Molecular correlates of renal function in kidney transplant biopsies

Abstract

The molecular changes in the parenchyma that reflect disturbances in the function of kidney transplants are unknown. We studied the relationships among histopathology, gene expression, and renal function in 146 human kidney transplant biopsies performed for clinical indications. Impaired function (estimated GFR) correlated with tubular atrophy and fibrosis but not with inflammation or rejection. Functional deterioration before biopsy correlated with inflammation and tubulitis and was greater in cases of rejection. Microarray analysis revealed a correlation between impaired renal function and altered expression of sets of transcripts consistent with tissue injury but not with those consistent with cytotoxic T cell infiltration or IFN-gamma effects. Multivariate analysis of clinical variables, histologic lesions, and transcript sets confirmed that expression of injury-related transcript sets independently correlated with renal function. Analysis of individual genes confirmed that the transcripts with the greatest positive or negative correlations with renal function were those suggestive of response to injury and parenchymal dedifferentiation not inflammation. We defined new sets of genes based on individual transcripts that correlated with renal function, and these highly correlated with the previously developed injury sets and with atrophy and fibrosis. Thus, in biopsies performed for clinical reasons, functional disturbances are reflected in transcriptome changes representing tissue injury and dedifferentiation but not the inflammatory burden.

Figure 1.

Distribution of renal function and degree of functional deterioration from baseline in the population. To assess if the range of measurements in our study population is suitable for analysis of histologic and molecular correlates of renal function, we assessed (A) the distribution of renal function (GFR) at the time of biopsy and (B) the distribution of functional deterioration from baseline (delta eGFR) in our patient population. Delta eGFR was calculated by subtracting baseline eGFR (within 4 mo before the biopsy) from eGFR at the time of biopsy. (C) We analyzed the relationship between eGFR at the time of biopsy and delta eGFR to assess how much eGFR at the time of biopsy is influenced by recent changes in eGFR.

Figure 2.

Relationship between renal function at biopsy and functional deterioration before biopsy to diagnosis of rejection or time posttransplant. We assessed whether biopsies with rejection (A) had different levels of eGFR at the time of biopsy compared with biopsies without rejection or borderline rejection or (B) differed in the degree of functional deterioration from baseline. Lines represent the mean in each group. The relationships between time posttransplant and (C) eGFR at biopsy and (D) functional deterioration from baseline are also shown. * P < 0.05.

Figure 3.

Relationship between eGFR and expression of PBTs. To assess whether there is any relationship between eGFR at the time of biopsy and molecular changes in the biopsy, we analyzed the correlation between eGFR and expression of previously defined PBTs that reflect the T cell burden (CATs), IFN-γ effects (GRITs), macrophage burden (CMATs), the tissue injury response (IRITs_D3 and IRITs_D5), and epithelial cell integrity (KTs). Expression of each transcript set within each biopsy was summarized as the geometric mean expression of all transcripts in that set to derive a gene set score. The gene set score for each biopsy is plotted against the corresponding eGFR value for that biopsy. Numbers represent Spearman rank correlation coefficients. * P < 0.05; ** P < 0.01.

Figure 4.

Single gene analysis of molecular correlates of eGFR. We analyzed the associations between eGFR at the time of biopsy and transcript changes independent of previous annotations. We identified all transcripts with a significant correlation (uncorrected P value <0.05) between their expression level and eGFR values. To consider transcripts with increased or decreased expression, we analyzed positive (i.e., decreased with low eGFR) and negative (i.e., increased with low eGFR) correlations separately. To account for possible differences in biopsies with or without rejection or between early and late biopsies, the analysis was performed separately for these subgroups in addition to an analysis across all biopsies, resulting in a total of ten transcript sets (five with positive and five with negative correlations with eGFR). We searched these transcript sets for transcripts that had previously been identified as part of a PBT. The distribution of these annotations among those transcripts correlating with eGFR is shown as a percent of total correlating transcripts (A) for those with negative correlations (increased expression with low eGFR) and (B) for those transcripts with positive correlations (decreased expression with eGFR). Numbers represent the total number of correlating transcripts in each group. The distribution of these annotations was very similar between the biopsy subgroups, indicating that the presence or absence of rejection and the time of biopsy do not have a significant impact on the relationship between transcript expression and eGFR. (C) To create gene sets with robust correlations between transcript expression and eGFR across all biopsy subgroups, we identified those transcripts that were identified in the analyses shown in Panels A and B in all subgroup analyses. The resulting gene lists then comprise a new eGFR transcript set that can be used to assess its performance in the biopsies in relationship to histologic lesions and clinical parameters.

Figure 5.

Performance of eGFR transcript sets in relationship to previously defined transcript sets and eGFR at the time of biopsy. We assessed the relationship of the eGFR transcript sets in relation to our previously identified PBTs. The most striking relationships were observed with transcript sets reflecting tissue injury (IRITs_D3) and epithelial integrity (KTs). The results are shown in Panels A and B). We assess the performance of the eGFR transcript sets in relation to the actual eGFR of the biopsy (Panels C and D). An assessment of the outliers is presented in the text.