2006 ◽  
Vol 5 (11) ◽  
pp. 3038-3047 ◽  
Author(s):  
Heidi Hoi-Yee Ngai ◽  
Wai-Hung Sit ◽  
Ping-Ping Jiang ◽  
Ruo-Jun Xu ◽  
Jennifer Man-Fan Wan ◽  
...  

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2058 ◽  
Author(s):  
Xundou Li ◽  
Youhe Gao

Urine is a very good source for biomarker discovery because it accumulates changes in the body. However, a major challenge in urinary biomarker discovery is the fact that the urinary proteome is influenced by various elements. To circumvent these problems, simpler systems, such as animal models, can be used to establish associations between physiological or pathological conditions and alterations in the urinary proteome. In this study, the urinary proteomes of young (two months old) and old rats (20 months old; nine in each group) were analyzed using LC-MS/MS and quantified using the Progenesis LC-MS software. A total of 371 proteins were identified, 194 of which were shared between the young and old rats. Based on criteria of a fold change ≥2,P< 0.05 and identification in each rat of the high-abundance group, 33 proteins were found to be changed (15 increased and 18 decreased in old rats). By adding a more stringent standard (protein spectral counts from every rat in the higher group greater than those in the lower group), eight proteins showed consistent changes in all rats of the groups; two of these proteins are also altered in the urinary proteome of aging humans. However, no shared proteins between our results and the previous aging plasma proteome were identified. Twenty of the 33 (60%) altered proteins have been reported to be disease biomarkers, suggesting that aging may share similar urinary changes with some diseases. The 33 proteins corresponded to 28 human orthologs which, according to the Human Protein Atlas, are strongly expressed in the kidney, intestine, cerebellum and lung. Therefore, the urinary proteome may reflect aging conditions in these organs.


PROTEOMICS ◽  
2012 ◽  
Vol 12 (22) ◽  
pp. 3403-3406 ◽  
Author(s):  
Abdulqader A. Alhaider ◽  
Nervana Bayoumy ◽  
Evelyn Argo ◽  
Abdel G. M. A. Gader ◽  
David A. Stead

2008 ◽  
Vol 2 (7-8) ◽  
pp. 964-973 ◽  
Author(s):  
Joshua J. Coon ◽  
Petra Zürbig ◽  
Mohammed Dakna ◽  
Anna F. Dominiczak ◽  
Stéphane Decramer ◽  
...  

2014 ◽  
Vol 13 (11) ◽  
pp. 5206-5217 ◽  
Author(s):  
Min Jueng Kang ◽  
Yune-Jung Park ◽  
Sungyong You ◽  
Seung-Ah Yoo ◽  
Susanna Choi ◽  
...  

2010 ◽  
Vol 11 (1) ◽  
Author(s):  
Sten Andersen ◽  
Harald Mischak ◽  
Petra Zürbig ◽  
Hans-Henrik Parving ◽  
Peter Rossing

2019 ◽  
Vol 9 (1) ◽  
pp. 53-65 ◽  
Author(s):  
Yumi Watanabe ◽  
Yoshitoshi Hirao ◽  
Kensaku Kasuga ◽  
Takayoshi Tokutake ◽  
Yuka Semizu ◽  
...  

Background/Aims: The identification of predictive biomarkers for Alzheimer’s disease (AD) from urine would aid in screening for the disease, but information about biological and pathophysiological changes in the urine of AD patients is limited. This study aimed to explore the comprehensive profile and molecular network relations of urinary proteins in AD patients. Methods: Urine samples collected from 18 AD patients and 18 age- and sex-matched cognitively normal controls were analyzed by mass spectrometry and semiquantified with the normalized spectral index method. Bioinformatics analyses were performed on proteins which significantly increased by more than 2-fold or decreased by less than 0.5-fold compared to the control (p < 0.05) using DAVID bioinformatics resources and KeyMolnet software. Results: The levels of 109 proteins significantly differed between AD patients and controls. Among these, annotation clusters related to lysosomes, complement activation, and gluconeogenesis were significantly enriched. The molecular relation networks derived from these proteins were mainly associated with pathways of lipoprotein metabolism, heat shock protein 90 signaling, matrix metalloproteinase signaling, and redox regulation by thioredoxin. Conclusion: Our findings suggest that changes in the urinary proteome of AD patients reflect systemic changes related to AD pathophysiology.


2019 ◽  
Vol 146 (8) ◽  
pp. 2315-2325 ◽  
Author(s):  
Ashley Di Meo ◽  
Ihor Batruch ◽  
Marshall D. Brown ◽  
Chuance Yang ◽  
Antonio Finelli ◽  
...  

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