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      Novel Modular Rhodopsins from Green Algae Hold Great Potential for Cellular Optogenetic Modulation Across the Biological Model Systems

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          Abstract

          Light-gated ion channel and ion pump rhodopsins are widely used as optogenetic tools and these can control the electrically excitable cells as (1) they are a single-component system i.e., their light sensing and ion-conducting functions are encoded by the 7-transmembrane domains and, (2) they show fast kinetics with small dark-thermal recovery time. In cellular signaling, a signal receptor, modulator, and the effector components are involved in attaining synchronous regulation of signaling. Optical modulation of the multicomponent network requires either receptor to effector encoded in a single ORF or direct modulation of the effector domain through bypassing all upstream players. Recently discovered modular rhodopsins like rhodopsin guanylate cyclase (RhoGC) and rhodopsin phosphodiesterase (RhoPDE) paves the way to establish a proof of concept for utilization of complex rhodopsin (modular rhodopsin) for optogenetic applications. Light sensor coupled modular system could be expressed in any cell type and hence holds great potential in the advancement of optogenetics 2.0 which would enable manipulating the entire relevant cell signaling system. Here, we had identified 50 novel modular rhodopsins with variant domains and their diverse cognate signaling cascades encoded in a single ORF, which are associated with specialized functions in the cells. These novel modular algal rhodopsins have been characterized based on their sequence and structural homology with previously reported rhodopsins. The presented novel modular rhodopsins with various effector domains leverage the potential to expand the optogenetic tool kit to regulate various cellular signaling pathways across the diverse biological model systems.

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          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

            A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.
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              MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

              Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.
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                Author and article information

                Journal
                Life (Basel)
                Life (Basel)
                life
                Life
                MDPI
                2075-1729
                28 October 2020
                November 2020
                : 10
                : 11
                : 259
                Affiliations
                [1 ]Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA; awasthi9@ 123456umd.edu
                [2 ]Laboratory of Optobiology, School of Biotechnology, Jawaharlal Nehru University, New Delhi 110067, India; sushmi59_sbt@ 123456jnu.ac.in (K.S.); manish13587@ 123456gmail.com (M.S.K.)
                Author notes
                [* ]Correspondence: rpeeyush@ 123456umd.edu (P.R.); skateriya@ 123456jnu.ac.in (S.K.)
                [†]

                Equally contributed.

                Author information
                https://orcid.org/0000-0002-4395-0589
                https://orcid.org/0000-0001-9649-1446
                https://orcid.org/0000-0002-7112-2588
                https://orcid.org/0000-0001-6823-2033
                https://orcid.org/0000-0001-5428-4297
                Article
                life-10-00259
                10.3390/life10110259
                7693036
                33126644
                51e3ff4c-ef08-4e1c-8fb2-c85ced3e118f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 03 September 2020
                : 19 October 2020
                Categories
                Article

                enzyme-rhodopsin,channelrhodopsins,optogenetics,two-component system,cyclase,phosphodiesterase

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