Acidomonas methanolica (DSM 5432, ATCC 43581, IMET 10945)

Name: Acidomonas methanolica (DSM 5432, ATCC 43581, IMET 10945)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 33

Type strain

Strain Designation MB 58

Culture col. no. DSM 5432 ATCC 43581 IMET 10945 CCUG 37300 LMG 1668 9 more

NCBI tax ID(s) 1231351 437

Special Collections DSMZ CCUG JCM CIP

History <- IMET <- K. Karbaum, MB 58 T. Urakami TK 0705 <-- IMET 10945 <-- K. Karbaum MB58. CIP <- 1988, IMET <- Inst. Biotech. Akad. Wissenchaften, Leipzig, Germany: strain MB58, Acetobacter methanoliticus

Links

Acidomonas methanolica MB 58 is a bacterium that was isolated from yeast fermentation process.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 386976

LMG 1668,IMET 10945,NBRC 104435,ATCC 43581,CCUG 37300,CIP 103167,DSM 5432,JCM 6891,VKM B-2104,LMD 89.7,BCRC 16049,CCRC 16049,NCCB 89007

@ref 20215

Last LPSN update 2026-02-09 11:17:47

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Rhodospirillales

Family Acetobacteraceae

Genus Acidomonas

Species Acidomonas methanolica

Full scientific name Acidomonas methanolica (Uhlig et al. 1986) Urakami et al. 1989

Synonyms (1)

Acetobacter methanolicus

BacDive ID	Other strains from **Acidomonas methanolica** (18)	Type strain
100083	A. methanolica STI09058(IMET), 1031-003,
100084	A. methanolica STI09061(IMET), 1110-002,
100085	A. methanolica STI09081(IMET), 1136-035,
100086	A. methanolica STI09082(IMET), 1141-033,
100087	A. methanolica STI09063(IMET), 1110-006,
100088	A. methanolica STI09203or(IMET), 486-3,
100089	A. methanolica STI09083(IMET), 1141-035,
100090	A. methanolica STI09214(IMET), 485-12,
163304	A. methanolica JCM 25089, NRIC 0548
163305	A. methanolica JCM 25090, NRIC 0549
163306	A. methanolica JCM 25091, NRIC 0550
163307	A. methanolica JCM 25092, NRIC 0551
163308	A. methanolica JCM 25093, NRIC 0552
163309	A. methanolica JCM 25094, NRIC 0553
163310	A. methanolica JCM 25095, NRIC 0554
163311	A. methanolica JCM 25096, NRIC 0555
163312	A. methanolica JCM 25097, NRIC 0556
163313	A. methanolica JCM 25098, NRIC 0557

Morphology

Cell morphology

@ref	Gram stain	Confidence
125438	negative	95.828

Culture and growth conditions

Culture medium

@ref	Name	Medium link	Composition
2164	SABOURAND GLUCOSE MEDIUM (DSMZ Medium 1429)	Medium recipe at MediaDive	Name: SABOURAUD GLUCOSE MEDIUM (DSMZ Medium 1429) Composition: SABOURAUD- Glucose-Bouillon 30.0 g/l Agar 15.0 g/l Distilled water
2164	MEDIUM 125 WITH METHANOL (DSMZ Medium 569)	Medium recipe at MediaDive	Name: MEDIUM 125 WITH METHANOL (DSMZ Medium 569) Composition: Agar 12.0 g/l Methanol 7.92 g/l KNO3 1.0 g/l Na2HPO4 0.23 g/l MgSO4 x 7 H2O 0.2 g/l NaH2PO4 0.07 g/l CaCl2 x 2 H2O 0.02 g/l FeSO4 x 7 H2O 0.001 g/l ZnSO4 x 7 H2O 7e-05 g/l MoO3 1e-05 g/l MnSO4 x 5 H2O 1e-05 g/l H3BO3 1e-05 g/l CuSO4 x 5 H2O 5e-06 g/l Distilled water
40546	MEDIUM 2 - for Acidomonas methanolica		Distilled water make up to (1000.000 ml);Manganese II sulphate monohydrate(0.050 g);Maltose (5.000 g);Magnesium sulphate heptahydrate (0.200 g);Agar (20.000 g);Glucose (15.000 g);Yeast extract (1.000 g);Tween 80 (1.000 ml);Proteose Peptone N°3 (8.000 g);C
120923	CIP Medium 2	Medium recipe at CIP

Culture temp

@ref	Growth	Type	Temperature (°C)
2164	positive	growth	30
40546	positive	growth	30
67770	positive	growth	30

Physiology and metabolism

Spore formation

@ref	Spore formation	Confidence
125438		92.348

Observation

67770

Observationquinones: Q-10

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2
#Engineered	#Biodegradation
#Host	#Yeast

Isolation

@ref	Sample type	Sampling date	Geographic location	Country	Country ISO 3 Code	Continent
2164	yeast fermentation process		Leipzig	Germany	DEU	Europe
53589	Fermentation of methanol with Candida sp.	1980	Leipzig	Germany	DEU	Europe
67770	Septic methanol-yeast process
120923	Other, Septic methanol-yeast process			Germany	DEU	Europe

Taxonmaps

69479

Global distribution of 16S sequence X77468 (>99% sequence identity) for Acidomonas methanolica subclade from Microbeatlas

Aquatic Samples	2020
Soil Samples	3564
Animal Samples	3308
Plant Samples	1165
Total Samples	10057

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
2164	1	Risk group (German classification) According to TRBA 466
120923	1	Risk group (French classification) According to TRBA 466

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM434603v1 assembly for Acidomonas methanolica DSM 5432	contig	GCA_004346035	437.4	2784746772	437	59.14
66792	ASM798892v1 assembly for Acidomonas methanolica NBRC 104435	contig	GCA_007988925	1231351.7	2887682946	1231351	49.47
67770	ASM61786v1 assembly for Acidomonas methanolica NBRC 104435 MB58	scaffold	GCA_000617865	1231351.3		1231351
124043	ASM2599527v1 assembly for Acidomonas methanolica NRIC 0498	contig	GCA_025995275	437.8		437

16S sequences

@ref	Description	Accession	Length	NCBI tax ID
20218	Acidomonas methanolica gene for 16S ribosomal RNA	D30770	1411	437
20218	Acidomonas methanolica 16S rRNA, partial sequence	AB003976	155	437
20218	A.methanolicus 16S rRNA gene	X77468	1481	437
20218	Acidomonas methanolica gene for 16S rRNA, complete sequence, strain: NRIC0498	AB110702	1409	437
124043	Acidomonas methanolica DNA, 16S-23S rRNA ITS region. strain: BCC 12263.	AB210135	761	437
124043	Acidomonas methanolica gene for 16S rRNA, partial sequence, strain: NBRC 104435.	AB682176	1414	437

GC content

@ref	GC-content (mol%)	Method
67770	62	high performance liquid chromatography (HPLC)
67770	62.3	thermal denaturation, midpoint method (Tm)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Acidomonas methanolica NBRC 104435 MB58
NCBI: GCA_000617865

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	facultative aerobe	85.20	no
125439	gram_stain	BacteriaNetⓘ	negative	73.50	no
125439	motility	BacteriaNetⓘ	yes	50.50	no
125439	spore_formation	BacteriaNetⓘ	no	85.40	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Acidomonas methanolica NBRC 104435
PATRIC: 1231351.7

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	95.83	no
125438	anaerobic	anaerobicⓘ	no	92.21	no
125438	spore-forming	spore-formingⓘ	no	92.35	no
125438	aerobic	aerobicⓘ	yes	80.14	no
125438	thermophilic	thermophileⓘ	no	97.41	yes
125438	flagellated	motile2+ⓘ	yes	56.03	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Acidomonas methanolica-associated necrotizing lymphadenitis in a patient with chronic granulomatous disease.	Chase JM, Holland SM, Greenberg DE, Marshall-Batty K, Zelazny AM, Church JA.	J Clin Immunol	10.1007/s10875-012-9728-6	2012
	Serologic reactivity to the emerging pathogen Granulibacter bethesdensis.	Greenberg DE, Shoffner AR, Marshall-Batty KR, Arora K, Zhao M, Martin R, Ding L, Hammer CH, Shaw PA, Kuhns DB, Malech HL, Gallin JI, Zarember KA, Holland SM.	J Infect Dis	10.1093/infdis/jis431	2012
Metabolism	Requirement of carbon dioxide for initial growth of facultative methylotroph, Acidomonas methanolica MB58.	Mitsui R, Katayama H, Tanaka M.	J Biosci Bioeng	10.1016/j.jbiosc.2014.11.016	2015
	Characterisation of Plant Growth-Promoting Endophytic Bacteria from Sugarcane and Their Antagonistic Activity against Fusarium moniliforme.	Pitiwittayakul N, Wongsorn D, Tanasupawat S.	Trop Life Sci Res	10.21315/tlsr2021.32.3.6	2021
	Sugarcane Pokkah Boeng Disease: Insights and Future Directions for Effective Management.	Poorniammal R, Jernisha J, Prabhu S, Dufosse L.	Life (Basel)	10.3390/life14121533	2024
Metabolism	Draft Genomic DNA Sequence of the Facultatively Methylotrophic Bacterium Acidomonas methanolica type strain MB58.	Higashiura N, Hadano H, Hirakawa H, Matsutani M, Takebe S, Matsushita K, Azuma Y.	FEMS Microbiol Lett	10.1111/1574-6968.12357	2014
	Distribution and genome structures of temperate phages in acetic acid bacteria.	Omata K, Hibi N, Nakano S, Komoto S, Sato K, Nunokawa K, Amano S, Ueda K, Takano H.	Sci Rep	10.1038/s41598-021-00998-w	2021
	Methylotroph Infections and Chronic Granulomatous Disease.	Falcone EL, Petts JR, Fasano MB, Ford B, Nauseef WM, Neves JF, Simoes MJ, Tierce ML, de la Morena MT, Greenberg DE, Zerbe CS, Zelazny AM, Holland SM.	Emerg Infect Dis	10.3201/eid2203.151265	2016
Enzymology	MxaF gene, a gene encoding alpha subunit of methanol dehydrogenase in and false growth of acetic acid bacteria on methanol.	Suzuki R, Lisdiyanti P, Komagata K, Uchimura T.	J Gen Appl Microbiol	10.2323/jgam.55.101	2009
	Unusual 1-3 peptidoglycan cross-links in Acetobacteraceae are made by L,D-transpeptidases with a catalytic domain distantly related to YkuD domains.	Alaman-Zarate MG, Rady BJ, Evans CA, Pian B, Greetham D, Marecos-Ortiz S, Dickman MJ, Lidbury IDEA, Lovering AL, Barstow BM, Mesnage S.	J Biol Chem	10.1016/j.jbc.2023.105494	2024
Genetics	Metagenome Sequencing Reveals the Midgut Microbiota Makeup of Culex pipiens quinquefasciatus and Its Possible Relationship With Insecticide Resistance.	Wang YT, Shen RX, Xing D, Zhao CP, Gao HT, Wu JH, Zhang N, Zhang HD, Chen Y, Zhao TY, Li CX.	Front Microbiol	10.3389/fmicb.2021.625539	2021
Metabolism	Influence of Dissolved-Aluminum Concentration on Sulfur-Oxidizing Bacterial Activity in the Biodeterioration of Concrete.	Buvignier A, Peyre-Lavigne M, Robin O, Bounouba M, Patapy C, Bertron A, Paul E.	Appl Environ Microbiol	10.1128/aem.00302-19	2019
Metabolism	The phylogeny of acetic acid bacteria based on the partial sequences of 16S ribosomal RNA: the elevation of the subgenus Gluconoacetobacter to the generic level.	Yamada Y, Hoshino K, Ishikawa T.	Biosci Biotechnol Biochem	10.1271/bbb.61.1244	1997
	Early Intracellular Trafficking of Granulibacter bethesdensis in Human Macrophages.	Chu J, Smelkinson MG, Dorward DW, Zarember KA, Gallin JI.	Infect Immun	10.1128/iai.00847-16	2017
Enzymology	Molecular characterization of RNase III protein of Asaia sp. for developing a robust RNAi-based paratransgensis tool to affect the sexual life-cycle of Plasmodium or Anopheles fitness.	Asgari M, Ilbeigikhamsehnejad M, Rismani E, Dinparast Djadid N, Raz A.	Parasit Vectors	10.1186/s13071-020-3889-6	2020
	Molecular analysis and essentiality of Aro1 shikimate biosynthesis multi-enzyme in Candida albicans.	Stogios PJ, Liston SD, Semper C, Quade B, Michalska K, Evdokimova E, Ram S, Otwinowski Z, Borek D, Cowen LE, Savchenko A.	Life Sci Alliance	10.26508/lsa.202101358	2022
Genetics	Complete genome and gene expression analyses of Asaia bogorensis reveal unique responses to culture with mammalian cells as a potential opportunistic human pathogen.	Kawai M, Higashiura N, Hayasaki K, Okamoto N, Takami A, Hirakawa H, Matsushita K, Azuma Y.	DNA Res	10.1093/dnares/dsv018	2015
Phylogeny	Diversity and functional analysis of bacterial communities associated with natural hydrocarbon seeps in acidic soils at Rainbow Springs, Yellowstone National Park.	Hamamura N, Olson SH, Ward DM, Inskeep WP.	Appl Environ Microbiol	10.1128/aem.71.10.5943-5950.2005	2005
Metabolism	Simultaneous Host-Pathogen Transcriptome Analysis during Granulibacter bethesdensis Infection of Neutrophils from Healthy Subjects and Patients with Chronic Granulomatous Disease.	Greenberg DE, Sturdevant DE, Marshall-Batty KR, Chu J, Pettinato AM, Virtaneva K, Lane J, Geller BL, Porcella SF, Gallin JI, Holland SM, Zarember KA.	Infect Immun	10.1128/iai.00778-15	2015
	Microbial ecology of an extreme acidic environment, the Tinto River.	Gonzalez-Toril E, Llobet-Brossa E, Casamayor EO, Amann R, Amils R.	Appl Environ Microbiol	10.1128/aem.69.8.4853-4865.2003	2003
Genetics	Analysis of 1,000+ Type-Strain Genomes Substantially Improves Taxonomic Classification of Alphaproteobacteria.	Hordt A, Lopez MG, Meier-Kolthoff JP, Schleuning M, Weinhold LM, Tindall BJ, Gronow S, Kyrpides NC, Woyke T, Goker M.	Front Microbiol	10.3389/fmicb.2020.00468	2020
Metabolism	Preliminary Evaluation of Glyceric Acid-producing Ability of Acidomonas methanolica NBRC104435 from Glycerol Containing Methanol.	Sato S, Kitamoto D, Habe H	J Oleo Sci	10.5650/jos.ess16236	2017
Metabolism	Formaldehyde uptake by Methylobacterium sp. MF1 and Acidomonas methanolica MB 58 with the different formaldehyde assimilation pathways.	Mitsui R, Kitazawa H, Sato T, Tanaka M	Environ Sci		2006
Phylogeny	Emendation of the genus Acidomonas Urakami, Tamaoka, Suzuki and Komagata 1989.	Yamashita SI, Uchimura T, Komagata K	Int J Syst Evol Microbiol	10.1099/ijs.0.02946-0	2004
Metabolism	The toxicity of substituted phenolic compounds to a detoxifying and an acetic acid bacterium.	Loffhagen N, Hartig C, Babel W	Ecotoxicol Environ Saf	10.1006/eesa.1996.1516	1997
Metabolism	Structure of the capsular polysaccharide and the O-side-chain of the lipopolysaccharide from Acetobacter methanolicus MB 70, and of oligosaccharides resulting from their degradation by the bacteriophage Acm6.	Grimmecke HD, Knirel YA, Shashkov AS, Kiesel B, Lauk W, Voges M	Carbohydr Res	10.1016/0008-6215(94)80072-3	1994
Metabolism	Phage Acm1-mediated transduction in the facultatively methanol-utilizing Acetobacter methanolicus MB 58/4.	Kiesel B, Wunsche L	J Gen Virol	10.1099/0022-1317-74-9-1741	1993
Metabolism	Structure of the capsular polysaccharide and the O-side-chain of the lipopolysaccharide from Acetobacter methanolicus MB 58/4 (IMET 10945), and of oligosaccharides resulting from their degradation by the bacteriophage Acml.	Grimmecke HD, Mamat U, Lauk W, Shashkov AS, Knirel YA, Vinogradov EV, Kochetkov NK	Carbohydr Res	10.1016/0008-6215(91)80014-e	1991
	Mutagenesis of acetobacter methanolicus MB58 with the transposon Tn5.	Dobrowolski P, Grundig MW	J Basic Microbiol	10.1002/jobm.3620300417	1990
	Brytella acorum gen. nov., sp. nov., a novel acetic acid bacterium from sour beverages.	Sombolestani AS, Bongaerts D, Depoorter E, Cleenwerck I, Wieme AD, Britton SJ, Weckx S, De Vuyst L, Vandamme P.	Syst Appl Microbiol	10.1016/j.syapm.2023.126440	2023
	Endosaccharibacter trunci gen. nov., sp. nov. and Rhizosaccharibacter radicis gen. nov., sp. nov., two novel bacteria of the family Acetobacteraceae isolated from sugarcane.	Pitiwittayakul N, Yukphan P, Charoenyingcharoen P, Tanasupawat S.	Heliyon	10.1016/j.heliyon.2024.e32825	2024
Enzymology	Tanticharoenia sakaeratensis gen. nov., sp. nov., a new osmotolerant acetic acid bacterium in the alpha-Proteobacteria.	Yukphan P, Malimas T, Muramatsu Y, Takahashi M, Kaneyasu M, Tanasupawat S, Nakagawa Y, Suzuki K, Potacharoen W, Yamada Y.	Biosci Biotechnol Biochem	10.1271/bbb.70319	2008

References

#2164	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 5432
#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 )
#20218	Verslyppe, B., De Smet, W., De Baets, B., De Vos, P., Dawyndt P.: StrainInfo introduces electronic passports for microorganisms.. Syst Appl Microbiol. 37: 42 - 50 2014 ( DOI 10.1016/j.syapm.2013.11.002 , PubMed 24321274 )
#40546	; Curators of the CIP;
#53589	Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 37300
#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 .
#120923	Collection of Institut Pasteur ; Curators of the CIP; CIP 103167
#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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Acidomonas methanolica (DSM 5432, ATCC 43581, IMET 10945)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Spore formation

Observation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_004346035

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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