Commonly available clinical parameters fail to predict early acute cellular rejection (EAR, occurring within 6 months after transplant), a major risk factor for graft loss after kidney transplantation. We performed whole-blood RNA sequencing at the time of transplant in 235 kidney transplant recipients enrolled in a prospective cohort study (Genomics of Chronic Allograft Rejection [GoCAR]) and evaluated the relationship of pretransplant transcriptomic profiles with EAR. EAR was associated with downregulation of NK and CD8+ T cell gene signatures in pretransplant blood. We identified a 23-gene set that predicted EAR in the discovery (n = 81, and AUC = 0.80) and validation (n = 74, and AUC = 0.74) sets. Exclusion of recipients with 5 or 6 HLA donor mismatches increased the AUC to 0.89. The risk score derived from the gene set was also significantly associated with acute cellular rejection after 6 months, antibody-mediated rejection and/or de novo donor-specific antibodies, and graft loss in a cohort of 154 patients, combining the validation set and additional GoCAR patients with surveillance biopsies between 6 and 24 months (n = 80) posttransplant. This 23-gene set is a potentially important new tool for determination of the recipient’s immunological risk before kidney transplantation, and facilitation of an individualized approach to immunosuppressive therapy.
Weijia Zhang, Zhengzi Yi, Chengguo Wei, Karen L. Keung, Zeguo Sun, Caixia Xi, Christopher Woytovich, Samira Farouk, Lorenzo Gallon, Madhav C. Menon, Ciara Magee, Nader Najafian, Milagros D. Samaniego, Arjang Djamali, Stephen I. Alexander, Ivy A. Rosales, Rex Neal Smith, Philip J. O’Connell, Robert Colvin, Paolo Cravedi, Barbara Murphy
Usage data is cumulative from June 2019 through June 2020.
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.