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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09 Nov 2021
TL;DR: In this article, two machine learning approaches for drug repurposing are presented for SARS-CoV-2 infection/replication in COVID-19 patients, one based on matrix factorization and the other based on network medicine.
Abstract: We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorisation algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 Repositioning Explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.
17 citations
28 Apr 2020
TL;DR: The hypothesis that modulation of TMPRSS2 expression is a promising therapeutic avenue for COVID-19 is raised, as 20 independent studies that implicate estrogenic and androgenic compounds as transcriptional modulators of TM PRSS2 are found.
Abstract: There is an urgent need to identify effective therapies for COVID-19. The SARS-CoV-2 host factor protease TMPRSS2 is required for viral entry and thus an attractive target for therapeutic intervention. In mouse, knockout of tmprss2 led to protection against SARS-CoV-1 with no deleterious phenotypes, and in human populations genetic loss of TMPRSS2 does not appear to be selected against. Here, we mined publicly available gene expression data to identify several compounds that down-regulate TMPRSS2. Recognizing the need for immediately available treatment options, we focused on FDA-approved drugs. We found 20 independent studies that implicate estrogenic and androgenic compounds as transcriptional modulators of TMPRSS2, suggesting these classes of drugs may be promising therapeutic candidates for clinical testing and observational studies of COVID-19. We also note that expression of TMPRSS2 is highly variable and skewed in humans, with a minority of individuals having extremely high expression. Combined with literature showing that inhibition of TMPRSS2 protease activity reduces SARS-CoV-2 viral entry in human cells, our results raise the hypothesis that modulation of TMPRSS2 expression is a promising therapeutic avenue for COVID-19. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 April 2020 doi:10.20944/preprints202003.0360.v2 © 2020 by the author(s). Distributed under a Creative Commons CC BY license. Introduction The rapid international spread of the novel pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the disease known as COVID-19, poses a global health emergency. As of April 5, 2020, there have been over 1,133,000 confirmed cases and 62,500 deaths worldwide. The clinical presentation of COVID-19 ranges from mild respiratory symptoms to severe progressive pneumonia, multiorgan failure, and death. Therapeutic interventions beyond supportive care in the literature have included oseltamivir, remdesivir, ganciclovir, α-interferon, hydroxychloroquine and lopinavir. Lopinavir, a protease inhibitor, is the only drug with a completed clinical trial but failed to shorten time to improvement or viral shedding. Any effective intervention rapidly mobilized to the frontlines could profoundly impact resource allocation. Effective treatments are therefore vital to handle the surge of COVID-19 infections. SARS-CoV-2 host factors are attractive targets for therapeutic intervention. The SARS-CoV-2 spike (S) glycoprotein binds the angiotensin-converting enzyme 2 (ACE2), allowing the viral particle to enter host cells. Viral entry into host cells also requires cleavage of the viral S protein by host proteases; this cleavage results in irreversible conformational changes to the S protein that allow the virus and host cell membranes to fuse. S protein cleavage, called priming, can use the host serine protease TMPRSS2 or the cysteine proteases cathepsin B or L (CatB/L). A recent single-cell RNA-sequencing study of human and non-human primate tissues revealed three major cell types that co-express TMPRSS2 and ACE2: type II pneumocytes in the lung, absorptive enterocytes in the terminal ileum, and nasal goblet secretory cells. Computational and in vitro screens are useful to identify compounds that either act directly against viral proteins, or that disrupt protein interactions between SARS-CoV-2 and host proteins required for its viral life cycle. Here we propose and develop a complementary approach seeking to identify transcriptional regulators of the host proteins most critical to viral entry and replication within host cells. Given the aggressiveness of this pandemic and the urgency of deploying effective treatments, our first efforts focus on the repurposing of existing drugs as an attractive alternative to novel compound discovery. We note, however, that this screening approach could also be applied to the discovery of new chemical entities with more desirable properties than already available approved medicines. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 April 2020 doi:10.20944/preprints202003.0360.v2
17 citations
TL;DR: Open Cancer TherApeutic Discovery (OCTAD) as discussed by the authors is a software pipeline for finding drugs that alter gene expression in such a way that they are likely to reverse the expression pattern of the disease.
Abstract: As the field of precision medicine progresses, treatments for patients with cancer are starting to be tailored to their molecular as well as their clinical features. The emerging cancer subtypes defined by these molecular features require that dedicated resources be used to assist the discovery of drug candidates for preclinical evaluation. Voluminous gene expression profiles of patients with cancer have been accumulated in public databases, enabling the creation of cancer-specific expression signatures. Meanwhile, large-scale gene expression profiles of cellular responses to chemical compounds have also recently became available. By matching the cancer-specific expression signature to compound-induced gene expression profiles from large drug libraries, researchers can prioritize small molecules that present high potency to reverse expression of signature genes for further experimental testing of their efficacy. This approach has proven to be an efficient and cost-effective way to identify efficacious drug candidates. However, the success of this approach requires multiscale procedures, imposing considerable challenges to many labs. To address this, we developed Open Cancer TherApeutic Discovery (OCTAD; http://octad.org
): an open workspace for virtually screening compounds targeting precise groups of patients with cancer using gene expression features. Its database includes 19,127 patient tissue samples covering more than 50 cancer types and expression profiles for 12,442 distinct compounds. The program is used to perform deep-learning-based reference tissue selection, disease gene expression signature creation, drug reversal potency scoring and in silico validation. OCTAD is available as a web portal and a standalone R package to allow experimental and computational scientists to easily navigate the tool. OCTAD is a software pipeline for finding drugs that alter gene expression in such a way that they are likely to reverse the expression pattern of the disease. This protocol describes how to use both the web portal and the desktop version of OCTAD.
16 citations
TL;DR: It is demonstrated that honeysuckle and Huangqi have the potential to be used as an inhibitor of SARS-CoV-2 virus entry that warrants further in vivo analysis and functional assessment of miRNAs to confirm their clinical importance.
Abstract: COVID-19 is threatening human health worldwide but no effective treatment currently exists for this disease. Current therapeutic strategies focus on the inhibition of viral replication or using anti-inflammatory/immunomodulatory compounds to improve host immunity, but not both. Traditional Chinese medicine (TCM) compounds could be promising candidates due to their safety and minimal toxicity. In this study, we have developed a novel in silico bioinformatics workflow that integrates multiple databases to predict the use of honeysuckle (Lonicera japonica) and Huangqi (Astragalus membranaceus) as potential anti-SARS-CoV-2 agents. Using extracts from honeysuckle and Huangqi, these two herbs upregulated a group of microRNAs including let-7a, miR-148b, and miR-146a, which are critical to reduce the pathogenesis of SARS-CoV-2. Moreover, these herbs suppressed pro-inflammatory cytokines including IL-6 or TNF-α, which were both identified in the cytokine storm of acute respiratory distress syndrome, a major cause of COVID-19 death. Furthermore, both herbs partially inhibited the fusion of SARS-CoV-2 spike protein-transfected BHK-21 cells with the human lung cancer cell line Calu-3 that was expressing ACE2 receptors. These herbs inhibited SARS-CoV-2 Mpro activity, thereby alleviating viral entry as well as replication. In conclusion, our findings demonstrate that honeysuckle and Huangqi have the potential to be used as an inhibitor of SARS-CoV-2 virus entry that warrants further in vivo analysis and functional assessment of miRNAs to confirm their clinical importance. This fast-screening platform can also be applied to other drug discovery studies for other infectious diseases.
16 citations
TL;DR: In this paper , the authors optimize and validate prime-seq, an early barcoding bulk RNA-seq method, and show that it performs equivalently to TruSeq, but is four times more cost-efficient due to almost 50 times cheaper library costs.
Abstract: Cost-efficient library generation by early barcoding has been central in propelling single-cell RNA sequencing. Here, we optimize and validate prime-seq, an early barcoding bulk RNA-seq method. We show that it performs equivalently to TruSeq, a standard bulk RNA-seq method, but is fourfold more cost-efficient due to almost 50-fold cheaper library costs. We also validate a direct RNA isolation step, show that intronic reads are derived from RNA, and compare cost-efficiencies of available protocols. We conclude that prime-seq is currently one of the best options to set up an early barcoding bulk RNA-seq protocol from which many labs would profit.
16 citations
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