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: Zhang et al. as mentioned in this paper identified imatinib, methazolamide, and harpagoside as direct enzymatic activators of ACE2 and showed that these three compounds can improve metabolic perturbations in vivo in an ACE2-dependent manner under the insulin-resistant state and SARS-CoV-2-infected state.
18 citations
TL;DR: Using Gene Set Enrichment Analysis, it is shown that brain EV-related transcripts are positively enriched in rodent models of epileptogenesis and epilepsy, and altered in response to anti-seizure drugs, suggesting that EVs show promise as biomarkers, treatments and drug targets for epilepsy.
Abstract: Extracellular vesicles (EVs) are small vesicles involved in intercellular communication. Data is emerging that EVs and their cargo have potential as diagnostic biomarkers and treatments for brain diseases, including traumatic brain injury and epilepsy. Here, we summarize the current knowledge regarding changes in EV numbers and cargo in status epilepticus (SE) and traumatic brain injury (TBI), which are clinically significant etiologies for acquired epileptogenesis in animals and humans. We also review encouraging data, which suggests that EVs secreted by stem cells may serve as recovery-enhancing treatments for SE and TBI. Using Gene Set Enrichment Analysis, we show that brain EV-related transcripts are positively enriched in rodent models of epileptogenesis and epilepsy, and altered in response to anti-seizure drugs. These data suggest that EVs show promise as biomarkers, treatments and drug targets for epilepsy. In parallel to gathering conceptual knowledge, analytics platforms for the isolation and analysis of EV contents need to be further developed.
18 citations
Cites methods from "A Next Generation Connectivity Map:..."
...To investigate whether anti-seizure drugs modify EV gene expression, we performed an in silico analysis using LINCS database, containing drug-induced transcriptomics profiles of cell lines to assess the possible effect of anti-seizure drugs on the expression of EV-related genes [110]....
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TL;DR: Zhang et al. as discussed by the authors analyzed the hub genes of GBM via weighted gene co-expression network analysis (WGCNA) of the cancer genome atlas (TCGA) dataset, and using the connectivity map (CMAP) platform for drug repurposing, found that multiple azole compounds had potential anti-GBM activity.
Abstract: Glioblastoma multiforme (GBM) is the most malignant and lethal primary brain tumor in adults accounting for about 50% of all gliomas. The only treatment available for GBM is the drug temozolomide, which unfortunately has frequent drug resistance issue. By analyzing the hub genes of GBM via weighted gene co-expression network analysis (WGCNA) of the cancer genome atlas (TCGA) dataset, and using the connectivity map (CMAP) platform for drug repurposing, we found that multiple azole compounds had potential anti-GBM activity. When their anti-GBM activity was examined, however, only three benzimidazole compounds, i.e. flubendazole, mebendazole and fenbendazole, potently and dose-dependently inhibited proliferation of U87 and U251 cells with IC50 values below 0.26 μM. Benzimidazoles (0.125-0.5 μM) dose-dependently suppressed DNA synthesis, cell migration and invasion, and regulated the expression of key epithelial-mesenchymal transition (EMT) markers in U87 and U251 cells. Benzimidazoles treatment also dose-dependently induced the GBM cell cycle arrest at the G2/M phase via the P53/P21/cyclin B1 pathway. Furthermore, the drugs triggered pyroptosis of GBM cells through the NF-κB/NLRP3/GSDMD pathway, and might also concurrently induced mitochondria-dependent apoptosis. In a nude mouse U87 cell xenograft model, administration of flubendazole (12.5, 25, and 50 mg · kg-1 · d-1, i.p, for 3 weeks) dose-dependently suppressed the tumor growth without obvious adverse effects. Taken together, our results demonstrated that benzimidazoles might be promising candidates for the treatment of GBM.
18 citations
TL;DR: Kamy et al. as mentioned in this paper proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer, which extracted the drug signatures and topological structure information contained in the drug relationships.
Abstract: Motivation Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. Results In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data, and the drug-drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. Availability The source code of our model and datasets are available at: https://github.com/cckamy/GraphRepur and https://figshare.com/articles/software/GraphRepur_Breast_Cancer_Drug_Repurposing/14220050. Supplementary information Supplementary data are available at Bioinformatics online.
18 citations
TL;DR: Six popular published methods for the prediction performance of drug-drug relationships based on the partial area under the receiver operating characteristic (ROC) curve are evaluated and ZhangScore was generally superior to other methods and exhibited the highest accuracy at the gene signature sizes ranging from 10 to 200.
Abstract: The methodologies for evaluating similarities between gene expression profiles of different perturbagens are the key to understanding mechanisms of actions (MoAs) of unknown compounds and finding new indications for existing drugs. L1000-based next-generation Connectivity Map (CMap) data is more than a thousand-fold scale-up of the CMap pilot dataset. Although several systematic evaluations have been performed individually to assess the accuracy of the methodologies for the CMap pilot study, the performance of these methodologies needs to be re-evaluated for the L1000 data. Here, using the drug-drug similarities from the Drug Repurposing Hub database as a benchmark standard, we evaluated six popular published methods for the prediction performance of drug-drug relationships based on the partial area under the receiver operating characteristic (ROC) curve at false positive rates of 0.001, 0.005 and 0.01 (AUC0.001, AUC0.005 and AUC0.01). The similarity evaluating algorithm called ZhangScore was generally superior to other methods and exhibited the highest accuracy at the gene signature sizes ranging from 10 to 200. Further, we tested these methods with an experimentally derived gene signature related to estrogen in breast cancer cells, and the results confirmed that ZhangScore was more accurate than other methods. Moreover, based on scoring results of ZhangScore for the gene signature of TOP2A knockdown, in addition to well-known TOP2A inhibitors, we identified a number of potential inhibitors and at least two of them were the subject of previous investigation. Our studies provide potential guidelines for researchers to choose the suitable connectivity method. The six connectivity methods used in this report have been implemented in R package (https://github.com/Jasonlinchina/RCSM).
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References
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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