Single nucleotide variant counts computed from RNA sequencing and cellular traffic into human kidney allografts

Abstract

Advances in bioinformatics allow identification of single nucleotide polymorphisms (variants) from RNA sequence data. In an allograft biopsy, 2 genomes contribute to the RNA pool, 1 from the donor organ and the other from the infiltrating recipient's cells. We hypothesize that imbalances in genetic variants of RNA sequence data of kidney allograft biopsies provide an objective measure of cellular infiltration of the allograft. We performed mRNA sequencing of 40 kidney allograft biopsies, selected to represent a comprehensive range of diagnostic categories. We analyzed the sequencing reads of these biopsies and of 462 lymphoblastoid cell lines from the 1000 Genomes Project, for RNA variants. The ratio of heterozygous to nonreference genome homozygous variants (Het/Hom ratio) on all autosomes was determined for each sample, and the estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) score was computed as a complementary estimate of the degree of cellular infiltration into biopsies. The Het/Hom ratios (P = .02) and the ESTIMATE scores (P < .001) were associated with the biopsy diagnosis. Both measures correlated significantly (r = .67, P < .0001), even though the Het/Hom ratio is based on mRNA sequence variation, while the ESTIMATE score uses mRNA expression. Het/Hom ratio and the ESTIMATE score may offer unbiased and quantitative parameters for characterizing cellular traffic into human kidney allografts.

Keywords: genomics; kidney transplantation/nephrology; molecular biology: mRNA/mRNA expression; monitoring: immune; translational research/science.

Figure 1. Workflow of the study

Forty allograft biopsy specimens obtained from 40 unique kidney allograft recipients were RNA sequenced. For each sample: (i) Biopsy infiltration score was derived as the sum of Banff acute scores, reported by the transport pathologist; (ii) A purity score was computed using the aligned RNA sequencing reads as input in ESTIMATE software; and (iii) Het/Hom ratio was computed after variant calling was done from the RNA sequencing data. Throughout the manuscript, the ratio of heterozygous to non-reference genome homozygous variants on all autosomes, for each sample, is called as the Het/Hom ratio, and the ESTIMATE score and 1-Purity score are used interchangeably.

Figure 2. Histopathological characteristics of the 40 kidney allograft biopsies

The histopathological characteristics of the 40 kidney allograft biopsies obtained from 40 kidney allograft recipients (“Sample #” 1 through 40) are shown. Biopsy tissue sections were stained with hematoxylin and eosin, periodic acid Schiff, and Masson trichrome for light microscopic evaluation. Staining for polyoma virus was done using affinity-purified and agarose-conjugated IgG_2a mouse monoclonal antibody (Calbiochem, San Diego, CA) that recognizes a 94-kDa SV40 large T antigen. Indirect immunofluorescence for complement factor 4 degradation (C4d) product was done on cryosections using a monoclonal anti-C4d antibody (Quidel, Santa Clara, CA). These biopsies were categorized using the Banff 2017 update of the Banff ‘97 classification. The Banff diagnostic categories include acute T-cell mediated rejection (“Acute TCMR, Sample # 1 through 11), active antibody mediated rejection (“Active ABMR, 12 through 18), chronic active antibody mediated rejection (19 through 29), interstitial fibrosis and tubular atrophy (“IFTA”, 30 through 35) and Normal (36 through 40). The median number of glomeruli per biopsy sample was 16 (range 13–24). Colors represent Banff scores 0 to 3. Banff acute scores include the “t” (tubulitis) score, “i” (interstitial inflammation) score, “g” (glomerulitis) score, “ptc” (peritubular capillary inflammation) score and “v” (vascular inflammation). Banff chronic scores include the “ci” (interstitial fibrosis) score, “ct” (tubular atrophy) score, “cg” (chronic glomerulopathy) score, “cv” (chronic vascular lesions) score and “ah” (arteriolar hyaline thickening) score). Also shown in the figure is the staining for complement factor 4d (C4d) in the peritubular capillaries. Sample 13, categorized as active ABMR and samples 20 and 29, categorized as chronic active ABMR, fulfilled the criteria for borderline changes suspicious for acute TCMR as well. Sample 19, categorized as chronic active ABMR, fulfilled the criteria for acute TCMR as well.

Figure 3. Het/Hom ratio by population derived from mRNA sequencing of 462 lymphoblastoid cell lines

In order to assess the variability of Het/Hom ratio at a population level for “normal” cells, we used the gEUVADIS (Genetic European Variation in Health and Disease), RNA sequencing project data. This is a European medical sequencing consortium with a publicly available database of mRNA and small RNA sequencing from 462 lymphoblastoid cell line samples from five populations of the 1000 Genomes Project. The five populations depicted in the above panels are: (i) CEU- Utah, USA, residents with Northern and Western European ancestry (N=92), (ii) FIN- Finnish in Finland (N=95), (iii) GBR- British in England and Scotland (N=94), (iv) TSI- Tuscany in Italy (N=93), and (v) YRI- Yoruba in Ibadan, Nigeria (N=89). The figure depicts the box plot of Het/Hom ratio derived from the RNA sequencing data of the five populations. The median Het/Hom ratio was 0.770 for CEU; 0.770 for FIN; 0.773 for GBR; 0.776 for TSI and 0.866 for YRI. The difference in the Het/Hom ratio among the groups was statistically significant (P<0.0001, Kruskal -Wallis test). By Dunn’s test, the difference in the Het/Hom ratio between YRI and each of the other four groups was statistically significant (P<0.05). None of the other pair-wise comparisons were statistically significant (all P>0.05).

Figure 4. Het/Hom ratio by diagnostic categories of the 40 allograft biopsies

Boxplot and individual data points of the Het/Hom ratios computed from RNA sequencing data of 40 kidney allograft biopsy samples, stratified by kidney allograft biopsy diagnosis category. Acute TCMR, acute T-cell mediated rejection, Active ABMR, active antibody-mediated rejection, Chronic active ABMR, chronic and active antibody-mediated rejection, IFTA, interstitial fibrosis and tubular atrophy or normal allograft biopsy (Normal). Biopsies were categorized using the Banff 2017 update of the Banff ‘97 classification scheme. The median Het/Hom ratio was 1.149 in acute TCMR, 0.997 in active ABMR, 1.020 in chronic active ABMR, 0.954 in IFTA and 0.898 in Normal. The difference in Het/Hom ratio among the diagnostic categories was significant (P=0.02, Kruskal-Wallis test). By Dunn’s test, difference in Het/Hom ratio between acute TCMR and IFTA (P,0.05), and between acute TCMR and Normal (P<0.05) was statistically significant. None of the other pair-wise comparisons were statistically significant (all P>0.05).

Figure 5. Association between the Het/Hom ratio and the biopsy infiltration score of the 40 allograft biopsies

Banff acute scores include the “t” [tubulitis] score, “i” [interstitial inflammation] score, “g” [glomerulitis] score, “ptc” [peritubular capillary inflammation] score and “v” [vascular inflammation] score. Each score ranges from 0 through 3. For each kidney allograft biopsy, we summed all the Banff acute scores and created a single numerical value, called the biopsy infiltration score, that ranges from 0 to 15. This figure depicts the infiltration score on the y-axis and Het/Hom ratio on the x-axis, for the 40 kidney allograft biopsy samples. The association between the Het/Hom ratio and the biopsy infiltration score was statistically significant (r=0.62, P<0.0001, Spearman rank-order correlation).

Figure 6. ESTIMATE score by diagnostic categories of the 40 allograft biopsies

The ESTIMATE score was derived from the RNA sequencing data using the ESTIMATE algorithm. We used the raw sequencing read counts (as generated by STAR aligner in quant mode) as input for the ESTIMATE software (25), a tool for predicting tumor purity and the presence of infiltrating stromal/immune cells in tumor tissues using gene expression data. The ESTIMATE algorithm combines gene expression of 141 stromal and 141 immune genes and performs single-sample gene set-enrichment analysis for each set of selected genes, calculate a stromal score and an immune score to predict the level of infiltrating stromal and immune cells, respectively, and combines these individual scores to provide a final score called the ESTIMATE score which is converted to a tumor purity score, ranging from 0 to 1, with 1 denoting a highly pure sample. For simplicity, we represent ESTIAMTE score as 1-Purity score, so that a higher score represents increasing degrees of immune cell infiltration in the biopsy specimens. In the y-axis, the 1-Purity score ranges from 0 to 1, where 0 denotes a highly pure sample. Biopsies were categorized using the Banff 2017 update of the Banff ‘97 classification. The median 1-Purity score was 0.394 in acute TCMR, 0.307 in active ABMR, 0.278 in chronic active ABMR, 0.170 in IFTA and 0.098 in Normal. The difference in the 1-Purity score among the groups was statistically significant (P<0.001, Kruskal-Wallis test). By Dunn’s test, difference in ESTIMATE score between acute TCMR and IFTA (P<0.01), and between acute TCMR and Normal (P<0.001) was statistically significant. None of the other pair-wise comparisons were statistically significant (all P>0.05).

Figure 7. Association between Het/Hom ratio and ESTIMATE score of the 40 allograft biopsies

The Het/Hom ratio and the ESTIMATE score was derived from the RNA sequencing data. We show the ESTIAMTE score as a 1-Purity score. The association between Het/Hom ratio and 1-Purity score was statistically significant (r=0.67, P<0.0001, Spearman rank-order correlation).

Figure 8. Lack of association between the time from transplantation to biopsy and Het/Hom ratio, ESTIMATE score, or the biopsy infiltration score

Scatterplot depicts the relationship between the time from transplantation to biopsy and Het/Hom ratio (Panel A), ESTIMATE score (1-Purity) (Panel B), and biopsy infiltration score (Panel C) for all 40 biopsies. Within each diagnostic category, there was no significant correlation between the time from transplantation to biopsy and Het/Hom ratio, ESTIMATE score, or the biopsy infiltration score (P>0.5, Spearman rank-order correlation).