Network-based modeling of herb combinations in traditional Chinese medicine.

Traditional Chinese medicine (TCM) has been practiced for thousands of years for treating human diseases. In comparison to modern medicine, one of the advantages of TCM is the principle of herb compatibility, known as TCM formulae. A TCM formula usually consists of multiple herbs to achieve the maximum treatment effects, where their interactions are believed to elicit the therapeutic effects. Despite being a fundamental component of TCM, the rationale of combining specific herb combinations remains unclear. In this study, we proposed a network-based method to quantify the interactions in herb pairs. We constructed a protein-protein interaction network for a given herb pair by retrieving the associated ingredients and protein targets, and determined multiple network-based distances including the closest, shortest, center, kernel, and separation, both at the ingredient and at the target levels. We found that the frequently used herb pairs tend to have shorter distances compared to random herb pairs, suggesting that a therapeutic herb pair is more likely to affect neighboring proteins in the human interactome. Furthermore, we found that the center distance determined at the ingredient level improves the discrimination of top-frequent herb pairs from random herb pairs, suggesting the rationale of considering the topologically important ingredients for inferring the mechanisms of action of TCM. Taken together, we have provided a network pharmacology framework to quantify the degree of herb interactions, which shall help explore the space of herb combinations more effectively to identify the synergistic compound interactions based on network topology.

The ENDS of assumptions: an online tool for the epistemic non-parametric drug-response scoring.

MOTIVATION: The drug sensitivity analysis is often elucidated from drug dose-response curves. These curves capture the degree of cell viability (or inhibition) over a range of induced drugs, often with parametric assumptions that are rarely validated. RESULTS: We present a class of non-parametric models for the curve fitting and scoring of drug dose-responses. To allow a more objective representation of the drug sensitivity, these epistemic models devoid of any parametric assumptions attached to the linear fit, allow the parallel indexing such as half-maximal inhibitory concentration and area under curve. Specifically, three non-parametric models including spline (npS), monotonic and Bayesian and the parametric logistic are implemented. Other indices including maximum effective dose and drug-response span gradient pertinent to the npS are also provided to facilitate the interpretation of the fit. The collection of these models is implemented in an online app, standing as useful resource for drug dose-response curve fitting and analysis. AVAILABILITY AND IMPLEMENTATION: The ENDS is freely available online at https://irscope.shinyapps.io/ENDS/ and source codes can be obtained from https://github.com/AmiryousefiLab/ENDS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of Celecoxib-Targeted Proteins Using Label-Free Thermal Proteome Profiling on Rat Hippocampus.

Celecoxib, or Celebrex, a nonsteroidal anti-inflammatory drug, is one of the most common medicines for treating inflammatory diseases. Recently, it has been shown that celecoxib is associated with implications in complex diseases, such as Alzheimer disease and cancer as well as with cardiovascular risk assessment and toxicity, suggesting that celecoxib may affect multiple unknown targets. In this project, we detected targets of celecoxib within the nervous system using a label-free thermal proteome profiling method. First, proteins of the rat hippocampus were treated with multiple drug concentrations and temperatures. Next, we separated the soluble proteins from the denatured and sedimented total protein load by ultracentrifugation. Subsequently, the soluble proteins were analyzed by nano-liquid chromatography tandem mass spectrometry to determine the identity of the celecoxib-targeted proteins based on structural changes by thermal stability variation of targeted proteins toward higher solubility in the higher temperatures. In the analysis of the soluble protein extract at 67°C, 44 proteins were uniquely detected in drug-treated samples out of all 478 identified proteins at this temperature. Ras-associated binding protein 4a, 1 out of these 44 proteins, has previously been reported as one of the celecoxib off targets in the rat central nervous system. Furthermore, we provide more molecular details through biomedical enrichment analysis to explore the potential role of all detected proteins in the biologic systems. We show that the determined proteins play a role in the signaling pathways related to neurodegenerative disease-and cancer pathways. Finally, we fill out molecular supporting evidence for using celecoxib toward the drug-repurposing approach by exploring drug targets. SIGNIFICANCE STATEMENT: This study determined 44 off-target proteins of celecoxib, a nonsteroidal anti-inflammatory and one of the most common medicines for treating inflammatory diseases. It shows that these proteins play a role in the signaling pathways related to neurodegenerative disease and cancer pathways. Finally, the study provides molecular supporting evidence for using celecoxib toward the drug-repurposing approach by exploring drug targets.

Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer.

With the advent of high-throughput technologies leading to big data generation, increasing number of gene signatures are being published to predict various features of diseases such as prognosis and patient survival. However, to use these signatures for identifying therapeutic targets, use of additional bioinformatic tools is indispensible part of research. Here, we have generated a pipeline comprised of nearly 15 bioinformatic tools and enrichment statistical methods to propose and validate a drug combination strategy from already approved drugs and present our approach using published pan-cancer epithelial-mesenchymal transition (EMT) signatures as a case study. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To confirm the anti-EMT efficacy of the proposed target combination in silico, we validated expression of targets in mesenchymal versus epithelial subtypes of ovarian cancer. Additionally, we inhibited the pinpointed proteins in vitro using an invasive lung cancer cell line. We found that whereas low-dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward cocultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.

Metabolomic signature of amino acids in plasma of patients with non-segmental Vitiligo.

INTRODUCTION: Vitiligo pathogenesis is complicated, and several possibilities were suggested. However, it is well-known that the metabolism of pigments plays a significant role in the pathogenicity of the disease. OBJECTIVES: We explored the role of amino acids in vitiligo using targeted metabolomics. METHODS: The amino acid profile was studied in plasma using liquid chromatography. First, 22 amino acids were derivatized and precisely determined. Next, the concentrations of the amino acids and the molar ratios were calculated in 31 patients and 34 healthy individuals. RESULTS: The differential concentrations of amino acids were analyzed and eight amino acids, i.e., cysteine, arginine, lysine, ornithine, proline, glutamic acid, histidine, and glycine were observed differentially. The ratios of cysteine, glutamic acid, and proline increased significantly in Vitiligo patients, whereas arginine, lysine, ornithine, glycine, and histidine decreased significantly compared to healthy individuals. Considering the percentage of skin area, we also showed that glutamic acid significantly has a higher amount in patients with less than 25% involvement compared to others. Finally, cysteine and lysine are considered promising candidates for diagnosing and developing the disorder with high accuracy (0.96). CONCLUSION: The findings are consistent with the previously illustrated mechanism of Vitiligo, such as production deficiency in melanin and an increase in immune activity and oxidative stress. Furthermore, new evidence was provided by using amino acids profile toward the pathogenicity of the disorder.

Downregulation of ITM2A Gene Expression in Macrophages of Patients with Ankylosing Spondylitis.

OBJECTIVES: Ankylosing spondylitis (AS) is a rheumatic disorder that is mostly determined by genetic and environmental factors. Given the known importance of macrophage in AS pathogenesis, we investigated the transcriptional profile of macrophage cells in the disease. METHODS AND RESULTS: Two approaches of differential expression and subsequently, weighted gene co-expression network analysis was utilized to analyze a publicly available microarray dataset of macrophages. Integral membrane protein 2A (ITM2A) was among the most significant genes with a decreased trend in the common results of both methods. In order to confirm the finding, the expression of ITM2A was evaluated in monocyte-derived (M2-like) and M1 macrophages obtained from 14 AS patients and 14 controls. Macrophages were differentiated from whole-blood separated monocytes by 7 days incubating with macrophage colony-stimulating factor and then macrophages specific markers were verified with the flow cytometer. M1 polarization was induced by IFN-γ and lipopolysaccharide. Finally, relative gene expression analysis by real-time polymerase chain reaction revealed a significant downregulation of the ITM2A gene in both M2 like and M1 macrophages of the AS group compared to the control. CONCLUSION: Since ITM2A plays a critical role in osteo- and chondrogenic cellular differentiation, our finding may provide new insights into AS pathogenesis.

DrugComb: an integrative cancer drug combination data portal.

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users' own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.

Efficacy of Persian medicine herbal formulations (capsules and decoction) compared to standard care in patients with COVID-19, a multicenter open-labeled, randomized, controlled clinical trial.

Persian medicine has recommended clinical experiences and proper herbal remedies for prevention and treatment of microbial infections and respiratory diseases. An open-label, randomized, controlled, multicenter trial was conducted at five hospitals in Tehran and Isfahan provinces of Iran on 358 hospitalized adult patients. A total of 174 patients received standard care and 184 received herbal remedies (polyherbal decoction every 8 hr and two herbal capsules every 12 hr) plus standard care for 7 days. The primary clinical endpoint was the duration of hospital stay, and secondary outcomes were clinical improvement of symptoms based on self-assessment questionnaire. Results demonstrated that these natural decoction and capsules treatment plus routine care significantly decreased duration of hospital dyspnea (3.291 day vs. 6.468 days), accelerated clinical improvement, and decreased symptoms such as dry cough, dyspnea, muscle pain, headache, fatigue, anorexia, chills, runny nose, sputum cough, and vertigo in the treatment group compared with standard-care group. Significant effects of these polyherbal formulations on improving the symptoms of COVID-19 could be incredibly promising for managing this pandemic with acceptable tolerability.

CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis.

MOTIVATION: Centrality analysis involves a series of ambiguities in that there are numerous well-known centrality measures with differing algorithms for establishing which nodes in a network are essential. There is no clearly preferred measure or means of deciding which measure is most germane to a given network with respect to node essentiality vis-à-vis topological features. Our aim here was to develop an instrument that enables comparisons among potentially appropriate centrality measures to be made with respect to network structure and thereby to support the identification of the most informative measure according to dimensional reduction methods. METHODS: The Central Informative Nodes in Network Analysis (CINNA) package introduced herein gathers all required functions for centrality analysis in weighted/unweighted and directed/undirected networks. Then, it compares, assorts and visualizes centrality measures to select which best describes the node importance. AVAILABILITY AND IMPLEMENTATION: CINNA is available in CRAN, including a tutorial. URL: https://cran.r-project.org/web/packages/CINNA/index.html.

Predicting Meridian in Chinese traditional medicine using machine learning approaches.

Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM.

Intraperitoneal Administration of Telmisartan Prevents Postsurgical Adhesion Band Formation.

BACKGROUND: Angiotensin II receptor blockers (ARBs) have a potential role in reducing inflammation and fibrosis. We have integrated systems and molecular biology approaches to investigate the therapeutic potential of ARBs in preventing postsurgical adhesion band formation. MATERIAL AND METHODS: we have followed the ARRIVE guidelines point by point during experimental studies. Telmisartan (1 and 9 mg/kg), valsartan (1 and 9 mg/kg), and losartan (1 and 10 mg/kg) were administered intraperitoneally in different groups of male albino Wistar rat. After 7 d of treatment, macroscopic evidence and score of fibrotic bands based on scaling methods was performed. Moreover, the anti-inflammatory and antifibrosis effects of telmisartan on reduction of fibrotic bands were investigated by using histopathology, ELISA, and real-time polymerase chain reaction methods. RESULTS: Telmisartan, but not losartan or valsartan, prevented the frequency as well as the stability of adhesion bands. Telmisartan appears to elicit anti-inflammatory responses by attenuating submucosal edema, suppressing proinflammatory cytokines, decreasing proinflammatory cell infiltration, and inhibiting oxidative stress at the site of peritoneal surgery. We also showed that telmisartan prevents fibrotic adhesion band formation by reducing excessive collagen deposition and suppression of profibrotic genes expression at the peritoneum adhesion tissues. CONCLUSIONS: These results support the potential application of telmisartan in preventing postsurgical adhesion band formation by inhibiting key pathologic responses of inflammation and fibrosis in postsurgery patients.

IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, mapping and mining analysis.

BACKGROUND: Reconstruction of protein-protein interaction networks (PPIN) has been riddled with controversy for decades. Particularly, false-negative and -positive interactions make this progress even more complicated. Also, lack of a standard PPIN limits us in the comparison studies and results in the incompatible outcomes. Using an evolution-based concept, i.e. interolog which refers to interacting orthologous protein sets, pave the way toward an optimal benchmark. RESULTS: Here, we provide an R package, IMMAN, as a tool for reconstructing Interolog Protein Network (IPN) by integrating several Protein-protein Interaction Networks (PPINs). Users can unify different PPINs to mine conserved common networks among species. IMMAN is designed to retrieve IPNs with different degrees of conservation to engage prediction analysis of protein functions according to their networks. CONCLUSIONS: IPN consists of evolutionarily conserved nodes and their related edges regarding low false positive rates, which can be considered as a gold standard network in the contexts of biological network analysis regarding to those PPINs which is derived from.

An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis.

BACKGROUND: Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP. RESULTS: High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein-protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12). CONCLUSIONS: High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.

Valproic acid inhibits the protective effects of stromal cells against chemotherapy in breast cancer: Insights from proteomics and systems biology.

Interaction between tumor and stromal cells is beginning to be decoded as a contributor to chemotherapy resistance. Here, we aim to take a system-level approach to explore a mechanism by which stromal cells induce chemoresistance in cancer cells and subsequently identify a drug that can inhibit such interaction. Using a proteomic dataset containing quantitative data on secretome of stromal cells, we performed multivariate analyses and found that bone-marrow mesenchymal stem cells (BM-MSCs) play the most protective role against chemotherapeutics. Pathway enrichment tests showed that secreted cytokines from BM-MSCs activated 4 signaling pathways including Janus kinase-signal transducer and activator of transcription, phosphatidylinositol 3-kinase-protein kinase B, and mitogen-activated protein kinase, transforming growth factor-ß in cancer cells collectively leading to nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) transcription factor activation. Based on the data from integrated Library of Integrated Network-Based Cellular Signatures (iLINCs) program, we found that among different drugs, valproic acid (VA) affected the expression of 34 genes within the identified pathways that are activated by stromal cells. Our in vitro experiments confirmed that VA inhibits NF-kB activation in cancer cells. In addition, analyzing gene expression data in patients taking oral VA showed that this drug decreased expression of antioxidant enzymes culminating in increased oxidative stress in tumor cells. These results suggest that VA confines the protective role of stromal cells by inhibiting the adaptation mechanisms toward oxidative stress which is potentiated by stromal cells. Since VA is an already prescribed drug manifesting anticancer effects, this study provides a mechanistic insight for combination of VA with chemotherapy in the clinical setting.

Human T-lymphotropic virus 1 (HTLV-1) pathogenesis: A systems virology study.

The main mechanisms of interaction between Human T-lymphotropic virus type 1 (HTLV-1) and its hosts in the manifestation of the related disease including HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP) and Adult T-cell leukemia/lymphoma (ATLL) are yet to be determined. It is pivotal to find out the changes in the genes expression toward an asymptomatic or symptomatic states. To this end, the systems virology analysis was performed. Firstly, the differentially expressed genes (DEGs) were taken pairwise among the four sample sets of Normal, Asymptomatic Carriers (ACs), ATLL, and HAM/TSP. Afterwards, the protein-protein interaction networks were reconstructed utilizing the hub genes. In conclusion, the pathways of cells proliferation and transformation were identified in the ACs state. In addition to immune pathways in ATLL, the inflammation and cancer pathways were discened in both diseases of ATLL and HAM/TSP. The outcomes can specify the genes involved in the pathogenesis and help to design the drugs in the future.

Phytosomal-curcumin antagonizes cell growth and migration, induced by thrombin through AMP-Kinase in breast cancer.

Here we explored the antitumor-activity of novel-formulated-form of curcumin (phytosomal-encapsulated-curcumin) or in combination with 5-FU in breast cancer. The antiproliferative activity was assessed in 2D and 3-dimensional cell-culture-model. The migratory-behaviors of the cells were determined by migration assay. The expression levels of CyclinD1,GSK3a/b, P-AMPK, MMP9, and E-cadherin were studied by qRT-PCR and/or Western blotting. The anti-inflammatory of nano-curcumin was assessed, while antioxidant activity was evaluated by malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), and total thiols (T-SH). To understand dynamic behavior of genes, we reconstructed a Boolean network, while the robustness of this model was evaluated by Hamming distance. phytosomal-curcumin suppressed cell-growth followed by tumor-shrinkage in 3D model through perturbation of AMP-activated protein kinase. Curcumin reduced the invasiveness of MCF-7 through perturbation of E-cadherin. Moreover, phytosomal-curcumin inhibited the tumor growth in xerograph model. Histological staining of tumor tissues revealed vascular disruption and RBC extravasation, necrosis, tumor stroma, and inflammation. Co-treatment of curcumin and 5-FU reduced the lipid-peroxidation and increased MDA/SOD level. Of note, curcumin reduced cyclinD-expression in breast cancer cell treated with thrombin, and activates AMPK in a time-dependent manner. Also suppression of AMPK abrogated inhibitory effect of phytosomal-curcumin on thrombin-induced cyclin D1 over-expression, suggesting that AMPK is essential for anti-proliferative effect of this agent in breast cancer. Our finding demonstrated that phytosomal-curcumin antagonizes cell growth and migration, induced by thrombin through AMP-Kinase in breast cancer, supporting further-investigations on the therapeutic potential of this novel anticancer agent in treatment of breast cancer.

Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database.

Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks.

Testis-Specific Y-Centric Protein-Protein Interaction Network Provides Clues to the Etiology of Severe Spermatogenic Failure.

Pinpointing causal genes for spermatogenic failure (SpF) on the Y chromosome has been an ever daunting challenge with setbacks during the past decade. Since complex diseases result from the interaction of multiple genes and also display considerable missing heritability, network analysis is more likely to explicate an etiological molecular basis. We therefore took a network medicine approach by integrating interactome (protein-protein interaction (PPI)) and transcriptome data to reconstruct a Y-centric SpF network. Two sets of seed genes (Y genes and SpF-implicated genes (SIGs)) were used for network reconstruction. Since no PPI was observed among Y genes, we identified their common immediate interactors. Interestingly, 81% (N = 175) of these interactors not only interacted directly with SIGs, but also they were enriched for differentially expressed genes (89.6%; N = 43). The SpF network, formed mainly by the dys-regulated interactors and the two seed gene sets, comprised three modules enriched for ribosomal proteins and nuclear receptors for sex hormones. Ribosomal proteins generally showed significant dys-regulation with RPL39L, thought to be expressed at the onset of spermatogenesis, strongly down-regulated. This network is the first global PPI network pertaining to severe SpF and if experimentally validated on independent data sets can lead to more accurate diagnosis and potential fertility recovery of patients.

Signaling network of lipids as a comprehensive scaffold for omics data integration in sputum of COPD patients.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous and progressive inflammatory condition that has been linked to the dysregulation of many metabolic pathways including lipid biosynthesis. How lipid metabolism could affect disease progression in smokers with COPD remains unclear. We cross-examined the transcriptomics, proteomics, metabolomics, and phenomics data available on the public domain to elucidate the mechanisms by which lipid metabolism is perturbed in COPD. We reconstructed a sputum lipid COPD (SpLiCO) signaling network utilizing active/inactive, and functional/dysfunctional lipid-mediated signaling pathways to explore how lipid-metabolism could promote COPD pathogenesis in smokers. SpLiCO was further utilized to investigate signal amplifiers, distributers, propagators, feed-forward and/or -back loops that link COPD disease severity and hypoxia to disruption in the metabolism of sphingolipids, fatty acids and energy. Also, hypergraph analysis and calculations for dependency of molecules identified several important nodes in the network with modular regulatory and signal distribution activities. Our systems-based analyses indicate that arachidonic acid is a critical and early signal distributer that is upregulated by the sphingolipid signaling pathway in COPD, while hypoxia plays a critical role in the elevated dependency to glucose as a major energy source. Integration of SpLiCo and clinical data shows a strong association between hypoxia and the upregulation of sphingolipids in smokers with emphysema, vascular disease, hypertension and those with increased risk of lung cancer.

Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms.

BACKGROUND: In the field of network science, exploring principal and crucial modules or communities is critical in the deduction of relationships and organization of complex networks. This approach expands an arena, and thus allows further study of biological functions in the field of network biology. As the clustering algorithms that are currently employed in finding modules have innate uncertainties, external and internal validations are necessary. METHODS: Sequence and network structure alignment, has been used to define the Interlog Protein Network (IPN). This network is an evolutionarily conserved network with communal nodes and less false-positive links. In the current study, the IPN is employed as an evolution-based benchmark in the validation of the module finding methods. The clustering results of five algorithms; Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Cartographic Representation (CR), Laplacian Dynamics (LD) and Genetic Algorithm; to find communities in Protein-Protein Interaction networks (GAPPI) are assessed by IPN in four distinct Protein-Protein Interaction Networks (PPINs). RESULTS: The MCL shows a more accurate algorithm based on this evolutionary benchmarking approach. Also, the biological relevance of proteins in the IPN modules generated by MCL is compatible with biological standard databases such as Gene Ontology, KEGG and Reactome. CONCLUSION: In this study, the IPN shows its potential for validation of clustering algorithms due to its biological logic and straightforward implementation.