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      Proteomic and Metabolomic Characteristics of Extremophilic Fungi Under Simulated Mars Conditions

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          Abstract

          Filamentous fungi have been associated with extreme habitats, including nuclear power plant accident sites and the International Space Station (ISS). Due to their immense adaptation and phenotypic plasticity capacities, fungi may thrive in what seems like uninhabitable niches. This study is the first report of fungal survival after exposure of monolayers of conidia to simulated Mars conditions (SMC). Conidia of several Chernobyl nuclear accident-associated and ISS-isolated strains were tested for UV-C and SMC sensitivity, which resulted in strain-dependent survival. Strains surviving exposure to SMC for 30 min, ISSFT-021-30 and IMV 00236-30, were further characterized for proteomic, and metabolomic changes. Differential expression of proteins involved in ribosome biogenesis, translation, and carbohydrate metabolic processes was observed. No significant metabolome alterations were revealed. Lastly, ISSFT-021-30 conidia re-exposed to UV-C exhibited enhanced UV-C resistance when compared to the conidia of unexposed ISSFT-021.

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          Most cited references63

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          The genetic landscape of a cell.

          A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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            Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources

            Background In order to improve gene prediction, extrinsic evidence on the gene structure can be collected from various sources of information such as genome-genome comparisons and EST and protein alignments. However, such evidence is often incomplete and usually uncertain. The extrinsic evidence is usually not sufficient to recover the complete gene structure of all genes completely and the available evidence is often unreliable. Therefore extrinsic evidence is most valuable when it is balanced with sequence-intrinsic evidence. Results We present a fairly general method for integration of external information. Our method is based on the evaluation of hints to potentially protein-coding regions by means of a Generalized Hidden Markov Model (GHMM) that takes both intrinsic and extrinsic information into account. We used this method to extend the ab initio gene prediction program AUGUSTUS to a versatile tool that we call AUGUSTUS+. In this study, we focus on hints derived from matches to an EST or protein database, but our approach can be used to include arbitrary user-defined hints. Our method is only moderately effected by the length of a database match. Further, it exploits the information that can be derived from the absence of such matches. As a special case, AUGUSTUS+ can predict genes under user-defined constraints, e.g. if the positions of certain exons are known. With hints from EST and protein databases, our new approach was able to predict 89% of the exons in human chromosome 22 correctly. Conclusion Sensitive probabilistic modeling of extrinsic evidence such as sequence database matches can increase gene prediction accuracy. When a match of a sequence interval to an EST or protein sequence is used it should be treated as compound information rather than as information about individual positions.
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              antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification

              Abstract Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                15 May 2019
                2019
                : 10
                : 1013
                Affiliations
                [1] 1Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California , Los Angeles, CA, United States
                [2] 2Biotechnology and Planetary Protection Group, Jet Propulsion Laboratory, California Institute of Technology , Pasadena, CA, United States
                [3] 3Department of Molecular Imaging and Therapy, Beckman Research Institute of City of Hope , Duarte, CA, United States
                [4] 4Department of Physics, Free University of Berlin , Berlin, Germany
                [5] 5Leiden Institute of Chemistry, Leiden University , Leiden, Netherlands
                [6] 6Department of Microbiology and Plant Pathology, Institute of Integrative Genome Biology, University of California, Riverside , Riverside, CA, United States
                [7] 7Department of Ecology, Lawrence Berkeley National Laboratory , Berkeley, CA, United States
                [8] 8Department of Chemistry, Dornsife College of Letters, Arts, and Sciences, University of Southern California , Los Angeles, CA, United States
                Author notes

                Edited by: Claudia P. Saavedra, Universidad Andrés Bello, Chile

                Reviewed by: Guilherme T. P. Brancini, Universidade de São Paulo Ribeirão Preto, Brazil; Yanchun Shao, Huazhong Agricultural University, China

                *Correspondence: Kasthuri Venkateswaran, kjvenkat@ 123456jpl.nasa.gov

                This article was submitted to Extreme Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2019.01013
                6529585
                31156574
                604e6b58-772a-4776-b828-890344b464e2
                Copyright © 2019 Blachowicz, Chiang, Elsaesser, Kalkum, Ehrenfreund, Stajich, Torok, Wang and Venkateswaran.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 07 January 2019
                : 24 April 2019
                Page count
                Figures: 6, Tables: 9, Equations: 0, References: 76, Pages: 16, Words: 0
                Funding
                Funded by: National Aeronautics and Space Administration 10.13039/100000104
                Award ID: 19-12829-26
                Funded by: Foundation for the National Institutes of Health 10.13039/100000009
                Award ID: P30 CA33572
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                fungi,simulated mars conditions,proteome,metabolome,chernobyl,international space station,extremophiles

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