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Proteomic and Transcriptomic Approaches for Studying Bone Regeneration in Health and Systemically Compromised Conditions
Author(s) -
Calciolari Elena,
Donos Nikolaos
Publication year - 2020
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.201900084
Subject(s) - context (archaeology) , omics , bioinformatics , regeneration (biology) , disease , computational biology , identification (biology) , medicine , biology , data science , computer science , pathology , paleontology , botany , microbiology and biotechnology
Bone regeneration is a complex biological process, where the molecular mechanisms are only partially understood. In an ageing population, where the prevalence of chronic diseases with an impact on bone metabolism is increasing, it becomes crucial to identify new strategies that would improve regenerative outcomes also in medically compromised patients. In this context, omics are demonstrating a great potential, as they offer new insights on the molecular mechanisms regulating physiologic/pathologic bone healing and, at the same time, allow the identification of new diagnostic and therapeutic targets. This review provides an overview on the current evidence on the use of transcriptomic and proteomic approaches in bone regeneration research, particularly in relation to type 1 diabetes and osteoporosis, and discusses future scenarios and potential benefits and limitations on the integration of multi‐omics. It is suggested that future research will leverage the synergy of omics with statistical modeling and bioinformatics to prompt the understanding of the biology underpinning bone formation in health and medically compromised conditions. With an eye toward personalized medicine, new strategies combining the mining of large datasets and bioinformatic data with a detailed characterization of relevant phenotypes will need to be pursued to further the understanding of disease mechanisms.

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