Kidney transplants fail over time. Injury due to antibodies that target cells of the blood vessels within the transplant kidney (microcirculatory endothelium) is a major reason why the transplants fail. Clinical tools to predict this injury are not available. MicroRNAs in the blood could serve as markers of injury. We propose to develop microRNA (miRNA)-based blood tests for the diagnosis of antibody-mediated microcirculatory inflammation/injury. We have carefully selected a group of 180 kidney transplant recipients at our hospital who had serum samples collected and stored in our laboratory at the time of undergoing an allograft biopsy. The AMI group (n=60) consists of 60 serum samples from 60 kidney transplant recipients with biopsy diagnosis of antibody-mediated microcirculatory inflammation. The No AMI group (n=120) consists of 120 serum samples from 120 kidney transplant recipients whose biopsy did not have such inflammation. This group consists of patients with biopsy diagnosis of acute cellular rejection, acute tubular injury, and normal allograft biopsy (n=40 each).
The Specific Aims are to: (1) Identify circulating extracellular miRNAs in serum of kidney graft recipients that are diagnostic of AMI in the kidney allograft, (2) Develop a statistical model (signature) diagnostic of AMI in the kidney allograft, (3) Validate the diagnostic model in serum samples obtained from an independent external cohort of kidney transplant recipients. We will use high throughput barcoded deep sequencing to characterize the serum miRNA transcriptome. We will use miRNA-specific qPCR assay to accurately quantify selected individual miRNAs, for their quick translation to the clinic. We will compare the serum miRNA transcriptome from the two groups (AMI vs. No AMI) and identify miRNAs that are different between the two. We will then develop PCR assays to accurately quantify the selected miRNAs. We will develop statistical models consisting of combination of miRNAs that best predict the diagnosis. We will statistically determine the clinical benefit of using such a model fr diagnosing AMI. To ensure that the statistical model works in different groups of patients, we will do PCR assays and test the statistical model (signature) in another group of 105 kidney transplant recipients (35 AMI and 70 No AMI) from a different hospital.

Public Health Relevance

Nearly half of all kidney transplants fail by 10 years, mainly as a result of injury due to antibodies. Currently, it is not possible to definitely say, without an invasive needle biopsy, whether there is injury due to antibodies. We will use new technologies to discover novel genes in the blood of kidney transplant patients and develop simple tests to identify, without biopsy, whether there is injury due to antibodies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Small Research Grants (R03)
Project #
1R03DK105270-01A1
Application #
9035912
Study Section
Kidney, Urologic and Hematologic Diseases D Subcommittee (DDK)
Program Officer
Rankin, Tracy L
Project Start
2016-02-01
Project End
2018-01-31
Budget Start
2016-02-01
Budget End
2017-01-31
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Weill Medical College of Cornell University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
060217502
City
New York
State
NY
Country
United States
Zip Code
10065
Thareja, Gaurav; Yang, Hua; Hayat, Shahina et al. (2018) Single nucleotide variant counts computed from RNA sequencing and cellular traffic into human kidney allografts. Am J Transplant 18:2429-2442