Pseudomonas mendocina S5.2 is a mesophilic prokaryote that was isolated from vineyard soil.
mesophilic genome sequence| @ref 20215 |
|
|
| Domain Bacteria |
| Phylum Pseudomonadota |
| Class Gammaproteobacteria |
| Order Pseudomonadales |
| Family Pseudomonadaceae |
| Genus Pseudomonas |
| Species Pseudomonas mendocina |
| Full scientific name Pseudomonas mendocina Palleroni 1970 (Approved Lists 1980) |
| Synonyms (1) |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 23668 | 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 | |
|---|---|---|---|---|---|
| 23668 | positive | growth | 28 | mesophilic |
| Cat1 | Cat2 | Cat3 | |
|---|---|---|---|
| #Engineered | #Agriculture | #Vineyard | |
| #Environmental | #Terrestrial | #Soil |
| @ref | Sample type | Geographic location | Country | Country ISO 3 Code | Continent | |
|---|---|---|---|---|---|---|
| 23668 | vineyard soil | Riquewihr | France | FRA | Europe |
| @ref | Biosafety level | Biosafety level comment | |
|---|---|---|---|
| 23668 | 2 | Risk group (German classification) |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | oxygen_tolerance | BacteriaNetⓘ | aerobe | 93.10 | no |
| 125439 | gram_stain | BacteriaNetⓘ | negative | 99.90 | no |
| 125439 | motility | BacteriaNetⓘ | yes | 93.40 | no |
| 125439 | spore_formation | BacteriaNetⓘ | no | 99.30 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | no | 99.50 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 98.34 | no |
| 125438 | aerobic | aerobicⓘ | yes | 91.34 | no |
| 125438 | spore-forming | spore-formingⓘ | no | 87.13 | no |
| 125438 | thermophilic | thermophileⓘ | no | 99.49 | yes |
| 125438 | flagellated | motile2+ⓘ | yes | 91.90 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Phenotypic and genomic survey on organic acid utilization profile of Pseudomonas mendocina strain S5.2, a vineyard soil isolate. | Chong TM, Chen JW, See-Too WS, Yu CY, Ang GY, Lim YL, Yin WF, Grandclement C, Faure D, Dessaux Y, Chan KG | AMB Express | 10.1186/s13568-017-0437-7 | 2017 | ||
| Genetics | Heavy-metal resistance of a France vineyard soil bacterium, Pseudomonas mendocina strain S5.2, revealed by whole-genome sequencing. | Chong TM, Yin WF, Mondy S, Grandclement C, Dessaux Y, Chan KG | J Bacteriol | 10.1128/JB.01702-12 | 2012 | |
| Phylogeny | Arsenic Resistance Mechanisms in Pseudomonas mendocina SMSKVR-3 Strain Isolated from Khetri Copper Mines, Rajasthan, India. | Mishra S, Kumar S, Verma SK | Curr Microbiol | 10.1007/s00284-021-02749-6 | 2022 |
| #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 ) |
| #23668 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 102033 |
| #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) . |
| #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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BacDive in 2025: the core database for prokaryotic strain data