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A Universal Strategy to Characterize Allograft Infiltrating Cells by Bioinformatics Deconvolution of Next Generation Sequencing Data.

G. Thareja,1 H. Yang,2 S. Hayat,1 C. Li,2 F. Mueller,2 C. Snopkowski,2 L. Perry,2 M. Magruder,2 J. Lee,2 D. Dadhania,2 K. Suhre,1 M. Suthanthiran,2 T. Muthukumar.2

1Weill Cornell Medicine-Qatar, Doha, Qatar
2Weill Cornell Medicine, New York

Meeting: 2017 American Transplant Congress

Abstract number: 88

Keywords: Genomics, Rejection

Session Information

Session Name: Concurrent Session: Predicting Tolerance and Rejection

Session Type: Concurrent Session

Date: Sunday, April 30, 2017

Session Time: 2:30pm-4:00pm

 Presentation Time: 3:42pm-3:54pm

Location: E351

RNA sequencing characterizes mRNA transcriptome at an unprecedented level of precision. Because organ transplantation is a unique situation of mRNAs encoded by two (donor and recipient) genomes sequenced simultaneously, we investigated the novel hypothesis that the Het/Hom ratio (ratio of heterozygous genotypes to non-reference allele homozygous genotypes), currently used to identify contamination in DNA-Seq data, is informative of allograft-infiltrating cells.

We isolated total RNA from 42 human kidney allograft biopsies from 42 recipients; 37 for-cause biopsies (acute cellular rejection-ACR=7; antibody rejection-AMR=8; plasma cell-rich acute rejection-PCRR=7, transplant glomerulopathy-TG=9 and interstitial fibrosis/tubular atrophy-IFTA=6) and 5 surveillance biopsies reported as normal (Normal=5).

We used Illumina HiSeq to generate RNA-Seq data and GATK best practice protocol for RNA-Seq variant calling using iGenome Ensembl gene annotation. We calculated Het/Hom ratios using an in-house python script. We used ESTIMATE algorithm, where a score of 1=no admixture of recipient 's graft infiltrating cells (GICs) and donor kidney parenchymal cells in the biopsy.

ACR biopsies with GICs had the highest Het/Hom ratio (median 1.29) and the Normal biopsies without the GICs had the lowest ratio (median 0.898).Het/Hom ratios were significantly associated with the Banff inflammation score (r=0.55, P<0.001) and the ESTIMATE purity score (r=0.77, P<0.0001).Our demonstration that bioinformatics deconvolution of RNA-Seq data yields Het/Hom ratios reflective of recipient cells' infiltrating human kidney allografts, offers a universal approach to monitor cellular infiltration into any type organ grafts.

CITATION INFORMATION: Thareja G, Yang H, Hayat S, Li C, Mueller F, Snopkowski C, Perry L, Magruder M, Lee J, Dadhania D, Suhre K, Suthanthiran M, Muthukumar T. A Universal Strategy to Characterize Allograft Infiltrating Cells by Bioinformatics Deconvolution of Next Generation Sequencing Data. Am J Transplant. 2017;17 (suppl 3).

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To cite this abstract in AMA style:

Thareja G, Yang H, Hayat S, Li C, Mueller F, Snopkowski C, Perry L, Magruder M, Lee J, Dadhania D, Suhre K, Suthanthiran M, Muthukumar T. A Universal Strategy to Characterize Allograft Infiltrating Cells by Bioinformatics Deconvolution of Next Generation Sequencing Data. [abstract]. Am J Transplant. 2017; 17 (suppl 3). https://atcmeetingabstracts.com/abstract/a-universal-strategy-to-characterize-allograft-infiltrating-cells-by-bioinformatics-deconvolution-of-next-generation-sequencing-data/. Accessed May 28, 2025.

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