ER stress has emerged as a signaling platform underlying the pathogenesis of various kidney diseases. Thus, there is an urgent need to develop ER stress biomarkers in the incipient stages of ER stress–mediated kidney disease, when a kidney biopsy is not yet clinically indicated, for early therapeutic intervention. Cysteine-rich with EGF-like domains 2 (CRELD2) is a newly identified protein that is induced and secreted under ER stress. For the first time to our knowledge, we demonstrate that CRELD2 can serve as a sensitive urinary biomarker for detecting ER stress in podocytes or renal tubular cells in murine models of podocyte ER stress–induced nephrotic syndrome and tunicamycin- or ischemia-reperfusion–induced acute kidney injury (AKI), respectively. Most importantly, urinary CRELD2 elevation occurs in patients with autosomal dominant tubulointerstitial kidney disease caused by UMOD mutations, a prototypical tubular ER stress disease. In addition, in pediatric patients undergoing cardiac surgery, detectable urine levels of CRELD2 within postoperative 6 hours strongly associate with severe AKI after surgery. In conclusion, our study has identified CRELD2 as a potentially novel urinary ER stress biomarker with potential utility in early diagnosis, risk stratification, treatment response monitoring, and directing of ER-targeted therapies in selected patient subgroups in the emerging era of precision nephrology.
Yeawon Kim, Sun-Ji Park, Scott R. Manson, Carlos A.F. Molina, Kendrah Kidd, Heather Thiessen-Philbrook, Rebecca J. Perry, Helen Liapis, Stanislav Kmoch, Chirag R. Parikh, Anthony J. Bleyer, Ying Maggie Chen
This article was first published December 7, 2017. Usage data is cumulative from December 2017 through March 2018.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.