The concept of « model organisms » is being revisited in the light of the latest advances in multi-omics technologies that can now capture the full range of molecular events that occur over time, regardless of the organism studied. Classic, well-studied models, such as Escherichia coli, Saccharomyces cerevisiae, to name a few, have long been valuable for hypothesis testing, reproducibility, and sharing common platforms among researchers. However, they are not suitable for all types of research. The complexity of unanswered questions in biology demands more elaborated systems, particularly to study plant and animal biodiversity, microbial ecosystems and their interactions with their hosts if any. More integrated systems, known as « holobionts », are emerging to describe and unify host organisms and associated microorganisms, providing an overview of all their possible interactions and trajectories. Comparative evolutionary proteomics offers interesting prospects for extrapolating knowledge from a few selected model organisms to others. This approach enables a deeper characterization of the diversity of proteins and proteoforms across the three branches of the tree of life, i.e. Bacteria, Archaea, and Eukarya. It also provides a powerful means to address remaining biological questions, such as identifying the key molecular players in organisms when they are confronted to environmental challenges, like anthropogenic toxicants, pathogens, dietary shifts or climate stressors, and proposing long-term sustainable solutions. SIGNIFICANCE: In this commentary, we reevaluated the concept of "model organisms" in light of advancements in multi-omics technologies. Traditional models have proven invaluable for hypothesis testing, reproducibility, and fostering shared research frameworks. However, we discussed that they are not universally applicable. To address complexities such as biodiversity and understand microbial ecosystems and their host interactions, integrated systems like "holobionts," which encompass host organisms and their associated microbes, are gaining prominence. Comparative evolutionary proteomics further enhances our understanding by enabling detailed exploration of protein diversity across organisms. This approach also facilitates the identification of critical molecular players in organisms facing environmental challenges, such as pollutants, pathogens, dietary changes, or climate stress, and contributes to developing sustainable long-term solutions.
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Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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Site | Modification | Modifier | Source | Reference |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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