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.

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 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. A three-mRNA signature TPPP3, FAT1, and NCALD, emerged as a robust classifier for non-ulcerative BPS. The ML-based framework used to define BPS classifiers establishes a solid foundation for comprehending the gene expression changes in the bladder during BPS and 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-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.

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment.

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

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.

A Comprehensive Transcriptomic Analysis of Arsenic-Induced Bladder Carcinogenesis.

Arsenic (sodium arsenite: NaAsO2) is a potent carcinogen and a known risk factor for the onset of bladder carcinogenesis. The molecular mechanisms that govern arsenic-induced bladder carcinogenesis remain unclear. We used a physiological concentration of NaAsO2 (250 nM: 33 µg/L) for the malignant transformation of normal bladder epithelial cells (TRT-HU1), exposed for over 12 months. The increased proliferation and colony-forming abilities of arsenic-exposed cells were seen after arsenic exposure from 4 months onwards. Differential gene expression (DEG) analysis revealed that a total of 1558 and 1943 (padj < 0.05) genes were deregulated in 6-month and 12-month arsenic-exposed TRT-HU1 cells. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that cell proliferation and survival pathways, such as the MAPK, PI3K/AKT, and Hippo signaling pathways, were significantly altered. Pathway analysis revealed that the enrichment of stem cell activators such as ALDH1A1, HNF1b, MAL, NR1H4, and CDH1 (p < 0.001) was significantly induced during the transformation compared to respective vehicle controls. Further, these results were validated by qPCR analysis, which corroborated the transcriptomic analysis. Overall, the results suggested that stem cell activators may play a significant role in facilitating the arsenic-exposed cells to gain a survival advantage, enabling the healthy epithelial cells to reprogram into a cancer stem cell phenotype, leading to malignant transformation.

Neuropilin 2 Is a Novel Regulator of Distal Colon Contractility.

Appropriate coordination of smooth muscle contraction and relaxation is essential for normal colonic motility. The impact of perturbed motility ranges from moderate, in conditions such as colitis, to potentially fatal in the case of pseudo-obstruction. The mechanisms underlying aberrant motility and the extent to which they can be targeted pharmacologically are incompletely understood. This study identified colonic smooth muscle as a major site of expression of neuropilin 2 (Nrp2) in mice and humans. Mice with inducible smooth muscle-specific knockout of Nrp2 had an increase in evoked contraction of colonic rings in response to carbachol at 1 and 4 weeks following initiation of deletion. KCl-induced contractions were also increased at 4 weeks. Colonic motility was similarly enhanced, as evidenced by faster bead expulsion in Nrp2-deleted mice versus Nrp2-intact controls. In length-tension analysis of the distal colon, passive tension was similar in Nrp2-deficient and Nrp2-intact mice, but at low strains, active stiffness was greater in Nrp2-deficient animals. Consistent with the findings in conditional Nrp2 mice, Nrp2-null mice showed increased contractility in response to carbachol and KCl. Evaluation of selected proteins implicated in smooth muscle contraction revealed no significant differences in the level of α-smooth muscle actin, myosin light chain, calponin, or RhoA. Together, these findings identify Nrp2 as a novel regulator of colonic contractility that may be targetable in conditions characterized by dysmotility.

Wnt Site Signaling Inhibitor Secreted Frizzled-Related Protein 3 Protects Mitral Valve Endothelium From Myocardial Infarction-Induced Endothelial-to-Mesenchymal Transition.

Background The onset and mechanisms of endothelial-to-mesenchymal transition (EndMT) in mitral valve (MV) leaflets following myocardial infarction (MI) are unknown, yet these events are closely linked to stiffening of leaflets and development of ischemic mitral regurgitation. We investigated whether circulating molecules present in plasma within days after MI incite EndMT in MV leaflets. Methods and Results We examined the onset of EndMT in MV leaflets from 9 sheep with inferior MI, 8 with sham surgery, and 6 naïve controls. Ovine MVs 8 to 10 days after inferior MI displayed EndMT, shown by increased vascular endothelial cadherin/α-smooth muscle actin-positive cells. The effect of plasma on EndMT in MV endothelial cells (VECs) was assessed by quantitative polymerase chain reaction, migration assays, and immunofluorescence. In vitro, post-MI plasma induced EndMT marker expression and enhanced migration of mitral VECs; sham plasma did not. Analysis of sham versus post-MI plasma revealed a significant drop in the Wnt signaling antagonist sFRP3 (secreted frizzled-related protein 3) in post-MI plasma. Addition of recombinant sFRP3 to post-MI plasma reversed its EndMT-inducing effect on mitral VECs. RNA-sequencing analysis of mitral VECs exposed to post-MI plasma showed upregulated FOXM1 (forkhead box M1). Blocking FOXM1 reduced EndMT transcripts in mitral VECs treated with post-MI plasma. Finally, FOXM1 induced by post-MI plasma was downregulated by sFRP3. Conclusions Reduced sFRP3 in post-MI plasma facilitates EndMT in mitral VECs by increasing the transcription factor FOXM1. Restoring sFRP3 levels or inhibiting FOXM1 soon after MI may provide a novel strategy to modulate EndMT in the MV to prevent ischemic mitral regurgitation and heart failure.

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.

Knockin mouse models demonstrate differential contributions of synaptotagmin-1 and -2 as receptors for botulinum neurotoxins.

Botulinum neurotoxins (BoNTs) are the most potent toxins known and are also utilized to treat a wide range of disorders including muscle spasm, overactive bladder, and pain. BoNTs' ability to target neurons determines their specificity, potency, and therapeutic efficacy. Homologous synaptic vesicle membrane proteins synaptotagmin-1 (Syt1) and synaptotagmin-2 (Syt2) have been identified as receptors for BoNT family members including BoNT/B, DC, and G, but their contributions at physiologically relevant toxin concentrations in vivo have yet to be validated and established. Here we generated two knockin mutant mouse models containing three designed point-mutations that specifically disrupt BoNT binding in endogenous Syt1 or Syt2, respectively. Utilizing digit abduction score assay by injecting toxins into the leg muscle, we found that Syt1 mutant mice showed similar sensitivity as the wild type mice, whereas Syt2 mutant mice showed reduced sensitivity to BoNT/B, DC, and G, demonstrating that Syt2 is the dominant receptor at skeletal neuromuscular junctions. We further developed an in vivo bladder injection assay for analyzing BoNT action on bladder tissues and demonstrated that Syt1 is the dominant toxin receptor in autonomic nerves controlling bladder tissues. These findings establish the critical role of protein receptors for the potency and specificity of BoNTs in vivo and demonstrate the differential contributions of Syt1 and Syt2 in two sets of clinically relevant target tissues.

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.

Interaction of TLK1 and AKTIP as a Potential Regulator of AKT Activation in Castration-Resistant Prostate Cancer Progression.

Prostate cancer (PCa) progression is characterized by the emergence of resistance to androgen deprivation therapy (ADT). AKT/PKB has been directly implicated in PCa progression, often due to the loss of PTEN and activation of PI3K>PDK1>AKT signaling. However, the regulatory network of AKT remains incompletely defined. Here, we describe the functional significance of AKTIP in PCa cell growth. AKTIP, identified in an interactome analysis as a substrate of TLK1B (that itself is elevated following ADT), enhances the association of AKT with PDK1 and its phosphorylation at T308 and S473. The interaction between TLK1 and AKTIP led to AKTIP phosphorylation at T22 and S237. The inactivation of TLK1 led to reduced AKT phosphorylation, which was potentiated with AKTIP knockdown. The TLK1 inhibitor J54 inhibited the growth of the LNCaP cells attributed to reduced AKT activation. However, LNCaP cells that expressed constitutively active, membrane-enriched Myr-AKT (which is expected to be active, even in the absence of AKTIP) were also growth-inhibited with J54. This suggested that other pathways (like TLK1>NEK1>YAP) regulating proliferation are also suppressed and can mediate growth inhibition, despite compensation by Myr-AKT. Nonetheless, further investigation of the potential role of TLK1>AKTIP>AKT in suppressing apoptosis, and conversely its reversal with J54, is warranted.