SPECIAL COMMUNICATION
Use of serial analysis of gene expression to generate kidney
expression libraries
M. Ashraf
El-Meanawy1,
Jeffrey R.
Schelling1,
Fatima
Pozuelo1,
Matthew M.
Churpek1,
Eckhard K.
Ficker,
Sudha
Iyengar3, and
John R.
Sedor1,2
Departments of 1 Medicine, 2 Physiology and
Biophysics, and 3 Epidemiology and Biostatistics, School of
Medicine, Case Western Reserve University, and Rammelkamp Center for
Research and Education, MetroHealth Medical Center, Cleveland, Ohio
44109
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ABSTRACT |
Chronic
renal disease initiation and progression remain incompletely
understood. Genome-wide expression monitoring should clarify mechanisms
that cause progressive renal disease by determining how clusters of
genes coordinately change their activity. Serial analysis of gene
expression (SAGE) is a technique of expression profiling, which permits
simultaneous, comparative, and quantitative analysis of gene-specific,
9- to 13-bp sequence tags. Using SAGE, we have constructed a tag
expression library from ROP-+/+ mouse kidney. Tag sequences were sorted
by abundance, and identity was determined by sequence homology
searching. Analyses of 3,868 tags yielded 1,453 unique kidney
transcripts. Forty-two percent of these transcripts matched mRNA
sequence entries with known function, 35% of the transcripts
corresponded to expressed sequence tag (EST) entries or cloned genes,
whose function has not been established, and 23% represented
unidentified genes. Previously characterized transcripts were clustered
into functional groups, and those encoding metabolic enzymes, plasma
membrane proteins (transporters/receptors), and ribosomal proteins were
most abundant (39, 14, and 12% of known transcripts, respectively).
The most common, kidney-specific transcripts were kidney
androgen-regulated protein (4% of all transcripts), sodium-phosphate
cotransporter (0.3%), renal cytochrome P-450 (0.3%),
parathyroid hormone receptor (0.1%), and kidney-specific cadherin
(0.1%). Comprehensively characterizing and contrasting gene expression
patterns in normal and diseased kidneys will provide an alternative
strategy to identify candidate pathways, which regulate nephropathy
susceptibility and progression, and novel targets for therapeutic intervention.
end-stage renal disease; mouse model; chronic renal failure; genetic techniques; mRNA
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INTRODUCTION |
THE INCIDENCE OF END-STAGE renal disease has
risen at an annual rate of 7-9% for the last decade
(3). This increase is due, at least in part, to incomplete
understanding of renal disease pathophysiology and limited therapeutic
options to prevent disease progression. Kidney disease-oriented
research has primarily focused on candidate molecules, emphasizing
fibrogenic pathways (7) and hemodynamic alterations in the
setting of reduced nephron number (8) as key mechanisms
regulating chronic renal disease initiation and progression. However,
chronic renal failure results from complex interaction of genetic and
environmental risk factors, and interruption of a single effector
pathway is unlikely to result in significant therapeutic benefit
(31).
In contrast to focusing on candidate effector pathways, gene expression
profiling is an alternative but powerful tool to better understand
renal disease pathogenesis by generating a detailed analysis of mRNA
expression profiles. Novel molecular techniques used to generate
transcript libraries simultaneously can determine net consequences of
gene-gene and gene-environment interactions on expression of thousands
of genes. Rather than applying a priori assumptions (i.e., hypothesis
testing), kidney transcript profiles from normal animals and animals
with progressive kidney disease could be "mined" by analytic
methods developed to discover unexpected relationships between genes or
pathways (i.e., "bottom-up approach") (6). Although
hypothesis-driven experimentation remains critical for knowledge
discovery, thoughtful analysis of expression profiles generated with
other model systems has yielded unanticipated results and defined new
paradigms. For example, cluster analysis of transcript profiles showed
that serum specifically activated wound-healing processes in
fibroblasts rather than serving as a general signal for cell growth
(18). Global expression monitoring also demonstrated that
receptor tyrosine kinases, with unique ligand specificities and
distinct biological effects, induce broadly overlapping, rather than
independent, sets of genes (13;27), suggesting that cellular responses
depend less on the specific ligand than cellular differentiation state,
signal strength, or combinatorial interactions between activated
signaling pathways. Finally, clustering of transcripts with similar
expression patterns can assign a precise biological pathway to a gene
of "generic" function (e.g., kinase) and provide clues to function
of an unknown gene by relating it to characterized genes
(1, 10).
Gene expression profiles can be generated and compared by multiple
techniques. Subtractive hybridization (33), subtraction libraries (14), and differential display (2,
5) are semiquantitative methods, which may not detect
small, pathophysiologically important variations in gene expression. In
contrast, DNA microarrays quantitatively assay expression levels of
thousands of genes, using discrete DNA sequences robotically imprinted
on glass microscope slides (9), but require technology and
instrumentation not available to most investigators. Serial analysis of
gene expression (SAGE) also allows simultaneous, quantitative analysis
of a large number of transcripts (36) but does not require
expensive instrumentation. Using SAGE, an individual investigator with
access to an automated sequencer can quantify mRNAs, expressed at a
level of 100 copies/cell, within months (36) and can
identify transcripts expressed as low as 1 transcript/cell in larger
SAGE libraries (37). To date, SAGE expression libraries
have been generated predominantly from yeast, tumor specimens, and
cultured cells (37, 42). Because quantitative
analyses of transcript levels appear to be critical for a better
understanding of normal cell function and cellular response to injury,
we have utilized SAGE to generate a cDNA tag library, which is composed
of unique 9- to 13-bp cDNA sequence signatures, from mouse kidney mRNA.
The results provide feasibility of applying SAGE for comparison of gene
expression between normal and diseased kidney to discover new
mechanisms of renal disease pathogenesis and to identify novel targets
for renal disease therapy.
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MATERIALS AND METHODS |
RNA preparation.
As a prelude to comparing kidney expression libraries generated from
wild-type animals and animals with progressive chronic renal failure,
kidneys were harvested from 26-wk-old male ROP-Es/+ mice (Jackson
Laboratories, Bar Harbor, ME), which are glomerulosclerosis prone but
have normal kidney structure and function in the absence of a
triggering event such as hyperglycemia or nephron reduction (12, 40). One and three-quarters kidney were
used for polyadenylated RNA (A+) extraction by using RNeasy and Oligotx
extraction kits (Qiagen, Valencia, CA) according to the manufacturer's recommendation.
Production of ROP-Es/+ kidney tag library.
SAGE kidney libraries were generated as described by Velculescu et al.
(36) with modifications to maximize the final yield. A
schematic of SAGE can be found on the Internet (http://www.sagenet.org) and in Fig. 1. Briefly,
biotin-TEG-oligo-dT (Integrated DNA Technologies, Coralville, IA) was
used to drive first-strand cDNA synthesis of mouse kidney A+ RNA using
a MMLV-RT cDNA synthesis kit (GIBCO-BRL, Gaithersburg, MD).
Second-strand cDNA was then generated with a final yield efficiency of
75-85%. Biotinylated cDNA was digested with the restriction
enzyme Nla III (New England Biolaboratories, Beverly, MA)
and 3'-cDNA end fragments isolated with streptavidin-coated magnetic
beads (Dynal, Oslo, Norway). 3'-cDNAs were split into two pools, and
each pool was ligated by using T4 DNA ligase (GIBCO-BRL) to custom-made
40-bp SAGE oligonucleotide DNA linkers (L1 and L2; Integrated DNA
Technologies). To release linker-cDNA tag hybrids, the ligation
reactions were then digested with the class II-shift restriction
endonuclease Bsm FI (New England Biolaboratories) at 60°C
for 2 h with continuous rotation in a hybridization oven. Magnetic
beads were pelleted, and released linker-tags were blunted with T4-DNA
polymerase (New England Biolaboratories) in the presence of
deoxynucleotides. The L1- and L2-linker-tag complexes were then ligated
together to form linker-ditag-linker constructs. To determine the
optimum input of the linker-ditag-linker for large scale PCR, aliquots
of serially diluted (1:10-1:1,280) ligation products were PCR
amplified for 25 cycles (95°C for 30 min, 55°C for 1 min, then
70°C for 1 min) and analyzed by 8% PAGE. Subsequently, a large-scale
(96 tubes) PCR reaction was conducted at optimum template dilution, and
PCR products were pooled and digested with Nla III to
release kidney cDNA ditag sequences. Released ditags were separated
from linkers by 12% PAGE, eluted, and ligated together to produce
concatemers. Concatemers were size fractionated by using 8% PAGE, and
those between 600 and 1,200 bp were isolated and cloned into pZero
(Invitrogen, San Diego, CA), which had been digested with Sph
I. After electroporation into DH10B (GIBCO-BRL), clones were
analyzed for recombinants by direct PCR using M13 forward and M13
reverse primers. PCR products >500 bp in size were isolated by 1.2%
agarose gel electrophoresis and sequenced by using a BigDye terminator
cycle sequencing kit (Perkin-Elmer, Foster City, CA). Sequencing
reactions were analyzed by using an ABI-377 automated sequencer
(Perkin-Elmer).
Data analysis.
Concatemer sequences were analyzed by using SAGE software v1.0
(provided by Dr. Kenneth Kinzler's laboratory, Johns Hopkins University, Baltimore, MD), which automatically detects and counts tags
from sequence files. Tag counts directly reflect transcript abundance
(36, 42). SAGE software excludes replicate
ditags from the tag sequence catalogue, because the probability of any two tags being coupled in the same ditag is small, even for abundant transcripts (36). The identities of the genes
corresponding to the tags were determined by homology searches of
public databases at the National Center for Biotechnology web site
(http://www.ncbi.nlm.nih.gov), including GenBank, European Molecular
Biology Laboratory (EMBL), DNA Database of Japan (DDBJ), Protein Data
Bank (PDB), and the expressed sequence tag (EST) division of GenBank,
using basic alignment research tool (BLAST)-N v2.1 (cutoff 70, no
filters) (35). This version of the BLAST search
algorithm is no longer available, but our results can be replicated by
using Advanced BLAST v2.0 [advanced search options: ungapped
alignment, expect set to 100-10,000, no filters and word
size set to 6 (see BLAST help file
http://www.ncbi.nlm.nih.gov/BLAST/blast_help.htm)]. The nonredundant
Mus musculus GenBank database was initially searched with
the tag sequences. If no appropriate matches were obtained, the entire
nonredundant GenBank database was next searched, and if necessary, the
tag sequence was submitted to dbEST, the nonredundant GenBank, EMBL,
DDBJ EST database. Frequently, SAGE tag sequences matched more than one
transcript. In these cases, genes matching the SAGE tags were
identified using the published algorithm (36, 39). First, GenBank entries from mammalian organisms were
identified (for searches which were not limited to M. musculus). Matches with nonmammalian sequences were excluded.
Second, genomic, non-mRNA sequences were eliminated. Finally, search
results were analyzed for multiple entries for the same gene, and the
final match was checked to verify that the SAGE sequence flanked the
3'-most Nla III restriction endonuclease recognition site.
Immunoblotting.
Tissue samples were isolated from neonatal rat kidney and heart (as a
positive control). DRK23 cells overexpressing rat Kv2.1 were analyzed
as a negative control. Samples were homogenized in 5-10 vol of 0.3 M sucrose, 10 mM NaPO4 (pH 7.4) supplemented with protease
inhibitor mix (Complete; Roche, Indianapolis, IN). After removal of
debris and nuclei (3,000 g, 10 min), the supernatant was
spun at 50,000 g for 1 h at 4°C to pellet a crude
membrane fraction. Protein concentrations were determined by the
bicinchoninic acid method (Pierce, Rockford, IL). Total protein was
separated on 11% SDS polyacrylamide gels and transferred to
polyvinylidene difluoride membranes. Membranes were blocked overnight
with 5% nonfat dry milk in PBS plus 0.1% Tween and immunoblotted with a commercially available mouse monoclonal anti-Kv1.5 antibody (1:250
dilution; 1 h at room temperature; Transduction Laboratories, Lexington, KY) followed by horseradish peroxidase-conjugated secondary antibody (1:3,000; 1 h at room temperature; Amersham Pharmacia Biotech, Piscataway, NJ). Western blots were developed with the ECL-Plus detection system (Amersham Pharmacia, Arlington Heights, IL),
as previously described (32).
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RESULTS |
Tag library generation.
As described in MATERIALS AND METHODS, a pilot PCR
reaction was performed to determine the optimum dilution for the input DNA by using serial dilutions (1:10-1:1,280) of recovered
linker-ditag-linker constructs (Fig. 2).
As expected, amplified linker-ditag-linker constructs were ~102 bp. A
1:40 dilution of the ligation reaction gave optimum results, with the
intensity of the ethidium bromide-stained, amplified
linker-ditag-linker being greater than the background products of ~80
(ligated linkers without kidney tag sequence) and 90 bp (ligated
linkers with a single kidney tag sequence). A large-scale PCR reaction
using the previously determined optimum conditions was subsequently
performed, and the 102-bp linker-ditag-linker products were digested
with Nla III to free ditags from the linkers. Ditags contain
two 9- to 13-bp sequences, which have been derived from kidney
transcripts and are joined in the sequence
5'-CATG(N)22-26CATG-3'. The first half of the ditag
represents sense sequence of an ROP-Es/+ kidney RNA, and the second
half represents the antisense sequence of a different kidney
transcript. Ditags migrated with 22- to 26-bp-molecular-size marker,
whereas linkers migrated at 40 bp (Fig.
3). In our experience, purification of
the 102-bp band by preparative PAGE, before Nla III
treatment, improves digestion efficiency and ultimately the quality of
the tag library. Eluted ditags were ligated together to generate
concatemers (Fig. 4), which were ligated
into pZero and transformed into bacteria as described in
MATERIALS AND METHODS. Bacterial clones were screened for
plasmids containing a kidney tag concatemer insert by direct PCR (Fig.
5) and concatemer sequence determined as
described in MATERIALS AND METHODS.

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Fig. 2.
Determination of optimum template dilution for
large-scale PCR. An ethidium bromide-stained, nondenaturing 8%
polyacrylamide gel demonstrates PCR products obtained from serial
dilutions (top) of the linker-ditag-linker template. The
1:10 dilution is not shown. Amplified linker-ditag-linker migrate at
102 bp. Background bands of other sizes stain with equal or lower
intensity. The 1:40 dilution of template had an optimal signal-to-noise
ratio and was used for the large scale PCR (see MATERIALS AND
METHODS).
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Fig. 3.
Nla III digestion of the 102-bp
linker-ditag-linker large scale PCR product yields 40-bp band (linkers)
and a 26-bp ditag bands. The Nla III digestion products were
analyzed by 16% PAGE, and the gel was stained with Sybr green-I. The
26-bp ditag band was excised from the gel and concatemerized as
described in MATERIALS AND METHODS.
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Fig. 4.
Sybr green-I stained, nondenaturing 5% PAGE of 26-bp
ditags concatemerized as described in MATERIALS AND
METHODS. Concatemers ranging in size from 600 to 1,200 bp were
subcloned in the pZero cloning vector.
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Fig. 5.
A
representative 1.2% agarose gel electrophoresis of cloned kidney SAGE
tag concatemers, which were generated by direct PCR of recombinant
bacterial clones. The gel has been ethidium bromide stained. The PCR
products vary in size from 700 to 1,400 bp, which include ~300 bp
derived from the multiple cloning site.
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Tag library analysis.
Our library contained 3,868 sequence tags, which represented 1,575 unique mRNA transcripts. Ditags, containing identical tag sequences,
were encountered 90 times. In each case, the tags were counted only
once in tag abundance calculations. Such ditags are potentially
produced by biased PCR, because the probability of any two tags being
coupled in the same ditag is small even for abundant transcripts
(36). The actual number of unique genes (1,575) identified by 3,868 kidney tags is
consistent with previous predictions (37). Assuming that a
cell contains 15,000 mRNA molecules (16), the 3,868-tag
sequence library provides 1-fold coverage for mRNA molecules present at
a minimum of 4 copies/cell. However, previous analyses of SAGE
libraries have suggested that three- to fourfold coverage is necessary
to identify all the transcripts at any level of abundance with high
certainty. The 100 most abundant tags (6.3% of the unique transcripts
represented in this expression library) represented 36% of isolated
mRNAs. As expected, the frequency distribution pattern demonstrated
that a minority of genes were responsible for the majority of
transcripts, indicating that most genes are expressed at low abundance.
Table 1 shows the 50 most abundant
transcripts with GenBank matches and corresponding sequence tags from
the normal mouse kidney tag library. The entire kidney library is
available at http://www.metrohealth.org/research/kidneytag.html.
Seventeen of these high- abundance transcripts were identified in
the EST databases, and of these, 15 ESTs identified genes with
characterized functions. As expected, housekeeping and mitochondrial
genes were among the most highly expressed genes. In addition, several
kidney-predominant or kidney-specific genes were also identified. Mouse
androgen-regulated protein previously has been shown to be expressed in
the S3 region of proximal tubules and accounted for ~4% of
transcripts. Other transcripts, known to be kidney specific, included
renal cytochrome P-450 (0.3% of all transcripts),
parathyroid hormone receptor (0.1%), and kidney-specific cadherin
(0.1%). Other genes that are highly, but not exclusively, expressed in
the kidney, were also identified in the group of highly expressed
genes, including the renal sodium phosphate
(Na+-Pi) cotransporter (0.3% of all
transcripts); plasma glutathione peroxidase (a superoxide scavenger
pathway enzyme, 2.3% of all transcripts); mouse kidney
testosterone-regulated RP2 mRNA (a transcript regulated in patients
with polycystic kidney disease, 0.2% of transcripts); the tetraspanin
CD63 (predominantly expressed in glomerulus and postulated to be
necessary for normal glomerular function, 0.2% of transcripts); and
prolidase (0.2% of transcripts). Prolidase encodes an enzyme that
recycles dipeptides with COOH-terminus proline or hydroxyproline
residues and has been previously been shown to be expressed at a high
level in mouse kidney (17). Interestingly, the
imidopeptide substrate of prolidase has been incriminated in
inflammation and tissue damage in prolidase-deficient humans
(21), suggesting that prolidase activity may be necessary for healing after inflammatory injury to the kidney. Although one
report demonstrates kidney Kv1.5 expression by Northern blotting (30), the Kv1.5 potassium channel was unexpectantly
abundant (3.4% of all transcripts). This gene is predominantly
expressed in ventricle, nerve cells, and vascular smooth muscle, and
the high level of expression may reflect contribution of renal
resistance vasculature or renal nerve transcripts to the SAGE tags.
Because such a high frequency of tags corresponding to Kv1.5 mRNA was found in the kidney SAGE library, Kv1.5 expression was confirmed by
immunoblot analysis (Fig. 6). Kv1.5 was
abundantly expressed in rat heart, as previously published
(4), and in neonatal rat kidney but not in a cell line
overexpressing a related potassium channel (Kv2.1).
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Table 1.
Fifty most frequent SAGE tags identified in a sample of 3,868 tags
isolated from a normal ROP-Es/+ mouse kidney (26 wk)
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Fig. 6.
Kv1.5 expression was analyzed by immunoblotting as
described in MATERIALS AND METHODS. Lysates from rat
neonatal kidney (lanes 1 and 2), heart
(lane 3, positive control) and DRK23 cells overexpressing
Kv2.1 (lane 4, negative control) were resolved by SDS-PAGE.
Left: molecular mass markers (in kDa); right
arrow: position of Kv1.5.
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Table 2 catalogues the genes identified
in the kidney tag library by function, which were assigned by applying
a classification system of cDNA clones isolated in an expression
profile of mouse proximal tubule (34). Genes identified by
microarray technology have also been grouped by function in a similar
manner (18). Approximately 40% of kidney transcripts,
identified by SAGE tags, matched mRNA sequence entries with known
function, 37% of the transcripts corresponded to EST entries or cloned
genes, with undetermined function, and 23% represented unidentified
genes. Of the tag sequences identified in the EST databases, 26 were identified only in kidney cDNA libraries that have been used in EST
projects (http://www.ncbi.nlm.nih.gov/UniGene/Mm.Home.html). Of these
26 tags, 24 were identified in libraries generated from C57BL kidneys,
an interesting finding because the C57BL and ROP mice share a large
amount of their genetic backgrounds (20). The remaining
two tags were identified in the cDNA ESTs generated from Barstead
Balb/c mouse kidney.
The functional categories, with the highest numbers of identified
genes, should be predicted by the physiology of the normal kidney. For
example, genes encoding enzymes regulating metabolic enzymes,
mitochondrial function or membrane proteins (transporters/receptors) accounted for 19% of all unique tag sequences and 47% of genes, whose
function has been established. Consistent with the high energy
requirement of filtration and solute transport, transcripts for 59 proteins involved in mitochondrial respiration were identified. These
included the mouse cytochrome-c oxidase Vb subunit gene, cytochrome c-oxidase polypeptides I and III, adrenodoxin,
and NADH-ubiquinone oxidoreductase chain 3 genes. Enzymes involved in
the Kreb's cycle, the final common pathway for oxidation of amino
acids, fatty acids, and carbohydrates and, ultimately, ATP generation,
were also identified, including succinyl-CoA synthase, isocitrate
dehydrogenase, and malate dehydrogenase. Glucose synthesis is an
important kidney metabolic function, and enzymes involved in
gluconeogenesis, such as hexokinase and fructose-1,6-bisphosphatase, were represented in this kidney tag library. Arachidonate acid metabolites have been implicated in normal renal physiology. In addition to CYB4B1, other genes involved in arachidonate metabolism were identified, including arachidonate 5-lipoxygenase and
phospholipase A2.
Because solute transport is a major physiological function of the
kidney, it was predicted that transcripts encoding transport proteins
would also be identified in the expression library. In addition to the
renal Na+-Pi transporter already mentioned,
other expressed genes with known transport functions included the
-subunit of Na+-K+-ATPase,
H+-ATPase, the inward rectifier potassium channel, the
mouse basolateral thick ascending limb of Henle chloride channel,
carbonic anhydrase, and the furosemide-sensitive Na-K-2Cl cotransporter.
In contrast to these functional categories of genes that regulate
normal renal physiology, transcripts promoting tissue injury and
remodeling, including proteases, cytokines, and matrix genes, were
uncommonly identified, which was expected in this control (wild-type)
mouse strain. However, genes encoding proteins that would limit tissue
injury were expressed. Some examples include I
B, the p58 cellular
inhibitor of the interferon-inducible, double- stranded RNA-dependent,
regulated protein kinase; clusterin (a complement regulator); type 2 plasminogen activator inhibitor; matrix Gla protein (an inhibitor of
ectopic calcification); GM2 protein (a platelet-activating factor
inhibitor); and p57KIP2 (a cyclin-dependent kinase inhibitor). Genes
required for maintenance of renal structure, cytoskeletal proteins, and
nuclear matrix proteins, as well as transcription factors, were
expressed at intermediate levels. Interestingly, tuberin, the tuberous
sclerosis complex 2 (TSC2) gene product that is mutated in tuberous
sclerosis, was also identified. Tuberous sclerosis patients, expressing
the mutated TSC2 gene, have renal hamartomas and cysts. Identification of the TSC2 transcript in the normal kidney suggests constitutive expression of this gene may maintain normal renal structure. Recently, tuberin expression has been demonstrated predominantly in intercalated cells of the distal convoluted tubule and the cortical and medullary collecting duct (24).
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DISCUSSION |
Our data demonstrate the feasibility of generating expression
libraries from kidney, and in aggregate, identify transcripts whose
expression would be predicted by the physiology of the normal kidney.
Genes regulating energy generation and solute transport dominated
identified transcripts with known functions, whereas, genes normally
expressed in inflammation or ongoing tissue remodeling or injury were
not identified, as expected, in normal (wild-type) kidney. These data
also demonstrate the potential power of expression profiling, which not
only catalogues expressed genes but also allows an assessment of how
the expression pattern of one gene relates to others. We suggest
analysis of expression profiles by SAGE or other methodologies,
generated from normal and diseased kidneys, should provide valuable new
insights into renal disease pathogenesis and could identify critical
regulators of renal disease initiation and progression.
SAGE provides a technique for high-throughput evaluation of gene
expression and is based on two principles. First, a short sequence of
9-13 bp can be generated from DNA digestion with appropriate combinations of restriction endonucleases. This sequence tag contains sufficient information to uniquely identify a transcript, provided that
it derived from a specific location within the mRNA sequence. Second,
many transcript tags can be concatenated and sequenced, revealing the
sequence of multiple tags simultaneously (36). A major
strength of the technique is that it provides quantitative gene
expression data (37), in contrast to differential display and subtraction hybridization. The quantitative aspect of SAGE has been
validated in other laboratories (36-38). For example, quantitative hybridization experiments by Iyer and Struhl determined that the SUP44/RPS4 is expressed at ~75 copies in yeast. A
yeast transcript profile, generated by SAGE, determined the abundance of this gene to be 63 copies/cell (37). Reproducibility
and reliability of tag libraries generated from different amounts of
input RNA from the same kidney sample have recently been reported (38). The abundance of the same tags correlated between
the two libraries, suggesting this technique and its modifications for
small samples will provide similar results in different laboratories.
The SAGE technique has limitations, however. First, a small number of
transcripts would be predicted to lack a Nla III restriction site and would not be detected. Second, transcripts expressed at low
abundance, or only in a fraction of the cell population, may not be
reliably detected. However, SAGE has been used to analyze gene
expression in yeast arrested at different phases of the cell cycle,
yielding transcripts as low as 0.3 copies/cell (37). Therefore, the size of the SAGE library depends on the level of confidence desired for detecting low- abundance mRNA molecules. Monte
Carlo simulations suggest the probability of identifying a single-copy
transcript in a library containing 30,000 tags is between 72 and 97%
(37). In addition, in an organ with as many different cell
types as the kidney, a large tag library will need to be screened to
identify transcripts unique for a subpopulation of cells. However, SAGE
has been recently adapted for small samples and validated in
microdissected kidney tubules containing ~50,000 cells
(38). Third, differential mRNA expression may not be
reflected at the protein level (15). Finally, identities
of transcripts may remain unassigned (i.e., no match in either the nr
or dbEST databases), but the percentage of these unknown transcripts
will diminish as large-scale sequencing of the mouse and human genomes nears completion. Alternative high-throughput screening techniques, such as cDNA or oligonucleotide microarrays, may ultimately render SAGE
obsolete. Recently, commercially available cDNA arrays on nylon
membranes containing 588 unique murine genes were used to identify
changes in gene expression in an in vitro model of branching morphogenesis (26). At present, though, microarray
technology is not widely available, and only at considerable cost if
robotically generated glass chips are used. In addition, the repertoire
of mouse genes on chips and nylon membranes is limited, and SAGE analysis can result in discovery of novel genes. No matter which method
is used to generate expression profiles, tools for exploring gene
expression libraries are in their infancy. Data analysis of expression
profiles ultimately will depend on integrating kidney mRNA libraries
with external information resources and will require software
development, such as the VectorArray application used to analyze the
gene expression profiles during branching morphogenesis (26). Necessary bioinformatics tools include links between
kidney genes identified in an expression profile and Genbank, links to user-friendly biological pathway databases, and access to databases that can identify functionally important nucleotide (e.g., regulatory elements) or protein (e.g., kinase domain) motifs.
Some of the most abundant genes expressed in ROP-Es/+ kidney were also
identified in a previously published abundance profile, which was
produced by sequencing randomly selected cDNA clones from
microdissected renal tubules (34). Examples of common
transcripts identified in this study and ours, included the
androgen-regulated protein and cytochrome-c oxidase. Not
surprisingly, SAGE reliably identified common tubular transcripts,
validating the technique in kidney given that proximal tubules account
for ~60% of renal mass. As a more comprehensive approach, SAGE
should emerge as a preferable method for generating kidney expression
libraries. The profile of abundant genes in our library (Table 1) also
compares favorably to the abundant, nonmitochondrial genes recently
identified by SAGE in C57BL/6J kidney (38). Of these 26 genes, we have identified 21 in similar abundance in our partial,
ROP-Es/+ kidney SAGE library.
SAGE has been used to generate gene expression profiles from normal
human skeletal muscle (39), pancreas (36),
and Saccharomyces cerevisiae yeast (37).
Integration of the yeast gene expression data with the S. cerevisiae genomic map allowed generation of chromosomal
expression maps, which identified regions that were transcriptionally
active and discovered genes that had not been predicted by sequence
information alone (37). More recently, gene expression has
been generated from in vitro models of normal and disease states, as
well as from actual tissues, to gain insights into pathogenesis. SAGE
expression libraries generated from cancerous and normal colonic
epithelia and p53- and mock-transfected cells have compared 300,000 and
7,200 transcripts, respectively (29, 37).
Fewer than 1% of the transcripts were expressed at significantly different levels in these comparative analyses. Because genes exhibiting the greatest difference in expression between normal and
diseased tissue are likely to be the most relevant to disease processes
(42), SAGE expression libraries have the potential to
identify candidate pathways important in disease pathogenesis, novel
diagnostic markers, and potential therapeutic targets.
Expression libraries are a powerful tool to apply to mechanisms of
renal disease, particularly because available treatments are limited,
only marginally effective, and rarely curative. Progressive kidney
disease probably results from complex interactions of traits (both
environmental and genetic) (31). Further understanding of
chronic renal failure disease pathogenesis and development of new
chronic renal failure therapies will require molecular tools, such as
expression profiling, designed to dissect complex diseases that result
from interactions of multiple genes. Expression profiling has already
been applied to in vitro and in vivo models of kidney disease.
Suppressive subtractive hybridization identified connective tissue
growth factor in glucose-stimulated mesangial cells, a finding
confirmed in streptozotocin-induced diabetic nephropathy
(22). Differential display has been used to identify candidate diabetic nephropathy genes in the GK rat (25),
and PCR-based subtractive hybridization has been used to analyze kidney gene profiles after 5/6 nephrectomy (41) and injection of
anti-Thy1.1 antibody (23). In each case, transcripts were
identified, whose induction in diseased kidney was previously
unrecognized. Interestingly, the most abundant transcript identified in
our ROP-Es/+ kidney library, mitochondrial cytochrome
c-oxidase I, was shown to undergo a 4.5-fold induction in
expression in 5/6 nephrectomized mice compared with sham-operated
control animals (41). Previous reports also showed
increased cytochrome oxidase I activity after unilateral nephrectomy
(19). The compensatory increase in cytochrome-c oxidase I expression may be linked to kidney cell deletion through enhanced oxygen radical generation and apoptosis (40).
However, the power of expression profiling for identification of
mechanisms of kidney disease will really occur only when sophisticated
analyses, such as Monte Carlo simulations and hierarchal cluster
analysis, are applied (6, 11). These
techniques will allow identification of key candidate genes or pathways
regulating kidney disease pathogenesis by 1) assigning a
precise biological pathway to a gene of generic function (e.g.,
kinase), 2) relating an unknown gene to characterized genes,
and 3) revealing unexpected relationships between previously known gene(s) or pathways.
In summary, SAGE is a powerful tool that enables quantitative
identification of differentially expressed genes. Alternative methods
of gene expression analysis (differential display, chip microarray,
subtractive hybridization) either lack the quantitative analytical
capacity or require prohibitively expensive equipment. Our data confirm
that it is feasible for a small laboratory to generate and analyze
comprehensive mRNA expression libraries by using tissue from in vivo
animal models and SAGE technology. On the basis of these studies, we
believe analysis of kidney expression libraries could identify renal
disease susceptibility genes and new pathogenic mechanisms by comparing
mRNA expression profiles from diseased and control animals. Such an
approach certainly should improve on simple models that appear
sufficient to explain a pathogenic process, but fail to improve outcome
due to the true complexity of disease mechanisms. Finally, the SAGE
technique may also have potential implications for the diagnosis of
human renal disease, if the methods can be modified to accommodate
smaller tissue volumes from biopsy specimens. Expression profiles from human renal biopsies could be used to stage disease and to identify patients at risk for progression. Microarray technology already has
been used to stratify human breast cancer tissues (28). Further understanding of kidney disease pathogenesis and development of
new kidney disease therapies will require application of genetic and
genomic tools, such as SAGE and other expression profiling methodologies, which are designed to dissect complex pathogenic mechanisms that result from interaction of multiple genes.
 |
ACKNOWLEDGEMENTS |
The detailed SAGE protocol and SAGE software were generously
provided by Drs. V. E. Velculescu and K.W. Kinzler (Oncology Center and the Program in Human Genetics and Molecular Biology, Johns
Hopkins University, Baltimore, MD). The authors gratefully acknowledge
Dr. Velculescu's helpful discussions concerning the technical aspects
of expression library generation using SAGE.
 |
FOOTNOTES |
Support for this project was provided by National Institute of Diabetes
and Digestive and Kidney Diseases Grants DK-38558, DK-02281, DK-07470,
DK-54644, and DK-54178 and the Kidney Foundation of Ohio. Dr. Schelling
is an Established Investigator of the American Heart Association.
Address for reprint requests and other correspondence: J. R. Sedor, Dept. of Medicine, BG 531, MetroHealth Medical Center, 2500 MetroHealth Dr., Cleveland, Ohio 44109-1998 (E-mail:
jrs4{at}po.cwru.edu).
The costs of publication of this
article were defrayed in part by the
payment of page charges. The article
must therefore be hereby marked
"advertisement"
in accordance with 18 U.S.C. §1734 solely to indicate this fact.
Received 25 October 1999; accepted in final form 22 March 2000.
 |
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