2C-Cas9: a versatile tool for clonal analysis of gene function

CRISPR/Cas9-mediated targeted mutagenesis allows efficient generation of loss-of-function alleles in zebrafish. To date this technology has been primarily used to generate genetic knockout animals. Nevertheless, the study of the function of certain loci might require tight spatiotemporal control of gene inactivation. Here, we show that tissue-specific gene disruption can be achieved by driving Cas9 expression with the Gal4/UAS system. Furthermore, by combining the Gal4/UAS and Cre/loxP systems, we establish a versatile tool to genetically label mutant cell clones, enabling their phenotypic analysis. Our technique has the potential to be applied to diverse model organisms, enabling tissue-specific loss-of-function and phenotypic characterization of live and fixed tissues.

Performance of gene-expression profiling test score variability to predict future clinical events in heart transplant recipients.

BACKGROUND A single non-invasive gene expression profiling (GEP) test (AlloMap®) is often used to discriminate if a heart transplant recipient is at a low risk of acute cellular rejection at time of testing. In a randomized trial, use of the test (a GEP score from 0-40) has been shown to be non-inferior to a routine endomyocardial biopsy for surveillance after heart transplantation in selected low-risk patients with respect to clinical outcomes. Recently, it was suggested that the within-patient variability of consecutive GEP scores may be used to independently predict future clinical events; however, future studies were recommended. Here we performed an analysis of an independent patient population to determine the prognostic utility of within-patient variability of GEP scores in predicting future clinical events. METHODS We defined the GEP score variability as the standard deviation of four GEP scores collected ≥315 days post-transplantation. Of the 737 patients from the Cardiac Allograft Rejection Gene Expression Observational (CARGO) II trial, 36 were assigned to the composite event group (death, re-transplantation or graft failure ≥315 days post-transplantation and within 3 years of the final GEP test) and 55 were assigned to the control group (non-event patients). In this case-controlled study, the performance of GEP score variability to predict future events was evaluated by the area under the receiver operator characteristics curve (AUC ROC). The negative predictive values (NPV) and positive predictive values (PPV) including 95 % confidence intervals (CI) of GEP score variability were calculated. RESULTS The estimated prevalence of events was 17 %. Events occurred at a median of 391 (inter-quartile range 376) days after the final GEP test. The GEP variability AUC ROC for the prediction of a composite event was 0.72 (95 % CI 0.6-0.8). The NPV for GEP score variability of 0.6 was 97 % (95 % CI 91.4-100.0); the PPV for GEP score variability of 1.5 was 35.4 % (95 % CI 13.5-75.8). CONCLUSION In heart transplant recipients, a GEP score variability may be used to predict the probability that a composite event will occur within 3 years after the last GEP score. TRIAL REGISTRATION Clinicaltrials.gov identifier NCT00761787.

Clinical usefulness of gene-expression profile to rule out acute rejection after heart transplantation: CARGO II.

AIMS A non-invasive gene-expression profiling (GEP) test for rejection surveillance of heart transplant recipients originated in the USA. A European-based study, Cardiac Allograft Rejection Gene Expression Observational II Study (CARGO II), was conducted to further clinically validate the GEP test performance. METHODS AND RESULTS Blood samples for GEP testing (AlloMap(®), CareDx, Brisbane, CA, USA) were collected during post-transplant surveillance. The reference standard for rejection status was based on histopathology grading of tissue from endomyocardial biopsy. The area under the receiver operating characteristic curve (AUC-ROC), negative (NPVs), and positive predictive values (PPVs) for the GEP scores (range 0-39) were computed. Considering the GEP score of 34 as a cut-off (>6 months post-transplantation), 95.5% (381/399) of GEP tests were true negatives, 4.5% (18/399) were false negatives, 10.2% (6/59) were true positives, and 89.8% (53/59) were false positives. Based on 938 paired biopsies, the GEP test score AUC-ROC for distinguishing ≥3A rejection was 0.70 and 0.69 for ≥2-6 and >6 months post-transplantation, respectively. Depending on the chosen threshold score, the NPV and PPV range from 98.1 to 100% and 2.0 to 4.7%, respectively. CONCLUSION For ≥2-6 and >6 months post-transplantation, CARGO II GEP score performance (AUC-ROC = 0.70 and 0.69) is similar to the CARGO study results (AUC-ROC = 0.71 and 0.67). The low prevalence of ACR contributes to the high NPV and limited PPV of GEP testing. The choice of threshold score for practical use of GEP testing should consider overall clinical assessment of the patient's baseline risk for rejection.