Ethanoligenens harbinense YUAN-3 is an anaerobe, mesophilic, Gram-positive prokaryote that was isolated from anaerobic sludge of molasses wastewater in a CSTR.
Gram-positive motile rod-shaped anaerobe mesophilic genome sequence 16S sequence| @ref 20215 |
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| Domain Bacteria |
| Phylum Bacillota |
| Class Clostridia |
| Order Eubacteriales |
| Family Oscillospiraceae |
| Genus Ethanoligenens |
| Species Ethanoligenens harbinense |
| Full scientific name Ethanoligenens harbinense Xing et al. 2006 |
| BacDive ID | Other strains from Ethanoligenens harbinense (1) | Type strain |
|---|---|---|
| 161289 | E. harbinense JCM 12960, CGMCC 1151 |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 7556 | ETHANOLIGENES MEDIUM (DSMZ Medium 1057) | Medium recipe at MediaDive | Name: ETHANOLIGENES MEDIUM (DSMZ Medium 1057) Composition: D-Glucose 9.98004 g/l Tryptone 3.99202 g/l Beef extract 1.99601 g/l K2HPO4 1.49701 g/l Yeast extract 0.998004 g/l L-Cysteine HCl x H2O 0.499002 g/l MgCl2 x 6 H2O 0.199601 g/l FeSO4 x 7 H2O 0.0998004 g/l Nitrilotriacetic acid 0.0127745 g/l FeCl2 x 4 H2O 0.000998004 g/l NaCl 0.000998004 g/l Sodium resazurin 0.000499002 g/l MnCl2 x 4 H2O 9.98004e-05 g/l ZnCl2 9.98004e-05 g/l NiCl2 x 6 H2O 9.98004e-05 g/l CaCl2 x 2 H2O 9.98004e-05 g/l Pyridoxine hydrochloride 9.98004e-05 g/l p-Aminobenzoic acid 4.99002e-05 g/l Calcium D-(+)-pantothenate 4.99002e-05 g/l (DL)-alpha-Lipoic acid 4.99002e-05 g/l Nicotinic acid 4.99002e-05 g/l Riboflavin 4.99002e-05 g/l Thiamine HCl 4.99002e-05 g/l Na2WO4 x 2 H2O 3.99202e-05 g/l CoCl2 x 6 H2O 2.99401e-05 g/l Na2MoO4 x 2 H2O 2.99401e-05 g/l Na2SeO3 x 5 H2O 2.99401e-05 g/l Folic acid 1.99601e-05 g/l Biotin 1.99601e-05 g/l CuCl2 1.99601e-05 g/l H3BO3 9.98004e-06 g/l Vitamin B12 9.98004e-07 g/l Distilled water |
| 31627 | Observationaggregates in chains |
| @ref | Chebi-ID | Metabolite | Utilization activity | Kind of utilization tested | |
|---|---|---|---|---|---|
| 31627 | 17057 ChEBI | cellobiose | + | carbon source | |
| 31627 | 28757 ChEBI | fructose | + | carbon source | |
| 31627 | 28260 ChEBI | galactose | + | carbon source | |
| 31627 | 17234 ChEBI | glucose | + | carbon source | |
| 31627 | 17716 ChEBI | lactose | + | carbon source | |
| 31627 | 17306 ChEBI | maltose | + | carbon source | |
| 31627 | 37684 ChEBI | mannose | + | carbon source | |
| 31627 | 28053 ChEBI | melibiose | + | carbon source | |
| 31627 | 33942 ChEBI | ribose | + | carbon source | |
| 31627 | 17992 ChEBI | sucrose | + | carbon source |
| Cat1 | Cat2 | Cat3 | |
|---|---|---|---|
| #Engineered | #Industrial | - | |
| #Engineered | #Waste | #Industrial wastewater | |
| #Environmental | #Terrestrial | #Mud (Sludge) | |
| #Condition | #Anoxic (anaerobic) | - |
Global distribution of 16S sequence AY295777 (>99% sequence identity) for Ethanoligenens harbinense subclade from Microbeatlas ![]()
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | IMG accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|---|
| 66792 | ASM302004v1 assembly for Ethanoligenens harbinense YUAN-3 | complete | 663278 | 99.18 | ||||
| 66792 | ASM17811v2 assembly for Ethanoligenens harbinense YUAN-3 | complete | 663278 | 98.84 |
| @ref | Description | Accession | Length | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 7556 | Ethanoligenens harbinense strain YUAN-3 16S ribosomal RNA gene, partial sequence | AY295777 | 1499 | 663278 |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | no | 96.00 | no |
| 125439 | motility | BacteriaNetⓘ | yes | 50.10 | no |
| 125439 | gram_stain | BacteriaNetⓘ | negative | 94.50 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | obligate anaerobe | 94.80 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | yes | 70.19 | yes |
| 125438 | anaerobic | anaerobicⓘ | yes | 94.41 | yes |
| 125438 | aerobic | aerobicⓘ | no | 96.94 | no |
| 125438 | spore-forming | spore-formingⓘ | no | 51.64 | yes |
| 125438 | thermophilic | thermophileⓘ | no | 75.79 | no |
| 125438 | flagellated | motile2+ⓘ | yes | 66.76 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Functional Comparison of Bacteria from the Human Gut and Closely Related Non-Gut Bacteria Reveals the Importance of Conjugation and a Paucity of Motility and Chemotaxis Functions in the Gut Environment. | Dobrijevic D, Abraham AL, Jamet A, Maguin E, van de Guchte M. | PLoS One | 10.1371/journal.pone.0159030 | 2016 | ||
| Metabolism | Linking genome content to biofuel production yields: a meta-analysis of major catabolic pathways among select H2 and ethanol-producing bacteria. | Carere CR, Rydzak T, Verbeke TJ, Cicek N, Levin DB, Sparling R. | BMC Microbiol | 10.1186/1471-2180-12-295 | 2012 | |
| Metabolism | Quantitative proteomic analysis reveals the ethanologenic metabolism regulation of Ethanoligenens harbinense by exogenous ethanol addition. | Li H, Mei X, Liu B, Xie G, Ren N, Xing D | Biotechnol Biofuels | 10.1186/s13068-019-1511-y | 2019 | |
| Metabolism | Insights on acetate-ethanol fermentation by hydrogen-producing Ethanoligenens under acetic acid accumulation based on quantitative proteomics. | Li H, Mei X, Liu B, Li Z, Wang B, Ren N, Xing D | Environ Int | 10.1016/j.envint.2019.05.013 | 2019 | |
| Metabolism | Composition of extracellular polymeric substances influences the autoaggregation capability of hydrogen-producing bacterium Ethanoligenens harbinense. | Ren N, Xie T, Xing D | Bioresour Technol | 10.1016/j.biortech.2009.05.021 | 2009 | |
| Phylogeny | Caproiciproducens galactitolivorans gen. nov., sp. nov., a bacterium capable of producing caproic acid from galactitol, isolated from a wastewater treatment plant. | Kim BC, Seung Jeon B, Kim S, Kim H, Um Y, Sang BI | Int J Syst Evol Microbiol | 10.1099/ijsem.0.000665 | 2015 | |
| Phylogeny | Ethanoligenens harbinense gen. nov., sp. nov., isolated from molasses wastewater. | Xing D, Ren N, Li Q, Lin M, Wang A, Zhao L | Int J Syst Evol Microbiol | 10.1099/ijs.0.63926-0 | 2006 |
| #7556 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 18485 |
| #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 ) |
| #27912 | IJSEM 755 2006 ( DOI 10.1099/ijs.0.63926-0 , PubMed 16585689 ) |
| #31627 | 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 #27912 |
| #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) . |
| #67770 | Japan Collection of Microorganism (JCM) ; Curators of the JCM; |
| #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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BacDive in 2025: the core database for prokaryotic strain data