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Link prediction (LP) is a task for the identification of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the gene prediction. LSIs discovered potential links between disease related genes, which were obtained from OMIM database for gastric, colorectal, breast, prostate and lung cancers. LSIs based disease related genes were ranked due to their LSI scores in descending order for retrieving the top 10, 50 and 100 disease related genes. SMV integrated four LSIs based predictions to obtain SMV based the top 10, 50 and 100 disease related genes. The performance of LSIs based and SMV based genes were evaluated separately by employing overlap analyses, which were performed with GeneCard disease-gene relation dataset and Gene Ontology (GO) terms. The GO-terms were used for biological assessment for the inferred gene lists by LSIs and SMV on all cancer types. Adamic-Adar (AA), Resource Allocation Index (RAI), and SMV based gene lists are generally achieved good performance results on all cancers in both overlap analyses. SMV also outperformed on breast cancer data. The increment in the selection of the number of the top ranked disease related genes also enhanced the performance results of SMV.

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Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference, clustered regularly interspaced short palindromic repeat, and overexpression. TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26 945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery.

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Biological networks serve a crucial role in elucidating intricate biological processes. While interspecies environmental interactions have been extensively studied, the exploration of gene interactions within species, particularly among individual microorganisms, is less developed. The increasing amount of microbiome genomic data necessitates a more nuanced analysis of microbial genome structures and functions. In this context, we introduce a complex structure using higher-order network theory, "Solid Motif Structures (SMS)", via a hierarchical biological network analysis of genomes within the same genus, effectively linking microbial genome structure with its function. Leveraging 162 high-quality genomes of Microcystis, a key freshwater cyanobacterium within microbial ecosystems, we established a genome structure network. Employing deep learning techniques, such as adaptive graph encoder, we uncovered 27 critical functional subnetworks and their associated SMSs. Incorporating metagenomic data from seven geographically distinct lakes, we conducted an investigation into Microcystis' functional stability under varying environmental conditions, unveiling unique functional interaction models for each lake. Our work compiles these insights into an extensive resource repository, providing novel perspectives on the functional dynamics within Microcystis. This research offers a hierarchical network analysis framework for understanding interactions between microbial genome structures and functions within the same genus.

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The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features' connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering's user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose the Multi-layer Bundling (MLB) method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters "bundles". This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating the bundle co-cluster matrix with the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed.

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Shaofuzhuyu Decoction (SFZYD) is a classical formula for treating endometriosis of cold coagulation and blood stasis (ECB). The clinical efficacy is definite, but the potential mechanisms require further exploration. The study aimed to reveal the metabolic mechanisms of SFZYD for treating ECB using mass spectrum oriented metabolomics. Firstly, the study has used metabolomics data to identify biomarkers and to investigate metabolic pathways. Then, the targets of SFZYD for treating ECB were dug by building and analyzing a biological network of biomarkers. Finally, the obtained targets were validated by molecular docking. This study found that SFZYD could significantly improve the biochemical indicators and metabolic abnormalities of ECB. A total of 18 ECB-related biomarkers in 7 pathways were identified. SFZYD was able to regulate the levels of 14 biomarkers that were involved in 5 metabolic pathways. Furthermore, the study yielded 119 SFZYD active ingredients, 1119 target proteins associated with endometriosis, 610 targets associated with biomarkers, 727 GO functions, and 159 KEGG pathways. Biological network analysis constructed a network diagram of herbs-ingredients-targets-biomarkers, and found 6 key active ingredients and 9 core targets. Molecular docking showed high affinities between key ingredients and core targets. This study elucidated that SFZYD plays a role in treating ECB through multi-component, multi-target, and multi-pathway.

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Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

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ETHNOPHARMACOLOGICAL RELEVANCE: Ginseng Radix and Astragali Radix are commonly combined to tonify Qi and alleviate fatigue. Previous studies have employed biological networks to investigate the mechanisms of herb pairs in treating different diseases. However, these studies have only elucidated a single network for each herb pair, without emphasizing the superiority of the herb combination over individual herbs. AIM OF THE STUDY: This study proposes an approach of comparing biological networks to highlight the synergistic effect of the pair in treating cancer-related fatigue (CRF). METHODS: The compounds and targets of Ginseng Radix, Astragali Radix, and CRF diseases were collected and predicted using different databases. Subsequently, the overlapping targets between herbs and disease were imported into the STRING and DAVID tools to build protein-protein interaction (PPI) networks and analyze enriched KEGG pathways. The biological networks of Ginseng Radix and Astragali Radix were compared separately or together using the DyNet application. Molecular docking was used to verify the predicted results. Further, in vitro experiments were conducted to validate the synergistic pathways identified in in silico studies. RESULTS: In the PPI network comparison, the combination created 89 new interactions and an increased average degree (11.260) when compared to single herbs (10.296 and 9.394). The new interactions concentrated on HRAS, STAT3, JUN, and IL6. The topological analysis identified 20 core targets of the combination, including three Ginseng Radix-specific targets, three Astragali Radix-specific targets, and 14 shared targets. In KEGG enrichment analysis, the combination regulated additional signaling pathways (152) more than Ginseng Radix (146) and Astragali Radix (134) alone. The targets of the herb pair synergistically regulated cancer pathways, specifically hypoxia-inducible factor 1 (HIF-1) signaling pathway. In vitro experiments including enzyme-linked immunosorbent assay and Western blot demonstrated that two herbs combination could up-regulate HIF-1α signaling pathway at different combined concentrations compared to either single herb alone. CONCLUSION: The herb pair increased protein interactions and adjusted metabolic pathways more than single herbs. This study provides insights into the combination of Ginseng Radix and Astragali Radix in clinical practice.

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As species diverge, a wide range of evolutionary processes lead to changes in protein-protein interaction (PPI) networks and metabolic networks. The rate at which molecular networks evolve is an important question in evolutionary biology. Previous empirical work has focused on interactomes from model organisms to calculate rewiring rates, but this is limited by the relatively small number of species and sparse nature of network data across species. We present a proxy for variation in network topology: variation in drug-drug interactions (DDIs), obtained by studying drug combinations (DCs) across taxa. Here, we propose the rate at which DDIs change across species as an estimate of the rate at which the underlying molecular network changes as species diverge. We computed the evolutionary rates of DDIs using previously published data from a high-throughput study in gram-negative bacteria. Using phylogenetic comparative methods, we found that DDIs diverge rapidly over short evolutionary time periods, but that divergence saturates over longer time periods. In parallel, we mapped drugs with known targets in PPI and cofunctional networks. We found that the targets of synergistic DDIs are closer in these networks than other types of DCs and that synergistic interactions have a higher evolutionary rate, meaning that nodes that are closer evolve at a faster rate. Future studies of network evolution may use DC data to gain larger-scale perspectives on the details of network evolution within and between species.

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Immune checkpoint blockade (ICB) therapy has brought significant advancements to the field of oncology. However, the diverse responses among patients highlight the need for more accurate predictive tools. In this study, insights are drawn from tumor-immunology pathways, and a novel network-based ICB immunotherapeutic signature, termed ICBnetIS, is constructed. The signature is derived from advanced biological network-based computational strategies involving co-expression networks and molecular interactions networks. The efficacy of ICBnetIS is established through its association with enhanced patient survival and a robust immune response characterized by diverse immune cell infiltration and active anti-tumor immune pathways. The validation process positions ICBnetIS as an effective tool in predicting responses to ICB therapy, analyzing ICB data from a broad collection of over 700 samples from multiple cancer types of more than 15 datasets. It achieves an aggregated prediction AUC of 0.784, which outperforms the other nine renowned immunotherapeutic signatures, indicating the superior predictive capability of ICBnetIS. To sum up, the findings suggest ICBnetIS as a potent tool in predicting ICB therapy responses, offering significant implications for patient selection and treatment optimization in oncology. The study highlights the role of ICBnetIS in advancing personalized treatment strategies, potentially transforming the clinical landscape of ICB therapy.

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BACKGROUND: Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank. RESULTS: We first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics. CONCLUSION: Overall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

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Biological macromolecules, such as DNA, RNA, and proteins in living organisms, form an intricate network that plays a key role in many biological processes. Many attempts have been made to build new networks by connecting non-communicable proteins with network mediators, especially using antibodies. In this study, we devised an aptamer-based switching system that enables communication between non-interacting proteins. As a proof of concept, two proteins, Cas13a and T7 RNA polymerase (T7 RNAP), were rationally connected using an aptamer that specifically binds to T7 RNAP. The proposed switching system can be modulated in both signal-on and signal-off manners and its responsiveness to the target activator can be controlled by adjusting the reaction time. This study paves the way for the expansion of biological networks by mediating interactions between proteins using aptamers.

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Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein-protein interaction networks and predicting novel drug functions.

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Motivation: A common task in scientific research is the comparison of lists or sets of diverse biological entities such as biomolecules, ontologies, sequences and expression profiles. Such comparisons rely, one way or another, on calculating a measure of similarity either by means of vector correlation metrics, set operations such as union and intersection, or specific measures to capture, for example, sequence homology. Subsequently, depending on the data type, the results are often visualized using heatmaps, Venn, Euler, or Alluvial diagrams. While most of the abovementioned representations offer simplicity and interpretability, their effectiveness holds only for a limited number of lists and specific data types. Conversely, network representations provide a more versatile approach where data lists are viewed as interconnected nodes, with edges representing pairwise commonality, correlation, or any other similarity metric. Networks can represent an arbitrary number of lists of any data type, offering a holistic perspective and most importantly, enabling analytics for characterizing and discovering novel insights in terms of centralities, clusters and motifs that can exist in such networks. While several tools that implement the translation of lists to the various commonly used diagrams, such as Venn and Euler, have been developed, a similar tool that can parse, analyze the commonalities and generate networks from an arbitrary number of lists of the same or heterogenous content does not exist. Results: To address this gap, we introduce List2Net, a web-based tool that can rapidly process and represent lists in a network context, either in a single-layer or multi-layer mode, facilitating network analysis on multi-source/multi-layer data. Specifically, List2Net can seamlessly handle lists encompassing a wide variety of biological data types, such as named entities or ontologies (e.g., lists containing gene symbols), sequences (e.g., protein/peptide sequences), and numeric data types (e.g., omics-based expression or abundance profiles). Once the data is imported, the tool then (i) calculates the commonalities or correlations (edges) between the lists (nodes) of interest, (ii) generates and renders the network for visualization and analysis and (iii) provides a range of exporting options, including vector, raster format visualization but also the calculated edge lists and metrics in tabular format for further analysis in other tools. List2Net is a fast, lightweight, yet informative application that provides network-based holistic insights into the conditions represented by the lists of interest (e.g., disease-to-disease, gene-to-phenotype, drug-to-disease, etc.). As a case study, we demonstrate the utility of this tool applied on publicly available datasets related to Multiple Sclerosis (MS). Using the tool, we showcase the translation of various ontologies characterizing this specific condition on disease-to-disease subnetworks of neurodegenerative, autoimmune and infectious diseases generated from various levels of information such as genetic variation, genes, proteins, metabolites and phenotypic terms.

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CONTEXT: Zi Xue Powder (ZXP) is a traditional formula for the treatment of fever. However, the potential mechanism of action of ZXP remains unknown. OBJECTIVE: This study elucidates the antipyretic characteristics of ZXP and the mechanism by which ZXP alleviates fever. MATERIALS AND METHODS: The key targets and underlying fever-reducing mechanisms of ZXP were predicted using network pharmacology and molecular docking. The targets of ZXP anti-fever active ingredient were obtained by searching TCMSP, STITCH and HERB. Moreover, male Sprague-Dawley rats were randomly divided into four groups: control, lipopolysaccharide (LPS), ZXP (0.54, 1.08, 2.16 g/kg), and positive control (acetaminophen, 0.045 g/kg); the fever model was established by intraperitoneal LPS injection. After the fever model was established at 0.5 h, the rats were administered treatment by gavage, and the anal temperature changes of each group were observed over 10 h after treatment. After 10 h, ELISA and Western blot analysis were used to further investigate the mechanism of ZXP. RESULTS: Network pharmacology analysis showed that MAPK was a crucial pathway through which ZXP suppresses fever. The results showed that ZXP (2.16 g/kg) decreased PGE2, CRH, TNF-a, IL-6, and IL-1ß levels while increasing AVP level compared to the LPS group. Furthermore, the intervention of ZXP inhibited the activation of MAPK pathway in LPS-induced fever rats. CONCLUSIONS: This study provides new insights into the mechanism by which ZXP reduces fever and provides important information and new research ideas for the discovery of antipyretic compounds from traditional Chinese medicine.

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BACKGROUND: Preeclampsia has been associated with maternal epigenetic changes, in particular DNA methylation changes in the placenta. It has been suggested that preeclampsia could also cause DNA methylation changes in the neonate. We examined DNA methylation in relation to gene expression in the cord blood of offspring born to mothers with preeclampsia. METHODS: This study included 128 mother-child pairs who participated in the Vitamin D Antenatal Asthma Reduction Trial (VDAART), where assessment of preeclampsia served as secondary outcome. We performed an epigenome-wide association study of preeclampsia and cord blood DNA methylation (Illumina 450 K chip). We then examined gene expression of the same subjects for validation and replicated the gene signatures in independent DNA methylation datasets. Lastly, we applied functional enrichment and network analyses to identify biological pathways that could potentially be involved in preeclampsia. FINDINGS: In the cord blood samples (n = 128), 263 CpGs were differentially methylated (FDR <0.10) in preeclampsia (n = 16), of which 217 were annotated. Top pathways in the functional enrichment analysis included apelin signaling pathway and other endothelial and cardiovascular pathways. Of the 217 genes, 13 showed differential expression (p's < 0.001) in preeclampsia and 11 had been previously related to preeclampsia (p's < 0.0001). These genes were linked to apelin, cGMP and Notch signaling pathways, all having a role in angiogenic process and cardiovascular function. INTERPRETATION: Preeclampsia is related to differential cord blood DNA methylation signatures of cardiovascular pathways, including the apelin signaling pathway. The association of these cord blood DNA methylation signatures with offspring's long-term morbidities due to preeclampsia should be further investigated. FUNDING: VDAART is funded by National Heart, Lung, and Blood Institute grants of R01HL091528 and UH3OD023268. HMK is supported by Jane and Aatos Erkko Foundation, Paulo Foundation, and the Pediatric Research Foundation. HM is supported by K01 award from NHLBI (1K01HL146977-01A1). PK is supported by K99HL159234 from NIH/NHLBI.

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MOTIVATION: Drug repurposing (DR) is an imminent approach for identifying novel therapeutic indications for the available drugs and discovering novel drugs for previously untreatable diseases. Nowadays, DR has major attention in the pharmaceutical industry due to the high cost and time of launching new drugs to the market through traditional drug development. DR task majorly depends on genetic information since the drugs revert the modified Gene Expression (GE) of diseases to normal. Many of the existing studies have not considered the genetic importance of predicting the potential candidates. METHOD: We proposed a novel multimodal framework that utilizes genetic aspects of drugs and diseases such as genes, pathways, gene signatures, or expression to enhance the performance of DR using various data sources. Firstly, the heterogeneous biological network (HBN) is constructed with three types of nodes namely drug, disease, and gene, and 4 types of edges similarities (drug, gene, and disease), drug-gene, gene-disease, and drug-disease. Next, a modified graph auto-encoder (GAE*) model is applied to learn the representation of drug and disease nodes using the topological structure and edge information. Secondly, the HBN is enhanced with the information extracted from biomedical literature and ontology using a novel semi-supervised pattern embedding-based bootstrapping model and novel DR perspective representation learning respectively to improve the prediction performance. Finally, our proposed system uses a neural network model to generate the probability score of drug-disease pairs. RESULTS: We demonstrate the efficiency of the proposed model on various datasets and achieved outstanding performance in 5-fold cross-validation (AUC = 0.99, AUPR = 0.98). Further, we validated the top-ranked potential candidates using pathway analysis and proved that the known and predicted candidates share common genes in the pathways.

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BACKGROUND: Hydrogen-rich water (HRW) has been shown to prevent cognitive impairment caused by ionizing radiation. This study aimed to investigate the pharmacological effects and mechanisms of HRW on ionizing radiation by coupling the brain metabolomics and biological target network methods. METHODS AND RESULTS: HRW significantly improves the cognitive impairment in rats exposed to ionizing radiation. Based on metabolomics and biological network results, we identified 54 differential metabolites and 93 target genes. The KEGG pathway indicates that glutathione metabolism, ascorbic acid and aldehyde acid metabolism, pentose and glucuronic acid interconversion, and glycerophospholipid metabolism play important roles in ionizing radiation therapy. CONCLUSION: Our study has systematically elucidated the molecular mechanism of HRW against ionizing radiation, which can be mediated by modulating targets, pathways and metabolite levels. This provides a new perspective for identifying the underlying pharmacological mechanism of HRW.

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Biological networks are known to be highly modular, and the dysfunction of network modules may cause diseases. Defining the key modules from the omics data and establishing the classification model is helpful in promoting the research of disease diagnosis and prognosis. However, for applying modules in downstream analysis such as disease states discrimination, most methods only utilize the node information, and ignore the node interactions or topological information, which may lead to false positives and limit the model performance. In this study, we propose an omics data analysis method based on feature linear relationship and graph convolutional network (LCNet). In LCNet, we adopt a way of applying the difference of feature linear relationships during disease development to characterize physiological and pathological changes and construct the differential linear relation network, which is simple and interpretable from the perspective of feature linear relationship. A greedy strategy is developed for searching the highly interactive modules with a strong discrimination ability. To fully utilize the information of the detected modules, the personalized sub-graphs for each sample based on the modules are defined, and the graph convolutional network (GCN) classifiers are trained to predict the sample labels. The experimental results on public datasets show the superiority of LCNet in classification performance. For Breast Cancer metabolic data, the identified metabolites by LCNet involve important pathways. Thus, LCNet can identify the module biomarkers by feature linear relationship and a greedy strategy, and label samples by personalized sub-graphs and GCN. It provides a new manner of utilizing node (molecule) information and topological information in the defined modules for better disease classification.

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INTRODUCTION AND OBJECTIVE: Interstitial cystitis and bladder pain syndrome (IC/BPS) presents with symptoms of debilitating bladder pain and is typically a diagnosis of exclusion. The cystoscopic detection of Hunner's lesions increases the likelihood of detecting tissue inflammation on bladder biopsy and increases the odds of therapeutic success with anti-inflammatory drugs. However, the identification of this subgroup remains challenging with the current lack of surrogate biomarkers of IC/BPS. On the path towards identifying biomarkers of IC/BPS, we modeled the dynamic evolution of inflammation in an experimental IC/BPS rodent model using computational biological network analysis of inflammatory mediators (cytokines and chemokines) released into urine. The use of biological network analysis allows us to identify urinary proteins that could be drivers of inflammation and could therefore serve as therapeutic targets for the treatment of IC/BPS. METHODS: Rats subjected to cyclophosphamide (CYP) injection (150 mg/kg) were used as an experimental model for acute IC/BPS (n = 8). Urine from each void was collected from the rats over a 12-h period and was assayed for 13 inflammatory mediators using Luminex™. Time-interval principal component analysis (TI-PCA) and dynamic network analysis (DyNA), two biological network algorithms, were used to identify biomarkers of inflammation characteristic of IC/BPS over time. RESULTS: Compared to vehicle-treated rats, nearly all inflammatory mediators were elevated significantly (p < 0.05) in the urine of CYP treated rats. TI-PCA highlighted that GRO-KC, IL-5, IL-18, and MCP-1 account for the greatest variance in the inflammatory response. At early time points, DyNA indicated a positive correlation between IL-4 and IL-1ß and between TNF-α and IL-1ß. Analysis of TI-PCA and DyNA at later time points showed the emergence of IL-5, IL-6, and IFNγ as additional key mediators of inflammation. Furthermore, DyNA network complexity rose and fell before peaking at 9.5 h following CYP treatment. This pattern of inflammation may mimic the fluctuating severity of inflammation associated with IC/BPS flares. CONCLUSIONS: Computational analysis of inflammation networks in experimental IC/BPS analysis expands on the previously accepted inflammatory signatures of IC by adding IL-5, IL-18, and MCP-1 to the prior studies implicating IL-6 and GRO as IC/BPS biomarkers. This analysis supports a complex evolution of inflammatory networks suggestive of the rise and fall of inflammation characteristic of IC/BPS flares.

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