Literature DB >> 24206226

Redox proteomics gives insights into the role of oxidative stress in alkaptonuria.

Daniela Braconi1, Lia Millucci, Lorenzo Ghezzi, Annalisa Santucci.   

Abstract

Alkaptonuria (AKU) is an ultra-rare metabolic disorder of the catabolic pathway of tyrosine and phenylalanine that has been poorly characterized at molecular level. As a genetic disease, AKU is present at birth, but its most severe manifestations are delayed due to the deposition of a dark-brown pigment (ochronosis) in connective tissues. The reasons for such a delayed manifestation have not been clarified yet, though several lines of evidence suggest that the metabolite accumulated in AKU sufferers (homogentisic acid) is prone to auto-oxidation and induction of oxidative stress. The clarification of the pathophysiological molecular mechanisms of AKU would allow a better understanding of the disease, help find a cure for AKU and provide a model for more common rheumatic diseases. With this aim, we have shown how proteomics and redox proteomics might successfully overcome the difficulties of studying a rare disease such as AKU and the limitations of the hitherto adopted approaches.

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Year:  2013        PMID: 24206226     DOI: 10.1586/14789450.2013.858020

Source DB:  PubMed          Journal:  Expert Rev Proteomics        ISSN: 1478-9450            Impact factor:   3.940


  4 in total

1.  A role for interleukins in ochronosis in a chondrocyte in vitro model of alkaptonuria.

Authors:  J B Mistry; D J Jackson; M Bukhari; A M Taylor
Journal:  Clin Rheumatol       Date:  2015-10-16       Impact factor: 2.980

Review 2.  Amyloidosis in alkaptonuria.

Authors:  Lia Millucci; Daniela Braconi; Giulia Bernardini; Pietro Lupetti; Josef Rovensky; Lakshminaryan Ranganath; Annalisa Santucci
Journal:  J Inherit Metab Dis       Date:  2015-04-14       Impact factor: 4.982

3.  Chondroptosis in alkaptonuric cartilage.

Authors:  Lia Millucci; Giovanna Giorgetti; Cecilia Viti; Lorenzo Ghezzi; Silvia Gambassi; Daniela Braconi; Barbara Marzocchi; Alessandro Paffetti; Pietro Lupetti; Giulia Bernardini; Maurizio Orlandini; Annalisa Santucci
Journal:  J Cell Physiol       Date:  2015-05       Impact factor: 6.384

4.  Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease.

Authors:  Ottavia Spiga; Vittoria Cicaloni; Cosimo Fiorini; Alfonso Trezza; Anna Visibelli; Lia Millucci; Giulia Bernardini; Andrea Bernini; Barbara Marzocchi; Daniela Braconi; Filippo Prischi; Annalisa Santucci
Journal:  Orphanet J Rare Dis       Date:  2020-02-12       Impact factor: 4.123

  4 in total

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