Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling.

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs' protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective combinations with the least toxicity and the best synergistic effect on blast cells. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment.

Bipartite network models to design combination therapies in acute myeloid leukaemia.

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.

A community challenge for a pancancer drug mechanism of action inference from perturbational profile data.

The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.

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.

Rigosertib elicits potent anti-tumor responses in colorectal cancer by inhibiting Ras signaling pathway.

BACKGROUND: The therapeutic potency of Rigosertib (RGS) in the treatment of the myelodysplastic syndrome has been investigated previously, but little is known about its mechanisms of action. METHODS: The present study integrates systems and molecular biology approaches to investigate the mechanisms of the anti-tumor effects of RGS, either alone or in combination with 5-FU in cellular and animal models of colorectal cancer (CRC). RESULTS: The effects of RGS were more pronounced in dedifferentiated CRC cell types, compared to cell types that were epithelial-like. RGS inhibited cell proliferation and cell cycle progression in a cell-type specific manner, and that was dependent on the presence of mutations in KRAS, or its down-stream effectors. RGS increased both early and late apoptosis, by regulating the expression of p53, BAX and MDM2 in tumor model. We also found that RGS induced cell senescence in tumor tissues by increasing ROS generation, and impairing oxidant/anti-oxidant balance. RGS also inhibited angiogenesis and metastatic behavior of CRC cells, by regulating the expression of CD31, E-cadherin, and matrix metalloproteinases-2 and 9. CONCLUSION: Our findings support the therapeutic potential of this potent RAS signaling inhibitor either alone or in combination with standard regimens for the management of patients with CRC.

Metformin inhibits polyphosphate-induced hyper-permeability and inflammation.

Circulating inflammatory factor inorganic polyphosphate (polyP) released from activated platelets could enhance factor XII and bradykinin resulted in increased capillary leakage and vascular permeability. PolyP induce inflammatory responses through mTOR pathway in endothelial cells, which is being reported in several diseases including atherosclerosis, thrombosis, sepsis, and cancer. Systems and molecular biology approaches were used to explore the regulatory role of the AMPK activator, metformin, on polyP-induced hyper-permeability in different organs in three different models of polyP-induced hyper-permeability including local, systemic short- and systemic long-term approaches in murine models. Our results showed that polyP disrupts endothelial barrier integrity in skin, liver, kidney, brain, heart, and lung in all three study models and metformin abrogates the disruptive effect of polyP. We also showed that activation of AMPK signaling pathway, regulation of oxidant/anti-oxidant balance, as well as decrease in inflammatory cell infiltration constitute a set of molecular mechanisms through which metformin elicits it's protective responses against polyP-induced hyper-permeability. These results support the clinical values of AMPK activators including the FDA-approved metformin in attenuating vascular damage in polyP-associated inflammatory diseases.

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.

Angiotensin receptor blocker Losartan inhibits tumor growth of colorectal cancer.

The renin-angiotensin system (RAS) is up-regulated in patients with colorectal cancer (CRC) and is reported to be associated with poor prognosis and chemo-resistance. Here we explored the therapeutic potential of targeting RAS in CRC using Losartan, an angiotensin receptor blocker. An integrative-systems biology approach was used to explore a proteome-level dataset of a gene signature that is modulated by Losartan. The anti-proliferative activity of Losartan was evaluated using 2- and 3-dimensional cell culture models. A xenograft model of colon cancer was used to investigate tumor growth with Losartan alone and in combination with 5-FU followed by histological staining (Hematoxylin & Eosin and Masson trichrome staining), biochemical analyses, gene expression analyses by RT-PCR, western blot/IHC, or MMP Gelatin Zymography studies. Effects on cell cycle and cell death were assessed by flow cytometry. Losartan inhibited cell growth and suppressed cell cycle progression, causing an increase in CRC cells in the G1 phase. Losartan significantly reduced tumor growth and enhanced tumor cell necrosis. An impact on the inflammatory response, including up-regulation of pro-inflammatory cytokines and chemokines in CRC cells are potential mechanisms that could partially explain Losartan's anti-proliferative effects. Moreover, metastasis and angiogenesis were reduced in Losartan-treated mice as observed by inhibited matrix metalloproteinase-2 and -9 activities and decreased tumor vasculature. These data demonstrate the therapeutic potential of combining chemotherapeutic regimens with Losartan to synergistically enhance its activity and target the renin-angiotensin system as a new approach in colorectal cancer treatment.

HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) versus adult T-cell leukemia/lymphoma (ATLL).

OBJECTIVES: Human T cell leukemia virus-1 (HTLV-1) infection may lead to one or both diseases including HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) or adult T cell leukemia lymphoma (ATLL). The complete interactions of the virus with host cells in both diseases is yet to be determined. This study aims to construct an interaction network for distinct signaling pathways in these diseases based on finding differentially expressed genes (DEGs) between HAM/TSP and ATLL. RESULTS: We identified 57 hub genes with higher criteria scores in the primary protein-protein interaction network (PPIN). The ontology-based enrichment analysis revealed following important terms: positive regulation of transcription from RNA polymerase II promoter, positive regulation of transcription from RNA polymerase II promoter involved in meiotic cell cycle and positive regulation of transcription from RNA polymerase II promoter by histone modification. The upregulated genes TNF, PIK3R1, HGF, NFKBIA, CTNNB1, ESR1, SMAD2, PPARG and downregulated genes VEGFA, TLR2, STAT3, TLR4, TP53, CHUK, SERPINE1, CREB1 and BRCA1 were commonly observed in all the three enriched terms in HAM/TSP vs. ATLL. The constructed interaction network was then visualized inside a mirrored map of signaling pathways for ATLL and HAM/TSP, so that the functions of hub genes were specified in both diseases.

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.

Rigosertib potently protects against colitis-associated intestinal fibrosis and inflammation by regulating PI3K/AKT and NF-κB signaling pathways.

AIMS: Rigosertib (RGS) is a PI3K inhibitor that exerts protective effects against tumor progression and cancer-related inflammation. This study was aimed to explore the regulatory effects of RGS on proliferative, pro-fibrotic and inflammatory factors in DSS- induced colitis mice model. MATERIALS AND METHODS: The present study integrates systems and molecular biology approaches to investigate the therapeutic potency of RGS in an experimental model of colitis specifically examining its effects on the PI3K/AKT and NF-κB signaling pathways. KEY FINDINGS: Analysis of time-resolved proteome profiling showed that PI3K-AKT inhibitors regulate expression of many proteins in all stages of inflammation, fibrogenesis and extracellular matrix remodeling. Consistent with our in-silico findings, RGS improved colitis disease activity as assessed by changes in body weight, degree of stool consistency, rectal bleeding and prolapse. RGS also reduced oxidative stress markers and colon histopathological score by decreasing inflammatory responses in colon tissues. Moreover, expression of pro-fibrotic and pro-inflammatory factors including Acta 2, Col 1a1, Col 1a2, IL-1ß, TNF-α, INF-γ, and MCP-1 were suppressed in the mice treated with RGS compared to the control group. The protective effects of RGS were mediated by inactivation of PI3K/AKT and NF-kB signaling pathways. SIGNIFICANCE: This study clearly demonstrates the anti-proliferative, anti-inflammatory and anti-fibrotic effects of RGS in colitis that may have implications for the treatment of colitis and colitis-associated cancer.

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.

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.

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.

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.

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.

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.

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.

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.

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.