Use of “Connectivity Map” (Cmap) to Identify Novel, Safe, Effective Antiaging Ingredients for Topical Use

2017 ◽  
pp. 1495-1510
Author(s):  
Johanna M. Gillbro ◽  
Eve Merinville ◽  
Mia Nilsson ◽  
Eva Hagforsen ◽  
Garrett Moran ◽  
...  
Keyword(s):  
Connectivity Map

scholarly journals Connectivity characterization of the mouse basolateral amygdalar complex

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Houri Hintiryan ◽  
Ian Bowman ◽  
David L. Johnson ◽  
Laura Korobkova ◽  
Muye Zhu ◽  
...  
Keyword(s):  
Granular Cell ◽  
Cell Types ◽  
Projection Neurons ◽  
Cell Type ◽  
Connectivity Map ◽  
Analysis Techniques ◽  
Domain Specific ◽  
Cell Type Specific ◽  
Unique Domain

AbstractThe basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks.

scholarly journals Connectivity Map Analysis Indicates PI3K/Akt/mTOR Inhibitors as Potential Anti-Hypoxia Drugs in Neuroblastoma

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2809
Author(s):  
Paolo Uva ◽  
Maria Carla Bosco ◽  
Alessandra Eva ◽  
Massimo Conte ◽  
Alberto Garaventa ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Mtor Inhibitors ◽  
Gene Set Enrichment Analysis ◽  
Low Oxygen ◽  
Connectivity Map ◽  
Hypoxic Conditions ◽  
Connectivity Score

Neuroblastoma (NB) is one of the deadliest pediatric cancers, accounting for 15% of deaths in childhood. Hypoxia is a condition of low oxygen tension occurring in solid tumors and has an unfavorable prognostic factor for NB. In the present study, we aimed to identify novel promising drugs for NB treatment. Connectivity Map (CMap), an online resource for drug repurposing, was used to identify connections between hypoxia-modulated genes in NB tumors and compounds. Two sets of 34 and 21 genes up- and down-regulated between hypoxic and normoxic primary NB tumors, respectively, were analyzed with CMap. The analysis reported a significant negative connectivity score across nine cell lines for 19 compounds mainly belonging to the class of PI3K/Akt/mTOR inhibitors. The gene expression profiles of NB cells cultured under hypoxic conditions and treated with the mTORC complex inhibitor PP242, referred to as the Mohlin dataset, was used to validate the CMap findings. A heat map representation of hypoxia-modulated genes in the Mohlin dataset and the gene set enrichment analysis (GSEA) showed an opposite regulation of these genes in the set of NB cells treated with the mTORC inhibitor PP242. In conclusion, our analysis identified inhibitors of the PI3K/Akt/mTOR signaling pathway as novel candidate compounds to treat NB patients with hypoxic tumors and a poor prognosis.

scholarly journals Generation of geometric interpolations of building types with deep variational autoencoders

2020 ◽  
Vol 6 ◽  
Author(s):  
Jaime de Miguel Rodríguez ◽  
Maria Eugenia Villafañe ◽  
Luka Piškorec ◽  
Fernando Sancho Caparrini
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Large Data ◽  
Learning Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Connectivity Map ◽  
Data Set ◽  
3D Objects ◽  
Machine Learning Model

Abstract This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

scholarly journals Connectivity Map-based discovery of parbendazole reveals targetable human osteogenic pathway

2015 ◽  
Vol 112 (41) ◽  
pp. 12711-12716 ◽  
Author(s):  
Andrea M. Brum ◽  
Jeroen van de Peppel ◽  
Cindy S. van der Leije ◽  
Marijke Schreuders-Koedam ◽  
Marco Eijken ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alkaline Phosphatase ◽  
Osteoblast Differentiation ◽  
Target Genes ◽  
Focal Adhesions ◽  
Bone Fragility ◽  
Bone Morphogenetic Protein 2 ◽  
Marker Genes ◽  
Connectivity Map ◽  
Morphogenetic Protein

Osteoporosis is a common skeletal disorder characterized by low bone mass leading to increased bone fragility and fracture susceptibility. In this study, we have identified pathways that stimulate differentiation of bone forming osteoblasts from human mesenchymal stromal cells (hMSCs). Gene expression profiling was performed in hMSCs differentiated toward osteoblasts (at 6 h). Significantly regulated genes were analyzed in silico, and the Connectivity Map (CMap) was used to identify candidate bone stimulatory compounds. The signature of parbendazole matches the expression changes observed for osteogenic hMSCs. Parbendazole stimulates osteoblast differentiation as indicated by increased alkaline phosphatase activity, mineralization, and up-regulation of bone marker genes (alkaline phosphatase/ALPL, osteopontin/SPP1, and bone sialoprotein II/IBSP) in a subset of the hMSC population resistant to the apoptotic effects of parbendazole. These osteogenic effects are independent of glucocorticoids because parbendazole does not up-regulate glucocorticoid receptor (GR) target genes and is not inhibited by the GR antagonist mifepristone. Parbendazole causes profound cytoskeletal changes including degradation of microtubules and increased focal adhesions. Stabilization of microtubules by pretreatment with Taxol inhibits osteoblast differentiation. Parbendazole up-regulates bone morphogenetic protein 2 (BMP-2) gene expression and activity. Cotreatment with the BMP-2 antagonist DMH1 limits, but does not block, parbendazole-induced mineralization. Using the CMap we have identified a previously unidentified lineage-specific, bone anabolic compound, parbendazole, which induces osteogenic differentiation through a combination of cytoskeletal changes and increased BMP-2 activity.

scholarly journals HOXB7 acts as an oncogenic biomarker in head and neck squamous cell carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiang Wu ◽  
Jin Li ◽  
Tingyuan Yan ◽  
Xueping Ke ◽  
Xin Li ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Expression Pattern ◽  
Squamous Cell ◽  
Neck Squamous Cell Carcinoma ◽  
Clinicopathological Parameters ◽  
Migration And Invasion ◽  
Connectivity Map

Abstract Background The homeobox gene Homeobox B7 (HOXB7) is overexpressed across a range of cancers and promotes tumorigenesis through varying effects on proliferation, survival, migration and invasion. However, its expression pattern and oncogenic role of HOXB7 in head and neck squamous cell carcinoma (HNSCC) remain largely unexplored. Here, we aimed to explore the expression pattern of HOXB7, its clinical significance as well as functional roles in HNSCC. Methods HOXB7 mRNA expression in HNSCC was determined by data mining and analyses from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) datasets. The protein abundance of HOXB7 was measured by immunohistochemistry in 119 primary HNSCC samples and associations between its expression and clinicopathological parameters and patient survival were evaluated. The pro-tumorigenic roles of HOXB7 in HNSCC were further delineated in vitro by loss-of-function assay. And a xenograft tumor model was established in nude mice to assess the role of HOXB7 in tumor growth. Connectivity Map (CMap) analysis was performed to identify bioactive small molecules which might be potential inhibitors for HOXB7. Results Bioinformatics analyses showed that HOXB7 mRNA was significantly overexpressed in 8 independent HNSCC datasets from TCGA and GEO databases. HOXB7 protein was markedly upregulated in HNSCC samples as compared to normal counterparts and its overexpression significantly associated with high pathological grade, advanced clinical stage, cervical node metastasis (P = 0.0195, 0.0152, 0.0300) and reduced overall and disease-free survival (P = 0.0014, 0.0007). Univariate and multivariate Cox regression analyses further revealed HOXB7 as an independent prognostic factor for patients’ overall survival. Moreover, HOXB7 knockdown significantly inhibited cell proliferation, migration and invasion and induced cell apoptosis in HNSCC cells, and resulted in compromised tumour growth in vivo. Furthermore, CMap (Connectivity map) analysis has identified three potential bioactive small molecule inhibitors (NU-1025, thiamine, vinburnine) for HOXB7 targeted therapy in HNSCC. Conclusions Our findings revealed that overexpression of HOXB7 was associates with tumour aggressiveness and unfavourable prognosis by serving a novel prognostic biomarker in HNSCC. Moreover, HOXB7 might be involved in the development and progression of HNSCC as an oncogene, and thereby might be a potential therapeutic target for HNSCC.

scholarly journals Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis

2015 ◽  
Vol 11 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Aakash Chavan Ravindranath ◽  
Nolen Perualila-Tan ◽  
Adetayo Kasim ◽  
Georgios Drakakis ◽  
Sonia Liggi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ligand Binding ◽  
Gene Expression Data ◽  
Mechanism Of Action ◽  
In Silico ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Connectivity Map ◽  
Action Analysis

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein–ligand binding.

scholarly journals Prioritizing Small Molecule as Candidates for Drug Repositioning using Machine Learning

2018 ◽  
Author(s):  
Khader Shameer ◽  
Kipp W. Johnson ◽  
Benjamin S. Glicksberg ◽  
Rachel Hodos ◽  
Ben Readhead ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Small Molecule ◽  
Chemical Space ◽  
Drug Repositioning ◽  
Chemical Properties ◽  
Support Vector ◽  
Feature Engineering ◽  
Connectivity Map ◽  
Molecular Features

ABSTRACTDrug repositioning, i.e. identifying new uses for existing drugs and research compounds, is a cost-effective drug discovery strategy that is continuing to grow in popularity. Prioritizing and identifying drugs capable of being repositioned may improve the productivity and success rate of the drug discovery cycle, especially if the drug has already proven to be safe in humans. In previous work, we have shown that drugs that have been successfully repositioned have different chemical properties than those that have not. Hence, there is an opportunity to use machine learning to prioritize drug-like molecules as candidates for future repositioning studies. We have developed a feature engineering and machine learning that leverages data from publicly available drug discovery resources: RepurposeDB and DrugBank. ChemVec is the chemoinformatics-based feature engineering strategy designed to compile molecular features representing the chemical space of all drug molecules in the study. ChemVec was trained through a variety of supervised classification algorithms (Naïve Bayes, Random Forest, Support Vector Machines and an ensemble model combining the three algorithms). Models were created using various combinations of datasets as Connectivity Map based model, DrugBank Approved compounds based model, and DrugBank full set of compounds; of which RandomForest trained using Connectivity Map based data performed the best (AUC=0.674). Briefly, our study represents a novel approach to evaluate a small molecule for drug repositioning opportunity and may further improve discovery of pleiotropic drugs, or those to treat multiple indications.

scholarly journals Systematical assessment of cancer stemness traits via one-class logistic regression (OCLR) algorithm and associations with tumor immune infiltrates in prostate cancer based on seven cohorts

2020 ◽  
Author(s):  
Hongxi Chen ◽  
Jinliang Xie ◽  
Peng Jin
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Progression Free Survival ◽  
Consensus Clustering ◽  
Connectivity Map ◽  
Cancer Stemness ◽  
Differential Abundance ◽  
Free Survival ◽  
Cell Cycle Inhibitor

Abstract Background: Prostate cancer stemness (PCS) cells have been reported to drive tumor progression, recurrence and drug resistance. However, there is lacking systematical assessment of stemness traits and associations with immunological properties in prostate adenocarcinoma (PRAD). Methods We collected 7 PRAD cohorts with 1465 men and calculated the stemness indices for each sample using the innovative one-class logistic regression (OCLR) machine learning algorithm. We selected the mRNAsi to quantify the stemness traits that correlated significantly with prognosis and accordingly identified 21 PCS-related CpG loci and 13 pivotal signature. Meanwhile, we conducted consensus clustering and classified the total cohorts into 5 PCS clusters with distinct outcomes based on the 13-gene panel. Additionally, we implemented the CIBERSORT algorithm to infer the differential abundance across 5 PCS clusters. Lastly, we used the Connectivity Map (CMap) resource to screen potential compounds for targeting PRAD stemness. Results: The 13-gene based PCS model possessed high predictive significance for progression-free survival (PFS) that was trained and validated in 7 independent cohorts. We found that PCScluster5 possessed the highest stemness fractions and suffered from the worst prognosis. Immune infiltration analysis shows that the activated immune cells (CD8+ T cell and dendritic cells) infiltrated significantly less in PCScluster5 than other clusters, especially PCScluster1, supporting the negative regulations between stemness and anticancer immunity. High mRNAsi was also found to be associated with up-regulation of immunosuppressive checkpoints, like PDL1. Finally, several potential compounds, including the top hits of cell cycle inhibitor and FOXM1 inhibitor, were identified for targeting PRAD stemness. Conclusion: Our study comprehensively evaluated the PRAD stemness traits based on large cohorts and established a 13-gene based classifier for predicting prognosis or potential strategies for stemness treatment.

Sign in / Sign up

Export Citation Format

Share Document