'Omics' Approaches to Understanding Interstitial Cystitis/Painful Bladder Syndrome/Bladder Pain Syndrome

Diagnosis and Treatment of Interstitial Cystitis

Interstitial cystitis/painful bladder syndrome/bladder pain syndrome (hereafter IC) is a chronic disorder of the urinary bladder that manifests as long-term symptoms of pelvic and or perineal pain, thinning or ulceration of the bladder epithelial lining, nocturia, urinary frequency, and urgency [1-3]. Approximately 3 to 8 million women and 1 to 4 million men in the US have been diagnosed with IC [1,2], and IC significantly reduces the quality of life of patients and suppresses physical function and social activity. The financial burden and medical cost associated with IC is increasing and is currently estimated to exceed $100 million/yr [4]. However, etiologies are largely unknown and effective treatment or medications have not been found.

Patients are diagnosed with IC by the presence of certain clinical features (e.g., symptoms of urinary urgency, frequency, and chronic pelvic pain, urodynamics, potassium sensitivity assay, cystoscopic bladder evaluation) in the absence of other identifiable causes for similar symptoms (e.g., urinary tract infection). Diagnosis is generally made according to recent the National Institute of Diabetes and Digestive and Kidney Diseases and American Urological Association guidelines [3,5-8]. Histological analysis of bladder biopsies obtained from IC patients has been reported to show a thinning of the urothelial layers and an increased infiltration of mast cells, suggesting that the disease is mediated by a defect in the regenerative potential of bladder epithelial cells, and/or by immune impairment [1,2]. Although there are multiple hypotheses about the primary cause of IC, the underlying pathologic mechanism remains completely undefined, leading to delays in identifying effective therapeutic modalities. Approaches to the treatment of IC have included procedures such as hydrodistension, and oral pharmaceutical drugs such as pentosan polysulfate (Elmiron, the U.S. Food and Drug Administration approved), antihistamines, tricyclic antidepressants, and immune modulators. In general, symptom relief with any of these strategies has been reported in only a small percentage of patients and recurrence typically is evident within a few months [1,9].

Molecular Signature of IC

In IC patients, several abnormalities were observed in epithelial, endothelial, smooth muscle (detrusor), neuronal, or immune cells, compared to controls. In particular, histological characteristics of IC include thinning or erosion of the bladder epithelium. Proliferation is suppressed [10] and cell permeability is altered in urothelium from IC patients [11,12], compared to controls. At the molecular level, expression of pro-proliferative proteins (e.g., cyclin D1) and barrier tight junction proteins (e.g., zonula occludens-1, occludin, and claudin 1, 4, and 8) is dysregulated [11,12]. IC bladder epithelial cells exhibited intrinsic alteration in differentiation, neurotransmitter release and potassium channel activity, with elevated nitric oxide production, nuclear factor κB (NF-κB) activation, nerve fiber number, serum C-reactive protein, neuropeptide Y and nerve growth factor production [13-15]. Bladder endothelial abnormalities in IC include increased levels of platelet derived endothelial cell growth factor/thymidine phosphorylase, vascular endothelial growth factor, and substance P expression [16-18].

A unique urinary biomarker of IC, a small glycopeptide named antiproliferative factor (APF), was originally identified and characterized by Keay et al. [19] APF bioactivity is increased in urine specimens of IC patients and measurement of APF in urine identifies IC with 94% sensitivity and 95% specificity [20-22]. APF has been suggested as a potential mediator of IC symptoms by regulating important signaling pathways, such as Akt, mitogen-activated protein kinase, β-catenin or p53 signaling, leading to suppression of cell proliferation and increases in transurothelial cellular permeability [19,23-25]. In order to understand the APF signaling network in a global way, a recent quantitative proteomics and systems biology approach was employed that identified a provisional APF network and several apparently key signaling nodes in urothelial cells downstream from APF [23,25-28]. Because we have described this APF signaling network in IC in a recent review [26], we will not cover signaling mechanisms perturbed by APF here.

Biomarker Discovery

Proteins are the major executors of life activities and are several steps closer to influencing a person's health than DNA and even RNA. Thus, proteins frequently have the greatest clinical significance for the diagnosis of diseases. Studies in the field of proteomics aim to systematically catalog and understand the identity, quantity, modification, localization, interaction, and function of all proteins in a cell or organism [40]. In the past two decades, a number of powerful proteomics technologies were developed to comprehensively compare the protein differences between patients suffering from a wide range of ailments and healthy controls to discover biomarkers for diagnosis, prognosis, and monitoring treatment response.

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) technology was developed in 1993 [41] and later commercialized as the ProteinChip system. This method combines surface-enhanced selective capture with high-sensitivity TOF MS for protein profiling, biomarker discovery, and immunoassay applications. SELDI-TOF MS is a high-throughput technology-assays can be performed in 96-well microplates and only limited preprocessing is required. In 2003, Van et al. [42] used weak cation exchange ProteinChip arrays as well as high-resolution proton nuclear magnetic resonance (NMR) to profile proteins and metabolites of urine samples obtained from 50 patients with IC, 30 patients with acute bacterial cystitis, and 50 asymptomatic controls. They found that urine samples obtained from healthy vs. IC-affected individuals can be segregated using eight different mass-to-charge (m/z) values.

Two-dimensional electrophoresis (2DE) coupled with matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS is a widely used proteomics approach. 2DE is a high-resolution protein separation method that sorts proteins according to two independent properties (i.e., isoelectric point and mass) [43]. After 2DE separation, gels are treated with a protein stain (Coomassie, silver, or SYPRO) to visualize protein spots. Gel images are analyzed using software to identify differentially expressed protein spots. To reduce the inter-gel variation, a method called two-dimensional difference gel electrophoresis (2D-DIGE) was developed in 1997 [44]. In this method, up to three different protein samples can be differentially tagged with chemically similar fluorescent dyes (e.g., Cy3, Cy5, or Cy2), mixed, and resolved on the same 2D gel. The gel is scanned with the excitation wavelength of each dye one after the other, so the gel image derived from each sample can be obtained separately and compared. After gel electrophoresis and image analysis, protein spots of interest (e.g., differentially expressed proteins) are excised and digested with an endoproteinase (e.g., trypsin) to generate peptides. The resulting peptides are mixed with a matrix (e.g., α-cyano-4-hydroxycinnamic acid) solution and the mixture is spotted onto a MALDI plate and analyzed with a MALDI-TOF MS to generate a peptide-mass fingerprint, which serves to identify the protein spot. The peptides can also be analyzed with nano-liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) to sequence each peptide and thus identify the protein [40].

Canter et al. [45] used 2DE to resolve proteins in urine samples from nine IC patients and nine asymptomatic controls. Four protein spots were significantly differentially expressed between the IC group and the control group. Subsequent MALDI-TOF analysis identified the protein spots significantly down-regulated in the IC group as a uromodulin and two kininogens (precursors of kinin) and the protein spot significantly upregulated in the IC group as inter-α-trypsin inhibitory heavy chain H4. Kuromitsu et al. [46] applied the 2D-DIGE method to compare proteins in urine samples from three IC patients and three healthy subjects. Several protein spots around 40 kDa were significantly increased in the IC group. These proteins were identified as fragments of human serum albumin by nano LC-MS/MS. Subsequently, the protease cleaving albumin was purified and identified as neutrophil elastase. The comparison of neutrophil elastase activity in urine samples of 86 IC patients and 89 healthy controls showed that the concentration of neutrophil elastase was significantly higher in an IC patient subset with bladder pain and small bladder capacity than in other IC patients and healthy controls. Lemberger et al. [47] applied 2DE to separated urine supernatants of eighteen cats with spontaneous feline idiopathic cystitis (FIC), a cat model of IC, and eighteen healthy controls. MALDI-TOF/TOF analysis identified ten spots significantly upregulated in the FIC group as albumin and one spot significantly downregulated in the FIC group as trefoil factor 2 [47].

Although the 2DE technology has many advantages such as robustness, the ability to run gels in parallel, and the unique ability to analyze complete proteins at high resolution, it has its intrinsic limitations, most importantly that low-abundance and hydrophobic proteins are difficult to detect [43]. To overcome these limitations, nanoLC-MS/MS has been developed and dramatically improved. Currently, using the nanoLC-MS/MS approach and high-end mass spectrometers, over 10,000 proteins can be identified from human cancer cells as well as archival formalin fixed and paraffin embedded clinical samples [48-50].

Kuromitsu et al. [46] reported on an active substance in urine from IC patients that drives the expression of the serum response element-dependent luciferase reporter gene in GPR18-expressing HEK293 cells. This material was identified by nanoLC-MS/MS as epidermal growth factor. Goo et al. [51] compared the urinary proteome of ten IC patients and ten healthy controls. A total of 889 and 1,003 proteins were identified from IC patients and healthy controls, respectively, using LC-MS/MS. Among the 78 significantly changed proteins, α-1B-glycoprotein and orosomucoid-1 were upregulated in at least 60% of IC patients compared to control subjects. In contrast, transthyretin and hemopexin were decreased in at least 60% of IC patients compared to healthy controls.

In summary, much effort has been made to identify and characterize potential urinary biomarkers for IC by proteomic analysis of a relatively small number of specimens. Complimentary to the high-content but low-throughput discovery MS methods, recently developed targeted MS methods allow fast and multiplex assays of candidate biomarkers with high sensitivity and accuracy [52]. It is anticipated that the candidate IC biomarkers will be validated in a large number of urine samples using targeted MS in the near future. In addition to biomarker discovery, quantitative proteomics methods will be increasingly applied to dissect the signaling networks perturbed in IC, thus opening up new avenues to the identification of novel drug targets.

Urinary Metabolites as Disease Biomarkers

For the diagnosis of IC, invasive procedures such as cystoscopy and the potassium sensitivity test and the noninvasive use of biofluids (e.g., urine, blood, saliva, fecal extract, and sputum specimens)-based assays have both been used. The best diagnostic test would include noninvasive biomarker(s) with high sensitivity and specificity. Urine-based diagnosis is attractive, since it is noninvasive with no required preparation by the patient. Global profiling of urinary metabolites using MS or NMR has been attempted [42,53]. NMR spectroscopy, which detects the resonance of a particular substance within a magnetic field, can provide structure as well as quantitative and qualitative information with great reproducibility and sensitivity [53-55]. Sample preparation steps for NMR analysis are simple, which is a great advantage for clinical applications but the sensitivity of NMR-based metabolomics analysis is less than MS-based metabolomics. In attempts to detect low abundance metabolites, several approaches have been tried, such as increasing NMR magnetic field strength, to enhance the sensitivity of NMR technology. Metabolic profiling provides a global chemical fingerprint of the physiology and metabolism of cells, and indicates physiological and pathological states of biological samples.

A few attempts to use metabolomics analysis to identify an IC signature have been published. Fukui et al. [53] used ultra-performance liquid chromatography-mass spectrometry and found that the urinary ratio of phenylacetylglutamine to creatine can be correlated to clinical grade of IC (e.g., mild to severe based on symptoms) [53]. In 2003, Van et al. [42] has suggested that MS and NMR spectral patterns distinguish IC-affected from non-IC patients including bacterial cystitis. With further validation in a larger cohort, global metabolite profiling combined with multivariate statistical and bioinformatics analysis would suggest important biomarker metabolites contributing to the biological responses such as the drug-induced toxicity or response as metabolic biomarkers.

Identification of Disease-Associated Molecules and Construction of Network Models

To identify disease-associated molecules, various types of feature selection procedures can be employed. In most cases, multiple hypotheses testing with false discovery rate control procedures are applied for various types of 'omics' data. In order to subgroup and select molecules with significant difference of measurement or large variability in subset of samples, a cluster analysis or discriminant analysis can be alternatively used. To translate the identified molecular signature into biologically meaningful hypotheses, functional enrichment analysis by Bioconductor [61], DAVID (Database for Annotation, Visualization and Integrated Discovery) and GSEA (gene set enrichment analysis) software can be used [62-64]. To identify a subset of molecules associated with diseases, several useful resources including Gendoo (gene, disease features ontology-based overview system), Genotator, and Metabolite Set Enrichment Analysis (MSEA) can be applied [65-67]. To construct a disease-perturbed network, several stand alone software and web-based tools can be useful, including: 1) integrative querying systems for interaction information (PSICQUIC [68]); 2) network modeling and analysis tools (STRING [69] and Cytoscape [70]); and pathway analysis (KEGG [71]). In addition to these instruments, other commercial tools such as GeneGo and Ingenuity Pathway Analysis (IPA) can be used to reconstruct a network, as well as identify significantly enriched pathways characterized by key network nodes.

Identification of Potential Molecular Targets and Indicators

Once network models are developed as described above, the network can be queried to select potential target molecules using two types of network analysis algorithms including 1) random walk algorithms and 2) master regulator analysis for prioritizing key molecules. The random walk approach has been applied to detecting disease genes related to a wide range of diseases [72,73]. Master regulator analysis is generally used to find a key transcription factor that has the largest number of targets using transcription factor-target and protein-protein interaction data [74,75].