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      Evaluating biological activity of compounds by transcription factor activity profiling

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          Abstract

          Transcription factor activity profiling reveals invariant signatures of perturbed biological pathways and cell systems.

          Abstract

          Assessing the biological activity of compounds is an essential objective of biomedical research. We show that one can infer the bioactivity of compounds by assessing the activity of transcription factors (TFs) that regulate gene expression. Using a multiplex reporter system, the FACTORIAL, we characterized cell response to a compound by a quantitative signature, the TF activity profile (TFAP). We found that perturbagens of biological pathways elicited distinct TFAP signatures in human cells. Unexpectedly, perturbagens of the same pathway all produced identical TFAPs, regardless of where or how they interfered. We found invariant TFAPs for mitochondrial, histone deacetylase, and ubiquitin/proteasome pathway inhibitors; cytoskeleton disruptors; and DNA-damaging agents. Using these invariant signatures permitted straightforward identification of compounds with specified bioactivities among uncharacterized chemicals. Furthermore, this approach allowed us to assess the multiple bioactivities of polypharmacological drugs. Thus, TF activity profiling affords straightforward assessment of the bioactivity of compounds through the identification of perturbed biological pathways.

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          Most cited references53

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          Ontological analysis of gene expression data: current tools, limitations, and open problems.

          Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis. More importantly, in spite of the fact that this type of analysis has been generally adopted, this approach has several important intrinsic drawbacks. These drawbacks are associated with all tools discussed and represent conceptual limitations of the current state-of-the-art in ontological analysis. We propose these as challenges for the next generation of secondary data analysis tools.
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            Discovery of drug mode of action and drug repositioning from transcriptional responses.

            A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compound-targeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).
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              Mitochondrial retrograde signaling.

              Mitochondrial retrograde signaling is a pathway of communication from mitochondria to the nucleus under normal and pathophysiological conditions. The best understood of such pathways is retrograde signaling in the budding yeast Saccharomyces cerevisiae. It involves multiple factors that sense and transmit mitochondrial signals to effect changes in nuclear gene expression; these changes lead to a reconfiguration of metabolism to accommodate cells to defects in mitochondria. Analysis of regulatory factors has provided us with a mechanistic view of regulation of retrograde signaling. Here we review advances in the yeast retrograde signaling pathway and highlight its regulatory factors and regulatory mechanisms, its physiological functions, and its connection to nutrient sensing, TOR signaling, and aging.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                September 2018
                26 September 2018
                : 4
                : 9
                : eaar4666
                Affiliations
                [1 ]Attagene Inc., P.O. Box 12054, Research Triangle Park, NC 27709, USA.
                [2 ]U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, D343-03, Research Triangle Park, NC 27711, USA.
                Author notes
                [*]

                Present address: Lineberger Cancer Research Center, University of North Carolina at Chapel Hill, Room 22-062, Chapel Hill, NC 27599–7295, USA.

                []Corresponding author. Email: smak@ 123456attagene.com
                Author information
                http://orcid.org/0000-0001-6983-1687
                http://orcid.org/0000-0002-1368-5171
                http://orcid.org/0000-0002-9725-102X
                http://orcid.org/0000-0001-9240-0770
                http://orcid.org/0000-0002-0055-2249
                http://orcid.org/0000-0003-1314-6909
                Article
                aar4666
                10.1126/sciadv.aar4666
                6157966
                30263952
                36c7a39e-0846-4587-bf89-176d7d0f78d0
                Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 10 November 2017
                : 21 August 2018
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R44GM125469
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Signal Transduction
                Signal Transduction
                Custom metadata
                Rochelle Abragante

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