The Chronic Toxicity of Endocrine-Disrupting Chemical to Daphnia magna: A Transcriptome and Network Analysis of TNT Exposure.

Endocrine-disrupting chemicals (EDCs) impair growth and development. While EDCs can occur naturally in aquatic ecosystems, they are continuously introduced through anthropogenic activities such as industrial effluents, pharmaceutical production, wastewater, and mining. To elucidate the chronic toxicological effects of endocrine-disrupting chemicals (EDCs) on aquatic organisms, we collected experimental data from a standardized chronic exposure test using Daphnia magna (D. magna), individuals of which were exposed to a potential EDC, trinitrotoluene (TNT). The chronic toxicity effects of this compound were explored through differential gene expression, gene ontology, network construction, and putative adverse outcome pathway (AOP) proposition. Our findings suggest that TNT has detrimental effects on the upstream signaling of Tcf/Lef, potentially adversely impacting oocyte maturation and early development. This study employs diverse bioinformatics approaches to elucidate the gene-level toxicological effects of chronic TNT exposure on aquatic ecosystems. The results provide valuable insights into the molecular mechanisms of the adverse impacts of TNT through network construction and putative AOP proposition.

Temporally complex inflammatory networks in an animal model reveal signatures for interstitial cystitis and bladder pain syndrome phenotype.

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.

Network pharmacology integrated with experimental verification reveals the antipyretic characteristics and mechanism of Zi Xue powder.

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.

Mechanism of Elian granules in the treatment of precancerous lesions of gastric cancer in rats through the MAPK signalling pathway based on network pharmacology.

CONTEXT: Elian Granules have been applied in the treatment of precancerous lesions of gastric cancer (PLGC) and achieved good results. However, its exact mechanism remains unclear. OBJECTIVES: To explore the mechanism of Elian granules in treating PLGC through the mitogen-activated protein kinase (MAPK) signalling pathway based on network pharmacology. MATERIALS AND METHODS: Through network pharmacological methods, the targets of the active component of Elian granules against PLGC were obtained. Subsequently, Specific Pathogen Free (SPF) male Sprague Dawley (SD) rats were randomly divided into normal, model, and Elian granule groups. The N-methyl-N'-nitro-N-nitrosoguanidine comprehensive method was used to establish the PLGC rat model. The model and Elian granule groups were given normal saline and Elian granule aqueous solution (3.24 g/kg/d) intragastric administration, respectively, for 24 weeks. The pathological changes in gastric tissues were observed by hematoxylin-eosin staining. The protein expression of p-JNK and p-p38 was verified by western blotting. RESULTS: 394 and 4,395 targets were identified in Elian granules and PLGC, respectively. The 190 common targets were mainly enriched in MAPK signalling pathways. The gastric mucosal epithelium was still intact, the glands were arranged regularly, and no goblet cells or apparent inflammatory cell infiltration were observed in the Elian granule group. The expression of p-JNK and p-p38 protein of the Elian granule group (0.83 ± 0.08; 1.18 ± 0.40) was significantly higher than the model group (0.27 ± 0.14; 0.63 ± 0.14) (p < 0.01; p < 0.05). DISCUSSION AND CONCLUSIONS: Elian granules may play a critical role in the treatment of rat PLGC by up-regulating the expression of p-JNK and p-p38 proteins in the MAPK signalling pathway, thus providing a scientific basis for clinical application.

Automated and accurate segmentation of leaf venation networks via deep learning.

Leaf vein network geometry can predict levels of resource transport, defence and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales due to the difficulties both in segmenting networks from images and in extracting multiscale statistics from subsequent network graph representations. Here we developed deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty-eight CNNs were trained on subsets of manually defined ground-truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of six independently trained CNNs were used to segment networks from larger leaf regions (c. 100 mm2 ). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry. The CNN approach gave a precision-recall harmonic mean of 94.5% ± 6%, outperforming other current network extraction methods, and accurately described the widths, angles and connectivity of veins. Multiscale statistics then enabled the identification of previously undescribed variation in network architecture across species. We provide a LeafVeinCNN software package to enable multiscale quantification of leaf vein networks, facilitating the comparison across species and the exploration of the functional significance of different leaf vein architectures.

Assessment of weighted topological overlap (wTO) to improve fidelity of gene co-expression networks.

BACKGROUND: For more than a decade, gene expression data sets have been used as basis for the construction of co-expression networks used in systems biology investigations, leading to many important discoveries in a wide range of subjects spanning human disease to evolution and the development of organisms. A commonly encountered challenge in such investigations is first that of detecting, then subsequently removing, spurious correlations (i.e. links) in these networks. While access to a large number of measurements per gene would reduce this problem, often only a small number of measurements are available. The weighted Topological Overlap (wTO) measure, which incorporates information from the shared network-neighborhood of a given gene-pair into a single score, is a metric that is frequently used with the implicit expectation of producing higher-quality networks. However, the actual extent to which wTO improves on the accuracy of a co-expression analysis has not been quantified. RESULTS: Here, we used a large-sample biological data set containing 338 gene-expression measurements per gene as a reference system. From these data, we generated ensembles consisting of 10, 20 and 50 randomly selected measurements to emulate low-quality data sets, finding that the wTO measure consistently generates more robust scores than what results from simple correlation calculations. Furthermore, for the data sets consisting of only 10 and 20 samples per gene, we find that wTO serves as a better predictor of the correlation scores generated from the full data set. However, we find that using wTO as a score for network building substantially alters several topographical aspects of the resulting networks, with no conclusive evidence that the resulting structure is more accurate. Importantly, we find that the much used approach of applying a soft-threshold modifier to link weights prior to computing the wTO substantially decreases the robustness of the resulting wTO network, but increases the predictive power of wTO networks with regards to the reference correlation (soft threshold) network, particularly as the size of the data sets increases. CONCLUSION: Our analysis demonstrates that, in agreement with previous assumptions, the wTO approach is capable of significantly improving the fidelity of co-expression networks, and that this effect is especially evident for cases of low-sample number gene-expression data sets.

cuRnet: an R package for graph traversing on GPU.

BACKGROUND: R has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms. RESULTS: This work presents cuRnet, a R package that provides a parallel implementation for GPUs of the breath-first search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows offloading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis. CONCLUSIONS: cuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We show the efficiency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms.

PhosphoPath: Visualization of Phosphosite-centric Dynamics in Temporal Molecular Networks.

Protein phosphorylation is an essential post-translational modification (PTM) regulating many biological processes at the cellular and multicellular level. Continuous improvements in phosphoproteomics technology allow the analysis of this PTM in an expanding biological content, yet up until now proteome data visualization tools are still very gene centric, hampering the ability to comprehensively map and study PTM dynamics. Here we present PhosphoPath, a Cytoscape app designed for the visualization and analysis of quantitative proteome and phosphoproteome data sets. PhosphoPath brings knowledge into the biological network by importing publically available data and enables PTM site-specific visualization of information from quantitative time series. To showcase PhosphoPath performance we use a quantitative proteomics data set comparing patient-derived melanoma cell lines grown in either conventional cell culture or xenografts.

DTIP-TC2A: An analytical framework for drug-target interactions prediction methods.

Identifying drug-target interactions through computational methods is raised an important and key step in the process of drug discovery and drug-oriented research during the last years. In addition to the advantages of existing computational methods, there are also challenges that affect methods' efficiency and provide obstacles in the direction of developing these computational methods. However, the literature suffers from lacking a comprehensive and comparative analysis concerning drug-target interactions prediction (DTIP) focusing on the analysis of technical and challenging aspects. It seems necessary to provide a comparative perspective and a different analysis on a macro level due to the importance of the DTIP problem. In this paper, we presented the quadruple framework of analytical, named DTIP-TC2A consists of four main components for DTIP. The first component, categorizing DTIP methods based on the technical aspect ahead and investigating the strengths and weaknesses of different DTIP methods. Second, classify DTIP challenges with a major focus on a well-organized and coherent investigation of challenges and presenting a macro view of the DTIP challenges by systematic identification of them. Third, recommending some general criteria to analyze DTIP methods in form of the proposed classifications. Suggesting a suitable set of qualitative criteria along with using quantitative criteria can lead to a more proper choice of DTIP methods. Fourth, performing a two-phase qualitative analysis and comparison between each class of DTIP approaches based on the proposed functional criteria and the identified challenges ahead in order to understand the superiority of each class of DTIP methods over the other class. We believed that the DTIP-TC2A framework can offer a proper context for efficient selection of DTIP methods, improving the efficiency of a DTIP system due to the nature of computational methods, upgrading DTIP methods by removing the barriers, and presenting new directions of research for further studies through systematic identification of DTIP challenges and purposeful evaluation of challenges and methods.

Network Medicine-Based Analysis of Association Between Gynecological Cancers and Metabolic and Hormonal Disorders.

Different metabolic and hormonal disorders like type 2 diabetes mellitus (T2DM), obesity, and polycystic ovary syndrome (PCOS) have tangible socio-economic impact. Prevalence of these metabolic and hormonal disorders is steadily increasing among women. There are clinical evidences that these physiological conditions are related to the manifestation of different gynecological cancers and their poor prognosis. The relationship between metabolic and hormonal disorders with gynecological cancers is quite complex. The need for gene level association study is extremely important to find markers and predicting risk factors. In the current work, we have selected metabolic disorders like T2DM and obesity, hormonal disorder PCOS, and 4 different gynecological cancers like endometrial, uterine, cervical, and triple negative breast cancer (TNBC). The gene list was downloaded from DisGeNET database (v 6.0). The protein interaction network was constructed using HIPPIE (v 2.2) and shared proteins were identified. Molecular comorbidity index and Jaccard coefficient (degree of similarity) between the diseases were determined. Pathway enrichment analysis was done using ReactomePA and significant modules (clusters in a network) of the constructed network was analyzed by MCODE plugin of Cytoscape. The comorbid conditions like PCOS-obesity found to increase the risk factor of ovarian and triple negative breast cancers whereas PCOS alone has highest contribution to the endometrial cancer. Different gynecological cancers were found to be differentially related to the metabolic/hormonal disorders and comorbid condition.

Network-based Drug Repurposing: A Critical Review.

New drug development for a disease is a tedious, time-consuming, complex, and expensive process. Even if it is done, the chances for success of newly developed drugs are still very low. Modern reports state that repurposing the pre-existing drugs will have more efficient functioning than newly developed drugs. This repurposing process will save time, reduce expenses and provide more success rate. The only limitation for this repurposing is getting a desired pharmacological and characteristic parameter of various drugs from vast data about medications, their effects, and target mechanisms. This drawback can be avoided by introducing computational methods of analysis. This includes various network analysis types that use various biological processes and relationships with various drugs to simplify data interpretation. Some of the data sets now available in standard, and simplified forms include gene expression, drug-target interactions, protein networks, electronic health records, clinical trial results, and drug adverse event reports. Integrating various data sets and interpretation methods allows a more efficient and easy way to repurpose an exact drug for the desired target and effect. In this review, we are going to discuss briefly various computational biological network analysis methods like gene regulatory networks, metabolic networks, protein-protein interaction networks, drug-target interaction networks, drugdisease association networks, drug-drug interaction networks, drug-side effects networks, integrated network-based methods, semantic link networks, and isoform-isoform networks. Along with this, we briefly discussed the drug's limitations, prediction methodologies, and data sets utilised in various biological networks for drug repurposing.

Formaldehyde exposure and leukemia risk: a comprehensive review and network-based toxicogenomic approach.

Formaldehyde is a widely used but highly reactive and toxic chemical. The International Agency for Research on Cancer classifies formaldehyde as a Group 1 carcinogen, based on nasopharyngeal cancer and leukemia studies. However, the correlation between formaldehyde exposure and leukemia incidence is a controversial issue. To understand the association between formaldehyde exposure and leukemia, we explored biological networks based on formaldehyde-related genes retrieved from public and commercial databases. Through the literature-based network approach, we summarized qualitative associations between formaldehyde exposure and leukemia. Our results indicate that oxidative stress-mediated genetic changes induced by formaldehyde could disturb the hematopoietic system, possibly leading to leukemia. Furthermore, we suggested major genes that are thought to be affected by formaldehyde exposure and associated with leukemia development. Our suggestions can be used to complement experimental data for understanding and identifying the leukemogenic mechanism of formaldehyde.

Systematic Network and Meta-analysis on the Antiviral Mechanisms of Probiotics: A Preventive and Treatment Strategy to Mitigate SARS-CoV-2 Infection.

With the alarming rise of infected cases and deaths, COVID-19 is a pandemic, affecting 220 countries worldwide. Until now, no specific treatment is available against SARS-CoV-2. The causal virus SARS-CoV-2 primarily infects lung cells, leading to respiratory illness ranging in severity from the common cold to deadly pneumonia. This, with comorbidities, worsens the clinical outcome, particularly for immunosuppressed individuals with COVID-19. Interestingly, the commensal gut microbiota has been shown to improve lung infections by modulating the immune system. Therefore, fine-tuning of the gut microbiome with probiotics could be an alternative strategy for boosting immunity and treating COVID-19. Here, we present a systematic biological network and meta-analysis to provide a rationale for the implementation of probiotics in preventing and/or treating COVID-19. We have identified 90 training genes from the literature analysis (according to PRISMA guidelines) and generated an association network concerning the candidate genes linked with COVID-19 and probiotic treatment. The functional modules and pathway enrichment analysis of the association network clearly show that the application of probiotics could have therapeutic effects on ACE2-mediated virus entry, activation of the systemic immune response, nlrp3-mediated immunomodulatory pathways, immune cell migration resulting in lung tissue damage and cardiovascular difficulties, and altered glucose/lipid metabolic pathways in the disease prognosis. We also demonstrate the potential mechanistic domains as molecular targets for probiotic applications to combat the viral infection. Our study, therefore, offers probiotics-mediated novel preventive and therapeutic strategies for COVID-19 warfare.

Community Detection in Large-Scale Bipartite Biological Networks.

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks-such as those that represent regulatory interactions, drug-gene, or gene-disease associations-are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a "hairball" of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches.

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target "ERBB4" and candidate drug "Wortmannin" provide insights on the possible personalized therapeutics for emerging COVID-19.

Arete - candidate gene prioritization using biological network topology with additional evidence types.

BACKGROUND: Refinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of specific causal genes. Given the qualitative and semantic complexity of biological data, successfully addressing this challenge requires development of flexible and interoperable solutions for making the best possible use of the largest possible fraction of all available data. RESULTS: We have developed an easily accessible framework that links two established network-based gene prioritization approaches with a supporting isolation forest-based integrative ranking method. The defining feature of the method is that both topological information of the biological networks and additional sources of evidence can be considered at the same time. The implementation was realized as an app extension for the Cytoscape graph analysis suite, and therefore can further benefit from the synergy with other analysis methods available as part of this system. CONCLUSIONS: We provide efficient reference implementations of two popular gene prioritization algorithms - DIAMOnD and random walk with restart for the Cytoscape system. An extension of those methods was also developed that allows outputs of these algorithms to be combined with additional data. To demonstrate the utility of our software, we present two example disease gene prioritization application cases and show how our tool can be used to evaluate these different approaches.

Gene Expression Analysis Through Network Biology: Bioinformatics Approaches.

Following the availability of high-throughput technologies, vast amounts of biological data have been generated. Gene expression is one example of the popular data that has been utilized for studying cellular systems in the transcriptional level. Several bioinformatics approaches have been developed to analyze such data. A typical expression analysis identifies a ranked list of individual significant differentially expressed genes between two conditions of interest. However, it has been accepted that biomolecules in a living organism are working together and interacting with each other. Study through network analysis could be complementary to typical expression analysis and provides more contexts to understanding the biological systems. Conversely, expression data could provide clues to functional links between biomolecules in biological networks. In this chapter, bioinformatics approaches to analyze expression data in network levels including basic concepts of network biology are described. Different concepts to integrate expression data with interactome data and example studies are explained.