ArticleBioinformatics and biology insights2014
Longitudinal analysis of whole blood transcriptomes to explore molecular signatures associated with acute renal allograft rejection.
Article in Bioinformatics and biology insights, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03575910 (HEARTBiT), which is not on this map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
HEARTBiT: A Novel Multi-Marker Blood Test for Management of Acute Cardiac Allograft Rejection
Who cites it
5 citing papers in PubMed.
- Bioinformatics strategies and biomarker refinement using high-throughput transcriptome data in transplantation.Frontiers in bioinformatics · 2026Article
- Virus-induced genetics revealed by multidimensional precision medicine transcriptional workflow applicable to COVID-19.Physiological genomics · 2020Article
- Longitudinal immunological characterization of the first presensitized recipient of a face transplant.JCI insight · 2017Article
- Novel multivariate methods for integration of genomics and proteomics data: applications in a kidney transplant rejection study.Omics : a journal of integrative biology · 2014Article
- Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.
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Registered trials
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