Date Published: October 5, 2018
Publisher: Public Library of Science
Author(s): Annelore Sacreas, Joshua Y. C. Yang, Bart M. Vanaudenaerde, Tara K. Sigdel, Juliane M. Liberto, Izabella Damm, Geert M. Verleden, Robin Vos, Stijn E. Verleden, Minnie M. Sarwal, Jules Lin.
Recent studies suggest that similar injury mechanisms are in place across different solid organ transplants, resulting in the identification of a common rejection module (CRM), consisting of 11 genes that are overexpressed during acute and, to a lesser extent, chronic allograft rejection.
We wanted to evaluate the usefulness of the CRM module in identifying acute rejection (AR) and different phenotypes of chronic lung transplant rejection (CLAD), i.e., bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), using transbronchial brushings, broncho-alveolar lavage (BAL) samples, and explant tissue.
Gene expression measurements for the 11 CRM genes (CD6, TAP1, CXCL10, CXCL9, INPP5D, ISG20, LCK, NKG7, PSMB9, RUNX3, and BASP1) were performed via qRT-PCR in 14 transbronchial brushings (AR, n = 4; no AR, n = 10), 32 BAL samples (stable, n = 13; AR, n = 8; BOS, n = 9; RAS, n = 10), and 44 tissue specimens (unused donor lungs, n = 15; BOS, n = 13; RAS, n = 16). A geometric mean score was calculated to quantitate overall burden of immune injury and a new computational model was built for the most significant genes in lung transplant injury.
Acute rejection showed a significant difference in almost every gene analysed, validating previous observations from microarray analysis. RAS tissue demonstrated a higher geometric mean score (6.35) compared to donor tissue (4.09, p = 0.018). Analysis of individual CRM genes showed an increased expression of ISG20, CXCL10 and CXCL9 in RAS. In BAL samples, no differences were detected in gene expression or geometric mean scores between the various groups (stable, 5.15; AR, 5.81; BOS, 5.62; RAS, 7.31). A newly modelled 2-gene tissue CRM score did not demonstrate any difference between BOS and RAS (p>0.05). However, the model was able to discriminate RAS from BOS tissue (AUC = 0.75, 95% CI = 0.55–0.94, p = 0.025).
Transcriptional tissue analysis for CRM genes in CLAD can identify acute rejection and distinguish RAS from BOS. The immune activation in RAS seems similar to acute rejection after kidney/liver/heart transplantation.
Chronic transplant rejection remains one of the major complications following solid organ transplantation, leading to graft loss and mortality, with acute rejection being a major risk factor to develop subsequent chronic rejection . Although the mechanisms of rejection remain largely unknown, it is considered to be a chronic humoral and cell-mediated response of the recipient towards the implanted non-self donor organ, leading to irreversible tissue fibrosis, failure of the organ, and eventually graft loss . Therefore, one could assume that similar mechanisms of rejection are in place across different engrafted organs.
Non-invasive and reliable monitoring of both acute and chronic rejection is indispensable in transplant medicine. Nevertheless, there are currently few–if any–biomarkers to monitor and predict allograft rejection. The development of a system to assess messenger RNA levels in kidney transplant biopsies has enabled the diagnosis of specific disease phenotypes within renal failure and improved risk stratification [16,17]. Moreover, the system has recently been adapted to assess heart transplant specimens . The recent identification of a CRM score for rejection in microarray datasets from different types of solid tissue allografts (kidney, heart, liver, lung) and its further validation in renal allografts encouraged the idea that increased risk of allograft-injury could be predicted, maybe even before the actual damage has occurred in LTx patients [3,4,19]. A computational score across all 11 genes in kidney transplantation also predicted which patients would more likely develop chronic rejection over time in the same study.