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<p>Analyzing single omics and integrating multimodal omics datasets to capture functional dysregulation in disease remains challenging. Here, we propose a bioinformatics framework that leverages curated datasets of protein complexes (‘complexome’) as a foundation for proteomics data integration. Available for human and other model organisms, the complexome provides a global view of cellular function, enabling queries with proteomics datasets. We first benchmarked how protein abundances across human tissues shape distinct complexomic profiles, serving to fingerprint biological activity. Next, we analyzed complexome remodeling using disease versus control proteomics quantifications. Using proteomics data from fibroblasts of patients with genetically confirmed metabolic defects, we identified significant perturbations in mitochondrial oxidative phosphorylation complexes and additional complexes involved in wider mitochondrial functions. The complexome provides a systems-wide approach to dissect mechanisms underlying disease-related functional and phenotypic changes by mapping measured protein-level perturbations to specific molecular complexes. The software is available as a Python notebook at <a href="https://github.com/mguharoy/Complexome">https://github.com/mguharoy/Complexome</a>.</p>

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The complexome contextualizes proteomics data to fingerprint biological states and highlight perturbed functional modules in disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月25日

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  • Mainak Guharoy,
  • Isabelle Adant,
  • Matthew Bird,
  • Alexander Botzki,
  • James Collier,
  • Jonas Dehairs,
  • Stefaan Derveaux,
  • Simon Devos,
  • Geert Goeminne,
  • Francis Impens,
  • Andrea Jáñez Pedrayes,
  • Rekin’s Janky,
  • Ruth Maes,
  • Pedro Magalhães,
  • Teresa M. Maia,
  • Wouter Meersseman,
  • Daisy Rymen,
  • Johannes V. Swinnen,
  • Delphi Van Haver,
  • Dries Verdegem,
  • Peter Witters,
  • David Cassiman,
  • Bart Ghesquiere

摘要

Analyzing single omics and integrating multimodal omics datasets to capture functional dysregulation in disease remains challenging. Here, we propose a bioinformatics framework that leverages curated datasets of protein complexes (‘complexome’) as a foundation for proteomics data integration. Available for human and other model organisms, the complexome provides a global view of cellular function, enabling queries with proteomics datasets. We first benchmarked how protein abundances across human tissues shape distinct complexomic profiles, serving to fingerprint biological activity. Next, we analyzed complexome remodeling using disease versus control proteomics quantifications. Using proteomics data from fibroblasts of patients with genetically confirmed metabolic defects, we identified significant perturbations in mitochondrial oxidative phosphorylation complexes and additional complexes involved in wider mitochondrial functions. The complexome provides a systems-wide approach to dissect mechanisms underlying disease-related functional and phenotypic changes by mapping measured protein-level perturbations to specific molecular complexes. The software is available as a Python notebook at https://github.com/mguharoy/Complexome.