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Journal ArticleDOI

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.
Citations
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Journal ArticleDOI
TL;DR: RJQM has a protection effect on PAD by regulating a complex molecular network associated with the regulation of OXPHOS by 10 active components, which may alleviate mitochondrial dysfunction and pathological metabolic programming.
Abstract: Ruan Jian Qing Mai formula (RJQM), a multicomponent herbal formula, has been widely used to treat peripheral arterial disease (PAD) in China. However, its active compounds and mechanisms of action are still unknown. First, RNA sequencing analysis of 15 healthy and 16 PAD samples showed that 524 PAD differential genes were significantly enriched in Go Ontology (ribonucleotide metabolic process, oxidoreductase complex, and electron transfer activity), Kyoto Encyclopedia of Genes and Genomes (KEGG) and GSEA pathways (OXPHOS and TCA cycle), miRNA (MIR183), and kinase (PAK6). Fifty-three active ingredients in RJQM had similar structures to the seven drug molecules in CLUE. Then, network topology analysis of the 53 components-target-pathway-disease network yielded 10 active ingredients. Finally, computational toxicity estimations showed that the median lethal dose (LD50) of the 10 active ingredients was above 1000 mg/kg, and eight of them did not cause hepatotoxicity, mutagenicity, carcinogenicity, cytotoxicity, and immunotoxicity nor activate 12 toxic pathways. In conclusion, RJQM has a protection effect on PAD by regulating a complex molecular network. Part of the mechanism is associated with the regulation of OXPHOS by 10 active components, which may alleviate mitochondrial dysfunction and pathological metabolic programming.

5 citations

Journal ArticleDOI
TL;DR: It is indicated that HDAC6 inhibition by ACY1215 might reduce infarct size in rats with cardiac IR injury possibly through modulating HIF-1α expression and TGF-β and CRP should be useful biomarkers to monitor the use of ACY 1215 in cardiacIR injury.
Abstract: Despite the fact that histone deacetylase (HDAC) inhibitors have been tested to treat various cardiovascular diseases, the effects of selective HDAC6 inhibitor ACY1215 on infarct size during cardiac ischemia-reperfusion (IR) injury still remain unknown. In the present study we aimed to investigate the effects of ACY1215 on infarct size in rats with cardiac IR injury, as well as to examine the association between HDAC6 inhibitors and the gene expression of hypoxia inducible factor-1α (HIF-1α), a key regulator of cellular responses to hypoxia. By using computational analysis of high-throughput expression profiling dataset, the association between HDAC inhibitors (pan-HDAC inhibitors panobinostat and vorinostat, and HDAC6 inhibitor ISOX) and their effects on HIF-1α gene-expression were evaluated. The male Wistar rats treated with ligation of left coronary artery followed by reperfusion were used as a cardiac IR model. ACY1215 (50 mg/kg), pan-HDAC inhibitor MPT0E028 (25 mg/kg), and vehicle were intraperitoneally injected within 5 min before reperfusion. The infarct size in rat myocardium was determined by 2,3,5-triphenyltetrazolium chloride staining. The serum levels of transforming growth factor-β (TGF-β) and C-reactive protein (CRP) were also determined. The high-throughput gene expression assay showed that treatment of ISOX was associated with a more decreased gene expression of HIF-1α than that of panobinostat and vorinostat. Compared to control rats, ACY1215-treated rats had a smaller infarct size (49.75 ± 9.36% vs. 19.22 ± 1.70%, p < 0.05), while MPT0E028-treated rats had a similar infarct size to control rats. ACY-1215- and MPT0E028-treated rats had a trend in decreased serum TGF-β levels, but not statistically significant. ACY1215-treated rats also had higher serum CRP levels compared to control rats (641.6 μg/mL vs. 961.37 ± 64.94 μg/mL, p < 0.05). Our research indicated that HDAC6 inhibition by ACY1215 might reduce infarct size in rats with cardiac IR injury possibly through modulating HIF-1α expression. TGF-β and CRP should be useful biomarkers to monitor the use of ACY1215 in cardiac IR injury.

5 citations


Cites background or methods from "A Next Generation Connectivity Map:..."

  • ...Analysis of the association between HDAC inhibitors and expression of HIF-1α In the present study we analyzed the L1000 gene expression dataset in the Connectivity Map [18] which was obtained from the Broad Institute (https://clue.io/data/GCTX#GCTxDatasets)....

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  • ...The analysis of dataset generated by treating diverse cell types with pharmacological and genetic perturbagens has been proposed to discover the functional connections between genes, drugs, and diseases [18, 19]....

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  • ...In short, the L1000 is a highthroughput gene expression assay that measures the expression of 978 “landmark” genes from human cells [18] which can be used to computationally infer the expression of 11,350 genes....

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  • ...To address this knowledge gap, in this present study we conducted a computational analysis of high-throughput expression profiling dataset [18] to evaluate the association between HDAC6 inhibitors and gene expression of HIF-1α....

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  • ...expression of HIF-1α In the present study we analyzed the L1000 gene expression dataset in the Connectivity Map [18] which was obtained from the Broad Institute (https://clue....

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Journal ArticleDOI
TL;DR: This study employed systems biology approaches to unveil the diagnostic and treatment options from a precision medicine perspective by delineating differential inflammation-associated biomarkers associated with carcinogenesis for both subtypes by performing a meta-analysis of cervical cancer-associated transcriptomic datasets.
Abstract: Cervical cancer is the fourth most commonly diagnosed cancer worldwide and, in almost all cases is caused by infection with highly oncogenic Human Papillomaviruses (HPVs). On the other hand, inflammation is one of the hallmarks of cancer research. Here, we focused on inflammatory proteins that classify cervical cancer patients by considering individual differences between cancer patients in contrast to conventional treatments. We repurposed anti-inflammatory drugs for therapy of HPV-16 and HPV-18 infected groups, separately. In this study, we employed systems biology approaches to unveil the diagnostic and treatment options from a precision medicine perspective by delineating differential inflammation-associated biomarkers associated with carcinogenesis for both subtypes. We performed a meta-analysis of cervical cancer-associated transcriptomic datasets considering subtype differences of samples and identified the differentially expressed genes (DEGs). Using gene signature reversal on HPV-16 and HPV-18, we performed both signature- and network-based drug reversal to identify anti-inflammatory drug candidates against inflammation-associated nodes. The anti-inflammatory drug candidates were evaluated using molecular docking to determine the potential of physical interactions between the anti-inflammatory drug and inflammation-associated nodes as drug targets. We proposed 4 novels anti-inflammatory drugs (AS-601245, betamethasone, narciclasin, and methylprednisolone) for the treatment of HPV-16, 3 novel drugs for the treatment of HPV-18 (daphnetin, phenylbutazone, and tiaprofenoic acid), and 5 novel drugs (aldosterone, BMS-345541, etodolac, hydrocortisone, and prednisolone) for the treatment of both subtypes. We proposed anti-inflammatory drug candidates that have the potential to be therapeutic agents for the prevention and/or treatment of cervical cancer.

5 citations

Journal ArticleDOI
TL;DR: Oxaliplatin sensitivity in PDOs was shown to correlate to oxaliPlatin-mediated inhibition on PDO xenograft tumors in mice, and parallelled clinical outcomes of CRC patients who received FOLFOX treatment.
Abstract: Background Addition of oxaliplatin to adjuvant 5-FU has significantly improved the disease-free survival and served as the first line adjuvant chemotherapy in advanced colorectal cancer (CRC) patients. However, a fraction of patients remains refractory to oxaliplatin-based treatment. It is urgent to establish a preclinical platform to predict the responsiveness toward oxaliplatin in CRC patients as well as to improve the efficacy in the resistant patients. Methods A living biobank of organoid lines were established from advanced CRC patients. Oxaliplatin sensitivity was assessed in patient-derived tumor organoids (PDOs) in vitro and in PDO-xenografted tumors in mice. Based on in vitro oxaliplatin IC50 values, PDOs were classified into either oxaliplatin-resistant (OR) or oxaliplatin-sensitive (OS) PDOs. The outcomes of patients undergone oxaliplatin-based treatment was followed. RNA-sequencing and bioinformatics tools were performed for molecular profiling of OR and OS PDOs. Oxaliplatin response signatures were submitted to Connectivity Map algorithm to identify perturbagens that may antagonize oxaliplatin resistance. Results Oxaliplatin sensitivity in PDOs was shown to correlate to oxaliplatin-mediated inhibition on PDO xenograft tumors in mice, and parallelled clinical outcomes of CRC patients who received FOLFOX treatment. Molecular profiling of transcriptomes revealed oxaliplatin-resistant and -sensitive PDOs as two separate entities, each being characterized with distinct hallmarks and gene sets. Using Leave-One-Out Cross Validation algorithm and Logistic Regression model, 18 gene signatures were identified as predictive biomarkers for oxaliplatin response. Candidate drugs identified by oxaliplatin response signature-based strategies, including inhibitors targeting c-ABL and Notch pathway, DNA/RNA synthesis inhibitors, and HDAC inhibitors, were demonstrated to potently and effectively increase oxaliplatin sensitivity in the resistant PDOs. Conclusions PDOs are useful in informing decision-making on oxaliplatin-based chemotherapy and in designing personalized chemotherapy in CRC patients.

5 citations

Book ChapterDOI
Y-h. Taguchi1
01 Jan 2020
TL;DR: This chapter will apply PCA based unsupervised FE to various bioinformatics problems, which ranges from biomarker identification and identification of disease causing genes to in silico drug discovery.
Abstract: Although PCA is often blamed as an old technology, if it is useful, no other reasons will be required to be used. In this chapter, I will apply PCA based unsupervised FE to various bioinformatics problems. As discussed in the earlier chapter, PCA based unsupervised FE is fitted to the situation that there are more number of features than the number of samples. This specific situation is very usual, because features are genes whose numbers are as many as several tens thousands, while the number of samples are as many as that of patients, which is often as many as a few tens. The application of PCA based unsupervised FE ranges from biomarker identification and identification of disease causing genes to in silico drug discovery. I try to mention studies where PCA based unsupervised FE is applied as many as possible, from the published papers by myself.

5 citations

References
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

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