"Bittarella massiliensis" (DSM 4029, ATCC 29733, VPI C48-50)

BacDive ID 2675

Type strain

Culture col. no. DSM 4029 ATCC 29733 VPI C48-50

Special Collections DSMZ

History <- ATCC; ATCC 29733 <- L. V. Holdeman, Virginia Polytechnical Institute and State University (VPI), Blacksburg, V., USA; VPI C48-50 (Clostridium sp.)

"Bittarella massiliensis" DSM 4029 is an anaerobe bacterium that was isolated from human faeces.

anaerobe genome sequence Bacteria

Name and taxonomic classification

Culture Collection Numbers

DSM 4029 ATCC 29733 VPI C48-50

@ref 20215

Last LPSN update 2026-02-09 11:18:20

Domain Bacteria

Phylum Bacillota

Class Clostridia

Order Eubacteriales

Family Oscillospiraceae

Genus "Bittarella"

Species "Bittarella massiliensis"

Full scientific name Bittarella massiliensis Durand et al. 2017

BacDive ID	Other strains from "Bittarella massiliensis" (1)	Type strain
169699	". massiliensis" GD6, DSM 101081

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	93.5

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
1578	CHOPPED MEAT MEDIUM (DSMZ Medium 78)		Medium recipe at MediaDive	Name: CHOPPED MEAT MEDIUM (DSMZ Medium 78) Composition: Ground beef 500.0 g/l Casitone 30.0 g/l Agar 15.0 g/l Ethanol 9.5 g/l (optional) K2HPO4 5.0 g/l Yeast extract 5.0 g/l L-Cysteine HCl 0.5 g/l Haemin 0.005 g/l (optional) Resazurin 0.001 g/l Vitamin K3 0.0005 g/l (optional) Vitamin K1 (optional) NaOH (optional) Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
1578	positive	growth	37

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
1578	anaerobe
125438	anaerobe	92.576
125439	anaerobe	99.9

Spore formation

@ref	Spore formation	Confidence
125439		91.3

Compound production

@ref	Compound
1578	12alpha hydroxysteroid dehydrogenase
1578	steroids

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host	#Human	-
#Host Body Product	#Gastrointestinal tract	#Feces (Stool)

Isolation

@ref	Sample type	Country	Country ISO 3 Code	Continent
1578	human faeces	USA	USA	North America

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	IMG-taxon 2695420950 annotated assembly for Bittarella massiliensis (ex Durand et al. 2017) DSM 4029	contig	GCA_900129515	1882754.4	2695420950	1720313	75.18
66792	ASM46660v1 assembly for Clostridium sp. ATCC 29733	scaffold	GCA_000466605	1507.3	2600254992	1507	29.96

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Clostridium sp. ATCC 29733
NCBI: GCA_000466605

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	91.30	no
125439	motility	BacteriaNetⓘ	no	56.40	no
125439	gram_stain	BacteriaNetⓘ	negative	93.50	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	99.90	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Clostridium sp. ATCC 29733 ATCC 29733
NCBI: GCA_000466605.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	70.81	no
125438	anaerobic	anaerobicⓘ	yes	92.58	yes
125438	aerobic	aerobicⓘ	no	97.16	yes
125438	spore-forming	spore-formingⓘ	no	55.80	no
125438	thermophilic	thermophileⓘ	no	93.41	yes
125438	flagellated	motile2+ⓘ	no	85.63	no

Literature

References

#1578	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 4029
#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) .
#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 .

Rate our new design

Content

About us