Journal of the American Society of Nephrology
2007 JASN IMPACT FACTOR 7.111 HOME   AUTHOR INFO   EDITORIAL BOARD   SUBSCRIBE   FEEDBACK   ALERTS   HELP 
    advanced
CURRENT ISSUE ARCHIVES JASN Express ONLINE SUBMISSION


This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Eikmans, M.
Right arrow Articles by Bruijn, J. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Eikmans, M.
Right arrow Articles by Bruijn, J. A.
J Am Soc Nephrol 14:899-907, 2003
© 2003 American Society of Nephrology

Renal mRNA Levels as Prognostic Tools in Kidney Diseases

Michael Eikmans*, Hans J. Baelde*, E. Chris Hagen{dagger}, Leendert C. Paul{ddagger}, Paul H. C. Eilers§, Emile de Heer* and Jan A. Bruijn*

Departments of *Pathology, {ddagger}Nephrology, and §Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands; and {dagger}Department of Nephrology, Eemland Hospital, Amersfoort, The Netherlands.

Correspondence to Dr. Michael Eikmans, LUMC, Dept. of Pathology, Building 1, L1-Q, PO Box 9600, 2300 RC, Leiden, The Netherlands. Phone: 31-71-526-6574; Fax: 31-71-524-8158;


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
ABSTRACT. Molecular biologic techniques are currently considered as new diagnostic and prognostic parameters with a sensitivity and specificity exceeding those of histologic and functional data currently used in clinical practice. The results in various clinical settings have been of limited value up to now. This study is an investigation of the use of tissue levels of RNA determined in routine clinical kidney biopsies as prognostic tools. The focus was on RNA encoding for molecules known to be involved in the pathogenesis of renal disorders. Fresh kidney biopsy tissue was obtained from 52 patients with various renal diseases. The GFR was followed for 12 mo. The extent of glomerulosclerosis and interstitial fibrosis in the biopsies was determined with quantitative digital image analysis. Glomerular and tubulointerstitial compartments from each biopsy specimen were separated, and mRNA levels of TGF-{beta}, collagen I, collagen IV, and fibronectin were quantitated by real-time PCR. Correlations, along with 95% confidence intervals (CI), between all variables tested at time biopsy were determined. To assess their prognostic value, these variables were correlated with the slope of GFR within several time intervals after biopsy. In addition, to evaluate the predictive value of the variables for outcome in individual patients, differences for each variable were tested between patients showing progressive decline in renal function (slope GFR < 0) and patients showing stable or improving renal function over time (slope GFR >= 0). In chronic renal diseases, the extent of histologic damage correlated with the GFR at the time of biopsy (r = -0.44; CI -0.68 to -0.11), but it did not correlate with the slope expressing a change in GFR after the biopsy. Tubulointerstitial TGF-{beta} mRNA levels correlated with the rate of change in GFR between time of biopsy and 1 mo later (r = 0.41; CI, 0.07 to 0.67). The GFR at the time of biopsy correlated with the slope of change in GFR between time of biopsy and 12 mo later (r = -0.50; CI, -0.73 to -0.18). In chronic renal diseases, glomerular fibronectin mRNA levels, in comparison with the GFR at time of biopsy, correlated relatively strongly with the slope of change in GFR between 3 and 12 mo (r = 0.50; CI, 0.16 to 0.74). Patients with favorable renal outcome after 12 mo showed significantly higher TGF-{beta} mRNA levels and lower proteinuria levels at time of biopsy (P < 0.05) than patients with a progressive decline in renal function. This study shows that mRNA levels measured in kidney biopsies can function as prognostic tools in human renal diseases. In particular, relatively high levels of tubulointerstitial TGF-{beta} mRNA and glomerular fibronectin mRNA are associated with less deterioration in renal function. E-mail: M.Eikmans@LUMC.NL


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Chronic renal disease may progress to end-stage renal failure. This event leads to requirement of dialysis or transplantation and is accompanied by high mortality rates and high costs. To determine the individual prognosis of a patient with a renal disease, physicians currently make use of functional parameters such as serum creatinine, GFR, proteinuria, and the histologic information obtained from the renal biopsy. Several studies have shown that the current functional measurements are inaccurate as measures of progression in chronic renal failure (1,2 ). An extensive number of studies have shown that certain histologic changes in the biopsy are associated with an adverse outcome. However, questions have been raised to the use of histologic alterations as predictors of outcome in the individual patient (3–5 ). The reason for this especially is that most of these alterations reflect existing tissue damage, which seems to exclude any form of progression. In addition, some renal lesions may be difficult to classify, resulting in a large interobserver and intraobserver variation.

The progression of chronic renal failure is accompanied by a fall in GFR and by an increase in accumulation of extracellular matrix (ECM) in the renal parenchyma, leading to glomerulosclerosis and interstitial fibrosis. Collagen IV, collagen I, and fibronectin are prominent components of the ECM in diseased kidneys (6–8 ). Transforming growth factor-{beta} (TGF-{beta}) plays a prime role in renal disease because it is both involved in tissue repair and tissue scarring, via the induction of ECM expression (9–11 ). In animal models, mRNA levels for TGF-{beta} and ECM components are upregulated early after the induction of renal disease, and they precede the development of glomerulosclerosis and interstitial fibrosis (12,13 ). Moreover, using renal mRNA levels of molecules involved in matrix deposition as predictors of disease progression has proven to be promising (14–17 ). In experimental glomerulonephritis, the predictive power of early collagen I mRNA levels for late histologic damage was higher than that of conventional functional and histologic parameters (17). These results warrant an evaluation of the prognostic value of mRNA levels in patients suffering from renal disease.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Patients
Between February 1998 and April 2000, 52 native kidney biopsies were collected from 52 patients suffering from renal diseases. Informed consent was given by the patients for use of part of the biopsy for scientific purposes. Patients were divided into two groups: those with chronic renal diseases (n = 37) and those with other renal diseases (n = 15). Diagnoses in the former group were: IgA nephropathy (n = 10), lupus nephritis (n = 7), focal and segmental glomerulosclerosis (n = 5), diabetic nephropathy (n = 2), pauci-immune glomerulonephritis (n = 3), light chain deposit disease (n = 3, two of whom additionally had interstitial nephritis), membranoproliferative glomerulonephritis type I (n = 1), membranous glomerulopathy (n = 1), interstitial nephritis (n = 1), rheumatoid arthritis-related renal disease (n = 1), hypertension-related renal disease (n = 1), end-stage renal disease (n = 2; as a result of diabetic nephropathy or malignant hypertension). Patients with other renal diseases were diagnosed having: minimal change disease (MCD, n = 10), no specific lesions that could account for the sudden deterioration in renal function (n = 3), post-infectious glomerulonephritis (n = 1), or radiation nephropathy (n = 1). Patients in the latter three groups showed a sharp increase in serum creatinine levels before biopsy, which returned to normal within several weeks after biopsy. Patients with MCD showed no specific lesions by light microscopy, and they retained normal renal function during the whole follow-up period. In the study, the patients with chronic renal disease and the patients with other renal diseases were analyzed separately, because we consider the kinetics of these particular disease entities different in terms of underlying molecular processes.

As controls, renal tissue was studied from 16 individuals with normal kidney function and histology. These individuals had no history of renal disease, and kidneys were obtained from Eurotransplant (cadaveric donor kidneys, n = 6) or at autopsy (n = 10).

Clinical Data
Renal function was measured as the GFR by using the Cockcroft-Gault equation (18). The GFR was followed for 12 mo. In the whole patient group, the mean number of available GFR data points for calculating slopes within the intervals between time of biopsy and 1 mo (T0 to 1 mo), T0 to 3 mo, T0 to 6 mo, T0 to 12 mo, and 3 mo to 12 mo were: 6.5 ± 5.1, 10.2 ± 7.4, 13.3 ± 8.8, 15.7 ± 9.6, and 8.0 ± 3.9, respectively. In the group of chronic renal diseases, these numbers were: 7.2 ± 5.4, 11.5 ± 7.5, 14.8 ± 9.1, 15.7 ± 10.1, and 8.0 ± 4.0, respectively. Urine protein was evaluated at time of biopsy and expressed as mg/24 h.

Assessment of Histologic Damage
Glomerulosclerosis.
Paraffin-embedded kidney tissue was sectioned at 4-µm thickness. Glomerulosclerosis was quantitated by evaluation of the glomerular deposition of periodic acid-Schiff (PAS)-positive material in PAS stainings. Digital image analysis was performed using a Zeiss microscope equipped with a full-color 3CCD camera (Sony DXC 950p) and KS-400 software version 3.0 (Zeiss-Kontron, Eichen, Germany). Further details on the method of image analysis have been described in a previous report (19). To calculate the amount of glomerulosclerosis in the PAS stainings, at least six glomeruli were randomly selected and analyzed at 400x magnification. Bowman’s capsules were left out of the analysis. Data are represented as the PAS-positive percentage of the total glomerular area measured.

Interstitial Fibrosis.
The Sirius red staining was used as a measure of the extent of interstitial fibrosis, as described in earlier studies (20,21 ). All sections were stained simultaneously in one session. Digital image analysis was applied to calculate the extent of surface staining by the Sirius red in the sections. Five adjacent microscopic fields were evaluated at 200x magnification. Glomeruli were left out of the analysis. Data are represented as the Sirius red-positive percentage of the "total tubulointerstitial area" measured.

Microdissection and RNA Extraction
For separation of glomeruli and tubulointerstitium in each biopsy, microdissection was performed under a stereomicroscope and according to a previously described protocol (22). For RNA extractions, Trizol Reagent (Life Technologies BRL) was used. RNA from the glomerular and the tubulointerstitial samples was extracted with 200 µl and 500 µl Trizol, respectively. This protocol has been described in a previous report (19).

cDNA Synthesis
RNA extracted from the glomerular and the tubulointerstitial tissues was converted into cDNA with the aid of the Sensiscript-RT kit and the Omniscript-RT kit (Qiagen, Westburg BV, Leusden, The Netherlands), respectively, according to the supplier’s manual. All cDNA reactions were performed in a total volume of 20 µl.

Real-Time PCR
Glomerular cDNA samples were diluted 25 times. Five microliters of each sample was used to measure mRNA levels of TGF-{beta}1, collagen {alpha}1(IV), fibronectin, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) by real-time PCR (ABI Prism 7700, Perkin Elmer). Tubulointerstitial cDNA samples were diluted 50 times. Five microliters of each sample was used to measure mRNA levels of TGF-{beta}1, collagen {alpha}1(I), collagen {alpha}1(IV), fibronectin, and GAPDH by real-time PCR. Sequences for TGF-{beta}1 and fibronectin of the forward primer, reverse primer (Biosource), and probe (Perkin Elmer) are: CCC AGC ATC TGC AAA GCT C, GTC AAT GTA CAG CTG CCG CA, ACA CCA ACT ATT GCT TCA GCT CCA CGG A [TGF-{beta}1]; GGA GAA TTC AAG TGT GAC CCT CA, AGG CAA CGT GTT ACG ATG ATG GGA AGA CAT, and TGC CAC TGT TCT CCT ACG TGG [fibronectin]. Sequences for collagen {alpha}1(I), collagen {alpha}1(IV), and GADPH have been described in a previous report (19). All measurements were done in duplicate. The real-time PCR protocol has been described in an earlier report (19). The mRNA level of each transcript was measured in one PCR run on a 96-wells plate. A standard was measured in duplicate on the same plate. The accompanying duplicate measurements of the patient samples were performed in a second PCR run on another plate and also included the standard in duplicate.

Correlations at Time of Biopsy
For the patient group with chronic renal diseases (n = 37) and for the patient group with other renal diseases (n = 15), correlations were tested between GFR, proteinuria, the extent of glomerulosclerosis, the extent of interstitial fibrosis, and the mRNA levels, all measured at time of biopsy.

Comparisons of Variables at Time of Biopsy with Outcome
Both in the whole patient group (n = 52) and in patients with chronic renal disease (n = 37), the prognostic value of the mRNA levels, histologic parameters, and renal function at time of biopsy was evaluated.

Correlations with Rate of Changes in GFR.
The mRNA levels, the extent of histologic damage, proteinuria, and the GFR at time of biopsy were correlated with the slopes of the regression lines through GFR data points within the following time intervals: T0 to 1 mo, T0 to 3 mo, T0 to 6 mo, T0 to 12 mo, and 3 mo to 12 mo.

Differences between Patients with Various Outcomes.
We wanted to evaluate the predictive value of clinical, morphologic, and molecular variables at time of biopsy for outcome in individual patients. For this, for each time interval indicated above, patients were separated into a group showing progressive decline in renal function (slope GFR < 0) and a group showing stable or improving renal function over time (slope GFR >= 0). Then, GFR, proteinuria, histology, and the mRNA levels were compared between the two groups, and statistical analyses were performed as indicated below.

Statistical Analyses
SPSS software version 10.0 was used for statistical analyses. Data are presented as means ± SD. Differences between controls and patients, and between patient groups with different outcomes, were evaluated by independent t tests. Correlations were evaluated by Pearson bivariate correlation tests (23) and presented with their 95% confidence intervals (CI) in Tables 2 and 3.


View this table:
[in this window]
[in a new window]

  		Table 2. Correlations at time of biopsya
 

View this table:
[in this window]
[in a new window]

  		Table 3. Prognostic value of variables at time of biopsya
 

   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Characteristics of Controls and Patients
Table 1 shows clinical variables, histologic variables, and mRNA levels in controls and in patients with renal diseases. GFR is significantly lower (P < 0.05) and proteinuria is significantly higher (P < 0.001) in patients compared with controls. The extent of glomerulosclerosis and interstitial fibrosis (P < 0.001), glomerular mRNA levels of TGF-{beta} (P < 0.001), collagen IV (P < 0.05), and fibronectin (P < 0.001), and tubulointerstitial mRNA levels of TGF-{beta} (P < 0.05) are significantly higher in renal patients than those in controls.


View this table:
[in this window]
[in a new window]

  		Table 1. Variables at time of biopsya
 
Correlations at Time of Biopsy
Correlations, with 95% CI, between variables at time of biopsy within the patient group with chronic renal diseases and the patient group with other renal diseases are shown in Table 2. In chronic renal diseases, relatively strong correlations were found between the extent of glomerulosclerosis and interstitial fibrosis (r = 0.56; CI, 0.27 to 0.77), between the extent of interstitial fibrosis and GFR (r = -0.44; CI, -0.68 to -0.11), between glomerular fibronectin mRNA and GFR (r = -0.41; CI, -0.66 to -0.08), and between tubulointerstitial TGF-{beta} mRNA and the extent of glomerulosclerosis (r = -0.38; CI -0.66 to -0.01). In the group with other renal diseases, the strongest correlations found were between glomerular TGF-{beta} mRNA levels and GFR (r = 0.71; CI, 0.09 to 0.94), between glomerular TGF-{beta} mRNA levels and the extent of interstitial fibrosis (r = -0.64; CI, -0.91 to -0.01), between glomerular fibronectin mRNA levels and proteinuria (r = 0.67; CI, 0.12 to 0.91), and between tubulointerstitial TGF-{beta} mRNA levels and GFR (r = -0.67; CI, -0.90 to -0.15).

Correlations of Variables at Time of Biopsy with Rate of Change in GFR
Table 3 shows correlations with 95% CI of the variables at time of biopsy with rate of change in GFR within different time intervals after biopsy, both in the whole patient group and in chronic renal diseases. In the whole patient group, renal function at time of biopsy correlated strongly with GFR slope values between time of biopsy and 1 mo later (Figure 1A; r = -0.63; CI, -0.78 to -0.42), with GFR slope between time of biopsy and 3 mo later (r = -0.60; CI, -0.77 to -0.38), with GFR slope between time of biopsy and 6 mo later (r = -0.51; CI, -0.71 to -0.26), and with GFR slope between time of biopsy and 12 mo later (r = -0.43; CI, -0.65 to -0.15). Tubulointerstitial TGF-mRNA levels were significantly correlated with GFR slope between time of biopsy and 1 mo later (Figure 1B; r = 0.39; CI, 0.10 to 0.63). In chronic renal diseases, several strong correlations were found. For instance, renal function at time of biopsy significantly correlated with GFR slope values between time of biopsy and 1 mo later (Figure 1C; r = -0.57; CI, -0.77 to -0.29), with GFR slope between time of biopsy and 3 mo later (r = -0.45; CI, -0.70 to -0.12), with GFR slope between time of biopsy and 6 mo later (r = -0.44; CI, -0.69 to -0.11), and with GFR slope between time of biopsy and 12 mo later (Figure 1D; r = -0.50; CI, -0.73 to -0.18). Tubulointerstitial TGF-{beta} mRNA levels correlated with the GFR slopes between time of biopsy and 1 mo later (Figure 1E; r = 0.41; CI, 0.07 to 0.67). The extent of histologic damage in the biopsies did not correlate with the change of renal function in time. Glomerular fibronectin mRNA levels, in comparison with the renal function at time of biopsy, correlated relatively strong with the rate of changes in GFR between 3 mo and 12 mo after the biopsy (Figure 1F; r = 0.50; CI, 0.16 to 0.74).



View larger version (24K):
[in this window]
[in a new window]
[as a PowerPoint slide]
  		Figure 1. Correlations of variables at time of biopsy with rate of changes in GFR. For each patient, the slope of the regression line through the GFR values between different time intervals after biopsy was calculated. Correlations with 95% confidence intervals (CI) evaluated in the whole patient group (n = 52; A and B) and in chronic renal diseases (n = 37; C, D, E, and F) are shown. (A) Correlation of the GFR at time of biopsy with the rate of change in GFR between time of biopsy and 1 mo later (r = -0.63; CI, -0.78 to -0.42). (B) Correlation of tubulointerstitial TGF-mRNA levels with rate of change in GFR between time of biopsy and 1 mo later (r = 0.39; CI, 0.10 to 0.63). Correlations of the GFR at time of biopsy with (C) the rate of change in GFR between time of biopsy and 1 mo later (r = -0.57; CI, -0.77 to -0.29) and with (D) the rate of change in GFR between time of biopsy and 12 mo later (r = -0.50; CI, -0.73 to -0.18). (E) Correlation of tubulointerstitial TGF-{beta} mRNA levels with rate of change in GFR between time of biopsy and 1 mo later (r = 0.41; CI, 0.07 to 0.67). (F) Correlation of glomerular fibronectin mRNA levels with the rate of change in GFR between 3 mo and 12 mo after biopsy (r = 0.50; CI, 0.16 to 0.74).

 
Differences in Variables between Patients with Various Outcomes
Patients with stable or improving renal function between T0 and 1 mo (slope GFR >= 0) had significantly higher tubulointerstitial TGF-{beta} mRNA levels (Figure 2A; relative mean level of 3.6 ± 2.9 versus 1.6 ± 1.0; P < 0.05), lower urine protein levels at time of biopsy (Figure 2B; 1.9 ± 1.9 mg/24 h versus 4.4 ± 3.2 mg/24 h; P < 0.05), and lower GFR at time of biopsy (Figure 2C; 53.6 ± 32.6 ml/min versus 85.6 ± 44.3 ml/min; P < 0.05) than patients showing progressive decline in renal function (slope GFR < 0). Within the time interval between T0 and 6 mo, patients with favorable prognosis had significantly lower urine protein levels (1.9 ± 1.7 mg/24 h versus 4.7 ± 3.4 mg/24 h; P < 0.01). Patients with stable or improving renal function between T0 and 12 mo had significantly higher TGF-{beta} mRNA levels (Figure 2D; relative level of 3.7 ± 2.9 versus 1.4 ± 1.2; P < 0.05) and lower urine protein levels at time of biopsy (Figure 2E; 2.1 ± 1.7 mg/24 h versus 4.3 ± 3.5 mg/24 h; P < 0.05).



View larger version (17K):
[in this window]
[in a new window]
[as a PowerPoint slide]
  		Figure 2. Predictive value of variables at time of biopsy in chronic renal disease for outcome in individual patients. For different time intervals after biopsy, patients with chronic renal diseases were separated into a group showing progressive decline in renal function (slope GFR < 0) and a group showing stable or improving renal function over time (slope GFR >= 0). For the time interval between time of biopsy and 1 mo, patient groups differed in (A) tubulointerstitial TGF-{beta} mRNA levels (P < 0.05), in (B) urine protein levels (P < 0.05), and in (C) GFR (P < 0.05). For the time interval between time of biopsy and 12 mo, patient groups differed in (D) tubulointerstitial TGF-{beta} mRNA levels (P < 0.05) and in (E) urine protein levels (P < 0.05).

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Adequate assessment of the rate of renal disease progression in the individual patient is essential in determining the risk of progression toward end-stage renal failure. The prognostic information obtained from clinical parameters and from renal biopsies has been questioned (2,4,5 ). This demands a refinement of the prospective analysis of progressive renal disease by the use of additional, perhaps more suitable, molecular techniques (24,25 ). In experimental kidney disease, measurement of mRNA levels in the renal tissue has proven to be a promising predictor of outcome. Alterations in mRNA synthesis for extracellular matrix (ECM) components and ECM-regulating molecules precede the appearance of histologic lesions (12,26 ). Moreover, levels of collagen mRNA predict for the amount of scarring (17) and for the slope of renal function deterioration (15). Studies in early biopsies taken from patients with renal transplants have demonstrated that cortical mRNA levels are associated with renal function deterioration and might be used as prognostic indicators (27–29 ). In sequential protocol transplant biopsies, levels of mRNA for TGF-{beta}, but not renal function, reflect disease progression (29). More recently, a study was conducted evaluating the use of mRNA levels as markers for risk for diabetic nephropathy (30). Although the data are limited, the study shows that glomerular mRNA for collagen IV and connective tissue growth factor can be used to predict progression from normoalbuminuria to microalbuminuria. The findings mentioned above are the rationales for evaluating the applicability of mRNA measurement in biopsy tissue from native kidneys in the clinical setting.

Our main objective was to determine the predictive value of mRNA levels in comparison with functional parameters and histologic data, irrespective of the patient’s diagnosis. It has been hypothesized that progressive kidney diseases develop via a final common pathway irrespective of the original etiology (31,32 ). This common pathway is the process of tissue scar formation (33), which is a result of alterations in ECM synthesis, and is mediated by TGF-{beta}. For this reason, we chose to measure mRNA levels of TGF-{beta} and ECM components in a heterogeneous group of patients with diverse renal diseases. For our study, the entire patient group of 52 patients was divided into a group of 37 patients with chronic renal diseases and a group of 15 patients with other renal diseases. In the latter group, the majority of patients were diagnosed having minimal change disease and did not show any deterioration of GFR over time. Other patients in this group had an acute form of renal disease, showing a renal function which rapidly deteriorated before biopsy, but which normalized within several weeks after biopsy. We believe that the kinetics of these particular entities are different in terms of underlying molecular processes; therefore, correlations between variables at time of biopsy and correlations with prognosis were analyzed separately in patients with chronic renal diseases and in patients with other renal diseases. Separate assessment of correlations at time of biopsy within subgroups indeed showed different results. The finding that in the group of chronic renal diseases the extent of interstitial fibrosis, in comparison with the extent of glomerulosclerosis, relatively strongly correlated with the GFR at time of biopsy confirms earlier reports that tubulointerstitial alterations correlate stronger with loss of renal function at time of biopsy than glomerular alterations (34,35 ).

As an outcome measurement for each patient, we used the rate of change in GFR, which is often used in clinical studies (36,37 ). The rate of change in renal function was determined according to the slope values of regression lines through GFR data points between set time intervals after taking the biopsy. For this study, we have performed a considerable number of pairwise correlation tests. When many non-independent correlations are being made, the possibility of finding significant differences by chance increases as the number of comparisons increases. This means that a multiple comparison problem exists. The use of a Bonferroni correction for multiple correlation testing is rather conservative; we have therefore chosen to present estimated correlation values with corresponding 95% CI rather than with P-values. We assessed correlations of clinical, histologic, and molecular variables with the change in renal function after biopsy. Both in patients with chronic renal diseases and in the whole patient group, TGF-{beta} mRNA levels in the tubulointerstitium correlated positively with the rate of change in GFR between time of biopsy and 1 mo later. In addition, patients who showed stable or improving renal function between time of biopsy and 1 mo had significantly higher levels of tubulointerstitial TGF-{beta} mRNA than patients who showed progressive decline in renal function. A similar observation was made for tubulointerstitial TGF-{beta} mRNA between groups when renal outcome was considered over a time interval between time of biopsy and 12 mo. This means that relatively high tubulointerstitial TGF-{beta} mRNA levels are associated with a favorable prognosis. This finding is in concert with observations from a study in transplanted kidneys, in which we showed that relatively high renal cortical mRNA levels of TGF-{beta} early after transplantation are associated with a stable graft function at later time points (28). Various beneficial effects have been ascribed to TGF-{beta}, such as its role in tissue repair and as an immunosuppressive agent (10,38,39 ). The TGF-{beta} mRNA levels measured in the tubulointerstitium may reflect the repair process after tissue injury, but it may also reflect the responsiveness of the patient to therapy within the first month after biopsy. Only in patients with chronic renal diseases, glomerular fibronectin mRNA levels correlated positively with the rate of change in GFR between 3 mo and 12 mo after the biopsy was taken. This correlation was very weak when evaluated in the whole patient group. This shows that the mRNA levels we assessed might be used as prognostic indicators only in certain subsets of renal disease entities. Although no significant difference was found in glomerular fibronectin mRNA levels between patients who showed progressive decline in renal function and patients with favorable outcome of renal function, glomerular fibronectin mRNA levels in the biopsies might still be associated with a relatively favorable prognosis. For example, fibronectin, regulated by the actions of TGF-{beta} (40), may play an important role in compensatory repair mechanisms of the tissue. Indeed, fibronectin has been found to be of major importance during wound healing (41,42 ). In particular, fibronectin mRNA is upregulated in regenerating tissue of damaged kidneys (43).

This study shows application of quantitative real-time PCR on fresh kidney biopsies for assessing the prognostic value of mRNA levels in patients with chronic renal diseases. The method of mRNA measurement may be applicable in other organ disorders, as has been hypothesized and found for instance in the liver (44), the lungs (45), the heart (46), and in cancer research (47,48 ). In conclusion, the extent of histologic damage is negatively correlated with the GFR at time of biopsy, but it does not predict the rate of change in GFR after taking of the biopsy. On the contrary, TGF-{beta} mRNA levels in the tubulointerstitium correlate with the rate of change in renal function of the patient between the time of biopsy and 1 mo later. Above all, patients with a favorable renal outcome between time of biopsy and 12 mo later show significantly higher tubulointerstitial TGF-{beta} mRNA levels than patients who show progressive decline in renal function during the same time period. In chronic renal diseases, glomerular fibronectin correlated better with the rate of changes in GFR between 3 mo and 12 mo than the GFR at time of biopsy did. The current findings indicate that renal mRNA levels may constitute a powerful prognostic tool for outcome in human kidney diseases.


   Acknowledgments
 
This work was supported by the Dutch ‘Praeventiefonds’ (Grant 28–2184–1). The authors thank Dr. I. M. Bajema and Dr. E. L. Lagaaij for critically reading the manuscript. Conclusions derived from this paper have been incorporated into a previous review article (Eikmans et al., Kidney Int 62, 1125–1135, 2002).


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Bauer JH, Brooks CS, Burch RN: Clinical appraisal of creatinine clearance as a measurement of glomerular filtration rate. Am J Kidney Dis 2: 337–346, 1982[Medline]
  2. Walser M, Drew HH, LaFrance ND: Creatinine measurements often yielded false estimates of progression in chronic renal failure. Kidney Int 34: 412–418, 1988[Medline]
  3. Schwartz MM, Lan SP, Bernstein J, Hill GS, Holley K, Lewis EJ: Irreproducibility of the activity and chronicity indices limits their utility in the management of lupus nephritis. Lupus Nephritis Collaborative Study Group. Am J Kidney Dis 21: 374–377, 1993[Medline]
  4. Schwartz MM, Lan SP, Bernstein J, Hill GS, Holley K, Lewis EJ: Role of pathology indices in the management of severe lupus glomerulonephritis. Lupus Nephritis Collaborative Study Group. Kidney Int 42: 743–748, 1992[Medline]
  5. Levey AS, Lau J, Pauker SG, Kassirer JP: Idiopathic nephrotic syndrome. Puncturing the biopsy myth. Ann Intern Med 107: 697–713, 1987
  6. Striker LM, Killen PD, Chi E, Striker GE: The composition of glomerulosclerosis: Studies in focal sclerosis, crescentic glomerulonephritis, and membranoproliferative glomerulonephritis. Lab Invest 51: 181–192, 1984[Medline]
  7. Buyukbabani N, Droz D: Distribution of the extracellular matrix components in human glomerular lesions. J Pathol 172: 199–207, 1994[CrossRef][Medline]
  8. Peten E, Striker LJ, Carome MA, Elliott SJ, Yang C-W, Striker GE: The contribution of increased collagen synthesis to human glomerulosclerosis: A quantitative analysis of a2IV collagen mRNA expression by competitive polymerase chain reaction. J Exp Med 176: 1571–1576, 1992[Abstract/Free Full Text]
  9. Border WA, Noble NA: Transforming growth factor beta in tissue fibrosis. N Engl J Med 331: 1286–1292, 1994[Free Full Text]
  10. Mustoe TA, Pierce GF, Thomason A, Gramates P, Sporn MB, Deuel TF: Accelerated healing of incisional wounds in rats induced by transforming growth factor-beta. Science 237: 1333–1336, 1987[Abstract/Free Full Text]
  11. Roberts AB, Sporn MB, Assoian RK, Smith JM, Roche NS, Wakefield LM, Heine UI, Liotta LA, Falanga V, Kehrl JH: Transforming growth factor type beta: rapid induction of fibrosis and angiogenesis in vivo and stimulation of collagen formation in vitro. Proc Natl Acad Sci USA 83: 4167–4171, 1986[Abstract/Free Full Text]
  12. Munaut C, Bergijk EC, Baelde JJ, Noël A, Foidart JM, Bruijn JA: A molecular biological study of extracellular matrix components during the development of glomerulosclerosis in murine chronic graft-versus-host disease (GvHD). Lab Invest 67: 580–587, 1992[Medline]
  13. Okuda S, Languino LR, Ruoslahti E, Border WA: Elevated expression of transforming growth factor-beta and proteoglycan production in experimental glomerulonephritis. Possible role in expansion of the mesangial extracellular matrix. J Clin Invest 86: 453–462, 1990
  14. Yang CW, Striker GE, Chen WY, Kopchick JJ, Striker LJ: Differential expression of glomerular extracellular matrix and growth factor mRNA in rapid and slowly progressive glomerulosclerosis: Studies in mice transgenic for native or mutated growth hormone. Lab Invest 76: 467–476, 1997[Medline]
  15. Striker LJ: Modern renal biopsy interpretation: Can we predict glomerulosclerosis? Semin Nephrol 13: 508–515, 1993[Medline]
  16. He C-J, Yang C-W, Peten EP, Liu Z-H, Patel A, Striker LJ, Striker GE: Collagen and collagenase mRNAs in normal and sclerotic glomeruli: Predictors of progression and response to therapy. Kidney Int 47 [Suppl 49]: S39–S43, 1995
  17. Lee SK, Goyal M, De Miguel M, Thomas P, Wharram B, Dysko R, Phan S, Killen PD, Wiggins RC: Renal biopsy collagen I mRNA predicts scarring in rabbit anti- GBM disease: Comparison with conventional measures. Kidney Int 52: 1000–1015, 1997[Medline]
  18. Cockcroft DW, Gault MH: Prediction of creatinine clearance from serum creatinine. Nephron 16: 31–41, 1976[Medline]
  19. Eikmans M, Baelde HJ, De Heer E, Bruijn JA: Effect of age and biopsy site on extracellular matrix mRNA and protein levels in human kidney biopsies. Kidney Int 60: 974–981, 2001[CrossRef][Medline]
  20. Masseroli M, O’Valle F, Andujar M, Ramirez C, Gomez-Morales M, de Dios LJ, Aguilar M, Aguilar D, Rodriguez-Puyol M, Del Moral RG: Design and validation of a new image analysis method for automatic quantification of interstitial fibrosis and glomerular morphometry. Lab Invest 78: 511–522, 1998[Medline]
  21. De Heer E, Sijpkens YW, Verkade M, den Dulk M, Langers A, Schutrups J, Bruijn JA, Van Es LA: Morphometry of interstitial fibrosis. Nephrol Dial Transplant 15 [Suppl 6]: 72–73, 2000[Abstract/Free Full Text]
  22. Eikmans M, Baelde HJ, De Heer E, Bruijn JA: Processing renal biopsies for diagnostic mRNA quantification: Improvement of RNA extraction and storage conditions. J Am Soc Nephrol 11: 868–873, 2000[Abstract/Free Full Text]
  23. Retherford RD, Choe MK: Bivariate linear regression. In: Statistical Models for Causal Analysis, New York, John Wiley & Sons, Inc., 1993, pp 1–28
  24. Kretzler M, Cohen CD, Doran P, Henger A, Madden S, Grone EF, Nelson PJ, Schlondorff D, Grone HJ: Repuncturing the renal biopsy: Strategies for molecular diagnosis in nephrology. J Am Soc Nephrol 13: 1961–1972, 2002[Abstract/Free Full Text]
  25. Eikmans M, Baelde HJ, De Heer E, Bruijn JA: RNA expression profiling as prognostic tool in renal patients: Toward nephrogenomics. Kidney Int 62: 1125–1135, 2002[CrossRef][Medline]
  26. Hugo C, Shankland SJ, Pichler RH, Couser WG, Johnson RJ: Thrombospondin 1 precedes and predicts the development of tubulointerstitial fibrosis in glomerular disease in the rat. Kidney Int 53: 302–311, 1998[CrossRef][Medline]
  27. Kirk AD, Jacobson LM, Heisey DM, Radke NF, Pirsch JD, Sollinger HW: Clinically stable human renal allografts contain histological and RNA- based findings that correlate with deteriorating graft function. Transplantation 68: 1578–1582, 1999[CrossRef][Medline]
  28. Eikmans M, Sijpkens YW, Baelde HJ, De Heer E, Paul LC, Bruijn JA: High transforming growth factor-beta and extracellular matrix mRNA response in renal allografts during early acute rejection is associated with absence of chronic rejection. Transplantation 73: 573–579, 2002[Medline]
  29. Baboolal K, Jones GA, Janezic A, Griffiths DR, Jurewicz WA: Molecular and structural consequences of early renal allograft injury. Kidney Int 61: 686–696, 2002[CrossRef][Medline]
  30. Adler SG, Kang SW, Feld S, Cha DR, Barba L, Striker L, Striker G, Riser BL, Lapage J, Nast CC: Can glomerular mRNAs in human type 1 diabetes be used to predict transition from normoalbuminuria to microalbuminuria? Am J Kidney Dis 40: 184–188, 2002[CrossRef][Medline]
  31. Vleming LJ, Baelde JJ, Westendorp RGJ, Daha MR, Van Es LA, Bruijn JA, Westendorp RG: The glomerular deposition of PAS positive material correlates with renal function in human kidney diseases. Clin Nephrol 47: 158–167, 1997[Medline]
  32. Van Vliet A, Baelde HJ, Vleming LJ, De Heer E, Bruijn JA: Distribution of fibronectin isoforms in human renal disease. J Pathol 193: 256–262, 2001[CrossRef][Medline]
  33. Morel-Maroger SL, Killen PD, Chi E, Striker GE: The composition of glomerulosclerosis. I. Studies in focal sclerosis, crescentic glomerulonephritis, and membranoproliferative glomerulonephritis. Lab Invest 51: 181–192, 1984
  34. Risdon RA, Sloper JC, De Wardener HE: Relationship between renal function and histological changes found in renal-biopsy specimens from patients with persistent glomerular nephritis. Lancet 2: 363–366, 1968[Medline]
  35. Vleming LJ, De Fijter JW, Westendorp RGJ, Daha MR, Bruijn JA, Van Es LA: Histomorphometric correlates of renal failure in IgA nephropathy. Clin Nephrol 49: 337–344, 1998[Medline]
  36. Ruggenenti P, Schieppati A, Remuzzi G: Progression, remission, regression of chronic renal diseases. Lancet 357: 1601–1608, 2001[CrossRef][Medline]
  37. Mitch WE: Measuring the rate of progression of renal insufficiency. In: The Progressive Nature of Renal Disease, edited by Mitch WE, New York, Churchill Livingstone, 1986, pp 203–220
  38. Tsunawaki S, Sporn M, Ding A, Nathan C: Deactivation of macrophages by transforming growth factor-beta. Nature 334: 260–262, 1988[CrossRef][Medline]
  39. Kulkarni AB, Huh CG, Becker D, Geiser A, Lyght M, Flanders KC, Roberts AB, Sporn MB, Ward JM, Karlsson S: Transforming growth factor beta 1 null mutation in mice causes excessive inflammatory response and early death. Proc Natl Acad Sci USA 90: 770–774, 1993[Abstract/Free Full Text]
  40. Ignotz RA, Endo T, Massague J: Regulation of fibronectin and type I collagen mRNA levels by transforming growth factor-beta. J Biol Chem 262: 6443–6446, 1987[Abstract/Free Full Text]
  41. Ffrench-Constant C, Van de WL, Dvorak HF, Hynes RO: Reappearance of an embryonic pattern of fibronectin splicing during wound healing in the adult rat. J Cell Biol 109: 903–914, 1989[Abstract/Free Full Text]
  42. Grinnell F, Billingham RE, Burgess L: Distribution of fibronectin during wound healing in vivo. J Invest Dermatol 76: 181–189, 1981[CrossRef][Medline]
  43. Basile DP, Martin DR, Hammerman MR: Extracellular matrix-related genes in kidney after ischemic injury: Potential role for TGF-beta in repair. Am J Physiol 275: F894–F903, 1998
  44. Ishimura N, Fukuda R, Fukumoto S: Relationship between the intrahepatic expression of interferon-alpha receptor mRNA and the histological progress of hepatitis C virus- associated chronic liver diseases. J Gastroenterol Hepatol 11: 712–717, 1996[Medline]
  45. Charpin JM, Valcke J, Kettaneh L, Epardeau B, Stern M, Israel-Biet D: Peaks of transforming growth factor-beta mRNA in alveolar cells of lung transplant recipients as an early marker of chronic rejection. Transplantation 65: 752–755, 1998[CrossRef][Medline]
  46. Birks EJ, Owen VJ, Burton PB, Bishop AE, Banner NR, Khaghani A, Polak JM, Yacoub MH: Tumor necrosis factor-alpha is expressed in donor heart and predicts right ventricular failure after human heart transplantation. Circulation 102: 326–331, 2000[Abstract/Free Full Text]
  47. Murray PA, Barrett-Lee P, Travers M, Luqmani Y, Powles T, Coombes RC: The prognostic significance of transforming growth factors in human breast cancer. Br J Cancer 67: 1408–1412, 1993[Medline]
  48. Olsson CA, De Vries GM, Buttyan R, Katz AE: Reverse transcriptase-polymerase chain reaction assays for prostate cancer. Urol Clin North Am 24: 367–378, 1997[CrossRef][Medline]
Received for publication July 26, 2002. Accepted for publication December 16, 2002.




This article has been cited by other articles:


Home page
 Am. J. Physiol. Renal Physiol.Home page
C. O. Chen, M. H. Park, M. S. Forbes, B. A. Thornhill, S. C. Kiley, K. H. Yoo, and R. L. Chevalier
Angiotensin-converting enzyme inhibition aggravates renal interstitial injury resulting from partial unilateral ureteral obstruction in the neonatal rat
Am J Physiol Renal Physiol, March 1, 2007; 292(3): F946 - F955.
[Abstract] [Full Text] [PDF]

Home page
 Nephrol Dial TransplantHome page
S.-M. Ka, A. Rifai, J.-H. Chen, C.-W. Cheng, H.-A. Shui, H.-S. Lee, Y.-F. Lin, L.-F. Hsu, and A. Chen
Glomerular crescent-related biomarkers in a murine model of chronic graft versus host disease
Nephrol. Dial. Transplant., February 1, 2006; 21(2): 288 - 298.
[Abstract] [Full Text] [PDF]

Home page
 Nephrol Dial TransplantHome page
M. Eikmans, H. J. Baelde, E. de Heer, and J. A. Bruijn
Messenger RNA assessment in clinical nephrology: perspectives and progress of methodology
Nephrol. Dial. Transplant., December 1, 2005; 20(12): 2598 - 2601.
[Full Text] [PDF]

Home page
 J. Am. Soc. Nephrol.Home page
M. Eikmans, M. C. Roos-van Groningen, Y. W.J. Sijpkens, J. Ehrchen, J. Roth, H. J. Baelde, I. M. Bajema, J. W. de Fijter, E. de Heer, and J. A. Bruijn
Expression of Surfactant Protein-C, S100A8, S100A9, and B Cell Markers in Renal Allografts: Investigation of the Prognostic Value
J. Am. Soc. Nephrol., December 1, 2005; 16(12): 3771 - 3786.
[Abstract] [Full Text] [PDF]

Home page
 J. Am. Soc. Nephrol.Home page
L. J. Striker and G. E. Striker
Windows on Renal Biopsy Interpretation: Does mRNA Analysis Represent a New Gold Standard?
J. Am. Soc. Nephrol., April 1, 2003; 14(4): 1096 - 1098.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Eikmans, M.
Right arrow Articles by Bruijn, J. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Eikmans, M.
Right arrow Articles by Bruijn, J. A.

HOME CURRENT ISSUE ARCHIVES JASN Express ONLINE SUBMISSION AUTHOR INFO
EDITORIAL BOARD SUBSCRIBE FEEDBACK ALERTS HELP