of atherosclerosis has been implemental in understanding the underlying mechanisms, the implicated cell types, the genes and the molecules at play in the onset and progression of atherosclerotic plaques. Although significant research advancements have been made, further research is necessary to delve into factors influencing plaque types, site preference within the vasculature, interactions with adjacent tissues (liver, pancreas and perivascular adipose tissue), inflammation and sex-based disparities, among others. The conventional low throughput methodologies which concentrate on individual cells, genes or metabolites are inadequate to tackle the complex and heterogeneous nature of atherosclerosis. With recent advancement in multi-omics and bioinformatics, research approaches have illuminated a clearer understanding of atherosclerosis. Consequently, these advancements pave the path to design novel therapeutics to complement currently approved lipid-lowering and other effective treatments. In this article, we summarize and critically evaluate the findings derived from recent high throughput single- or multi-omic studies conducted in animal models of atherosclerosis. We also delve into the challenges associated with using experimental animals to model human atherosclerosis and contemplate the essential enhancements needed to better mimic human conditions. We further discuss the requirement of establishing a structured multi-omic database for atherosclerosis research, enabling broader access and utilisation within the scientific community. Wed, 01 Jan 2025 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/144524 2025-01-01T00:00:00Z