Drawing networks of rejection - a systems biological approach to the identification of candidate genes in heart transplantation.

Technological development led to an increased interest in systems biological approaches to characterize disease mechanisms and candidate genes relevant to specific diseases. We suggested that the human peripheral blood mononuclear cells (PBMC) network can be delineated by cellular reconstruction to guide identification of candidate genes. Based on 285 microarrays (7370 genes) from 98 heart transplant patients enrolled in the Cardiac Allograft Rejection Gene Expression Observational study, we used an information-theoretic, reverse-engineering algorithm called ARACNe (algorithm for the reconstruction of accurate cellular networks) and chromatin immunoprecipitation assay to reconstruct and validate a putative gene PBMC interaction network. We focused our analysis on transcription factor (TF) genes and developed a priority score to incorporate aspects of network dynamics and information from published literature to supervise gene discovery. ARACNe generated a cellular network and predicted interactions for each TF during rejection and quiescence. Genes ranked highest by priority score included those related to apoptosis, humoural and cellular immune response such as GA binding protein transcription factor (GABP), nuclear factor of κ light polypeptide gene enhancer in B-cells (NFκB), Fas (TNFRSF6)-associated via death domain (FADD) and c-AMP response element binding protein. We used the TF CREB to validate our network. ARACNe predicted 29 putative first-neighbour genes of CREB. Eleven of these (37%) were previously reported. Out of the 18 unknown predicted interactions, 14 primers were identified and 11 could be immunoprecipitated (78.6%). Overall, 75% (n= 22) inferred CREB targets were validated, a significantly higher fraction than randomly expected (P < 0.001, Fisher's exact test). Our results confirm the accuracy of ARACNe to reconstruct the PBMC transcriptional network and show the utility of systems biological approaches to identify possible molecular targets and biomarkers.

The economic implications of noninvasive molecular testing for cardiac allograft rejection.

Endomyocardial biopsy is the mainstay for monitoring cardiac allograft rejection. A noninvasive strategy--peripheral blood gene expression profiling of circulating leukocytes--is an alternative with proven benefits, but unclear economic implications. Financial data were obtained from five cardiac transplant centers. An economic evaluation was conducted to compare the costs of outpatient biopsy with those of a noninvasive approach to monitoring cardiac allograft rejection. Hospital outpatient biopsy costs averaged 3297 US dollars, excluding reimbursement for professional fees. Costs to Medicare and private payers averaged 3581 US dollars and 4140 US dollars, respectively. A noninvasive monitoring test can reduce biopsy utilization. The savings to health care payers in the United States can be conservatively estimated at approximately 12.0 million US dollars annually. Molecular testing using gene expression profiling of peripheral circulating leukocytes is a new technology that offers physicians a noninvasive, less expensive alternative to endomyocardial biopsy for monitoring allograft rejection in cardiac transplant patients.

The heart-allocation simulation model: a tool for comparison of transplantation allocation policies.

BACKGROUND: Numerous studies have investigated prognostic factors for the survival of transplant candidates waiting for a donor organ, but little is known about the impact of allocation policies on waiting list outcome. Simulation models would allow a comparison of different policies for allocating donor hearts on pretransplant outcome. METHODS: A model was built for the Eurotransplant waiting list for heart transplantation. Survival and delisting distributions were estimated from the Eurotransplant transplant candidate inflow between 1995 and 2000 (n=7,142). Other characteristics were obtained directly from the transplant candidate inflow of 1999 and 2000 (n=2,097) and the donor organs of 1998 and 1999 (n=1,520). Overall and subgroup waiting list mortality were estimated for allocation policies differing by ABO blood group, border, and clinical profile rules. RESULTS: The model estimated that international organ exchange reduces waiting list mortality in the different countries by 1.9% to 12.4%. An allocation policy incorporating the initial clinical profile of the transplant candidates further reduced waiting list mortality by 1.7%. Changing ABO rules toward identical matching yielded a slightly more equitable survival for the different groups, without an overall effect on mortality. The best possible allocation policy is the policy where organs are allocated to patients that are at highest risk of dying, and withholding organs from patients that would eventually delist because of improvement. CONCLUSIONS: Patients benefit from international organ exchange and by a heart allocation scheme based on clinical profiles. Timely delisting of patients who are-temporarily-too well for transplantation is the best waiting list policy.