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1.
Front Cell Infect Microbiol. 2018 Nov 14;8:392. doi: 10.3389/fcimb.2018.00392. eCollection 2018.

Insecticidal Toxicity of Yersinia frederiksenii Involves the Novel Enterotoxin YacT.

Author information

1
Lehrstuhl für Mikrobielle Ökologie, Fakultät für Grundlagen der Biowissenschaften, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, Germany.
2
Friedrich-Loeffler-Institut, Institut für Molekulare Pathogenese, Jena, Germany.
3
Department of Computational Systems Biology, University of Vienna, Vienna, Austria.

Abstract

The genus Yersinia comprises 19 species of which three are known as human and animal pathogens. Some species display toxicity toward invertebrates using the so-called toxin complex (TC) and/or determinants that are not yet known. Recent studies showed a remarkable variability of insecticidal activities when representatives of different Yersinia species (spp.) were subcutaneously injected into the greater wax moth, Galleria mellonella. Here, we demonstrate that Y. intermedia and Y. frederiksenii are highly toxic to this insect. A member of Y. Enterocolitica phylogroup 1B killed G. mellonella larvae with injection doses of approximately 38 cells only, thus resembling the insecticidal activity of Photorhabdus luminescens. The pathogenicity Yersinia spp. displays toward the larvae was higher at 15°C than at 30°C and independent of the TC. However, upon subtraction of all genes of the low-pathogenic Y. enterocolitica strain W22703 from the genomes of Y. intermedia and Y. frederiksenii, we identified a set of genes that may be responsible for the toxicity of these two species. Indeed, a mutant of Y. frederiksenii lacking yacT, a gene that encodes a protein similar to the heat-stable cytotonic enterotoxin (Ast) of Aeromonas hydrophila, exhibited a reduced pathogenicity toward G. mellonella larvae and altered the morphology of hemocytes. The data suggests that the repertoire of virulence determinants present in environmental Yersinia species remains to be elucidated.

KEYWORDS:

Galleria mellonella; YacT; Yersinia; enterotoxin; insecticidal activity

2.
Nucleic Acids Res. 2018 Nov 12. doi: 10.1093/nar/gky1085. [Epub ahead of print]

eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses.

Author information

1
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
2
Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain.
3
Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland.
4
Experimental and Clinical Research Center, a cooperation of Charité-Universitätsmedizin Berlin and Max Delbruck Center for Molecular Medicine, 13125 Berlin, Germany.
5
The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N 2200, Denmark.
6
Daniel K. Inouye Center for Microbial Oceanography: Research and Education (C-MORE), University of Hawaii, Honolulu, HI 96822, USA.
7
Biobyte solutions GmbH, Bothestr 142, 69126 Heidelberg, Germany.
8
CUBE-Division of Computational Systems Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna 1090, Austria.
9
Germany Molecular Medicine Partnership Unit (MMPU), University Hospital Heidelberg and European Molecular Biology Laboratory, Heidelberg, Germany.
10
Max Delbrück Centre for Molecular Medicine, Berlin, Germany.
11
Department of Bioinformatics, Biocenter University of Würzburg, Würzburg, Germany.

Abstract

eggNOG is a public database of orthology relationships, gene evolutionary histories and functional annotations. Here, we present version 5.0, featuring a major update of the underlying genome sets, which have been expanded to 4445 representative bacteria and 168 archaea derived from 25 038 genomes, as well as 477 eukaryotic organisms and 2502 viral proteomes that were selected for diversity and filtered by genome quality. In total, 4.4M orthologous groups (OGs) distributed across 379 taxonomic levels were computed together with their associated sequence alignments, phylogenies, HMM models and functional descriptors. Precomputed evolutionary analysis provides fine-grained resolution of duplication/speciation events within each OG. Our benchmarks show that, despite doubling the amount of genomes, the quality of orthology assignments and functional annotations (80% coverage) has persisted without significant changes across this update. Finally, we improved eggNOG online services for fast functional annotation and orthology prediction of custom genomics or metagenomics datasets. All precomputed data are publicly available for downloading or via API queries at http://eggnog.embl.de.

3.
Phytochemistry. 2018 Dec;156:224-233. doi: 10.1016/j.phytochem.2018.10.012. Epub 2018 Oct 15.

A promiscuous beta-glucosidase is involved in benzoxazinoid deglycosylation in Lamium galeobdolon.

Author information

1
Chair of Plant Breeding, Technical University of Munich, Liesel-Beckmann-Str. 2, D-85354, Freising, Germany. Electronic address: laura.hannemann@tum.de.
2
Division of Computational Systems Biology, University of Vienna, Althanstr. 14 A-1090, Vienna, Austria. Electronic address: calin.rares.lucaciu@univie.ac.at.
3
Plant Genome and Systems Biology, Helmholtz Center Munich, Ingolstädter Landstraße 1, D-85764, Neuherberg, Germany. Electronic address: sapna.bioinfo@gmail.com.
4
Division of Computational Systems Biology, University of Vienna, Althanstr. 14 A-1090, Vienna, Austria. Electronic address: thomas.rattei@univie.ac.at.
5
Plant Genome and Systems Biology, Helmholtz Center Munich, Ingolstädter Landstraße 1, D-85764, Neuherberg, Germany; School of Life Sciences, Technical University Munich, Germany. Electronic address: k.mayer@helmholtz-muenchen.de.
6
Chair of Genetics, Technical University of Munich, Emil-Ramann-Str. 8, D-85354, Freising, Germany. Electronic address: gierl@tum.de.
7
Chair of Plant Breeding, Technical University of Munich, Liesel-Beckmann-Str. 2, D-85354, Freising, Germany. Electronic address: monika.frey@tum.de.

Abstract

In the plant kingdom beta-glucosidases (BGLUs) of the glycosidase hydrolase family 1 have essential function in primary metabolism and are particularly employed in secondary metabolism. They are essential for activation in two-component defence systems based on stabilisation of reactive compounds by glycosylation. Based on de novo assembly we isolated and functionally characterised BGLUs expressed in leaves of Lamium galeobdolon (LgGLUs). LgGLU1 could be assigned to hydrolysis of the benzoxazinoid GDIBOA (2,4-dihydroxy-1,4-benzoxazin-3-one glucoside). Within the Lamiaceae L. galeobdolon is distinguished by the presence GDIBOA in addition to the more common iridoid harpagide. Although LgGLU1 proved to be promiscuous with respect to accepted substrates, harpagide hydrolysis was not detected. Benzoxazinoids are characteristic defence compounds of the Poales but are also found in some unrelated dicots. The benzoxazinoid specific BGLUs have recently been identified for the grasses maize, wheat, rye and the Ranunculaceae Consolida orientalis. All enzymes share a general substrate ambiguity but differ in detailed substrate pattern. The isolation of the second dicot GDIBOA glucosidase LgGLU1 allowed it to analyse the phylogenetic relation of the distinct BGLUs also within dicots. The data revealed long periods of independent sequence evolution before speciation.

KEYWORDS:

Benzoxazinoids; Beta-glycosidase; Chemical defence; DIBOA; Harpagide; Lamiaceae; Repeated evolution; Substrate ambiguity

PMID:
30336442
DOI:
10.1016/j.phytochem.2018.10.012
[Indexed for MEDLINE]
Free full text
Icon for Elsevier Science
4.
mSphere. 2018 Oct 10;3(5). pii: e00412-18. doi: 10.1128/mSphere.00412-18.

The Genetic Transformation of Chlamydia pneumoniae.

Author information

1
Department of Infectious Diseases and Microbiology, University of Luebeck, Luebeck, Germany.
2
German Center for Infection Research (DZIF), Partner Site, Hamburg-Luebeck-Borstel-Riems, Germany.
3
Molecular Microbiology Group, Faculty of Medicine, University of Southampton, Southampton General Hospital, Southampton, United Kingdom.
4
Institute of Molecular Pathogenesis, Friedrich-Loeffler-lnstitute (Federal Research Institute for Animal Health), Jena, Germany.
5
Molecular and Experimental Mycobacteriology, Research Center Borstel, Borstel, Germany.
6
Division of Computational Systems Biology, University Vienna, Vienna, Austria.
7
Institute of Anatomy, University of Luebeck, Luebeck, Germany.
8
University of Sunshine Coast, Maroochydore, Australia.
9
RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich-Schiller-Universität Jena, Jena, Germany.
10
Department of Infectious Diseases and Microbiology, University of Luebeck, Luebeck, Germany jan.rupp@uksh.de.
#
Contributed equally

Abstract

We demonstrate the genetic transformation of Chlamydia pneumoniae using a plasmid shuttle vector system which generates stable transformants. The equine C. pneumoniae N16 isolate harbors the 7.5-kb plasmid pCpnE1. We constructed the plasmid vector pRSGFPCAT-Cpn containing a pCpnE1 backbone, plus the red-shifted green fluorescent protein (RSGFP), as well as the chloramphenicol acetyltransferase (CAT) gene used for the selection of plasmid shuttle vector-bearing C. pneumoniae transformants. Using the pRSGFPCAT-Cpn plasmid construct, expression of RSGFP in koala isolate C. pneumoniae LPCoLN was demonstrated. Furthermore, we discovered that the human cardiovascular isolate C. pneumoniae CV-6 and the human community-acquired pneumonia-associated C. pneumoniae IOL-207 could also be transformed with pRSGFPCAT-Cpn. In previous studies, it was shown that Chlamydia spp. cannot be transformed when the plasmid shuttle vector is constructed from a different plasmid backbone to the homologous species. Accordingly, we confirmed that pRSGFPCAT-Cpn could not cross the species barrier in plasmid-bearing and plasmid-free C. trachomatis, C. muridarum, C. caviae, C. pecorum, and C. abortus However, contrary to our expectation, pRSGFPCAT-Cpn did transform C. felis Furthermore, pRSGFPCAT-Cpn did not recombine with the wild-type plasmid of C. felis Taken together, we provide for the first time an easy-to-handle transformation protocol for C. pneumoniae that results in stable transformants. In addition, the vector can cross the species barrier to C. felis, indicating the potential of horizontal pathogenic gene transfer via a plasmid.IMPORTANCE The absence of tools for the genetic manipulation of C. pneumoniae has hampered research into all aspects of its biology. In this study, we established a novel reproducible method for C. pneumoniae transformation based on a plasmid shuttle vector system. We constructed a C. pneumoniae plasmid backbone shuttle vector, pRSGFPCAT-Cpn. The construct expresses the red-shifted green fluorescent protein (RSGFP) fused to chloramphenicol acetyltransferase in C. pneumoniae C. pneumoniae transformants stably retained pRSGFPCAT-Cpn and expressed RSGFP in epithelial cells, even in the absence of chloramphenicol. The successful transformation in C. pneumoniae using pRSGFPCAT-Cpn will advance the field of chlamydial genetics and is a promising new approach to investigate gene functions in C. pneumoniae biology. In addition, we demonstrated that pRSGFPCAT-Cpn overcame the plasmid species barrier without the need for recombination with an endogenous plasmid, indicating the potential probability of horizontal chlamydial pathogenic gene transfer by plasmids between chlamydial species.

KEYWORDS:

Chlamydia felis ; Chlamydia pneumoniae ; genetic manipulation; plasmid shuttle vector; plasmid tropism; transformation

5.
Curr Biol. 2018 Jul 23;28(14):2348-2355.e9. doi: 10.1016/j.cub.2018.05.067. Epub 2018 Jul 12.

The Iceman's Last Meal Consisted of Fat, Wild Meat, and Cereals.

Author information

1
Eurac Research - Institute for Mummy Studies, Viale Druso 1, 39100 Bolzano, Italy. Electronic address: frank.maixner@eurac.edu.
2
CUBE - Division of Computational Systems Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria.
3
SLING, Life Sciences Institute, National University of Singapore, Singapore; Department of Biochemistry, National University of Singapore, Singapore.
4
Institute of Materials Science, Physics of Surfaces, Technische Universität Darmstadt, Alarich-Weiss-Str. 2, 64287 Darmstadt, Germany; Center of Smart Interfaces, Technische Universität Darmstadt, Alarich-Weiss-Str. 10, 64287 Darmstadt, Germany.
5
Institute of Clinical Molecular Biology, Kiel University, Rosalind-Franklin-Str. 12, 24105 Kiel, Germany.
6
Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA.
7
Environmental Research and Innovation (ERIN), Luxembourg Institute of Science and Technology (LIST), Esch/Alzette, Luxembourg.
8
Eurac Research - Institute for Mummy Studies, Viale Druso 1, 39100 Bolzano, Italy.
9
Smurfit Institute of Genetics, University of Dublin, Trinity College, Dublin 2, Ireland.
10
Responsabile del Laboratorio di Archeozoologia della Soprintendenza Provinciale ai Beni culturali di Bolzano - Alto Adige, Ufficio Beni archeologica, 39100 Bolzano, Italy.
11
South Tyrol Museum of Archaeology, Museumstrasse 43, 39100 Bolzano, Italy.
12
Department of Molecular and Cellular Biology & Genome Center, University of California, Davis, Davis, CA, USA.
13
Department of Physical Anthropology, Institute of Forensic Medicine, University of Bern, Sulgenauweg 40, 3007 Bern, Switzerland.
14
Max Planck Institute for Evolutionary Biology, August-Thienemann-Strasse 2, D-24306, Plön, Germany.
15
Cancer Research Institute & Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, Korea.
16
Department of Radiodiagnostics, Central Hospital Bolzano, Bolzano, Italy.
17
Scuola Superiore Sanitaria Provinciale "Claudiana," Via Lorenz Böhler 13, 39100 Bolzano, Italy.
18
Department of Gastroenterology, Hepatology, and Infectious Diseases, Otto-von-Guericke University, Leipziger Strasse 44, 39120 Magdeburg, Germany.
19
Chair for Clinical Bioinformatics, Saarland University, Medical Faculty, Saarbrücken, Germany.
20
Elemental Bio-imaging Facility, University of Technology Sydney, Broadway, New South Wales, 2007, Australia.
21
Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 141 83 Stockholm, Sweden.
22
Institute of Botany, Sternwartestrasse 15, University of Innsbruck, 6020 Innsbruck, Austria.
23
Agilent Technologies, 5301 Stevens Creek Blvd, Santa Clara, CA 95051, USA.
24
Eurac Research - Institute for Mummy Studies, Viale Druso 1, 39100 Bolzano, Italy. Electronic address: albert.zink@eurac.edu.

Abstract

The history of humankind is marked by the constant adoption of new dietary habits affecting human physiology, metabolism, and even the development of nutrition-related disorders. Despite clear archaeological evidence for the shift from hunter-gatherer lifestyle to agriculture in Neolithic Europe [1], very little information exists on the daily dietary habits of our ancestors. By undertaking a complementary -omics approach combined with microscopy, we analyzed the stomach content of the Iceman, a 5,300-year-old European glacier mummy [2, 3]. He seems to have had a remarkably high proportion of fat in his diet, supplemented with fresh or dried wild meat, cereals, and traces of toxic bracken. Our multipronged approach provides unprecedented analytical depth, deciphering the nutritional habit, meal composition, and food-processing methods of this Copper Age individual.

KEYWORDS:

European Copper Age mummy; Iceman; ancient DNA; diet; last meal; lipidomics; microscopy; multi-omics study; proteomics; stomach content

7.
FASEB J. 2018 Jun 25:fj201800443. doi: 10.1096/fj.201800443. [Epub ahead of print]

Oxytocin-like signaling in ants influences metabolic gene expression and locomotor activity.

Author information

1
Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria.
2
Division of Computational Systems Biology (CUBE), Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.
3
Ludwig Boltzmann Institute for Cancer Research, Vienna, Austria.
4
Institute of Science and Technology (IST) Austria, Klosterneuburg, Austria.
5
Institute for Molecular Biosciences, The University of Queensland, St. Lucia, Queensland, Australia; and.
6
Faculty of Chemistry, Institute of Biological Chemistry, University of Vienna, Vienna, Austria.

Abstract

Ants are emerging model systems to study cellular signaling because distinct castes possess different physiologic phenotypes within the same colony. Here we studied the functionality of inotocin signaling, an insect ortholog of mammalian oxytocin (OT), which was recently discovered in ants. In Lasius ants, we determined that specialization within the colony, seasonal factors, and physiologic conditions down-regulated the expression of the OT-like signaling system. Given this natural variation, we interrogated its function using RNAi knockdowns. Next-generation RNA sequencing of OT-like precursor knock-down ants highlighted its role in the regulation of genes involved in metabolism. Knock-down ants exhibited higher walking activity and increased self-grooming in the brood chamber. We propose that OT-like signaling in ants is important for regulating metabolic processes and locomotion.-Liutkevičiūtė, Z., Gil-Mansilla, E., Eder, T., Casillas-Pérez, B., Di Giglio, M. G., Muratspahić, E., Grebien, F., Rattei, T., Muttenthaler, M., Cremer, S., Gruber, C. W. Oxytocin-like signaling in ants influences metabolic gene expression and locomotor activity.

KEYWORDS:

G protein–coupled receptor; inotocin; insect; vasopressin; vasotocin

8.
Sci Rep. 2018 Jun 21;8(1):9467. doi: 10.1038/s41598-018-27781-8.

Characterization of a community-acquired-MRSA USA300 isolate from a river sample in Austria and whole genome sequence based comparison to a diverse collection of USA300 isolates.

Author information

1
Austrian Agency for Health and Food Safety, National Reference Laboratory for Coagulase Positive Staphylococci including Staphylococcus aureus, Graz, Austria. sarah.lepuschitz@ages.at.
2
Vienna University of Technology, Research Area of Biochemical Technology, Institute of Chemical, Biological and Environmental Engineering, Vienna, Austria. sarah.lepuschitz@ages.at.
3
Austrian Agency for Health and Food Safety, National Reference Laboratory for Coagulase Positive Staphylococci including Staphylococcus aureus, Graz, Austria.
4
University of Vienna, Department of Microbiology and Ecosystem Science, Vienna, Austria.
5
Vienna University of Technology, Research Area of Biochemical Technology, Institute of Chemical, Biological and Environmental Engineering, Vienna, Austria.
6
University of Natural Resources and Life Sciences, Department of Biotechnology, Vienna, Austria.

Abstract

The increasing emergence of multi-resistant bacteria in healthcare settings, in the community and in the environment represents a major health threat worldwide. In 2016, we started a pilot project to investigate antimicrobial resistance in surface water. Bacteria were enriched, cultivated on selective chromogenic media and species identification was carried out by MALDI-TOF analysis. From a river in southern Austria a methicillin resistant Staphylococcus aureus (MRSA) was isolated. Whole genome sequence analysis identified the isolate as ST8, spa type t008, SCCmecIV, PVL and ACME positive, which are main features of CA-MRSA USA300. Whole genome based cgMLST of the water isolate and comparison to 18 clinical MRSA USA300 isolates from the Austrian national reference laboratory for coagulase positive staphylococci originating from 2004, 2005 and 2016 and sequences of 146 USA300 isolates arbitrarily retrieved from the Sequence Read Archive revealed a close relatedness to a clinical isolate from Austria. The presence of a CA-MRSA USA300 isolate in an aquatic environment might pose a public health risk by serving as a potential source of infection or a source for emergence of new pathogenic MRSA clones.

9.
Environ Microbiol. 2018 Mar 25. doi: 10.1111/1462-2920.14110. [Epub ahead of print]

Reef invertebrate viromics: diversity, host specificity and functional capacity.

Author information

1
Australian Institute of Marine Science, PMB 3, Townsville, QLD 4810, Australia.
2
Department of Microbiology and Ecosystem Science, Division of Computational Systems Biology, University of Vienna, Vienna, Austria.
3
College of Science and Engineering, James Cook University, Townsville, QLD, Australia.
4
AIMS@JCU, Australian Institute of Marine Science and James Cook University, Townsville, QLD, Australia.
5
School of Biosciences, University of Melbourne, Parkville, Melbourne, VIC 3010, Australia.
6
Austalian Centre for Ecogenomics, University of Queensland, Brisbane, QLD 4072, Australia.

Abstract

Recent metagenomic analyses have revealed a high diversity of viruses in the pelagic ocean and uncovered clear habitat-specific viral distribution patterns. Conversely, similar insights into the composition, host specificity and function of viruses associated with marine organisms have been limited by challenges associated with sampling and computational analysis. Here, we performed targeted viromic analysis of six coral reef invertebrate species and their surrounding seawater to deliver taxonomic and functional profiles of viruses associated with reef organisms. Sponges and corals' host species-specific viral assemblages with low sequence identity to known viral genomes. While core viral genes involved in capsid formation, tail structure and infection mechanisms were observed across all reef samples, auxiliary genes including those involved in herbicide resistance and viral pathogenesis pathways such as host immune suppression were differentially enriched in reef hosts. Utilising a novel OTU based assessment, we also show a prevalence of dsDNA viruses belonging to the Mimiviridae, Caudovirales and Phycodnaviridae in reef environments and further highlight the abundance of ssDNA viruses belonging to the Circoviridae, Parvoviridae, Bidnaviridae and Microviridae in reef invertebrates. These insights into coral reef viruses provide an important framework for future research into how viruses contribute to the health and evolution of reef organisms.

10.
Front Plant Sci. 2018 Feb 28;9:149. doi: 10.3389/fpls.2018.00149. eCollection 2018.

Great Cause-Small Effect: Undeclared Genetically Engineered Orange Petunias Harbor an Inefficient Dihydroflavonol 4-Reductase.

Author information

1
Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Vienna, Austria.
2
Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.
3
Thaer-Institute of Agricultural and Horticultural Sciences Humboldt University Berlin, Berlin, Germany.

Abstract

A recall campaign for commercial, orange flowering petunia varieties in spring 2017 caused economic losses worldwide. The orange varieties were identified as undeclared genetically engineered (GE)-plants, harboring a maize dihydroflavonol 4-reductase (DFR, A1), which was used in former scientific transgenic breeding attempts to enable formation of orange pelargonidin derivatives from the precursor dihydrokaempferol (DHK) in petunia. How and when the A1 cDNA entered the commercial breeding process is unclear. We provide an in-depth analysis of three orange petunia varieties, released by breeders from three countries, with respect to their transgenic construct, transcriptomes, anthocyanin composition, and flavonoid metabolism at the level of selected enzymes and genes. The two possible sources of the A1 cDNA in the undeclared GE-petunia can be discriminated by PCR. A special version of the A1 gene, the A1 type 2 allele, is present, which includes, at the 3'-end, an additional 144 bp segment from the non-viral transposable Cin4-1 sequence, which does not add any functional advantage with respect to DFR activity. This unequivocally points at the first scientific GE-petunia from the 1980s as the A1 source, which is further underpinned e.g., by the presence of specific restriction sites, parts of the untranslated sequences, and the same arrangement of the building blocks of the transformation plasmid used. Surprisingly, however, the GE-petunia cannot be distinguished from native red and blue varieties by their ability to convert DHK in common in vitro enzyme assays, as DHK is an inadequate substrate for both the petunia and maize DFR. Recombinant maize DFR underpins the low DHK acceptance, and, thus, the strikingly limited suitability of the A1 protein for a transgenic approach for breeding pelargonidin-based flower color. The effect of single amino acid mutations on the substrate specificity of DFRs is demonstrated. Expression of the A1 gene is generally lower than the petunia DFR expression despite being under the control of the strong, constitutive p35S promoter. We show that a rare constellation in flavonoid metabolism-absence or strongly reduced activity of both flavonol synthase and B-ring hydroxylating enzymes-allows pelargonidin formation in the presence of DFRs with poor DHK acceptance.

KEYWORDS:

A1 type 2 allele; Petunia × hybrida; Zea mays; anthocyanin; dihydroflavonol 4-reductase; orange flower color; pelargonidin; transgenic plant

11.
ISME J. 2018 Jun;12(7):1729-1742. doi: 10.1038/s41396-018-0077-1. Epub 2018 Feb 23.

Peatland Acidobacteria with a dissimilatory sulfur metabolism.

Author information

1
Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, Research Network Chemistry meets Microbiology, University of Vienna, Vienna, Austria.
2
Department of Biology, University of Konstanz, Konstanz, Germany.
3
Department of Chemistry and Bioscience, Center for Microbial Communities, Aalborg University, Aalborg, Denmark.
4
US Department of Energy Joint Genome Institute, Walnut Creek, CA, USA.
5
Division of Computational Systems Biology, Department of Microbiology and Ecosystem Science, Research Network Chemistry meets Microbiology, University of Vienna, Vienna, Austria.
6
Department for Microbiology and Cell Science, Fort Lauderdale Research and Education Center, UF/IFAS, University of Florida, Davie, FL, USA.
7
Department of Biology, University of Konstanz, Konstanz, Germany. michael.pester@dsmz.de.
8
Leibniz Institute DSMZ, Braunschweig, Germany. michael.pester@dsmz.de.

Abstract

Sulfur-cycling microorganisms impact organic matter decomposition in wetlands and consequently greenhouse gas emissions from these globally relevant environments. However, their identities and physiological properties are largely unknown. By applying a functional metagenomics approach to an acidic peatland, we recovered draft genomes of seven novel Acidobacteria species with the potential for dissimilatory sulfite (dsrAB, dsrC, dsrD, dsrN, dsrT, dsrMKJOP) or sulfate respiration (sat, aprBA, qmoABC plus dsr genes). Surprisingly, the genomes also encoded DsrL, which so far was only found in sulfur-oxidizing microorganisms. Metatranscriptome analysis demonstrated expression of acidobacterial sulfur-metabolism genes in native peat soil and their upregulation in diverse anoxic microcosms. This indicated an active sulfate respiration pathway, which, however, might also operate in reverse for dissimilatory sulfur oxidation or disproportionation as proposed for the sulfur-oxidizing Desulfurivibrio alkaliphilus. Acidobacteria that only harbored genes for sulfite reduction additionally encoded enzymes that liberate sulfite from organosulfonates, which suggested organic sulfur compounds as complementary energy sources. Further metabolic potentials included polysaccharide hydrolysis and sugar utilization, aerobic respiration, several fermentative capabilities, and hydrogen oxidation. Our findings extend both, the known physiological and genetic properties of Acidobacteria and the known taxonomic diversity of microorganisms with a DsrAB-based sulfur metabolism, and highlight new fundamental niches for facultative anaerobic Acidobacteria in wetlands based on exploitation of inorganic and organic sulfur molecules for energy conservation.

13.
Environ Microbiol. 2018 Mar;20(3):1041-1063. doi: 10.1111/1462-2920.14043. Epub 2018 Mar 12.

Genomic insights into the Acidobacteria reveal strategies for their success in terrestrial environments.

Author information

1
Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, Research Network "Chemistry Meets Biology", University of Vienna, Vienna, Austria.
2
Department of Energy, Joint Genome Institute, Walnut Creek, CA, USA.
3
Division of Computational Systems Biology, Department of Microbiology and Ecosystem Science, Research Network "Chemistry Meets Biology", University of Vienna, Vienna, Austria.

Abstract

Members of the phylum Acidobacteria are abundant and ubiquitous across soils. We performed a large-scale comparative genome analysis spanning subdivisions 1, 3, 4, 6, 8 and 23 (n = 24) with the goal to identify features to help explain their prevalence in soils and understand their ecophysiology. Our analysis revealed that bacteriophage integration events along with transposable and mobile elements influenced the structure and plasticity of these genomes. Low- and high-affinity respiratory oxygen reductases were detected in multiple genomes, suggesting the capacity for growing across different oxygen gradients. Among many genomes, the capacity to use a diverse collection of carbohydrates, as well as inorganic and organic nitrogen sources (such as via extracellular peptidases), was detected - both advantageous traits in environments with fluctuating nutrient environments. We also identified multiple soil acidobacteria with the potential to scavenge atmospheric concentrations of H2 , now encompassing mesophilic soil strains within the subdivision 1 and 3, in addition to a previously identified thermophilic strain in subdivision 4. This large-scale acidobacteria genome analysis reveal traits that provide genomic, physiological and metabolic versatility, presumably allowing flexibility and versatility in the challenging and fluctuating soil environment.

14.
Front Cell Infect Microbiol. 2017 Dec 5;7:499. doi: 10.3389/fcimb.2017.00499. eCollection 2017.

Growth of Chlamydia pneumoniae Is Enhanced in Cells with Impaired Mitochondrial Function.

Author information

1
Department of Infectious Diseases and Microbiology, University of Lübeck, Lübeck, Germany.
2
Research Unit Analytical BioGeoChemistry, Helmholtz Center Munich, Neuherberg, Germany.
3
Division of Computational Systems Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.
4
Genomics of Gene Expression Lab, Centro de Investigaciones Príncipe Felipe, Valencia, Spain.
5
Microbiology and Cell Science, IFAS, University of Florida, Gainesville, FL, United States.

Abstract

Effective growth and replication of obligate intracellular pathogens depend on host cell metabolism. How this is connected to host cell mitochondrial function has not been studied so far. Recent studies suggest that growth of intracellular bacteria such as Chlamydia pneumoniae is enhanced in a low oxygen environment, arguing for a particular mechanistic role of the mitochondrial respiration in controlling intracellular progeny. Metabolic changes in C. pneumoniae infected epithelial cells were analyzed under normoxic (O2 ≈ 20%) and hypoxic conditions (O2 < 3%). We observed that infection of epithelial cells with C. pneumoniae under normoxia impaired mitochondrial function characterized by an enhanced mitochondrial membrane potential and ROS generation. Knockdown and mutation of the host cell ATP synthase resulted in an increased chlamydial replication already under normoxic conditions. As expected, mitochondrial hyperpolarization was observed in non-infected control cells cultured under hypoxic conditions, which was beneficial for C. pneumoniae growth. Taken together, functional and genetically encoded mitochondrial dysfunction strongly promotes intracellular growth of C. pneumoniae.

KEYWORDS:

Chlamydia pneumoniae; host-pathogen interaction; hypoxia; metabolism; mitochondria

PMID:
29259924
PMCID:
PMC5723314
DOI:
10.3389/fcimb.2017.00499
[Indexed for MEDLINE]
Free PMC Article
Icon for PubMed Central
15.
Pathog Dis. 2017 Dec 29;75(9). doi: 10.1093/femspd/ftx120.

Genome sequencing of Chlamydia trachomatis serovars E and F reveals substantial genetic variation.

Author information

1
Ludwig Boltzmann Institute for Cancer Research, Währinger Straße 13A, 1090 Vienna, Austria.
2
CUBE Division of Computational Systems Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Althanstraße 14, 1090 Vienna, Austria.
3
Institute of Functional Microbial Genomics, Heinrich-Heine-University of Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany.
4
Biological-Medical Research Center, Heinrich-Heine-University of Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany.

Abstract

Chlamydia trachomatis (Ctr) is a bacterial pathogen that causes ocular, urogenital and lymph system infections in humans. It is highly abundant and among its serovars, E, F and D are most prevalent in sexually transmitted disease. However, the number of publicly available genome sequences of the serovars E and F, and thereby our knowledge about the molecular architecture of these serovars, is low. Here we sequenced the genomes of six E and F clinical isolates and one E lab strain, in order to study the genetic variance in these serovars. As observed before, the genomic variation inside the Ctr genomes is very low and the phylogenetic placement in comparison to publicly available genomes is as expected by ompA gene serotyping. However, we observed a large InDel carrying four to five open reading frames in one clinical E sample and in the E lab strain. We have also observed substantial variation on nucleotide and amino acid levels, especially in membrane proteins and secreted proteins. Furthermore, these two groups of proteins are also target for recombination events. One clinical F isolate was genetically heterogeneous and revealed the highest differences on nucleotide level in the pmpE gene.

KEYWORDS:

Chlamydia; adhesins; comparative genomics; evolution; genome

PMID:
29186396
PMCID:
PMC5827700
DOI:
10.1093/femspd/ftx120
[Indexed for MEDLINE]
Free PMC Article
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16.
PeerJ. 2017 Nov 17;5:e4054. doi: 10.7717/peerj.4054. eCollection 2017.

Coral-associated viral communities show high levels of diversity and host auxiliary functions.

Author information

1
Australian Institute of Marine Science, Townsville, Queensland, Australia.
2
School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia.
3
Department of Microbiology and Ecosystem Science, Division of Computational Systems Biology, University of Vienna, Vienna, Austria.
4
Australian Centre for Ecogenomics, University of Queensland, Brisbane, Queensland, Australia.
5
School of Biosciences, University of Melbourne, Melbourne, Victoria, Australia.

Abstract

Stony corals (Scleractinia) are marine invertebrates that form the foundation and framework upon which tropical reefs are built. The coral animal associates with a diverse microbiome comprised of dinoflagellate algae and other protists, bacteria, archaea, fungi and viruses. Using a metagenomics approach, we analysed the DNA and RNA viral assemblages of seven coral species from the central Great Barrier Reef (GBR), demonstrating that tailed bacteriophages of the Caudovirales dominate across all species examined, and ssDNA viruses, notably the Microviridae, are also prevalent. Most sequences with matches to eukaryotic viruses were assigned to six viral families, including four Nucleocytoplasmic Large DNA Viruses (NCLDVs) families: Iridoviridae, Phycodnaviridae, Mimiviridae, and Poxviridae, as well as Retroviridae and Polydnaviridae. Contrary to previous findings, Herpesvirales were rare in these GBR corals. Sequences of a ssRNA virus with similarities to the dinornavirus, Heterocapsa circularisquama ssRNA virus of the Alvernaviridae that infects free-living dinoflagellates, were observed in three coral species. We also detected viruses previously undescribed from the coral holobiont, including a virus that targets fungi associated with the coral species Acropora tenuis. Functional analysis of the assembled contigs indicated a high prevalence of latency-associated genes in the coral-associated viral assemblages, several host-derived auxiliary metabolic genes (AMGs) for photosynthesis (psbA, psbD genes encoding the photosystem II D1 and D2 proteins respectively), as well as potential nematocyst toxins and antioxidants (genes encoding green fluorescent-like chromoprotein). This study expands the currently limited knowledge on coral-associated viruses by characterising viral composition and function across seven GBR coral species.

KEYWORDS:

Coral; GBR; Holobiont; Metagenomics; Symbiodinium; Virus

Conflict of interest statement

Thomas Rattei is an Academic Editor for PeerJ.

17.
Virulence. 2017 Nov 17;8(8):1808-1819. doi: 10.1080/21505594.2017.1391446. Epub 2017 Nov 27.

Peripheral blood vessels are a niche for blood-borne meningococci.

Author information

1
a Institut Necker Enfants-Malades, INSERM U1151, Equipe 11 , Paris , France.
2
b Université Paris Descartes; Sorbonne Paris Cité, Faculté de Médecine , Paris , France.
3
c Assistance Publique - Hôpitaux de Paris, Hôpital Necker Enfants Malades , Paris , France.
4
d Department of Infectious Diseases and Immunology , Faculty of Veterinary Medicine, Utrecht University , Utrecht , The Netherlands.
5
e Plateforme génomique de l'Institut Imagine, INSERM UMR 1163, Paris Descartes Sorbonne Université Paris Cité , Paris , France.
6
f CUBE - Division of Computational Systems Biology, Dept. of Microbiology and Ecosystem Science , University of Vienna , Vienna , Austria.
7
g Service de Chirurgie Plastique Reconstructrice et Esthétique, Groupe Hospitalier Paris Saint Joseph , Paris , France.
8
h INSERM U1016, Institut Cochin , Paris , France.
9
i CNRS UMR8104 , Paris , France.

Abstract

Neisseria meningitidis is the causative agent of cerebrospinal meningitis and that of a rapidly progressing fatal septic shock known as purpura fulminans. Meningococcemia is characterized by bacterial adhesion to human endothelial cells of the microvessels. Host specificity has hampered studies on the role of blood vessels colonization in N. meningitidis associated pathogenesis. In this work, using a humanized model of SCID mice allowing the study of bacterial adhesion to human cells in an in vivo context we demonstrate that meningococcal colonization of human blood vessels is a prerequisite to the establishment of sepsis and lethality. To identify the molecular pathways involved in bacterial virulence, we performed transposon insertion site sequencing (Tn-seq) in vivo. Our results demonstrate that 36% of the genes that are important for growth in the blood of mice are dispensable when bacteria colonize human blood vessels, suggesting that human endothelial cells lining the blood vessels are feeding niches for N. meningitidis in vivo. Altogether, our work proposes a new paradigm for meningococcal virulence in which colonization of blood vessels is associated with metabolic adaptation and sustained bacteremia responsible for sepsis and subsequent lethality.

KEYWORDS:

Neisseria meningitidis; Tn-seq; host cell interaction; nutritional virulence; purpura fulminans

PMID:
29099305
PMCID:
PMC5810509
DOI:
10.1080/21505594.2017.1391446
[Indexed for MEDLINE]
Free PMC Article
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18.
Nat Methods. 2017 Nov;14(11):1063-1071. doi: 10.1038/nmeth.4458. Epub 2017 Oct 2.

Critical Assessment of Metagenome Interpretation-a benchmark of metagenomics software.

Author information

1
Faculty of Technology, Bielefeld University, Bielefeld, Germany.
2
Center for Biotechnology, Bielefeld University, Bielefeld, Germany.
3
Formerly Department of Algorithmic Bioinformatics, Heinrich Heine University (HHU), Duesseldorf, Germany.
4
Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany.
5
Braunschweig Integrated Centre of Systems Biology (BRICS), Braunschweig, Germany.
6
Mathematics Department, Oregon State University, Corvallis, Oregon, USA.
7
Department of Pediatrics, University of California, San Diego, California, USA.
8
Department of Computer Science and Engineering, University of California, San Diego, California, USA.
9
German Center for Infection Research (DZIF), partner site Hannover-Braunschweig, Braunschweig, Germany.
10
Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany.
11
Cluster of Excellence on Plant Sciences (CEPLAS).
12
Department of Environmental Science, Section of Environmental microbiology and Biotechnology, Aarhus University, Roskilde, Denmark.
13
Department of Microbiology, University of Copenhagen, Copenhagen, Denmark.
14
Department of Science and Environment, Roskilde University, Roskilde, Denmark.
15
Department of Energy, Joint Genome Institute, Walnut Creek, California, USA.
16
Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
17
Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, Russia.
18
Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.
19
The ithree institute, University of Technology Sydney, Sydney, New South Wales, Australia.
20
Department of Computer Science, Research Center in Computer Science (CRIStAL), Signal and Automatic Control of Lille, Lille, France.
21
National Centre of the Scientific Research (CNRS), Rennes, France.
22
Department of Computational and Systems Biology, Genome Institute of Singapore, Singapore.
23
Department of Microbiology and Infection, Warwick Medical School, University of Warwick, Coventry, UK.
24
Department of Computer Science, University of Tuebingen, Tuebingen, Germany.
25
Intel Corporation, Hillsboro, Oregon, USA.
26
GenScale-Bioinformatics Research Team, Inria Rennes-Bretagne Atlantique Research Centre, Rennes, France.
27
Institute of Research in Informatics and Random Systems (IRISA), Rennes, France.
28
Department of Molecular Infection Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany.
29
Algorizk-IT consulting and software systems, Paris, France.
30
Joint BioEnergy Institute, Emeryville, California, USA.
31
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
32
Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
33
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
34
Energy Engineering and Geomicrobiology, University of Calgary, Calgary, Alberta, Canada.
35
Department of Bioinformatics, Institute for Microbiology and Genetics, University of Goettingen, Goettingen, Germany.
36
Genevention GmbH, Goettingen, Germany.
37
Institute of Population Health Sciences, National Health Research Institutes, Zhunan Town, Taiwan.
38
Computational Science Research Center, San Diego State University, San Diego, California, USA.
39
Boyce Thompson Institute for Plant Research, New York, New York, USA.
40
Research Group Bioinformatics (NG4), Robert Koch Institute, Berlin, Germany.
41
Coordination for the Improvement of Higher Education Personnel (CAPES) Foundation, Ministry of Education of Brazil, Brasília, Brazil.
42
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
43
Department of Computer Science, University of Maryland, College Park, Maryland, USA.
44
School of Biology, Newcastle University, Newcastle upon Tyne, UK.
45
Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
46
Institute of Microbiology, ETH Zurich, Zurich, Switzerland.

Abstract

Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

PMID:
28967888
PMCID:
PMC5903868
DOI:
10.1038/nmeth.4458
[Indexed for MEDLINE]
Free PMC Article
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19.
Sci Rep. 2017 Sep 8;7(1):11047. doi: 10.1038/s41598-017-10369-z.

Sulfonolipids as novel metabolite markers of Alistipes and Odoribacter affected by high-fat diets.

Author information

1
Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. alesia.walker@helmholtz-muenchen.de.
2
Research Unit Microbe-Plant Interactions, Research Group Molecular Microbial Ecology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
3
Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
4
Chair of Analytical Food Chemistry, Technische Universität München, Freising-Weihenstephan, Germany.
5
Chair of Nutrition and Immunology, Technische Universität München, Freising-Weihenstephan, Germany.
6
Division of Computational System Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.
7
Scientific Computing Research Unit, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
8
ZIEL - Institute for Food & Health, Technische Universität München, Freising-Weihenstephan, Germany.
9
Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. schmitt-kopplin@helmholtz-muenchen.de.
10
Chair of Analytical Food Chemistry, Technische Universität München, Freising-Weihenstephan, Germany. schmitt-kopplin@helmholtz-muenchen.de.
11
ZIEL - Institute for Food & Health, Technische Universität München, Freising-Weihenstephan, Germany. schmitt-kopplin@helmholtz-muenchen.de.

Abstract

The gut microbiota generates a huge pool of unknown metabolites, and their identification and characterization is a key challenge in metabolomics. However, there are still gaps on the studies of gut microbiota and their chemical structures. In this investigation, an unusual class of bacterial sulfonolipids (SLs) is detected in mouse cecum, which was originally found in environmental microbes. We have performed a detailed molecular level characterization of this class of lipids by combining high-resolution mass spectrometry and liquid chromatography analysis. Eighteen SLs that differ in their capnoid and fatty acid chain compositions were identified. The SL called "sulfobacin B" was isolated, characterized, and was significantly increased in mice fed with high-fat diets. To reveal bacterial producers of SLs, metagenome analysis was acquired and only two bacterial genera, i.e., Alistipes and Odoribacter, were revealed to be responsible for their production. This knowledge enables explaining a part of the molecular complexity introduced by microbes to the mammalian gastrointestinal tract and can be used as chemotaxonomic evidence in gut microbiota.

20.
Nat Biotechnol. 2017 Aug 8;35(8):725-731. doi: 10.1038/nbt.3893.

Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea.

Author information

1
Department of Energy Joint Genome Institute, Walnut Creek, California, USA.
2
Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, USA.
3
United States Department of Agriculture, Agricultural Research Service, Genomics and Bioinformatics Research Unit, Gainesville, Florida, USA.
4
School of Natural Sciences, University of California Merced, Merced, California, USA.
5
Broad Institute, Cambridge, Massachusetts, USA.
6
Biosciences Division, Oak Ridge National Laboratory, Oakridge Tennessee, USA.
7
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
8
The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA.
9
Department of Microbiology &Molecular Genetics, Biomedical Physical Sciences, Michigan State University, East Lansing, Michigan, USA.
10
Department of Biology, California State University, San Bernardino, California, USA.
11
J. Craig Venter Institute, San Diego, California, USA.
12
J. Craig Venter Institute, Rockville, Maryland, USA.
13
Microbial Genomics and Bioinformatics Research Group, Max Planck Institute for Marine Microbiology, Bremen, Germany.
14
Biosciences Division, Argonne National Laboratory, Argonne, Illinois, USA.
15
Department of Surgery, University of Chicago, Chicago, Illinois, USA.
16
Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.
17
Department of Microbiology &Immunology, University of British Columbia, Vancouver, British Columbia, Canada.
18
Center for Dark Energy Biosphere Investigation, University of Southern California, Los Angeles, California, USA.
19
Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
20
Advanced Genomics Lab, University of Vermont Cancer Center, Burlington Vermont, USA.
21
Georgia Institute of Technology, School of Civil and Environmental Engineering, Atlanta, Georgia, USA.
22
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
23
Department of Marine Science, University of Texas-Austin, Marine Science Institute, Austin, Texas, USA.
24
Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.
25
Genome Center, University of California, Davis, California, USA.
26
School of Life Sciences, University of Nevada Las Vegas, Las Vegas, Nevada, USA.
27
Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, Nevada, USA.
28
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA.
29
Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
30
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA.
31
Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA.
32
Center for Microbiome Innovation, and Departments of Pediatrics and Computer Science &Engineering, University of California San Diego, La Jolla, California, USA.
33
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Trust Genome Campus, Hinxton, Cambridge, UK.
34
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
35
Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia.
36
Centre for Algorithmic Biotechnology, ITBM, St. Petersburg State University, St. Petersburg, Russia.
37
Knapp Center for Biomedical Discovery, Chicago, Illinois, USA.
38
National Cancer Institute, Frederick, Maryland, USA.
39
Department of Earth and Planetary Science, University of California, Berkeley, California, USA.

Abstract

We present two standards developed by the Genomic Standards Consortium (GSC) for reporting bacterial and archaeal genome sequences. Both are extensions of the Minimum Information about Any (x) Sequence (MIxS). The standards are the Minimum Information about a Single Amplified Genome (MISAG) and the Minimum Information about a Metagenome-Assembled Genome (MIMAG), including, but not limited to, assembly quality, and estimates of genome completeness and contamination. These standards can be used in combination with other GSC checklists, including the Minimum Information about a Genome Sequence (MIGS), Minimum Information about a Metagenomic Sequence (MIMS), and Minimum Information about a Marker Gene Sequence (MIMARKS). Community-wide adoption of MISAG and MIMAG will facilitate more robust comparative genomic analyses of bacterial and archaeal diversity.

PMID:
28787424
DOI:
10.1038/nbt.3893
[Indexed for MEDLINE]
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