Exogenous metal particles and ions from implant devices are known to cause severe toxic events with symptoms ranging from adverse local tissue reactions to systemic toxicities, potentially leading to the development of cancers, heart conditions, and neurological disorders. Toxicity mechanisms, also known as Adverse Outcome Pathways (AOPs), that explain these metal-induced toxicities are severely understudied. Therefore, we deployed in silico structure- and knowledge-based approaches to identify proteome-level perturbations caused by metals and pathways that link these events to human diseases. We captured 177 structure-based, 347 knowledge-based, and 402 imputed metal-gene/protein relationships for chromium, cobalt, molybdenum, nickel, and titanium. We prioritized 72 proteins hypothesized to directly contact implant surfaces and contribute to adverse outcomes. Results of this exploratory analysis were formalized as structured AOPs. We considered three case studies reflecting the following possible situations: (i) the metal-protein-disease relationship was previously known; (ii) the metal-protein, protein-disease, and metal-disease relationships were individually known but were not linked (as a unified AOP); and (iii) one of three relationships was unknown and was imputed by our methods. These situations were illustrated by case studies on nickel-induced allergy/hypersensitivity, cobalt-induced heart failure, and titanium-induced periprosthetic osteolysis, respectively. All workflows, data, and results are freely available in https://github.com/DnlRKorn/Knowledge_Based_Metallomics/. An interactive view of select data is available at the ROBOKOP Neo4j Browser at http://robokopkg.renci.org/browser/.
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