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Proceedings of the National Academy of Sciences of the United States of America logoLink to Proceedings of the National Academy of Sciences of the United States of America
. 2009 Mar 16;106(13):5330–5335. doi: 10.1073/pnas.0813121106

MicroRNA expression profiles predictive of human renal allograft status

Dany Anglicheau a,b,c, Vijay K Sharma a, Ruchuang Ding a, Aurélie Hummel a, Catherine Snopkowski a, Darshana Dadhania a,d, Surya V Seshan e, Manikkam Suthanthiran a,d,1
PMCID: PMC2663998  PMID: 19289845

Abstract

Immune rejection of organ transplants is a life-threatening complication and is exemplified by alterations in the expression of protein-encoding genes. Because microRNAs (miRNAs) regulate the expression of genes implicated in adaptive immunity, we investigated whether acute rejection (AR) is associated with alterations in miRNA expression within allografts and whether expression profiles are diagnostic of AR and predict allograft function. Seven of 33 renal allograft biopsies (12 AR and 21 normal) were profiled using microfluidic cards containing 365 mature human miRNAs (training set), and a subset of differentially expressed miRNAs were quantified in the remaining 26 allograft biopsies (validation set). We found a strong association between intragraft expression of miRNAs and messenger RNAs (mRNAs), and that AR, and renal allograft function, could be predicted with a high level of precision using intragraft levels of miRNAs. Our investigation of miRNA expression in normal human peripheral blood mononuclear cells (PBMCs) showed that miRNAs (miR-142–5p, -155, and -223) overexpressed in AR biopsies are highly expressed in PBMCs, and that stimulation with the mitogen phytohaemagglutinin results in an increase in the abundance of miR-155 and a decrease in miR-223 and let-7c. Quantification of miRNAs in primary cultures of human renal epithelial cells (HRECs) showed that miR-30a-3p, -10b, and let-7c are highly expressed in HRECs, and that stimulation results in a decreased expression of miR-30a-3p. Our studies, in addition to suggesting a cellular basis for the altered intragraft expression of miRNAs, propose that miRNA expression patterns may serve as biomarkers of human renal allograft status.

Keywords: acute rejection, biomarkers, miRNA, renal transplantation, mRNA

An important breakthrough in biology in recent years is the discovery of microRNAs (miRNAs) (1). miRNAs are small (≈19–25 nucleotides), naturally occurring, evolutionarily conserved, abundant, and noncoding RNAs that regulate gene expression, primarily by translational repression or by messenger RNA (mRNA) degradation (2). The first miRNA was described in 1993 (3, 4) and hundreds of miRNAs have now been cloned and thousands predicted bioinformatically (5). It has been shown that a single miRNA is directly responsible for the repression of hundreds of proteins and regulating the levels of thousands of others (6). Many diverse biological processes such as development (7), cell proliferation, differentiation, apoptosis, fat metabolism, and oncogenesis (8) are reported to be regulated by miRNAs. Emerging data suggest a critical role for miRNAs in the regulation of immune cell development and in the modulation of innate and adaptive immune responses (9–12).

Transplantation of organs has progressed from a risky experimental therapy to a safe and life-saving treatment modality in a relatively short span of 5 decades (13–15). However, transplant recipients require life-long treatment with nonspecific, toxic, and multiple immunosuppressive drugs and are ever under the threat of losing their allografts because of immune rejection of the transplanted organ. Immune rejection results from the activation of recipient's lymphocytes following recognition of donor antigens, generation of effector T lymphocytes, alloantibody production, and graft infiltration by alloreactive cells. A better understanding of the mechanisms of allograft rejection may lead to the identification of new targets for therapeutic intervention and to the development of safer treatment modalities.

Earlier studies of allograft rejection, including those carried out in our laboratory, have demonstrated striking alterations in the expression of multiple genes during acute rejection (AR) (16–21). We reported that acute rejection of human renal allografts is exemplified by increased expression of mRNAs encoding cytotoxic attack proteins granzyme B and perforin (18, 19), mRNAs for key chemokines and chemokine receptors such as chemokine gamma inducible protein of 10 kD (IP-10) and its receptor CXCR3 (20), and mRNA for FOXP3, a specification and functional factor for regulatory T cells (21). mRNA expression profiling studies using microarrays have identified that acute rejection is associated with major perturbations in the expression of multiple genes including those implicated in cell cycling, metabolism, and immunity (16, 17).

A critical and unresolved question is the mechanistic basis for the major perturbations in gene expression during an episode of acute rejection. miRNAs can regulate the expression of a vast array of genes including those involved in adaptive immunity. Therefore, we investigated whether acute rejection is associated with significant alterations in the expression of miRNAs within the allografts, and whether intragraft miRNA expression patterns are diagnostic of acute rejection and predict renal allograft function.

We found that a subset of 17 mature miRNAs differentiated, acutely rejecting allografts from normal allografts, and that acute rejection, and renal allograft function, could be predicted with a high level of precision using intragraft levels of miRNAs. We also found a strong positive association between intragraft expression of miRNAs and mRNAs. Our investigation of miRNA expression in normal human peripheral blood mononuclear cells (PBMCs) showed that miRNAs (miR-142–5p, -155, and -223) overexpressed in AR biopsies are highly expressed in PBMCs, and that activation results in an increase in the abundance of miR-155 and a decrease in miR-223 and let-7c. Quantification of miRNAs in primary cultures of human renal epithelial cells (HRECs) showed that miR-30a-3p, -10b, and let-7c are highly expressed in HRECs compared to PBMCs, and that stimulation results in a decreased expression of miR-30a-3p.

Results

MicroRNA Expression Profiles of Human Renal Allografts.

We first determined global miRNA expression profiles of human renal allografts using microfluidic cards containing TaqMan primers and probes for 365 mature human miRNAs. The characteristics of patients whose renal allograft biopsies were studied for global miRNA expression patterns (training set), or for a subset of differentially expressed miRNAs (validation set), are summarized in supporting information (SI) Table S1.

Among the 365 mature human miRNAs analyzed in the training set (4 normal and 3 AR biopsies), 174 ± 7 miRNAs (48%) were expressed in each biopsy sample (174 ± 10 miRNAs in the AR biopsies vs. 174 ± 4 miRNAs in the normal allograft biopsies). Unsupervised hierarchical clustering of miRNA expression patterns correctly classified the normal allograft biopsies and the AR biopsies (Fig. 1A).

Fig. 1.

Fig. 1.

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Unsupervised hierarchical clustering and principal component analysis of miRNA expression profiles differentiate acute rejection biopsies from normal allograft biopsies of human renal allografts. (A) MicroRNA (miRNA) expression patterns of 7 human kidney allograft biopsies [3 showing histological features of acute rejection (AR) and 4 with normal allograft biopsy results (N)] were examined using microfluidic cards containing TaqMan probes and primer pairs for 365 human mature miRNAs. A total of 174 ± 7 miRNAs were expressed at a significant level (i.e., CT < 35) in all samples. Gender, age, ethnicity, type of transplantation, and time from transplantation to biopsy were as follow: AR1 (Male, Black, 52 years, living donor, 161 days), AR2 (male, White, 32 years, living donor, 119 days), AR3 (Female, White, 48 years, deceased donor, 31 days), N1 (female, Black, 40 years, living donor, 203 days), N2 (Male, Indian, 50 years, living donor, 191 days), N3 (Male, Black, 31 years, living donor, 196 days), and N4 (Male, Asian, 51 years, deceased donor, 88 days). The biopsies were grouped by unsupervised hierarchical clustering on the basis of similarity in expression patterns. The degree of relatedness of the expression patterns in biopsy samples is represented by the dendrogram at the top of the panel. Branch lengths represent the degree of similarity between individual samples (Top) or miRNA (Left). Two major clusters (Top) accurately divided AR biopsies from normal allograft biopsies. Each column corresponds to the expression profile of a renal allograft biopsy, and each row corresponds to a miRNA. The color in each cell reflects the level of expression of the corresponding miRNA in the corresponding sample, relative to its mean level of expression in the entire set of biopsy samples. The increasing intensities of red mean that a specific miRNA has a higher expression in the given sample and the increasing intensities of green mean that this miRNA has a lower expression. The scale (Bottom Right) reflects miRNA abundance ratio in a given sample relative to the mean level for all samples. (B) Principal component analysis of 7 kidney allograft biopsies based on the expression of 174 small RNAs significantly expressed (i.e., CT < 35) in all of the samples. PCA is a bilinear decomposition method designed to reduce the dimensionality of multivariable systems and used for overviewing clusters within multivariate data. It transforms a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). The first PC accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. PCA showed evident clustering and confirmed the separation of AR samples from normal allograft biopsies. Samples were accurately grouped by PC1, which explained 45.91% of the overall miRNA expression variability, whereas PC2 explained 21.48% of variability and did not classify the samples according to their diagnosis.

The clear separation of AR biopsies from normal allograft biopsies was further confirmed by displaying the relationships among miRNA expression patterns using principal component analysis (PCA) (Fig. 1B). Samples were accurately grouped by PC1, which explained 46% of the overall miRNA expression variability, whereas PC2 explained 21% of the variability and did not classify the samples according to their diagnosis. Table S2 lists the miRNAs with high contribution to the overall variability of the samples.

MicroRNAs Distinguishing Acute Rejection Biopsies from Normal Allograft Biopsies.

Supervised analysis was used to detect miRNAs differentially expressed in AR biopsies and normal allograft biopsies. A subset of 17 miRNAs was differentially expressed at a P value <0.01. Among the 17 miRNAs, 10 (let-7c, miR-10a, miR-10b, miR-125a, miR-200a, miR-30a-3p, miR-30b, miR30c, miR30e-3p, and miR-32) were underexpressed in AR biopsies compared to normal allograft biopsies, and 7 (miR-142–5p, miR-142–3p, miR-155, miR-223, miR-146b, miR-146a, and miR-342) were overexpressed (Fig. 2 and Table S3). At a P value <0.05, 33 additional miRNAs were found to be underexpressed in AR biopsies compared to normal allograft biopsies, and only 3 miRNAs were found to be overexpressed (Fig. 2 and Table S3).

Fig. 2.

Fig. 2.

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Differential expression of miRNAs in acute rejection biopsies and normal allograft biopsies at a P value <0.05. MicroRNA (miRNA) expression patterns of 7 human kidney allograft biopsies [3 showing histological features of acute rejection (AR) and 4 with normal allograft biopsy results (N)] were examined using microfluidic cards containing TaqMan probes and primer pairs for 365 human mature miRNAs. Each column corresponds to the expression profile of a renal allograft biopsy, and each row corresponds to a miRNA. ABqPCR software was used to identify miRNAs that were differentially expressed between AR biopsies and normal allograft biopsies. CT filtering procedure was first performed. Assays with a CT value >35 in >50% of samples in each group were called undetected. Assays that were not detected in both groups were not included in the analysis. For the remaining assays, t test was used to detect differentially expressed miRNAs. The miRNA clustering tree is shown on the Left. Branch lengths represent the degree of similarity between individual miRNAs. The higher intensities of red mean higher expression level.

Validation of the MicroRNA Signatures Predictive of Renal Allograft Status.

An independent set of 26 renal allograft biopsies (9 AR biopsies and 17 normal allograft biopsies) was used to validate a subset of miRNAs identified by global expression profiling to be differentially expressed in AR biopsies and normal allograft biopsies. Fig. 3 confirms the differential expression of miRNAs in AR biopsies compared to normal allograft biopsies. As observed in the training set, miR-142–5p (P < 0.0001), -155 (P < 0.0001), and -223 (P < 0.0001) were overexpressed in AR biopsies in the validation set, and miR-10b (P = 0.01), miR-30a-3p (P = 0.04), and let-7C (P = 0.08) were underexpressed.

Fig. 3.

Fig. 3.

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Validation of differential expression of microRNAs in AR biopsies and normal allograft biopsies of human renal allografts. Intragraft expression levels of miR-142–5p, -155, -223, -10b, -30a-3p, and let-7c in an independent validation set of 9 acute rejection biopsies and 17 normal kidney allograft biopsies. Expression levels were quantified using modified TaqMan miRNA assays that allow absolute quantification of miRNAs (see SI Text for full details). miRNA copy numbers were normalized using the stably expressed RNU44 small nucleolar RNA, and are shown as mean (± SE) ratio of miRNA copies to RNU44 copy numbers. RNU44 copy numbers were not different between the 9 acute rejection biopsies (8.87 × 106 ± 1.48 × 106 copies/μg RNA) and the 17 normal allograft biopsies (8.72 × 106 ± 8.42 × 105 copies/μg RNA, P = 0.92). P value calculated using t test.

Intragraft miRNA Levels Are Biomarkers of Renal Allograft Status.

We investigated whether intragraft miRNA levels predict AR and renal allograft function. We used receiver-operating curves (ROCs) to analyze miRNA levels to determine cutoff points that yielded the highest combined sensitivity and specificity for predicting AR and allograft function. Our analysis showed that AR can be predicted very accurately using intragraft levels of miR-142–5p (100% sensitivity and 95% specificity, P < 0.0001, Table 1) or miR-155 (100% sensitivity and 95% specificity, P < 0.0001, Table 1). Intragraft levels of miR-223, -10b, -30a-3p, and let-7c were also diagnostic of AR but with a lesser level of accuracy (Table 1).

Table 1.

Diagnostic accuracy of intragraft miRNA/mRNA levels

Cutoff point % sensitivity % specificity AUC (95% CI) P value
miRNAs*
miR-142–5p 0.11 100 95 0.99 (0.96–1.02) <0.0001
miR-155 0.06 100 90 0.98 (0.94–1.01) <0.0001
miR-223 0.44 92 90 0.96 (0.90–1.02) <0.0001
miR-10b 1.33 100 62 0.83 (0.69–0.97) 0.002
miR-30a-3p 0.57 67 76 0.79 (0.63–0.95) 0.007
let-7c 0.64 92 61 0.73 (0.55–0.92) 0.03
RNU44 - - - 0.50 (0.29–0.72) 0.97
mRNAs
CD3 4.02 92 86 0.93 (0.85–1.01) <0.0001
CD20 1.04 100 80 0.89 (0.77–1.01) 0.0002
NKCC-2 12.47 67 90 0.77 (0.58–0.96) 0.01
USAG-1 3.04 67 100 0.83 (0.66–0.99) 0.002
18S - - - 0.52 (0.30–0.74) 0.85
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*Receiver-operating characteristic (ROC) curve analysis was used to determine the cutoff points that yielded the highest combined sensitivity and specificity. The ratio of miRNA copies to RNU44 copies and the ratio of mRNA copies to 18S rRNA copies were used to perform the ROC curve analysis. Analysis involving RNU44 and 18S rRNA was performed using copies per 1 μ g total RNA.

Analysis involving the ROC showed that AR can also be predicted using intragraft levels of T cell CD3 mRNA, B cell CD20 mRNA, and mRNA encoding renal tubular proteins NKCC-2 and USAG-1 but with much less sensitivity and specificity compared to intragraft levels of miR-142–5p, -155, or -223 (Table 1).

We examined whether intragraft miRNA levels predict renal allograft function. Renal graft function at the time of allograft biopsy was assessed by calculating glomerular filtration rate (eGFR) using the 4-parameter modified diet in renal disease (MDRD) formula (22). Our examination showed that intragraft levels of miR-142–5p (R = −0.66, P < 0.0001), -10b (R = 0.62, P < 0.0001), -155 (R = −0.59, P = 0.0003), -223 (R = −0.57, P = 0.0006), -30a-3p (R = 0.57, P = 0.0006), and let-7c (R = 0.37, P = 0.03) are significantly associated with eGFR. Among the intragraft mRNAs assessed, CD3 mRNA (R2 = 0.36, P = 0.0002), but not mRNA for CD20 (R2 = 0.04, P = 0.25), NKCC-2 (R2 = 0.01, P = 0.58), or USAG-1 (R2 = 0.08, P = 0.21), was associated with graft function.

Mechanisms for the Altered Intragraft Expression of miRNAs in AR Biopsies.

Fig. 4 illustrates that there is a strong positive association between intragraft levels of CD3 mRNA and intragraft levels of miR-142–5p (Fig. 4A, R2 = 0.72, P < 0.0001), miR-155 (Fig. 4B, R2 = 0.69, P < 0.0001), or miR-223 (Fig. 4C, R2 = 0.66, P < 0.0001). We also found a strong positive relationship between intragraft levels of CD20 mRNA and miR-142–5p (R2 = 0.61, P < 0.0001), miR-155 (R2 = 0.55, P < 0.0001), or miR-223 (R2 = 0.56, P < 0.0001). In contrast, there was no association between renal tubule NKCC-2 mRNA or USAG-1 mRNA and miR-142–5p, -155, or miR-223 (all P values >0.05).

Fig. 4.

Fig. 4.

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Positive association between miRNAs and mRNAs in human allograft biopsies. Intragraft levels of miRNAs were quantified with the use of TaqMan miRNA assays, and intragraft levels of mRNAs were quantified using real time quantitative PCR assays, and the relationship between the intragraft levels of miRNA and mRNA is shown, along with Pearson correlation (R2) and P values. A strong positive association between the levels of CD3 mRNA and the levels of miRNAs overexpressed in acute rejection biopsies was found: (A) miR-142–5p (R2 = 0.72, P < 0.0001); (B) miR-155 (R2 = 0.69, P < 0.0001); or (C) miR-223 (R2 = 0.66, P < 0.0001). A positive association between renal tubule specific NKCC-2 mRNA and miRNAs underexpressed in acute rejection biopsies was also observed: (E) miR-30a-3p (R2 = 0.53, P < 0.0001); (F) miR-10b (R2 = 0.36, P < 0.0001); or (G) let-7c (R2 = 0.13, P = 0.04). Results from all 33 renal allograft biopsies (red, 12 acute rejection biopsies; green, 21 normal allograft biopsies) are shown. The threshold cycle (CT) is the fractional cycle number at which the fluorescence crossed the fixed threshold in miRNA/mRNA assays. (D) The mean (± SD) CT values of the endogenous control for mRNAs (18S rRNA, 24.8 ± 1.3 vs. 24.7 ± 1.1, P = 0.86, t test) and (H) for miRNAs (RNU44 small nucleolar RNA, 27.1 ± 0.7 vs. 27.1 ± 0.5, P = 0.97, t test) were similar between the acute rejection samples and the normal renal allografts.

We examined whether an association exists between miRNAs underexpressed in AR biopsies and intragraft mRNA levels. We found a positive association between renal tubule-specific NKCC-2 mRNA and miR-30a-3p (Fig. 4E, R2 = 0.53, P < 0.0001), miR-10b (Fig. 4F, R2 = 0.36, P < 0.0001), or let-7c (Fig. 4G, R2 = 0.13, P = 0.04). In a similar fashion to NKCC-2, renal tubule-related USAG-1 mRNA levels were positively associated with the intragraft levels of miR-30a-3p (R2 = 0.44, P < 0.0001), miR-10b (R2 = 0.35, P = 0.0003), or let-7c (R2 = 0.19, P = 0.01). In contrast, there was no significant association between intragraft levels of CD3 mRNA or CD20 mRNA and miR-30a-3p, miR-10b, or let-7c (all P values >0.05).

To address whether the altered expression of miRNAs in AR biopsies is because of the relative proportions of graft-infiltrating immune cells and resident kidney parenchymal cells, we quantified the abundance of differentially expressed miRNAs in normal human PBMCs and in normal HRECs. We also investigated whether stimulation of PBMCs or HRECs altered the level of expression of miRNAs. Our investigation showed that whereas the absolute levels of RNU44 small nucleolar miRNA was similar in both PBMCs and HRECs (2.0 × 107 ± 1.2 × 107 vs. 2.35 × 107 ± 1.9 × 106, P > 0.05), the miRNAs overexpressed in AR biopsies (miR-142–5p, miR-155, and miR-223) were all expressed at a higher level in normal human PBMCs compared to miRNAs (miR-30a-3p, miR-10b, or let-7c) underexpressed in AR biopsies. Moreover, stimulation of PBMCs with the mitogen phytohaemagglutinin (PHA) results in an increase in the abundance of miR-155 (P = 0.0002) and a decrease in miR-223 (P = 0.02), let-7c (P = 0.02), or miR-142–5p (P = 0.08) (Fig. 5). H-ras (23) and c-myc (24) are targets of let-7c, and activation of PBMCs with PHA resulted in the increased expression of mRNAs for c-myc (P = 0.01) and H-ras (P = 0.07) (Fig. 5).

Fig. 5.

Fig. 5.

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Levels of miRNAs in resting or activated normal human peripheral blood mononuclear cells. Peripheral blood mononuclear cells (PBMCs) were obtained from healthy individuals and were incubated without (open bars) or with (filled bars) 2 μg/mL PHA for 24 h (A, F, and G) (n = 7 subjects), 48 h (D) (n = 4 subjects), or 24, 48, and 72 h (B, C, E, H, and I) (n = 2 subjects), and RNA was isolated for miRNA quantification (A–E) or mRNA quantification (F–I) (see SI Text for full details). miRNA copy numbers were normalized using the RNU44 small nucleolar RNA copy numbers and mRNA copy numbers were normalized using the 18S rRNA copy numbers and are shown as mean (± SE) ratio of miRNA copies to RNU44 copy numbers or ratio of mRNA copies to 18S rRNA copies. P value calculated using paired t test.

Quantification of miRNAs in primary cultures of HRECs showed that miR-30a-3p, miR-10b, or let-7c are expressed at a higher level in HRECs compared to PBMCs, and that stimulation of HRECs with cell-free supernatants of PHA-activated PBMCs results in a decrease in the abundance of miR-30a-3p (P = 0.02) (Fig. 6). As expected, activation of HRECs with cell-free supernatants of PHA-activated PBMCs increased the expression of mRNA-encoding proinflammatory cytokines MCP-1, RANTES, and IP-10 in HRECs (Fig. S1).

Fig. 6.

Fig. 6.

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Levels of miRNAs in resting or activated normal human renal epithelial cells. Primary cultures of normal human renal epithelial cells (HRECs) were incubated for 24 h (A, B) or 24 and 48 h (C) with cell-free supernatants of resting PBMCs (open bars) or cell-free supernatants of PBMCs activated with 2 μg/mL PHA (filled bars). Total RNA was isolated from HRECs and a subset of miRNAs found to be overexpressed (A) or underexpressed (B and C) in acute rejection biopsies were quantified with the use of modified TaqMan miRNA assays (see SI Text for full details). miRNA copy numbers were normalized using the RNU44 small nucleolar RNA copy numbers and are shown as mean (± SE) ratio of miRNA copies to RNU44 copy numbers. Results are from 2 consecutive experiments with 2 independent primary cultures of HRECs developed from 2 human kidneys. P values calculated using paired t test.

Discussion

We identified that intragraft miRNA profiles distinguish patients with AR of human allografts from patients with normal allograft biopsy results, and that AR can be diagnosed with a high degree of accuracy with the use of intragraft levels of miRNAs. Moreover, miRNA profiles were also predictive of renal allograft function. Our observations, together, support the hypothesis that intragraft miRNA expression patterns may serve as biomarkers of human renal allograft status.

We used a 2-step approach to develop miRNA signatures predictive of AR. First, we ascertained intragraft expression patterns of 365 mature human miRNAs in 7 human renal allograft biopsies classified as AR or normal. Global expression profiling identified miRNAs differentially expressed in AR biopsies compared to normal biopsies (Figs. 1 and 2). In the second step, with the use of modified TaqMan miRNA assays, we determined absolute copy numbers of miRNAs in 26 additional renal allograft biopsies (Fig. 3). Our approach resolved that intragraft levels of miR-142–5p, -155, -223, -10b, -30a-3p, and let-7c are diagnostic of AR, with miR-142–5p, miR-155, and miR-223 each predicting AR with >90% sensitivity and specificity (Table 1). Intragraft levels of mRNA for CD3, CD20, NKCC-2, and USAG-1 were also diagnostic of AR, but with less combined sensitivity and specificity.

Intragraft levels of miR-142–5p, -155, -223, -10b, -30a-3p, and let-7c predicted renal graft function with miR-142–5p and miR-10b showing the strongest association with graft function. Among the mRNAs analyzed, mRNA for CD3, but not mRNAs for CD20, NKCC-2, and USAG-1, predicted graft function, and the association between CD3 mRNA and graft function was weaker compared to that of miR-142–5p or miR-10b. Our observations that intragraft miRNA expression patterns are predictive of allograft status, in addition to the existing data that miRNAs are stable (25), present in high abundance, and can be examined in formalin-fixed tissues (26), advance the idea that miRNA expression patterns may be of value as biomarkers in clinical transplantation.

Several of the miRNAs found at a higher level in AR biopsies compared to normal allograft biopsies have been reported to play an important role in innate and adaptive immunity. For example, recent microarray studies of activated mouse macrophages characterized miR-155 as a common target of a broad range of inflammatory mediators (27) and miR-146a and miR-155 to be regulated in response to immune-cell stimulation by endotoxins (28). miR-155 is encoded within an exon of the nonprotein-encoding gene bic (B-cell integration cluster) and high levels of bic expression are induced upon antigen receptor stimulation of B and T cells, and Toll-like receptor stimulation of macrophages and dendritic cells (29). In this regard, our in vitro studies showed that activation with the polyclonal T-cell mitogen PHA increases miR-155 expression in normal human PBMCs.

Intragraft levels of miR-146 were higher in AR samples compared to normal allograft biopsies. miR-146 is expressed at low levels in naïve T cells, is upregulated in Th1 cells, but not in Th2 cells, and is considered as a Th1-specific miRNA (30). In support of Th1-type cells infiltrating rejecting human renal allografts, we found that intragraft level of mRNA for the type 1 cytokine IFN-γ but not the level of mRNA for the Th2 cytokine IL-4 were higher in AR biopsies compared to normal allograft biopsies (Fig. S2).

miR-223 has been found to be expressed in granulocytes, platelets, monocytes, T and B cells, with levels being lower in T cells and B cells compared to granulocytes or platelets (31). Among the miRNAs overexpressed in the AR biopsies, miR-223 levels were the highest. We also found the abundance of miR-223 to be higher compared to that of miR-142–5p or miR-155 in normal human PBMCs, and that activation of PBMCs with PHA results in a reduction of miR-223 expression.

Intragraft levels of miR-142 were also higher in AR biopsies compared to normal allograft biopsies. miR-142 expression is mostly restricted to lymphoid cells and is expressed at a lower level in differentiated Th1 cells or Th2 cells compared to naïve T cells (12, 30, 32). We found that activation results in a reduction, albeit not statistically significant (P = 0.08), in the expression of miR-142 in PBMCs.

Acute rejection of human renal allografts was also characterized by underexpression of miRNAs within the rejecting allografts compared to allografts with normal biopsy results. Indeed, among the 53 miRNAs differentially expressed between AR biopsies and normal biopsies, 43 were underexpressed and only 10 were overexpressed in the AR biopsies (Fig. 2).

Two members of the let-7 family (let-7a and let-7c) were underexpressed in AR biopsies. let-7 family members have been shown to regulate cell proliferation and to be potential tumor suppressors (33, 34), and let-7c was shown to inhibit expression of the oncogenes ras (23) and c-myc (24). Our investigation showed that let-7c is downregulated in PHA-activated PBMCs (Fig. 5 D and E) and mRNA for both H-ras and c-myc are upregulated (Fig. 5 F–I). However, the reduction in the expression of let-7c was not observed 24 h after activation and was evident after 48 h, whereas the maximum upregulation of H-ras and c-myc was observed 24 h following activation (Fig. 5 H and I). Thus, downregulation of let-7c does not appear to be an absolute prerequisite for the upregulation of H-ras or c-myc in normal human PBMCs.

miR-30a-3p and miR-10b were both underexpressed in AR biopsies compared to normal allograft biopsies (Fig. 3). These miRNAs were expressed at a greater abundance in HRECs compared to PBMCs, and activation of HRECs, as demonstrated by increased expression of mRNA for proinflammatory chemokines MCP-1, RANTES, and IP-10 (Fig. S1), was associated with a reduced expression of miR-30a-3p (Fig. 6).

What Might Be the Basis for the Altered Expression of MicroRNAs in Acutely Rejecting Allografts?

Acute rejection is distinguished by infiltration of the allograft by multiple cell types including T cells and B cells, and several of the miRNAs overexpressed in AR biopsies compared to normal allograft biopsies are highly expressed in hematopoietic cells (5). Our observations revealed: (i) miRNAs expressed in high abundance in human PBMCs are present at high levels in acutely rejecting allografts; (ii) a strong positive association exists between intragraft levels of overexpressed miRNAs and mRNA for T cell CD3 and B cell CD20 (Fig. 4); and (iii) the strong positive relationship between renal tubule-specific NKCC-2 mRNA and miR-30a-3p and miR-10b expressed in high abundance in HRECs are all consistent with the interpretation that the altered expression of miRNAs during AR is most likely because of the relative proportions of graft-infiltrating immune cells and resident renal parenchymal cells. Our in vitro studies with the use of PBMCs and HRECs showing that some but not all of the differentially expressed miRNAs are also regulated by stimulation with the mitogen PHA raises the possibility that there may be altered regulation of miRNAs within the cells themselves. Clearly, additional studies of quantification of miRNAs within graft-infiltrating cells and resident epithelial cells are required to resolve the basis for the altered miRNA levels observed in AR biopsies.

In summary, we found that a small number of mature miRNAs accurately differentiated acutely rejecting human renal allografts from normal allografts, and AR and renal allograft function could be predicted with a high level of precision using intragraft levels of miRNAs. We also found a strong positive association between intragraft expression of miRNAs and mRNAs. Our complementary in vivo and in vitro studies, in addition to suggesting the cellular basis for altered intragraft expression of miRNAs, suggest that miRNA expression patterns may serve as biomarkers of human renal allograft status.

Materials and Methods

Renal Allograft Recipients and Biopsy Specimens.

We investigated microRNA expression patterns of 33 renal allograft biopsies obtained from 32 adult recipients of human renal allografts: 12 biopsies from 11 recipients with graft dysfunction [mean (± SD) creatinine: 5.6 ± 3.5 mg/dL] and biopsy-confirmed AR according to the Banff 97 classification (35) [mean age (± SD): 38.5 ± 8.6 yr, 7 men and 4 women, 5 living and 6 deceased donors] and 21 biopsies from 21 recipients with stable allograft function (creatinine: 1.3 ± 0.3 mg/dL) and normal allograft biopsy (46.4 ± 11.5 yr, 8 men and 13 women, 15 living and 6 deceased donors). The mean (± SE) time to biopsy was 19.1 ± 7.0 months posttransplantation in the AR group and 6.3 ± 0.9 months in the group with stable graft function and normal biopsy results (P = 0.51, Mann-Whitney test). Additional information of the study subjects is given in SI Text and Table S1.

MicroRNA Expression Profiling.

Global miRNA profiling of allograft biopsy specimens was studied using the TaqMan low-density array human microRNA panel v1.0 containing 365 mature human miRNAs (Applied Biosystems). miRNAs found to be differentially expressed in AR biopsies compared to normal allograft biopsies were quantified using TaqMan miRNA assays (Applied Biosystems) modified by the incorporation of our standard curve protocol (36). Details for total RNA purification, miRNA profiling, quantification, and data analysis are provided in SI Text.

Measurement of Intragraft Levels of mRNA Using Kinetic Quantitative PCR Assay.

The expression level of mRNAs was quantified using real-time quantitative PCR assays as detailed in SI Text. Primers and probes sequences are shown in Table S4.

Cell Culture.

The procedures for the in vitro studies using peripheral blood mononuclear cells and normal human renal epithelial cells are described in SI Text.

Supplementary Material

Supporting Information
supp_106_13_5330__index.html (491B, html)

Acknowledgments.

We are indebted to Robert Genario and Chen Wang for their superb technical assistance, Heejung Bang for expert statistical help, and Maria Trantino for her meticulous assistance in the preparation of the manuscript. Supported in part by awards (AI51652 and AI72790 to M. Suthanthiran) from the National Institutes of Health. D.A. is supported by fellowship awards from the Les Entreprises du Medicament recherches and the Groupement Coopératif de Transplantation d'Ile-de-France.

Footnotes

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/cgi/content/full/0813121106/DCSupplemental.

References

  • 1.Grosshans H, Filipowicz W. Molecular biology: The expanding world of small RNAs. Nature. 2008;451:414–416. doi: 10.1038/451414a. [DOI] [PubMed] [Google Scholar]
  • 2.Shyu AB, Wilkinson MF, van Hoof A. Messenger RNA regulation: To translate or to degrade. EMBO J. 2008;27:471–481. doi: 10.1038/sj.emboj.7601977. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;75:843–854. doi: 10.1016/0092-8674(93)90529-y. [DOI] [PubMed] [Google Scholar]
  • 4.Wightman B, Ha I, Ruvkun G. Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell. 1993;75:855–862. doi: 10.1016/0092-8674(93)90530-4. [DOI] [PubMed] [Google Scholar]
  • 5.Landgraf P, et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell. 2007;129:1401–1414. doi: 10.1016/j.cell.2007.04.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Baek D, et al. The impact of microRNAs on protein output. Nature. 2008;455:64–71. doi: 10.1038/nature07242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Stefani G, Slack FJ. Small non-coding RNAs in animal development. Nat Rev Mol Cell Biol. 2008;9:219–230. doi: 10.1038/nrm2347. [DOI] [PubMed] [Google Scholar]
  • 8.Valencia-Sanchez MA, Liu J, Hannon GJ, Parker R. Control of translation and mRNA degradation by miRNAs and siRNAs. Genes Dev. 2006;20:515–524. doi: 10.1101/gad.1399806. [DOI] [PubMed] [Google Scholar]
  • 9.Gantier MP, Sadler AJ, Williams BR. Fine-tuning of the innate immune response by microRNAs. Immunol Cell Biol. 2007;85:458–462. doi: 10.1038/sj.icb.7100091. [DOI] [PubMed] [Google Scholar]
  • 10.Lodish HF, Zhou B, Liu G, Chen CZ. Micromanagement of the immune system by microRNAs. Nat Rev Immunol. 2008;8:120–130. doi: 10.1038/nri2252. [DOI] [PubMed] [Google Scholar]
  • 11.Pipkin ME, Monticelli S. Genomics and the immune system. Immunology. 2008;124:23–32. doi: 10.1111/j.1365-2567.2008.02818.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Tili E, Michaille JJ, Calin GA. Expression and function of micro-RNAs in immune cells during normal or disease state. Int J Med Sci. 2008;5:73–79. doi: 10.7150/ijms.5.73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Suthanthiran M, Strom TB. Renal transplantation. N Engl J Med. 1994;331:365–376. doi: 10.1056/NEJM199408113310606. [DOI] [PubMed] [Google Scholar]
  • 14.Sayegh MH, Carpenter CB. Transplantation 50 years later–progress, challenges, and promises. N Engl J Med. 2004;351:2761–2766. doi: 10.1056/NEJMon043418. [DOI] [PubMed] [Google Scholar]
  • 15.Morris PJ. Transplantation–a medical miracle of the 20th century. N Engl J Med. 2004;351:2678–2680. doi: 10.1056/NEJMp048256. [DOI] [PubMed] [Google Scholar]
  • 16.Flechner SM, et al. Kidney transplant rejection and tissue injury by gene profiling of biopsies and peripheral blood lymphocytes. Am J Transplant. 2004;4:1475–1489. doi: 10.1111/j.1600-6143.2004.00526.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Sarwal M, et al. Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. N Engl J Med. 2003;349:125–138. doi: 10.1056/NEJMoa035588. [DOI] [PubMed] [Google Scholar]
  • 18.Sharma VK, et al. Molecular executors of cell death–differential intrarenal expression of Fas ligand, Fas, granzyme B, and perforin during acute and/or chronic rejection of human renal allografts. Transplantation. 1996;62:1860–1866. doi: 10.1097/00007890-199612270-00031. [DOI] [PubMed] [Google Scholar]
  • 19.Li B, et al. Noninvasive diagnosis of renal-allograft rejection by measurement of messenger RNA for perforin and granzyme B in urine. N Engl J Med. 2001;344:947–954. doi: 10.1056/NEJM200103293441301. [DOI] [PubMed] [Google Scholar]
  • 20.Tatapudi RR, et al. Noninvasive detection of renal allograft inflammation by measurements of mRNA for IP-10 and CXCR3 in urine. Kidney Int. 2004;65:2390–2397. doi: 10.1111/j.1523-1755.2004.00663.x. [DOI] [PubMed] [Google Scholar]
  • 21.Muthukumar T, et al. Messenger RNA for FOXP3 in the urine of renal-allograft recipients. N Engl J Med. 2005;353:2342–2351. doi: 10.1056/NEJMoa051907. [DOI] [PubMed] [Google Scholar]
  • 22.Poggio ED, Wang X, Greene T, Van Lente F, Hall PM. Performance of the modification of diet in renal disease and Cockcroft-Gault equations in the estimation of GFR in health and in chronic kidney disease. J Am Soc Nephrol. 2005;16:459–466. doi: 10.1681/ASN.2004060447. [DOI] [PubMed] [Google Scholar]
  • 23.Johnson SM, et al. RAS is regulated by the let-7 microRNA family. Cell. 2005;120:635–647. doi: 10.1016/j.cell.2005.01.014. [DOI] [PubMed] [Google Scholar]
  • 24.Shah YM, et al. Peroxisome proliferator-activated receptor alpha regulates a microRNA-mediated signaling cascade responsible for hepatocellular proliferation. Mol Cell Biol. 2007;27:4238–4247. doi: 10.1128/MCB.00317-07. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Mitchell PS, et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci USA. 2009;105:10513–10518. doi: 10.1073/pnas.0804549105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Siebolts U, et al. Tissues from routine pathology archives are suitable for microRNA analyses by quantitative PCR. J Clin Pathol. 2008;62:84–88. doi: 10.1136/jcp.2008.058339. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.O'Connell RM, Taganov KD, Boldin MP, Cheng G, Baltimore D. MicroRNA-155 is induced during the macrophage inflammatory response. Proc Natl Acad Sci USA. 2007;104:1604–1609. doi: 10.1073/pnas.0610731104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Taganov KD, Boldin MP, Chang KJ, Baltimore D. NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci USA. 2006;103:12481–12486. doi: 10.1073/pnas.0605298103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Turner M, Vigorito E. Regulation of B- and T-cell differentiation by a single microRNA. Biochem Soc Trans. 2008;36:531–533. doi: 10.1042/BST0360531. [DOI] [PubMed] [Google Scholar]
  • 30.Monticelli S, et al. MicroRNA profiling of the murine hematopoietic system. Genome Biol. 2005;6:R71.1–R71.15. doi: 10.1186/gb-2005-6-8-r71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Merkerova M, Belickova M, Bruchova H. Differential expression of microRNAs in hematopoietic cell lineages. Eur J Haematol. 2008;81:304–310. doi: 10.1111/j.1600-0609.2008.01111.x. [DOI] [PubMed] [Google Scholar]
  • 32.Chen CZ, Li L, Lodish HF, Bartel DP. MicroRNAs modulate hematopoietic lineage differentiation. Science. 2004;303:83–86. doi: 10.1126/science.1091903. [DOI] [PubMed] [Google Scholar]
  • 33.Lee YS, Dutta A. MicroRNAs: Small but potent oncogenes or tumor suppressors. Curr Opin Investig Drugs. 2006;7:560–564. [PubMed] [Google Scholar]
  • 34.Zhang B, Pan X, Cobb GP, Anderson TA. microRNAs as oncogenes and tumor suppressors. Dev Biol. 2007;302:1–12. doi: 10.1016/j.ydbio.2006.08.028. [DOI] [PubMed] [Google Scholar]
  • 35.Solez K, et al. Banff 07 classification of renal allograft pathology: Updates and future directions. Am J Transplant. 2008;8:753–760. doi: 10.1111/j.1600-6143.2008.02159.x. [DOI] [PubMed] [Google Scholar]
  • 36.Kawai T, et al. HLA-mismatched renal transplantation without maintenance immunosuppression. N Engl J Med. 2008;358:353–361. doi: 10.1056/NEJMoa071074. [DOI] [PMC free article] [PubMed] [Google Scholar]

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