Methylobacterium oxalidis 35a is an aerobe, mesophilic, Gram-negative prokaryote that was isolated from leaves of the sorrel Oxalis corniculata.
Gram-negative motile rod-shaped aerobe mesophilic genome sequence 16S sequence| @ref 20215 |
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|
| Domain Pseudomonadati |
| Phylum Pseudomonadota |
| Class Alphaproteobacteria |
| Order Hyphomicrobiales |
| Family Methylobacteriaceae |
| Genus Methylobacterium |
| Species Methylobacterium oxalidis |
| Full scientific name Methylobacterium oxalidis Tani et al. 2012 |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 19152 | R2A MEDIUM (DSMZ Medium 830) | Medium recipe at MediaDive | Name: R2A MEDIUM (DSMZ Medium 830) Composition: Agar 15.0 g/l Casamino acids 0.5 g/l Starch 0.5 g/l Glucose 0.5 g/l Proteose peptone 0.5 g/l Yeast extract 0.5 g/l K2HPO4 0.3 g/l Na-pyruvate 0.3 g/l MgSO4 x 7 H2O 0.05 g/l Distilled water |
| @ref | Salt | Growth | Tested relation | Concentration | |
|---|---|---|---|---|---|
| 30303 | NaCl | positive | growth | <2 % |
| @ref | Chebi-ID | Metabolite | Utilization activity | Kind of utilization tested | |
|---|---|---|---|---|---|
| 30303 | 30089 ChEBI | acetate | + | carbon source | |
| 30303 | 15740 ChEBI | formate | + | carbon source | |
| 30303 | 28757 ChEBI | fructose | + | carbon source | |
| 30303 | 29987 ChEBI | glutamate | + | carbon source | |
| 30303 | 24996 ChEBI | lactate | + | carbon source | |
| 30303 | 15792 ChEBI | malonate | + | carbon source | |
| 30303 | 51850 ChEBI | methyl pyruvate | + | carbon source | |
| 30303 | 17272 ChEBI | propionate | + | carbon source | |
| 30303 | 30031 ChEBI | succinate | + | carbon source |
| Cat1 | Cat2 | Cat3 | |
|---|---|---|---|
| #Host | #Plants | #Herbaceous plants (Grass,Crops) | |
| #Host Body-Site | #Plant | #Leaf (Phyllosphere) |
| @ref | Sample type | Host species | Country | Country ISO 3 Code | Continent | |
|---|---|---|---|---|---|---|
| 19152 | leaves of the sorrel Oxalis corniculata | Oxalis corniculata | Japan | JPN | Asia |
Global distribution of 16S sequence AB607860 (>99% sequence identity) for Methylobacterium oxalidis subclade from Microbeatlas ![]()
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | IMG accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|---|
| 124043 | ASM3016099v1 assembly for Methylobacterium oxalidis NBRC 107715 | scaffold | 944322 | 42.13 | ||||
| 66792 | ASM2217950v1 assembly for Methylobacterium oxalidis DSM 24028 | contig | 944322 | 14.83 | ||||
| 66792 | ASM799219v1 assembly for Methylobacterium oxalidis NBRC 107715 | contig | 944322 | 6.82 |
| @ref | Description | Accession | Length | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 19152 | Methylobacterium oxalidis gene for 16S ribosomal RNA, partial sequence | AB607860 | 1446 | 944322 |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | no | 94.50 | no |
| 125439 | motility | BacteriaNetⓘ | yes | 67.60 | no |
| 125439 | gram_stain | BacteriaNetⓘ | negative | 93.80 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | obligate aerobe | 93.70 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | no | 95.78 | yes |
| 125438 | anaerobic | anaerobicⓘ | no | 94.05 | yes |
| 125438 | aerobic | aerobicⓘ | yes | 84.66 | yes |
| 125438 | spore-forming | spore-formingⓘ | no | 85.05 | yes |
| 125438 | thermophilic | thermophileⓘ | no | 97.12 | yes |
| 125438 | flagellated | motile2+ⓘ | yes | 76.32 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Prerequisites for amplicon pyrosequencing of microbial methanol utilizers in the environment. | Kolb S, Stacheter A. | Front Microbiol | 10.3389/fmicb.2013.00268 | 2013 | ||
| A conserved signaling pathway activates bacterial CBASS immune signaling in response to DNA damage. | Lau RK, Enustun E, Gu Y, Nguyen JV, Corbett KD. | EMBO J | 10.15252/embj.2022111540 | 2022 | ||
| Phylogeny | Methylobacterium nigriterrae sp. nov., isolated from black soil. | Chen LB, OuYang YT, Liu L, Jin PJ, Huang RR, Pan WY, Wang Y, Xing JY, She TT, Jiao JY, Wang S, Li WJ. | Antonie Van Leeuwenhoek | 10.1007/s10482-024-01981-x | 2024 | |
| Phylogeny | Methylobacterium segetis sp. nov., a novel member of the family Methylobacteriaceae isolated from soil on Jeju Island. | Ten LN, Li W, Elderiny NS, Kim MK, Lee SY, Rooney AP, Jung HY | Arch Microbiol | 10.1007/s00203-019-01784-z | 2019 | |
| Phylogeny | Methylobacterium oxalidis sp. nov., isolated from leaves of Oxalis corniculata. | Tani A, Sahin N, Kimbara K | Int J Syst Evol Microbiol | 10.1099/ijs.0.033019-0 | 2011 |
| #19152 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 24028 |
| #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 ) |
| #30303 | Barberan A, Caceres Velazquez H, Jones S, Fierer N.: Hiding in Plain Sight: Mining Bacterial Species Records for Phenotypic Trait Information. mSphere 2: 2017 ( DOI 10.1128/mSphere.00237-17 , PubMed 28776041 ) - originally annotated from #26644 (see below) |
| #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 . |
| #124043 | Isabel Schober, Julia Koblitz: Data extracted from sequence databases, automatically matched based on designation and taxonomy . |
| #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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