Methylorubrum extorquens ID 585-99 is a mesophilic prokaryote that was isolated from pine tissue cultures from meristems of trees.
mesophilic genome sequence 16S sequence| @ref 20215 |
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| Domain Bacteria |
| Phylum Pseudomonadota |
| Class Alphaproteobacteria |
| Order Hyphomicrobiales |
| Family Methylobacteriaceae |
| Genus Methylorubrum |
| Species Methylorubrum extorquens |
| Full scientific name Methylorubrum extorquens (Urakami and Komagata 1984 ex Bassalik 1913) Green and Ardley 2018 |
| Synonyms (2) |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 4891 | CASO AGAR (MERCK 105458) (DSMZ Medium 220) | Medium recipe at MediaDive | Name: CASO AGAR (Merck 105458) (DSMZ Medium 220; with strain-specific modifications) Composition: Agar 15.0 g/l Casein peptone 15.0 g/l Methanol 10.0 g/l NaCl 5.0 g/l Soy peptone 5.0 g/l Distilled water | ||
| 4891 | NUTRIENT AGAR (DSMZ Medium 1) | Medium recipe at MediaDive | Name: NUTRIENT AGAR (DSMZ Medium 1) Composition: Agar 15.0 g/l Peptone 5.0 g/l Meat extract 3.0 g/l Distilled water |
| @ref | Growth | Type | Temperature (°C) | Range | |
|---|---|---|---|---|---|
| 4891 | positive | growth | 25 | mesophilic |
| @ref | Sample type | Host species | Geographic location | Country | Country ISO 3 Code | Continent | |
|---|---|---|---|---|---|---|---|
| 4891 | pine (Pinus sylvestris) tissue cultures from meristems of trees | Pinus sylvestris | northern part | Finland | FIN | Europe |
Global distribution of 16S sequence AF267912 (>99% sequence identity) for Methylorubrum from Microbeatlas ![]()
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | IMG accession | NCBI tax ID | |
|---|---|---|---|---|---|---|---|
| 66792 | ASM24343v2 assembly for Methylorubrum extorquens DSM 13060 | contig | 882800 |
| @ref | Description | Accession | Length | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 4891 | Methylobacterium extorquens 16S ribosomal RNA gene, partial sequence | AF267912 | 1448 | 882800 |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | no | 96.40 | no |
| 125439 | motility | BacteriaNetⓘ | yes | 64.10 | no |
| 125439 | gram_stain | BacteriaNetⓘ | negative | 96.60 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | aerobe | 91.40 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | no | 98.32 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 92.80 | no |
| 125438 | aerobic | aerobicⓘ | yes | 84.57 | no |
| 125438 | spore-forming | spore-formingⓘ | no | 86.80 | no |
| 125438 | thermophilic | thermophileⓘ | no | 96.94 | no |
| 125438 | flagellated | motile2+ⓘ | yes | 76.53 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| The Role of Microorganisms and Carbon-to-Nitrogen Ratios for Microbial Protein Production from Bioethanol. | Van Peteghem L, Sakarika M, Matassa S, Rabaey K. | Appl Environ Microbiol | 10.1128/aem.01188-22 | 2022 | ||
| Metabolism | Monitoring the plant epiphyte Methylobacterium extorquens DSM 21961 by real-time PCR and its influence on the strawberry flavor. | Verginer M, Siegmund B, Cardinale M, Muller H, Choi Y, Miguez CB, Leitner E, Berg G. | FEMS Microbiol Ecol | 10.1111/j.1574-6941.2010.00942.x | 2010 | |
| Enzymology | Cultivation-independent characterization of methylobacterium populations in the plant phyllosphere by automated ribosomal intergenic spacer analysis. | Knief C, Frances L, Cantet F, Vorholt JA. | Appl Environ Microbiol | 10.1128/aem.02532-07 | 2008 | |
| Phylogeny | Laboratory divergence of Methylobacterium extorquens AM1 through unintended domestication and past selection for antibiotic resistance. | Carroll SM, Xue KS, Marx CJ. | BMC Microbiol | 10.1186/1471-2180-14-2 | 2014 | |
| Phylogeny | Associations between Ectomycorrhizal Fungi and Bacterial Needle Endophytes in Pinus radiata: Implications for Biotic Selection of Microbial Communities. | Rua MA, Wilson EC, Steele S, Munters AR, Hoeksema JD, Frank AC. | Front Microbiol | 10.3389/fmicb.2016.00399 | 2016 | |
| Metabolism | Evolution of mitochondria reconstructed from the energy metabolism of living bacteria. | Degli Esposti M, Chouaia B, Comandatore F, Crotti E, Sassera D, Lievens PM, Daffonchio D, Bandi C. | PLoS One | 10.1371/journal.pone.0096566 | 2014 | |
| Stress | The meristem-associated endosymbiont Methylorubrum extorquens DSM13060 reprograms development and stress responses of pine seedlings. | Koskimaki JJ, Pohjanen J, Kvist J, Fester T, Hartig C, Podolich O, Fluch S, Edesi J, Haggman H, Pirttila AM | Tree Physiol | 10.1093/treephys/tpab102 | 2022 | |
| Genetics | The intracellular Scots pine shoot symbiont Methylobacterium extorquens DSM13060 aggregates around the host nucleus and encodes eukaryote-like proteins. | Koskimaki JJ, Pirttila AM, Ihantola EL, Halonen O, Frank AC | mBio | 10.1128/mBio.00039-15 | 2015 | |
| Metabolism | Interaction with ectomycorrhizal fungi and endophytic Methylobacterium affects nutrient uptake and growth of pine seedlings in vitro. | Pohjanen J, Koskimaki JJ, Sutela S, Ardanov P, Suorsa M, Niemi K, Sarjala T, Haggman H, Pirttila AM | Tree Physiol | 10.1093/treephys/tpu062 | 2014 |
| #4891 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 13060 |
| #20215 | Parte, A.C., Sardà Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Göker, M.: List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. IJSEM ( DOI 10.1099/ijsem.0.004332 ) |
| #66792 | Julia Koblitz, Joaquim Sardà, Lorenz Christian Reimer, Boyke Bunk, Jörg Overmann: Automatically annotated for the DiASPora project (Digital Approaches for the Synthesis of Poorly Accessible Biodiversity Information) . |
| #69479 | João F Matias Rodrigues, Janko Tackmann,Gregor Rot, Thomas SB Schmidt, Lukas Malfertheiner, Mihai Danaila,Marija Dmitrijeva, Daniela Gaio, Nicolas Näpflin and Christian von Mering. University of Zurich.: MicrobeAtlas 1.0 beta . |
| #125438 | Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg Overmann: Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets. 2024 ( DOI 10.1101/2024.08.12.607695 ) |
| #125439 | Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardy: deepG: Deep Learning for Genome Sequence Data. R package version 0.3.1 . |
| #126262 | A. Lissin, I. Schober, J. F. Witte, H. Lüken, A. Podstawka, J. Koblitz, B. Bunk, P. Dawyndt, P. Vandamme, P. de Vos, J. Overmann, L. C. Reimer: StrainInfo—the central database for linked microbial strain identifiers. ( DOI 10.1093/database/baaf059 ) |
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