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      Mitochondria-encoded genes contribute to evolution of heat and cold tolerance in yeast

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          Abstract

          The mitochondrial genome is a hotspot for divergence in thermal growth differences in yeast.

          Abstract

          Genetic analysis of phenotypic differences between species is typically limited to interfertile species. Here, we conducted a genome-wide noncomplementation screen to identify genes that contribute to a major difference in thermal growth profile between two reproductively isolated yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum. The screen identified only a single nuclear-encoded gene with a moderate effect on heat tolerance, but, in contrast, revealed a large effect of mitochondrial DNA (mitotype) on both heat and cold tolerance. Recombinant mitotypes indicate that multiple genes contribute to thermal divergence, and we show that protein divergence in COX1 affects both heat and cold tolerance. Our results point to the yeast mitochondrial genome as an evolutionary hotspot for thermal divergence.

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          Most cited references52

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          Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads—a baiting and iterative mapping approach

          Christoph Hahn, Lutz Bachmann, Bastien Chevreux (2013)
          We present an in silico approach for the reconstruction of complete mitochondrial genomes of non-model organisms directly from next-generation sequencing (NGS) data—mitochondrial baiting and iterative mapping (MITObim). The method is straightforward even if only (i) distantly related mitochondrial genomes or (ii) mitochondrial barcode sequences are available as starting-reference sequences or seeds, respectively. We demonstrate the efficiency of the approach in case studies using real NGS data sets of the two monogenean ectoparasites species Gyrodactylus thymalli and Gyrodactylus derjavinoides including their respective teleost hosts European grayling (Thymallus thymallus) and Rainbow trout (Oncorhynchus mykiss). MITObim appeared superior to existing tools in terms of accuracy, runtime and memory requirements and fully automatically recovered mitochondrial genomes exceeding 99.5% accuracy from total genomic DNA derived NGS data sets in <24 h using a standard desktop computer. The approach overcomes the limitations of traditional strategies for obtaining mitochondrial genomes for species with little or no mitochondrial sequence information at hand and represents a fast and highly efficient in silico alternative to laborious conventional strategies relying on initial long-range PCR. We furthermore demonstrate the applicability of MITObim for metagenomic/pooled data sets using simulated data. MITObim is an easy to use tool even for biologists with modest bioinformatics experience. The software is made available as open source pipeline under the MIT license at https://github.com/chrishah/MITObim.
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            Sequencing and comparison of yeast species to identify genes and regulatory elements.

            Manolis Kellis, Nick Patterson, Matthew G. Endrizzi … (2003)
            Identifying the functional elements encoded in a genome is one of the principal challenges in modern biology. Comparative genomics should offer a powerful, general approach. Here, we present a comparative analysis of the yeast Saccharomyces cerevisiae based on high-quality draft sequences of three related species (S. paradoxus, S. mikatae and S. bayanus). We first aligned the genomes and characterized their evolution, defining the regions and mechanisms of change. We then developed methods for direct identification of genes and regulatory motifs. The gene analysis yielded a major revision to the yeast gene catalogue, affecting approximately 15% of all genes and reducing the total count by about 500 genes. The motif analysis automatically identified 72 genome-wide elements, including most known regulatory motifs and numerous new motifs. We inferred a putative function for most of these motifs, and provided insights into their combinatorial interactions. The results have implications for genome analysis of diverse organisms, including the human.
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              Studies on the transformation of intact yeast cells by the LiAc/SS-DNA/PEG procedure.

              R Gietz, R H Schiestl, A. Willems … (1995)
              An improved lithium acetate (LiAc)/single-stranded DNA (SS-DNA)/polyethylene glycol (PEG) protocol which yields > 1 x 10(6) transformants/micrograms plasmid DNA and the original protocol described by Schiestl and Gietz (1989) were used to investigate aspects of the mechanism of LiAc/SS-DNA/PEG transformation. The highest transformation efficiency was observed when 1 x 10(8) cells were transformed with 100 ng plasmid DNA in the presence of 50 micrograms SS carrier DNA. The yield of transformants increased linearly up to 5 micrograms plasmid per transformation. A 20-min heat shock at 42 degrees C was necessary for maximal yields. PEG was found to deposit both carrier DNA and plasmid DNA onto cells. SS carrier DNA bound more effectively to the cells and caused tighter binding of 32P-labelled plasmid DNA than did double-stranded (DS) carrier. The LiAc/SS-DNA/PEG transformation method did not result in cell fusion. DS carrier DNA competed with DS vector DNA in the transformation reaction. SS plasmid DNA transformed cells poorly in combination with both SS and DS carrier DNA. The LiAc/SS-DNA/PEG method was shown to be more effective than other treatments known to make cells transformable. A model for the mechanism of transformation by the LiAc/SS-DNA/PEG method is discussed.
              Article 0 comments Cited 452 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sci Adv
                Journal ID (iso-abbrev): Sci Adv
                Journal ID (publisher-id): SciAdv
                Journal ID (hwp): advances
                Title: Science Advances
                Publisher: American Association for the Advancement of Science
                ISSN (Electronic): 2375-2548
                Publication date Collection: January 2019
                Publication date (Electronic): 30 January 2019
                Volume: 5
                Issue: 1
                Electronic Location Identifier: eaav1848
                Affiliations
                [1 ]Molecular Genetics and Genomics Program, Division of Biology and Biomedical Sciences, Washington University, St. Louis, MO 63110, USA.
                [2 ]Department of Genetics, Washington University, St. Louis, MO 63110, USA.
                [3 ]Center for Genome Sciences and System Biology, Washington University, St. Louis, MO 63110, USA.
                [4 ]Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Genome Center of Wisconsin, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, University of Wisconsin–Madison, Madison, WI 53706, USA.
                [5 ]Department of Food Biotechnology, Institute of Agrochemistry and Food Technology (IATA), CSIC, Paterna, Valencia, Spain.
                [6 ]Department of Biology, University of Rochester, Rochester, NY 14627, USA.
                Author notes
                [* ]Corresponding author. Email: lixueying@ 123456wustl.edu (X.C.L.); justin.fay@ 123456rochester.edu (J.C.F.)
                Author information
                http://orcid.org/0000-0002-1100-4694
                http://orcid.org/0000-0001-9912-8802
                http://orcid.org/0000-0001-5088-7461
                http://orcid.org/0000-0002-9934-5468
                http://orcid.org/0000-0003-1893-877X
                Article
                Publisher ID: aav1848
                DOI: 10.1126/sciadv.aav1848
                PMC ID: 6353624
                PubMed ID: 30729162
                SO-VID: d3eabbea-a354-4018-b284-7cd5279ae905
                Copyright © Copyright © 2019 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).
                License:

                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
                Date received : 22 August 2018
                Date accepted : 13 December 2018
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DEB-1253634
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: GM080669
                Funded by: doi http://dx.doi.org/10.13039/100000875, Pew Charitable Trusts;
                Funded by: doi http://dx.doi.org/10.13039/100005825, National Institute of Food and Agriculture;
                Award ID: Hatch project 1003258
                Funded by: European Union’s Horizon 2020 research and innovation programme;
                Award ID: 747775
                Funded by: DOE Great Lakes Bioenergy Research Center;
                Award ID: DE-FC02-07ER64494
                Funded by: DOE Great Lakes Bioenergy Research Center;
                Award ID: DE-SC0018409
                Funded by: Vilas Trust Estate;
                Categories
                Subject: Research Article
                Subject: Research Articles
                Subject: SciAdv r-articles
                Subject: Genetics
                Custom metadata
                Copyeditor Eunice Diego

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