A Multi-Omics Framework for Decoding Disease Mechanisms: Insights from Methylmalonic Aciduria.

IF 6.1 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Jianbo Fu, Vito R T Zanotelli, Cedric Howald, Nylsa Chammartin, Ilya Kolpakov, Ioannis Xenarios, D Sean Froese, Bernd Wollscheid, Patrick G A Pedrioli, Sandra Goetze
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Abstract

The diverse perspectives offered by multi-omics data analysis can aid in identifying the most relevant molecular pathways involved in disease processes, and findings in one layer can substantiate findings in other layers of information. Integrating data from multiple omics sources is becoming increasingly important to improve disease diagnosis and treatment, especially for conditions with complex and poorly understood underlying pathomechanisms. Methylmalonic aciduria (MMA), an inherited metabolic disorder, serves as an illustrative example of such a disease with poorly understood pathogenesis for which published multi-omics data are readily available. Re-using these FAIR data, obtained from the multi-omics digitization of 230 MMA patient samples, we pursued advanced data integration and analysis strategies to integrate different levels of biological information, combining genomic, transcriptomic, proteomic, and metabolomic profiling with biochemical and clinical data, with the aim of elucidating molecular perturbations in individuals affected by MMA. The analysis of protein-quantitative trait loci highlighted the importance of glutathione metabolism in the pathogenesis of methylmalonic acidemia (MMA). This finding was supported by correlation network analyses that integrated proteomics and metabolomics data, alongside gene set enrichment and transcription factor analyses based on disease severity from transcriptomic data. The correlation network analysis also revealed that lysosomal function is compromised in MMA patients, which is critical for maintaining metabolic balance. Our research introduces a comprehensive data analysis framework that effectively addresses the challenge of prioritizing disruptions in molecular pathways by accumulating evidence from multiple omics levels.

解码疾病机制的多组学框架:来自甲基丙二酸尿症的见解
多组学数据分析提供的不同视角可以帮助确定参与疾病过程的最相关的分子途径,并且一层的发现可以证实其他信息层的发现。整合来自多个组学来源的数据对于改善疾病的诊断和治疗变得越来越重要,特别是对于具有复杂和知之甚少的潜在病理机制的疾病。甲基丙二酸尿症(MMA)是一种遗传性代谢紊乱,是这种疾病的一个例子,其发病机制尚不清楚,已发表的多组学数据很容易获得。利用从230例MMA患者样本的多组学数字化中获得的这些FAIR数据,我们采用先进的数据整合和分析策略,将基因组、转录组学、蛋白质组学和代谢组学分析与生化和临床数据结合起来,整合不同水平的生物信息,目的是阐明MMA患者的分子扰动。蛋白质数量性状位点的分析强调了谷胱甘肽代谢在甲基丙二酸血症(MMA)发病机制中的重要性。这一发现得到了相关网络分析的支持,该分析整合了蛋白质组学和代谢组学数据,以及基于转录组学数据的疾病严重程度的基因集富集和转录因子分析。相关网络分析还显示,MMA患者的溶酶体功能受损,这对维持代谢平衡至关重要。我们的研究引入了一个全面的数据分析框架,通过积累来自多个组学水平的证据,有效地解决了分子途径中破坏的优先次序的挑战。
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来源期刊
Molecular & Cellular Proteomics
Molecular & Cellular Proteomics 生物-生化研究方法
CiteScore
11.50
自引率
4.30%
发文量
131
审稿时长
84 days
期刊介绍: The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action. The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data. Scope: -Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights -Novel experimental and computational technologies -Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes -Pathway and network analyses of signaling that focus on the roles of post-translational modifications -Studies of proteome dynamics and quality controls, and their roles in disease -Studies of evolutionary processes effecting proteome dynamics, quality and regulation -Chemical proteomics, including mechanisms of drug action -Proteomics of the immune system and antigen presentation/recognition -Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease -Clinical and translational studies of human diseases -Metabolomics to understand functional connections between genes, proteins and phenotypes
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