Neomoorella caeni (DSM 21394, AMP)

Name: Neomoorella caeni (DSM 21394, AMP)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 16817

Type strain

Strain Designation AMP

Culture col. no. DSM 21394 AMP

NCBI tax ID(s) 3051332

Special Collections DSMZ

History DSMZ <- M. Balk, Wageningen University, Laboratory of Microbiology, Netherlands; AMP (Moorella thermoacetica) <- B. Jiang; {2004}

Links

Neomoorella caeni AMP is an anaerobe bacterium that was isolated from thermophilic methanogenic bioreactor.

anaerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 403570

JCM 35360,DSM 21394

@ref 20215

Last LPSN update 2026-05-29 08:28:52

Domain Bacteria

Phylum Bacillota

Class Clostridia

Order Desulfitibacterales

Family Neomoorellaceae

Genus Neomoorella

Species Neomoorella caeni

Full scientific name Neomoorella caeni (Santaella et al. 2023) Gtari and Ventura 2025

Synonyms (1)

Moorella caeni

Morphology

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
15673	DESULFOVIBRIO (MV) MEDIUM (DSMZ Medium 641)		Medium recipe at MediaDive	Name: DESULFOVIBRIO (MV) MEDIUM (DSMZ Medium 641; with strain-specific modifications) Composition: Methanol 3.94816 g/l Na2SO4 1.99402 g/l Yeast extract 0.997009 g/l Na2CO3 0.997009 g/l MgSO4 x 7 H2O 0.997009 g/l Na2S2O3 x 5 H2O 0.997009 g/l NH4Cl 0.997009 g/l KH2PO4 0.498504 g/l Na2S x 9 H2O 0.0997009 g/l CaCl2 x 2 H2O 0.0997009 g/l HCl 0.00249252 g/l FeCl2 x 4 H2O 0.00149551 g/l Sodium resazurin 0.000498504 g/l NaOH 0.000498504 g/l CoCl2 x 6 H2O 0.000189432 g/l Pyridoxine hydrochloride 9.97009e-05 g/l MnCl2 x 4 H2O 9.97009e-05 g/l ZnCl2 6.97906e-05 g/l Calcium D-(+)-pantothenate 4.98504e-05 g/l Nicotinic acid 4.98504e-05 g/l Thiamine HCl 4.98504e-05 g/l p-Aminobenzoic acid 4.98504e-05 g/l (DL)-alpha-Lipoic acid 4.98504e-05 g/l Riboflavin 4.98504e-05 g/l Na2MoO4 x 2 H2O 3.58923e-05 g/l NiCl2 x 6 H2O 2.39282e-05 g/l Folic acid 1.99402e-05 g/l Biotin 1.99402e-05 g/l H3BO3 5.98205e-06 g/l Na2WO4 x 2 H2O 3.98804e-06 g/l Na2SeO3 x 5 H2O 2.99103e-06 g/l CuCl2 x 2 H2O 1.99402e-06 g/l Vitamin B12 9.97009e-07 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
15673	positive	growth	60

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
15673	anaerobe
125439	anaerobe	97.178

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2
#Engineered	#Bioreactor
#Condition	#Anoxic (anaerobic)
#Condition	#Thermophilic (>45°C)

Isolation

@ref	Sample type	Country	Country ISO 3 Code	Continent
15673	thermophilic methanogenic bioreactor	Netherlands	NLD	Europe

Taxonmaps

69479

Global distribution of 16S sequence AY884087 (>99% sequence identity) for Moorella from Microbeatlas

Aquatic Samples	110
Soil Samples	38
Animal Samples	52
Plant Samples	8
Total Samples	208

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
15673	1	Risk group (German classification) According to TRBA 466

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM187532v1 assembly for Neomoorella thermoacetica DSM 21394	contig	GCA_001875325	1525.12	2703719211	1525	34.14

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
15673	Moorella thermoacetica strain AMP 16S ribosomal RNA gene, partial sequence	AY884087	1528		1525

GC content

@ref	GC-content (mol%)	Method
15673	57.3	high performance liquid chromatography (HPLC)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM187532v1 assembly for Neomoorella thermoacetica DSM 21394
BV-BRC: 1525.12

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	gram_stain	BacteriaNetⓘ	negative	72.33	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	97.18	no
125439	motility	BacteriaNetⓘ	no	63.70	no
125439	spore_formation	BacteriaNetⓘ	no	87.29	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Moorella thermoacetica strain DSM 21394
PATRIC: 1525.12

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	57.06	no
125438	anaerobic	anaerobicⓘ	yes	89.83	yes
125438	spore-forming	spore-formingⓘ	yes	78.67	no
125438	aerobic	aerobicⓘ	no	91.81	no
125438	thermophilic	thermophileⓘ	yes	73.95	yes
125438	flagellated	motile2+ⓘ	yes	72.50	no

Literature

Title	Authors	Journal	DOI	Year
Methanol and Carbon Monoxide Metabolism of the Thermophile Moorella caeni.	Vecchini Santaella NA, Stams AJM, Sousa DZ.	Environ Microbiol	10.1111/1462-2920.70096	2025
Genome-Based Comparison of All Species of the Genus Moorella, and Status of the Species Moorella thermoacetica and Moorella thermoautotrophica.	Redl S, Poehlein A, Esser C, Bengelsdorf FR, Jensen TO, Jendresen CB, Tindall BJ, Daniel R, Durre P, Nielsen AT	Front Microbiol	10.3389/fmicb.2019.03070	2020
Moorella caeni sp. nov., isolated from thermophilic anaerobic sludge from a methanol-fed reactor.	Vecchini Santaella N, Sousa DZ, Stams AJM.	Int J Syst Evol Microbiol	10.1099/ijsem.0.005905	2023

References

#15673	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 21394
#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 )
#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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Neomoorella caeni (DSM 21394, AMP)

Name and taxonomic classification

Morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_001875325

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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