A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.
TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.
About: This article is published in Cell.The article was published on 2017-11-30 and is currently open access. It has received 1943 citations till now.
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TL;DR: Wang et al. as discussed by the authors used the TF regulatory network analysis to reveal the common molecular regulatory networks and identify the potential therapeutic drugs of rosacea and Alzheimer's disease, and found that melatonin (MLT) treatment significantly improved the skin lesion by reducing keratinocyte-mediated inflammatory cytokines secretion and repressing the migration of HUVEC cells.
Abstract: Rosacea is significantly associated with dementia, particularly Alzheimer's disease (AD). However, the common underlying molecular mechanism connecting these two diseases remains limited. This study aimed to reveal the common molecular regulatory networks and identify the potential therapeutic drugs of rosacea and AD. 747 overlapped DEGs (ol-DEGs) were detected in AD and rosacea, enriched in inflammation-, metabolism-, apoptosis-related pathways. Using the TF regulatory network analysis, 37 common TFs and target genes were identified as the hub genes. They were used to predict therapeutic drugs of rosacea and AD using DGIdb/CMap database. Among the 113 predicted drugs, melatonin (MLT) was co-associated both with RORA and IFN-γ in AD and rosacea. Subsequently, network pharmacology analysis identified 19 pharmacological targets of MLT and demonstrated that MLT could help in treating AD/rosacea partly by modulating inflammatory and vascular signaling pathways. Finally, we verified the therapeutic role and mechanism of MLT on rosacea in vivo and vitro. We found that MLT treatment significantly improved rosacea-like skin lesion by reducing keratinocyte-mediated inflammatory cytokines secretion and repressing the migration of HUVEC cells. In conclusion, this study contributes to common pathologies shared by rosacea and AD and identified MLT as an effective treatment strategy for rosacea and AD via regulating inflammation and angiogenesis.
9 citations
TL;DR: MEK/ERK phosphoprotein activity testing of a number of TBAs showed that only MBZ increased the activity in both THP-1 monocytes and PMA differentiated macrophages, indicating ERK activation also in non-haematological cell lines.
Abstract: We recently showed that the anti-helminthic compound mebendazole (MBZ) has immunomodulating activity in monocyte/macrophage models and induces ERK signalling. In the present study we investigated whether MBZ induced ERK activation is shared by other tubulin binding agents (TBAs) and if it is observable also in other human cell types. Curated gene signatures for a panel of TBAs in the LINCS Connectivity Map (CMap) database showed a unique strong negative correlation of MBZ with MEK/ERK inhibitors indicating ERK activation also in non-haematological cell lines. L1000 gene expression signatures for MBZ treated THP-1 monocytes also connected negatively to MEK inhibitors. MEK/ERK phosphoprotein activity testing of a number of TBAs showed that only MBZ increased the activity in both THP-1 monocytes and PMA differentiated macrophages. Distal effects on ERK phosphorylation of the substrate P90RSK and release of IL1B followed the same pattern. The effect of MBZ on MEK/ERK phosphorylation was inhibited by RAF/MEK/ERK inhibitors in THP-1 models, CD3/IL2 stimulated PBMCs and a MAPK reporter HEK-293 cell line. MBZ was also shown to increase ERK activity in CD4+ T-cells from lupus patients with known defective ERK signalling. Given these mechanistic features MBZ is suggested suitable for treatment of diseases characterized by defective ERK signalling, notably difficult to treat autoimmune diseases.
9 citations
TL;DR: Multi-drug treatments as synergetic therapy for ovarian serous cystadenocarcinoma were investigated and their target actions and pathways were determined by the network pharmacology strategy, which provides a new prospect for medicamentous therapy.
Abstract: Pharmacological control against ovarian serous cystadenocarcinoma has received increasing attention. The purpose of this study was to investigate multi-drug treatments as synergetic therapy for ovarian serous cystadenocarcinoma and to explore their mechanisms of action by the network pharmacology method. Genes acting on ovarian serous cystadenocarcinoma were first collected from GEPIA and DisGeNET. Gene Ontology annotation, Kyoto Encyclopedia of Genes and Genomes pathway, Reactome pathway, and Disease Ontology analyses were then conducted. A connectivity map analysis was employed to identify compounds as treatment options for ovarian serous cystadenocarcinoma. Targets of these compounds were obtained from the Search Tool for Interacting Chemicals (STITCH). The intersections between the ovarian serous cystadenocarcinoma-related genes and the compound targets were identified. Finally, the Kyoto Encyclopedia of Genes and Genomes and Reactome pathways in which the overlapped genes participated were selected, and a correspondence compound-target pathway network was constructed. A total of 541 ovarian serous cystadenocarcinoma-related genes were identified. The functional enrichment and pathway analyses indicated that these genes were associated with critical tumor-related pathways. Based on the connectivity map analysis, five compounds (resveratrol, MG-132, puromycin, 15-delta prostaglandin J2, and valproic acid) were determined as treatment agents for ovarian serous cystadenocarcinoma. Next, 48 targets of the five compounds were collected. Following mapping of the 48 targets to the 541 ovarian serous cystadenocarcinoma-related genes, we identified six targets (PTGS1, FOS, HMOX1, CASP9, PPARG, and ABCB1) as therapeutic targets for ovarian serous cystadenocarcinoma by the five compounds. By analysis of the compound-target pathway network, we found the synergistic anti-ovarian serous cystadenocarcinoma potential and the underlying mechanisms of action of the five compounds. In summary, latent drugs against ovarian serous cystadenocarcinoma were acquired and their target actions and pathways were determined by the network pharmacology strategy, which provides a new prospect for medicamentous therapy for ovarian serous cystadenocarcinoma. However, further in-depth studies are indispensable to increase the validity of this study.
9 citations
Cites background from "A Next Generation Connectivity Map:..."
...Unraveling the connections among gene expressions, diseases, and compounds [34], it is widely used for exploring potential novel drugs in various diseases [35–37]....
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TL;DR: In this article , a biomarker called fibrosis progression signature (FPS) was defined to predict long-term progression of liver fibrosis in patients with hepatitis C virus and nonalcoholic fatty liver disease (NAFLD).
9 citations
TL;DR: Novel strategies in drug discovery such as phenotypical drug screening and gene expression profiling technologies could provide a solution for this impasse in Peyronie’s disease.
Abstract: INTRODUCTION Finding novel medical treatment for Peyronie's disease (PD) has suffered from similar limitations and difficulties as other fibrotic diseases.Areas covered: Underlying fibrosis, there is a vastly complex intertwining of several pathways. Focusing on a single target during antifibrotic drug development has not led to the development of many efficacious drugs, especially in PD. Inhibiting one cog in this large machinery usually leads to activation of compensatory mechanisms.Expert opinion: Novel strategies in drug discovery such as phenotypical drug screening and gene expression profiling technologies could provide a solution for this impasse.
9 citations
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
Journal Article•
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Abstract: We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.
30,124 citations
TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
Abstract: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
10,968 citations
TL;DR: How BLAT was optimized is described, which is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences.
Abstract: Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences BLAT's speed stems from an index of all nonoverlapping K-mers in the genome This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly BLAT has several major stages It uses the index to find regions in the genome likely to be homologous to the query sequence It performs an alignment between homologous regions It stitches together these aligned regions (often exons) into larger alignments (typically genes) Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible This paper describes how BLAT was optimized Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications http://genomeucscedu hosts a web-based BLAT server for the human genome
8,326 citations
TL;DR: This paper proposed parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples.
Abstract: SUMMARY Non-biological experimental variation or “batch effects” are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (>25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
6,319 citations