Fusobacterium mortiferum (DSM 108838, FSA-380-WT-2B)

Name: Fusobacterium mortiferum (DSM 108838, FSA-380-WT-2B)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 159509

Type strain

Strain Designation FSA-380-WT-2B

Culture col. no. DSM 108838 FSA-380-WT-2B

NCBI tax ID(s) 850

Special Collections DSMZ Pig DZIF

History DSMZ <- T. Clavel , TU Munich, Freising-Weihenstephan, Germany;

Links

Fusobacterium mortiferum FSA-380-WT-2B is a bacterium that was isolated from feces; pig, 5 month old, wild type.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 405274

DSM 108838

@ref 20215

Last LPSN update 2026-05-29 08:44:12

Domain Bacteria

Phylum Fusobacteriota

Class Fusobacteriia

Order Fusobacteriales

Family Fusobacteriaceae

Genus Fusobacterium

Species Fusobacterium mortiferum

Full scientific name Fusobacterium mortiferum (Harris 1901) Moore and Holdeman 1970 (Approved Lists 1980)

Synonyms (3)

"Hiblerillus rectus"
Clostridium rectum
"Bacillus mortiferus"

BacDive ID	Other strains from **Fusobacterium mortiferum** (2)	Type strain
5770	F. mortiferum 350A, DSM 19809, ATCC 25557, CCUG 14475, ... (type strain)
2654	F. mortiferum DSM 1295, ATCC 25751, NCIB 10651, VPI 2488, ...

Morphology

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
66333	WILKINS-CHALGREN ANAEROBE BROTH (DSMZ Medium 339)		Medium recipe at MediaDive	Name: WILKINS-CHALGREN ANAEROBE BROTH (DSMZ Medium 339) Composition: dehydrated Wilkins-Chalgren medium 33.0 g/l L-Cysteine HCl 0.3 g/l Sodium resazurin 0.0005 g/l Distilled water
66333	COLUMBIA BLOOD MEDIUM (DSMZ Medium 693)		Medium recipe at MediaDive	Name: COLUMBIA BLOOD MEDIUM (DSMZ Medium 693) Composition: Defibrinated sheep blood 50.0 g/l Columbia agar base

Culture temp

@ref	Growth	Type	Temperature (°C)
66333	positive	growth	37

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125438	anaerobe	91.796
125439	anaerobe	98.595

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
68380	29016 ChEBI	arginine	-	hydrolysis	from API rID32A
68380	16024 ChEBI	D-mannose	+	fermentation	from API rID32A
68380	29985 ChEBI	L-glutamate	-	degradation	from API rID32A
68380	17632 ChEBI	nitrate	-	reduction	from API rID32A
68380	16634 ChEBI	raffinose	-	fermentation	from API rID32A
68380	27897 ChEBI	tryptophan	-	energy source	from API rID32A
68380	16199 ChEBI	urea	-	hydrolysis	from API rID32A

Metabolite production

@ref	Chebi-ID	Metabolite	Production
68380	35581 ChEBI	indole		from API rID32A

Metabolite tests

@ref	Chebi-ID	Metabolite	Indole test
68380	35581 ChEBI	indole	-	from API rID32A

Enzymes

@ref	Value	Activity	Ec
68380	alanine arylamidase	-	3.4.11.2	from API rID32A
68380	alkaline phosphatase	+	3.1.3.1	from API rID32A
68380	alpha-arabinosidase	-	3.2.1.55	from API rID32A
68380	alpha-fucosidase	-	3.2.1.51	from API rID32A
68380	alpha-galactosidase	+	3.2.1.22	from API rID32A
68380	alpha-glucosidase	-	3.2.1.20	from API rID32A
68380	arginine dihydrolase	-	3.5.3.6	from API rID32A
68380	beta-galactosidase	+	3.2.1.23	from API rID32A
68380	beta-Galactosidase 6-phosphate	+		from API rID32A
68380	beta-glucosidase	-	3.2.1.21	from API rID32A
68380	beta-glucuronidase	-	3.2.1.31	from API rID32A
68380	glutamate decarboxylase	-	4.1.1.15	from API rID32A
68380	glutamyl-glutamate arylamidase	-		from API rID32A
68380	glycin arylamidase	-		from API rID32A
68380	histidine arylamidase	-		from API rID32A
68380	L-arginine arylamidase	-		from API rID32A
68380	leucine arylamidase	-	3.4.11.1	from API rID32A
68380	leucyl glycin arylamidase	-	3.4.11.1	from API rID32A
68380	N-acetyl-beta-glucosaminidase	-	3.2.1.52	from API rID32A
68380	phenylalanine arylamidase	-		from API rID32A
68380	proline-arylamidase	-	3.4.11.5	from API rID32A
68380	pyrrolidonyl arylamidase	+	3.4.19.3	from API rID32A
68380	serine arylamidase	-		from API rID32A
68380	tryptophan deaminase	-	4.1.99.1	from API rID32A
68380	tyrosine arylamidase	-		from API rID32A
68380	urease	-	3.5.1.5	from API rID32A

API rID32A

@ref	URE	ADH (Arg)	alpha GAL	beta GAL	beta-Galactosidase 6-phosphatebeta GP	alpha GLU	beta GLU	alpha ARA	beta GUR	beta-N-Acetyl-beta-glucosaminidasebeta NAG	MNE	RAF	GDC	alpha FUC	Reduction of nitrateNIT	IND	PAL	L-arginine arylamidaseArgA	ProA	LGA	Phenylalanine arylamidasePheA	Leucine arylamidaseLeuA	PyrA	Tyrosine arylamidaseTyrA	Alanine arylamidaseAlaA	Glycin arylamidaseGlyA	Histidine arylamidaseHisA	Glutamyl-glutamate arylamidaseGGA	Serine arylamidaseSerA
66333	-	-	+	+	+	-	-	-	-	-	+	-	-	-	-	-	+	-	-	-	-	-	+	-	-	-	-	-	-

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host Body Product	#Gastrointestinal tract	#Feces (Stool)
#Host	#Mammals	#Suidae (Pig,Swine)

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
66333	feces; pig, 5 month old, wild type	Kranzberg (Bavaria)	Germany	DEU	Europe

Taxonmaps

69479

Global distribution of 16S sequence MN537550 (>99% sequence identity) for Fusobacterium mortiferum from Microbeatlas

Aquatic Samples	2288
Soil Samples	447
Animal Samples	29675
Plant Samples	89
Total Samples	32499

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
66333	2	Risk group (German classification)

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM969610v1 assembly for Fusobacterium sp. FSA-380-WT-2B	contig	GCA_009696105	2605786.3	2900559953	2605786	70.26

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
66333	Fusobacterium mortiferum strain DSM 108838 16S ribosomal RNA gene, partial sequence	MN537550	1456		850

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM969610v1 assembly for Fusobacterium sp. FSA-380-WT-2B
BV-BRC: 2605786.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	98.60	no
125439	gram_stain	BacteriaNetⓘ	variable	46.79	no
125439	motility	BacteriaNetⓘ	yes	73.31	no
125439	spore_formation	BacteriaNetⓘ	yes	75.99	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Fusobacterium sp. FSA-380-WT-2B
PATRIC: 2605786.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	79.49	no
125438	anaerobic	anaerobicⓘ	yes	91.80	no
125438	spore-forming	spore-formingⓘ	no	75.12	no
125438	aerobic	aerobicⓘ	no	94.57	no
125438	thermophilic	thermophileⓘ	no	91.99	no
125438	flagellated	motile2+ⓘ	no	82.67	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Metabolism	The gut bacterium Extibacter muris produces secondary bile acids and influences liver physiology in gnotobiotic mice.	Streidl T, Karkossa I, Segura Munoz RR, Eberl C, Zaufel A, Plagge J, Schmaltz R, Schubert K, Basic M, Schneider KM, Afify M, Trautwein C, Tolba R, Stecher B, Doden HL, Ridlon JM, Ecker J, Moustafa T, von Bergen M, Ramer-Tait AE, Clavel T.	Gut Microbes	10.1080/19490976.2020.1854008	2021
Phylogeny	A collection of bacterial isolates from the pig intestine reveals functional and taxonomic diversity.	Wylensek D, Hitch TCA, Riedel T, Afrizal A, Kumar N, Wortmann E, Liu T, Devendran S, Lesker TR, Hernandez SB, Heine V, Buhl EM, M D'Agostino P, Cumbo F, Fischoder T, Wyschkon M, Looft T, Parreira VR, Abt B, Doden HL, Ly L, Alves JMP, Reichlin M, Flisikowski K, Suarez LN, Neumann AP, Suen G, de Wouters T, Rohn S, Lagkouvardos I, Allen-Vercoe E, Sproer C, Bunk B, Taverne-Thiele AJ, Giesbers M, Wells JM, Neuhaus K, Schnieke A, Cava F, Segata N, Elling L, Strowig T, Ridlon JM, Gulder TAM, Overmann J, Clavel T.	Nat Commun	10.1038/s41467-020-19929-w	2020

References

#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 )
#66333	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 108838
#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) .
#68380	Automatically annotated from API rID32A .
#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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Fusobacterium mortiferum (DSM 108838, FSA-380-WT-2B)

Name and taxonomic classification

Morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Metabolite utilization

Metabolite production

Metabolite tests

Enzymes

API rID32A

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_009696105

16S sequences

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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