Investigation of the impact of bromodomain inhibition on cytoskeleton stability and contraction.

BACKGROUND: Injury to contractile organs such as the heart, vasculature, urinary bladder and gut can stimulate a pathological response that results in loss of normal contractility. PDGF and TGFß are among the most well studied initiators of the injury response and have been shown to induce aberrant contraction in mechanically active cells of hollow organs including smooth muscle cells (SMC) and fibroblasts. However, the mechanisms driving contractile alterations downstream of PDGF and TGFß in SMC and fibroblasts are incompletely understood, limiting therapeutic interventions. METHODS: To identify potential molecular targets, we have leveraged the analysis of publicly available data, comparing transcriptomic changes in mechanically active cells stimulated with PDGF and TGFß. Additional Analysis of publicly available data sets were performed on SMC and fibroblasts treated in the presence or absence of the MYC inhibitor JQ1. Validation of in silico findings were performed with qPCR, immunoblots, and collagen gel contraction assays measure the effect of JQ1 on cytoskeleton associated genes, proteins and contractility in mechanically active cells. Likelihood ratio test and FDR adjusted p-values were used to determine significant differentially expressed genes. Student ttest were used to calculate statistical significance of qPCR and contractility analyses. RESULTS: Comparing PDGF and TGFß stimulated SMC and fibroblasts identified a shared molecular profile regulated by MYC and members of the AP-1 transcription factor complex. Additional in silico analysis revealed a unique set of cytoskeleton-associated genes that were sensitive to MYC inhibition with JQ1. In vitro validation demonstrated JQ1 was also able to attenuate TGFß and PDGF induced changes to the cytoskeleton and contraction of smooth muscle cells and fibroblasts in vitro. CONCLUSIONS: These findings identify MYC as a key driver of aberrant cytoskeletal and contractile changes in fibroblasts and SMC, and suggest that JQ1 could be used to restore normal contractile function in hollow organs.

De-obstruction of bladder outlet in humans reverses organ remodelling by normalizing the expression of key transcription factors.

BACKGROUND: Benign prostatic hyperplasia in elderly males often causes bladder outlet obstruction termed benign prostatic obstruction (BPO). BPO induces lower urinary tract symptoms and quantifiable urodynamic alterations in bladder function. When conservative medical treatments are exhausted, surgical interventions like transurethral resection of the prostate (TURP) are employed for bladder outlet de-obstruction. Elucidating the molecular changes in the human bladder resulting from BPO and their reversal post-de-obstruction is pivotal for defining the "point of no return", when the organ deterioration becomes irreversible. In this study we carried out a comprehensive molecular and urodynamic characterization of the bladders in men with BPO before TURP and 3 months after the relief of obstruction. METHODS: We report integrated transcriptome and proteome analysis of bladder samples from male patients with BPO before and 3 months after de-obstruction surgery (TURP). mRNA and protein profiles were correlated with urodynamic findings, specifically voiding detrusor pressure (PdetQmax) before TURP. We delineated the molecular classifiers of each group, pointing at the different pre-TURP bladder status. RESULTS: Age-matched patients with BPO without DO were divided into two groups based on the PdetQmax values recorded by UDI before de-obstruction: high and medium pressure (HP and MP) groups. Three months after de-obstruction surgery, the voiding parameters PdetQmax, Qmax and RV were significantly improved in both groups, without notable inter-group differences in the values after TURP. Patients with high PdetQmax showed less advanced remodeling and inflammatory changes than those with lower values. We detected significant dysregulation of gene expression, which was at least partially reversed by de-obstruction in both patients' groups. Transcription factor SOX21 and its target thrombospondin 4 (THBS4) demonstrated normalization post-TURP. CONCLUSIONS: Our findings reveal substantial yet incomplete reversal of cell signalling pathways three months after TURP, consistent with improved urodynamic parameters. We propose a set of biomarker genes, indicative of BPO, and possibly contributing to the bladder changes. This study unveils the stages of progressive obstruction-induced bladder decompensation and offers insights into selecting an optimal intervention point to mitigate loss of contractility.

Machine Learning-based Classification of transcriptome Signatures of non-ulcerative Bladder Pain Syndrome.

Lower urinary tract dysfunction (LUTD) presents a global health challenge with symptoms impacting a substantial percentage of the population. The absence of reliable biomarkers complicates the accurate classification of LUTD subtypes with shared symptoms such as non-ulcerative Bladder Pain Syndrome (BPS) and overactive bladder caused by bladder outlet obstruction with Detrusor Overactivity (DO). This study introduces a machine learning (ML)-based approach for the identification of mRNA signatures specific to non-ulcerative BPS. Using next-generation sequencing (NGS) transcriptome data from bladder biopsies of patients with BPS, benign prostatic obstruction with DO and controls, our statistical approach successfully identified 13 candidate genes capable of discerning BPS from control and DO patients. This set was subsequently validated using Quantitative Polymerase Chain Reaction (QPCR) in a larger patient cohort. To confirm our findings, we applied both supervised and unsupervised ML approaches to the QPCR dataset. Notably, a three-mRNA signature TPPP3, FAT1, and NCALD, emerged as a robust classifier, effectively distinguishing patients with non-ulcerative BPS from controls and patients with DO. This signature was universally selected by both supervised and unsupervised approaches. The ML-based framework used to define BPS classifiers not only establishes a solid foundation for comprehending the specific gene expression changes in the bladder of the patients with BPS but also serves as a valuable resource and methodology for advancing signature identification in other fields. The proposed ML pipeline demonstrates its efficacy in handling challenges associated with limited sample sizes, offering a promising avenue for applications in similar domains.

Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis.

BACKGROUND: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. RESULTS: To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating 4 essential functionalities-namely, Data Exploration, AutoML, CustomML, and Visualization-MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on 6 distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. CONCLUSION: MLme serves as a valuable resource for leveraging ML to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.

Integrated omics analysis unveils a DNA damage response to neurogenic injury.

Spinal cord injury (SCI) evokes profound bladder dysfunction. Current treatments are limited by a lack of molecular data to inform novel therapeutic avenues. Previously, we showed systemic inosine treatment improved bladder function following SCI in rats. Here, we applied multi-omics analysis to explore molecular alterations in the bladder and their sensitivity to inosine following SCI. Canonical pathways regulated by SCI included those associated with protein synthesis, neuroplasticity, wound healing, and neurotransmitter degradation. Upstream regulator analysis identified MYC as a key regulator, whereas causal network analysis predicted multiple regulators of DNA damage response signaling following injury, including PARP-1. Staining for both DNA damage (γH2AX) and PARP activity (poly-ADP-ribose) markers in the bladder was increased following SCI, and attenuated in inosine-treated tissues. Proteomics analysis suggested that SCI induced changes in protein synthesis-, neuroplasticity-, and oxidative stress-associated pathways, a subset of which were shown in transcriptomics data to be inosine-sensitive. These findings provide novel insights into the molecular landscape of the bladder following SCI, and highlight a potential role for PARP inhibition to treat neurogenic bladder dysfunction.

Investigation of the impact of bromodomain inhibition on cytoskeleton stability and contraction.

Injury to contractile organs such as the heart, vasculature, urinary bladder and gut can stimulate a pathological response that results in loss of normal contractility. PDGF and TGFß are among the most well studied initiators of the injury response and have been shown to induce aberrant contraction in mechanically active cells of hollow organs including smooth muscle cells (SMC) and fibroblasts. However the mechanisms driving contractile alterations downstream of PDGF and TGFß in SMC and fibroblasts are incompletely understood, limiting therapeutic interventions. To identify potential molecular targets, we have leveraged the analysis of publicly available data, comparing transcriptomic changes in mechanically active cells stimulated with PDGF and TGFß and identified a shared molecular profile regulated by MYC and members of the AP-1 transcription factor complex. We also analyzed data sets from SMC and fibroblasts treated in the presence or absence of the MYC inhibitor JQ1. This analysis revealed a unique set of cytoskeleton-associated genes that were sensitive to MYC inhibition. JQ1 was also able to attenuate TGFß and PDGF induced changes to the cytoskeleton and contraction of smooth muscle cells and fibroblasts in vitro. These findings identify MYC as a key driver of aberrant cytoskeletal and contractile changes in fibroblasts and SMC, and suggest that JQ1 could be used to restore normal contractile function in hollow organs.

SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis.

Background: In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. Results: To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. Conclusion: SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan.

Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis.

Background: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. Results: To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. Conclusion: MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.

Integrated longitudinal analysis of adult grade 4 diffuse gliomas with long-term relapse interval revealed upregulation of TGF-ß signaling in recurrent tumors.

BACKGROUND: Adult-type diffuse gliomas, CNS WHO grade 4 are the most aggressive primary brain tumors and represent a particular challenge for therapeutic intervention. METHODS: In a single-center retrospective study of matched pairs of initial and post-therapeutic glioma cases with a recurrence period greater than 1 year, we performed whole exome sequencing combined with mRNA and microRNA expression profiling to identify processes that are altered in recurrent gliomas. RESULTS: Mutational analysis of recurrent gliomas revealed early branching evolution in 75% of the patients. High plasticity was confirmed at the mRNA and miRNA levels. SBS1 signature was reduced and SBS11 was elevated, demonstrating the effect of alkylating agent therapy on the mutational landscape. There was no evidence for secondary genomic alterations driving therapy resistance. ALK7/ACVR1C and LTBP1 were upregulated, whereas LEFTY2 was downregulated, pointing towards enhanced Tumor Growth Factor ß (TGF-ß) signaling in recurrent gliomas. Consistently, altered microRNA expression profiles pointed towards enhanced Nuclear Factor Kappa B and Wnt signaling that, cooperatively with TGF-ß, induces epithelial to mesenchymal transition (EMT), migration, and stemness. TGF-ß-induced expression of pro-apoptotic proteins and repression of antiapoptotic proteins were uncoupled in the recurrent tumor. CONCLUSIONS: Our results suggest an important role of TGF-ß signaling in recurrent gliomas. This may have clinical implications since TGF-ß inhibitors have entered clinical phase studies and may potentially be used in combination therapy to interfere with chemoradiation resistance. Recurrent gliomas show high incidence of early branching evolution. High tumor plasticity is confirmed at the level of microRNA and mRNA expression profiles.

MLcps: machine learning cumulative performance score for classification problems.

BACKGROUND: Assessing the performance of machine learning (ML) models requires careful consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics to gain a comprehensive understanding of a trained model's performance, as each metric focuses on a specific aspect. However, comparing the scores of these individual metrics for each model to determine the best-performing model can be time-consuming and susceptible to subjective user preferences, potentially introducing bias. RESULTS: We propose the Machine Learning Cumulative Performance Score (MLcps), a novel evaluation metric for classification problems. MLcps integrates several precomputed evaluation metrics into a unified score, enabling a comprehensive assessment of the trained model's strengths and weaknesses. We tested MLcps on 4 publicly available datasets, and the results demonstrate that MLcps provides a holistic evaluation of the model's robustness, ensuring a thorough understanding of its overall performance. CONCLUSIONS: By utilizing MLcps, researchers and practitioners no longer need to individually examine and compare multiple metrics to identify the best-performing models. Instead, they can rely on a single MLcps value to assess the overall performance of their ML models. This streamlined evaluation process saves valuable time and effort, enhancing the efficiency of model evaluation. MLcps is available as a Python package at https://pypi.org/project/MLcps/.

SpheroScan: a user-friendly deep learning tool for spheroid image analysis.

BACKGROUND: In recent years, 3-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional 2-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. RESULTS: To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. CONCLUSION: SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan.

Urinary Tract Infections in Children with Vesicoureteral Reflux Are Accompanied by Alterations in Urinary Microbiota and Metabolome Profiles.

Children with vesicoureteral reflux (VUR) are at an increased risk of recurrent urinary tract infections (UTIs) and renal scarring. Gut microbiota are associated with disease phenotypes, but there has been no study that associates urinary microbiota (uMB) and metabolic profiles with VUR pathology. To identify dominant uMB genera and metabolites associated with UTIs in VUR, urine samples collected under sterile conditions underwent 16S ribosomal RNA sequencing (n = 49) and metabolomic analysis by mass spectrometry (n = 96). Alterations in uMB and metabolomic profiles in VUR patients suggest remodeling of urinary bacterial communities after UTIs: Dorea- and Escherichia-dominant uMB profiles were more frequently identified in participants with VUR. Prevotella- and Lactobacillus-dominant uMB profiles were more prevalent in controls (p < 0.001). Microbial composition varied based on recurrent febrile UTI status (p = 0.001). A total of 243 urinary metabolites involved in energy, amino acid, nucleotide, and lipid metabolism were altered in VUR patients with UTIs (p < 0.05). Importantly, VUR specimens revealed changes in the bacteria-associated metabolic pathways such as glutamate degradation, methyl-citrate cycle, and bile acid metabolism. PATIENT SUMMARY: Differences in urinary commensal bacteria and metabolites exist between children with and without vesicoureteral reflux (VUR). These changes may be utilized to identify patients at risk of VUR-associated kidney damage.

Integrated mRNA-miRNA transcriptome analysis of bladder biopsies from patients with bladder pain syndrome identifies signaling alterations contributing to the disease pathogenesis.

BACKGROUND: Interstitial cystitis, or bladder pain syndrome (IC/BPS), is a chronic bladder disorder characterized by lower abdominal pain associated with the urinary bladder and accompanied by urinary frequency and urgency in the absence of identifiable causes. IC/PBS can be separated into the classic Hunner's ulcerative type and the more prevalent non-ulcerative disease. Our aim was to unravel the biological processes and dysregulated cell signaling pathways leading to the bladder remodeling in non-ulcerative bladder pain syndrome (BPS) by studying the gene expression changes in the patients' biopsies. METHODS: We performed paired microRNA (miRNA) and mRNA expression profiling in the bladder biopsies of BPS patients with non-Hunner interstitial cystitis phenotype, using comprehensive Next-generation sequencing (NGS) and studied the activated pathways and altered biological processes based on the global gene expression changes. Paired mRNA-miRNA transcriptome analysis delineated the regulatory role of the dysregulated miRNAs by identifying their targets in the disease-induced pathways. RESULTS: EIF2 Signaling and Regulation of eIF4 and p70S6K Signaling, activated in response to cellular stress, were among the most significantly regulated processes during BPS. Leukotriene Biosynthesis nociceptive pathway, important in inflammatory diseases and neuropathic pain, was also significantly activated. The biological processes identified using Gene Ontology over-representation analysis were clustered into six main functional groups: cell cycle regulation, chemotaxis of immune cells, muscle development, muscle contraction, remodeling of extracellular matrix and peripheral nervous system organization and development. Compared to the Hunner's ulcerative type IC, activation of the immune pathways was modest in non-ulcerative BPS, limited to neutrophil chemotaxis and IFN-γ-mediated signaling. We identified 62 miRNAs, regulated and abundant in BPS and show that they target the mRNAs implicated in eIF2 signalling pathway. CONCLUSIONS: The bladders of non-ulcerative BPS patients recruited in this study had alterations consistent with a strong cell proliferative response and an up-regulation of smooth muscle contractility, while the contribution of inflammatory processes was modest. Pathway analysis of the integrated mRNA-miRNA NGS dataset pinpointed important regulatory miRNAs whose dysregulation might contribute to the pathogenesis. Observed molecular changes in the peripheral nervous system organization and development indicate the potential role of local bladder innervation in the pain perceived in this type of BPS.

Extracellular Vesicles Protect the Neonatal Lung from Hyperoxic Injury through the Epigenetic and Transcriptomic Reprogramming of Myeloid Cells.

Rationale: Mesenchymal stem/stromal cell (MSC)-small extracellular vesicle (MEx) treatment has shown promise in experimental models of neonatal lung injury. The molecular mechanisms by which MEx afford beneficial effects remain incompletely understood. Objectives: To investigate the therapeutic mechanism of action through assessment of MEx biodistribution and impact on immune cell phenotypic heterogeneity. Methods: MEx were isolated from the conditioned medium of human umbilical cord Wharton's jelly-derived MSCs. Newborn mice were exposed to hyperoxia (HYRX, 75% O2) from birth and returned to room air at Postnatal Day 14 (PN14). Mice received either a bolus intravenous MEx dose at PN4 or bone marrow-derived myeloid cells (BMDMy) pretreated with MEx. Animals were killed at PN4, PN7, PN14, or PN28 to characterize MEx biodistribution or for assessment of pulmonary parameters. The therapeutic role of MEx-educated BMDMy was determined in vitro and in vivo. Measurements and Main Results: MEx therapy ameliorated core histological features of HYRX-induced neonatal lung injury. Biodistribution and mass cytometry studies demonstrated that MEx localize in the lung and interact with myeloid cells. MEx restored the apportion of alveolar macrophages in the HYRX-injured lung and concomitantly suppressed inflammatory cytokine production. In vitro and ex vivo studies revealed that MEx promoted an immunosuppressive BMDMy phenotype. Functional assays demonstrated that the immunosuppressive actions of BMDMy are driven by phenotypically and epigenetically reprogrammed monocytes. Adoptive transfer of MEx-educated BMDMy, but not naive BMDMy, restored alveolar architecture, blunted fibrosis and pulmonary vascular remodeling, and improved exercise capacity. Conclusions: MEx ameliorate hyperoxia-induced neonatal lung injury though epigenetic and phenotypic reprogramming of myeloid cells.

Urinary miRNA profiles discriminate between obstruction-induced bladder dysfunction and healthy controls.

Urgency, frequency and incomplete emptying are the troublesome symptoms often shared between benign prostatic obstruction-induced (BLUTD) and neurogenic (NLUTD) lower urinary tract dysfunction. Previously, using bladder biopsies, we suggested a panel of miRNA biomarkers for different functional phenotypes of the bladder. Urine is a good source of circulating miRNAs, but sex- and age-matched controls are important for urinary metabolite comparison. In two groups of healthy subjects (average age 32 and 57 years old, respectively) the total protein and RNA content was very similar between age groups, but the number of secreted extracellular vesicles (uEVs) and expression of several miRNAs were higher in the young healthy male volunteers. Timing of urine collection was not important for these parameters. We also evaluated the suitability of urinary miRNAs for non-invasive diagnosis of bladder outlet obstruction (BOO). A three urinary miRNA signature (miR-10a-5p, miR-301b-3p and miR-363-3p) could discriminate between controls and patients with LUTD (BLUTD and NLUTD). This panel of representative miRNAs can be further explored to develop a non-invasive diagnostic test for BOO. The age-related discrepancy in the urinary miRNA content observed in this study points to the importance of selecting appropriate, age-matched controls.

Molecular mechanisms of esophageal epithelial regeneration following repair of surgical defects with acellular silk fibroin grafts.

Constructive remodeling of focal esophageal defects with biodegradable acellular grafts relies on the ability of host progenitor cell populations to repopulate implant regions and facilitate growth of de novo functional tissue. Intrinsic molecular mechanisms governing esophageal repair processes following biomaterial-based, surgical reconstruction is largely unknown. In the present study, we utilized mass spectrometry-based quantitative proteomics and in silico pathway evaluations to identify signaling cascades which were significantly activated during neoepithelial formation in a Sprague Dawley rat model of onlay esophagoplasty with acellular silk fibroin scaffolds. Pharmacologic inhibitor and rescue experiments revealed that epithelialization of neotissues is significantly dependent in part on pro-survival stimuli capable of suppressing caspase activity in epithelial progenitors via activation of hepatocyte growth factor receptor (c-MET), tropomyosin receptor kinase A (TrkA), phosphoinositide 3-kinase (PI3K), and protein kinase B (Akt) signaling mechanisms. These data highlight the molecular machinery involved in esophageal epithelial regeneration following surgical repair with acellular implants.

A Single Cell Dissociation Approach for Molecular Analysis of Urinary Bladder in the Mouse Following Spinal Cord Injury.

We describe the implementation of spinal cord injury in mice to elicit detrusor-sphincter dyssynergia, a functional bladder outlet obstruction, and subsequent bladder wall remodeling. To facilitate assessment of the cellular composition of the bladder wall in non-injured control and spinal cord injured mice, we developed an optimized dissociation protocol that supports high cell viability and enables the detection of discrete subpopulations by flow cytometry. Spinal cord injury is created by complete transection of the thoracic spinal cord. At the time of tissue harvest, the animal is perfused with phosphate-buffered saline under deep anesthesia and bladders are harvested into Tyrode's buffer. Tissues are minced prior to incubation in digestion buffer that has been optimized based on the collagen content of mouse bladder as determined by interrogation of publicly available gene expression databases. Following generation of a single cell suspension, material is analyzed by flow cytometry for assessment of cell viability, cell number and specific subpopulations. We demonstrate that the method yields cell populations with greater than 90% viability, and robust representation of cells of mesenchymal and epithelial origin. This method will enable accurate downstream analysis of discrete cell types in mouse bladder and potentially other organs.

Improved isolation strategies to increase the yield and purity of human urinary exosomes for biomarker discovery.

Circulating miRNAs are detected in extracellular space and body fluids such as urine. Circulating RNAs can be packaged in secreted urinary extracellular vesicles (uEVs) and thus protected from degradation. Urinary exosome preparations might contain specific miRNAs, relevant as biomarkers in renal and bladder diseases. Major difficulties in application of uEVs into the clinical environment are the high variability and low reproducibility of uEV isolation methods. Here we used five different methods to isolate uEVs and compared the size distribution, morphology, yield, presence of exosomal protein markers and RNA content of uEVs. We present an optimized ultracentrifugation and size exclusion chromatography approach for highly reproducible isolation for 50-150 nm uEVs, corresponding to the exosomes, from 50 ml urine. We profiled the miRNA content of uEVs and total urine from the same samples with the NanoString platform and validated the data using qPCR. Our results indicate that 18 miRNAs, robustly detected in uEVs were always present in the total urine. However, 15 miRNAs could be detected only in the total urine preparations and might represent naked circulating miRNA species. This is a novel unbiased and reproducible strategy for uEVs isolation, content normalization and miRNA cargo analysis, suitable for biomarker discovery studies.

Concordant miRNA and mRNA expression profiles in humans and mice with bladder outlet obstruction.

Bladder outlet obstruction (BOO) leads to lower urinary tract symptoms (LUTS) and urodynamic changes of the bladder function. Previously we identified microRNA (miRNA) and mRNA expression profiles associated with different states of BOO-induced LUTD in human patients. Bladder wall remodeling resulting from obstruction is widely studied in animal models of experimentally-induced partial BOO (pBOO). Here we determined the expression profiles of miRNAs and selected mRNAs in pBOO mice and compared the observed changes to human patients. Similar to results from human patients, we observed a down-regulation of smooth muscle-associated miRNAs mmu-miR-1, mmu-miR-143, mmu-miR-145, mmu-miR-486 and mmu-miR-133a in pBOO mouse bladders. Pro-fibrotic miRNAs mmu-miR-142-3p and mmu-miR-21 were up-regulated, and anti-fibrotic miRNA mmu-miR-29c was down-regulated. Pathway analysis in human BOO patients identified TNF-alpha as the top upstream regulator. Although there was evidence of hypertrophic changes in pBOO mice, contrary to human data, we observed no regulation of TNF-responsive genes in the mouse model. Experimentally-induced pBOO in mice led to significant gene expression changes, including alteration of pro-fibrotic mRNAs and miRNAs resembling human BOO patients. Gene expression changes were also validated in a mouse model of bladder inflammation. Lack of evidence of TNF-alpha-induced miRNA and mRNA regulation might indicate a different pathophysiological mechanism of organ remodeling in pBOO model compared to the human disease.