Methylocystis hirsuta (DSM 18500, ATCC BAA-1344, CSC 1)

Name: Methylocystis hirsuta (DSM 18500, ATCC BAA-1344, CSC 1)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 7218

Type strain

Culture col. no. DSM 18500 ATCC BAA-1344 CSC 1

NCBI tax ID(s) 369798

Special Collections DSMZ Literature strains

History DSMZ <- A. S. Lindner, Dept. Environmental Engineering Sci., Univ. Florida, USA; CSC1 <- S. M. Henry and D. Grbic-Galic, Dept. Civil Engineering, Stanford Univ., USA

Links

Methylocystis hirsuta DSM 18500 is an aerobe, Gram-negative bacterium that was isolated from uncontaminated groundwater aquifer in the mid-1980s from Moffett Naval Air Station.

Gram-negative aerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 298315

ATCC BAA-1344,DSM 18500,LMG 27832

@ref 20215

Last LPSN update 2026-05-29 08:26:35

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Hyphomicrobiales

Family Methylocystaceae

Genus Methylocystis

Species Methylocystis hirsuta

Full scientific name Methylocystis hirsuta Lindner et al. 2007

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Confidence
31857	negative	0.85 µm	0.45 µm
125439	negative			96.272
125438	negative			99.5

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
7564	METHYLOSARCINA QUISQUILLARUM AND M. FIBRATA MEDIUM (DSMZ Medium 921)		Medium recipe at MediaDive	Name: METHYLOSARCINA QUISQUILLARUM AND M. FIBRATA MEDIUM (DSMZ Medium 921) Composition: Agar 15.0 g/l MgSO4 x 6 H2O 1.0 g/l KNO3 1.0 g/l Na2HPO4 x 12 H2O 0.716 g/l KH2PO4 0.26 g/l CaCl2 x 2 H2O 0.2 g/l CuSO4 x 5 H2O 0.001 g/l FeSO4 x 7 H2O 0.0005 g/l ZnSO4 x 7 H2O 0.0004 g/l Fe(III)-EDTA 0.00038 g/l Na2MoO4 x 2 H2O 0.00026 g/l Na2-EDTA 0.00025 g/l CoCl2 x 6 H2O 5e-05 g/l MnCl2 x 4 H2O 2e-05 g/l H3BO3 1.5e-05 g/l NiCl2 x 6 H2O 1e-05 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
7564	positive	growth	28
31857	positive	optimum	30

Culture pH

@ref	Ability	Type	PH
31857	positive	optimum	7
31857	positive	growth	5.0-8.0

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
31857	aerobe
125439	obligate aerobe	99.269

Spore formation

@ref	Spore formation	Confidence
125439		98.04

Observation

31857

Observationaggregates in clumps

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
31857	22653 ChEBI	asparagine	+	carbon source
31857	35391 ChEBI	aspartate	+	carbon source
31857	18257 ChEBI	ornithine	+	carbon source
31857	17148 ChEBI	putrescine	+	carbon source

Enzymes

@ref	Value	Activity	Ec
31857	catalase	+	1.11.1.6
31857	cytochrome oxidase	+	1.9.3.1

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Groundwater

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
7564	uncontaminated groundwater aquifer in the mid-1980s from Moffett Naval Air Station	California, Mountain View	USA	USA	North America

Taxonmaps

69479

Global distribution of 16S sequence DQ364433 (>99% sequence identity) for Methylocystis from Microbeatlas

Aquatic Samples	940
Soil Samples	493
Animal Samples	169
Plant Samples	43
Total Samples	1645

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
7564	1	Risk group (German classification) According to TRBA 466

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	NCBI tax ID	Score
66792	ASM372235v1 assembly for Methylocystis hirsuta CSC1	contig	GCA_003722355	369798.5	369798	73.99

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
7564	Methylocystis hirsuta strain CSC1 16S ribosomal RNA gene, partial sequence	DQ364433	1450		369798

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM372235v1 assembly for Methylocystis hirsuta CSC1
NCBI: GCA_003722355

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.27	no
125439	gram_stain	BacteriaNetⓘ	negative	96.27	no
125439	motility	BacteriaNetⓘ	yes	43.35	no
125439	spore_formation	BacteriaNetⓘ	no	98.04	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Methylocystis hirsuta strain CSC1
PATRIC: 369798.5

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	99.50	yes
125438	anaerobic	anaerobicⓘ	no	87.63	yes
125438	spore-forming	spore-formingⓘ	no	85.56	no
125438	aerobic	aerobicⓘ	yes	76.43	yes
125438	thermophilic	thermophileⓘ	no	96.73	yes
125438	flagellated	motile2+ⓘ	no	72.83	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Enrichment of mixed methanotrophic cultures producing polyhydroxyalkanoates (PHAs) from various environmental sources.	Gesicka A, Gutowska N, Palaniappan S, Oleskowicz-Popiel P, Lezyk M.	Sci Total Environ	10.1016/j.scitotenv.2023.168844	2024
	Polyhydroxyalkanoate Production by Methanotrophs: Recent Updates and Perspectives.	Patel SKS, Singh D, Pant D, Gupta RK, Busi S, Singh RV, Lee JK.	Polymers (Basel)	10.3390/polym16182570	2024
Enzymology	Recent Trends in the Production and Recovery of Bioplastics Using Polyhydroxyalkanoates Copolymers.	Garcia A, Aguirre C, Perez A, Bahamonde SS, Urtuvia V, Diaz-Barrera A, Pena C.	Microorganisms	10.3390/microorganisms12112135	2024
	An Overview of Recent Advancements in Microbial Polyhydroxyalkanoates (PHA) Production from Dark Fermentation Acidogenic Effluents: A Path to an Integrated Bio-Refinery.	Saratale RG, Cho SK, Saratale GD, Kumar M, Bharagava RN, Varjani S, Kadam AA, Ghodake GS, Palem RR, Mulla SI, Kim DS, Shin HS.	Polymers (Basel)	10.3390/polym13244297	2021
	Volatile Fatty Acids as Carbon Sources for Polyhydroxyalkanoates Production.	Szacherska K, Oleskowicz-Popiel P, Ciesielski S, Mozejko-Ciesielska J.	Polymers (Basel)	10.3390/polym13030321	2021
Phylogeny	Diversity and Habitat Preferences of Cultivated and Uncultivated Aerobic Methanotrophic Bacteria Evaluated Based on pmoA as Molecular Marker.	Knief C.	Front Microbiol	10.3389/fmicb.2015.01346	2015
Genetics	Genome mining for methanobactins.	Kenney GE, Rosenzweig AC.	BMC Biol	10.1186/1741-7007-11-17	2013
Phylogeny	Methylocystis silviterrae sp.nov., a high-affinity methanotrophic bacterium isolated from the boreal forest soil.	Tikhonova EN, Grouzdev DS, Avtukh AN, Kravchenko IK	Int J Syst Evol Microbiol	10.1099/ijsem.0.005166	2021
Biotechnology	Elucidating the influence of environmental factors on biogas-based polyhydroxybutyrate production by Methylocystis hirsuta CSC1.	Rodriguez Y, Firmino PIM, Arnaiz E, Lebrero R, Munoz R	Sci Total Environ	10.1016/j.scitotenv.2019.135136	2019
Metabolism	Genome sequence of Methylocystis hirsuta CSC1, a polyhydroxyalkanoate producing methanotroph.	Bordel S, Rodriguez E, Munoz R	Microbiologyopen	10.1002/mbo3.771	2018
Phylogeny	Methylocystis hirsuta sp. nov., a novel methanotroph isolated from a groundwater aquifer.	Lindner AS, Pacheco A, Aldrich HC, Costello Staniec A, Uz I, Hodson DJ	Int J Syst Evol Microbiol	10.1099/ijs.0.64541-0	2007

References

#7564	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 18500
#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 )
#28120	IJSEM 1891 2007 ( DOI 10.1099/ijs.0.64541-0 , PubMed 17684277 )
#31857	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 #28120
#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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Methylocystis hirsuta (DSM 18500, ATCC BAA-1344, CSC 1)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

Observation

Metabolite utilization

Enzymes

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_003722355

16S sequences

Genome-based predictions

Predictions

Literature

Literature

References

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