Clostridium porci (DSM 100959, WCA-389-WT-23D1, JCM 34385)

Name: Clostridium porci (DSM 100959, WCA-389-WT-23D1, JCM 34385)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 159188

Type strain

Strain Designation WCA-389-WT-23D1

Culture col. no. DSM 100959 WCA-389-WT-23D1 JCM 34385

NCBI tax ID(s) 2605778

Special Collections DSMZ JCM Pig Literature strains DZIF

History <- T. Clavel , TU Munich, Freising-Weihenstephan, Germany; WCA-389-WT-23D1 B. Abt; DSMZ, Germany; DSM 100959 (=WCA-389-WT-23D1) <-- T. Clavel; RWTH Aachen, Germany.

Links

Clostridium porci WCA-389-WT-23D1 is an obligate anaerobe, rod-shaped bacterium that was isolated from feces; pig, 8 weeks old, wild type.

rod-shaped obligate anaerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 403330

JCM 34385,DSM 100959

@ref 20215

Last LPSN update 2026-02-09 11:38:32

Domain Bacteria

Phylum Bacillota

Class Clostridia

Order Eubacteriales

Family Clostridiaceae

Genus Clostridium

Species Clostridium porci

Full scientific name Clostridium porci Wylensek et al. 2021

Morphology

Cell morphology

69645

Cell shaperod-shaped

Culture and growth conditions

Culture medium

@ref	Name	Medium link	Composition
66011	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
69645	Wilkins-Chalgren Anaerobe broth		supplemented with L-cysteine and DTT (0.05% and 0.02% (w/v), respectively)
69645	modified YCFA

Culture temp

@ref	Growth	Type	Temperature (°C)
66011	positive	growth	37
67770	positive	growth	37
69645	positive	growth	37

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
69645	obligate anaerobe
125438	anaerobe	91.836
125439	anaerobe	99.9

Observation

69645

ObservationCells often occur in pairs

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
69645	17234 ChEBI	glucose	+	assimilation

Metabolite production

@ref	Chebi-ID	Metabolite
69645	30089 ChEBI	acetate
69645	15740 ChEBI	formate
69645	48944 ChEBI	isobutyrate

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	Enrichment culture	Enrichment culture duration	Enrichment culture temperature	Isolation procedure
66011	feces; pig, 8 weeks old, wild type	Kranzberg (Bavaria)	Germany	DEU	Europe
67770	Feces of a German Landrace pig	Freising	Germany	DEU	Europe
69645	Pig feces	Thalhausen, Bavaria	Germany	DEU	Europe	Wilkins-Chalgren Anaerobe broth	1-3 days	37	anaerobic

Taxonmaps

69479

Global distribution of 16S sequence MN537446 (>99% sequence identity) for Clostridium porci subclade from Microbeatlas

Aquatic Samples	95
Soil Samples	191
Animal Samples	13250
Plant Samples	37
Total Samples	13573

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM969637v1 assembly for Clostridium porci WCA-389-WT-23D1	contig	GCA_009696375	2605778.3	8021258258	2605778	63.3

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
66011	Clostridium porci strain WCA-389-WT-23D1 16S ribosomal RNA gene, partial sequence	MN537446	1468		2652270
124043	Clostridium porci DSM 100959 gene for 16S rRNA, partial sequence.	LC853104	1485		2605778

GC content

@ref	GC-content (mol%)	Method
69645	46.2	genome sequence analysis

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Clostridium porci WCA-389-WT-23D1
NCBI: GCA_009696375

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	yes	65.30	no
125439	motility	BacteriaNetⓘ	yes	66.50	no
125439	gram_stain	BacteriaNetⓘ	positive	60.50	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. WCA-389-WT-23D1
PATRIC: 2605778.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	67.06	no
125438	anaerobic	anaerobicⓘ	yes	91.84	no
125438	spore-forming	spore-formingⓘ	yes	52.61	no
125438	aerobic	aerobicⓘ	no	96.84	no
125438	thermophilic	thermophileⓘ	no	87.30	no
125438	flagellated	motile2+ⓘ	yes	73.30	no

Literature

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 )
#66011	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 100959
#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 .
#69645	David Wylensek, Thomas C. A. Hitch, Thomas Riedel, Afrizal Afrizal, Neeraj Kumar, Esther Wortmann, Tianzhe Liu, Saravanan Devendran, Till R. Lesker, Sara B. Hernández, Viktoria Heine, Eva M. Buhl, Paul M. D'Agostino, Fabio Cumbo, Thomas Fischöder, Marzena Wyschkon, Torey Looft, Valeria R. Parreira, Birte Abt, Heidi L. Doden, Lindsey Ly, João M. P. Alves, Markus Reichlin, Krzysztof Flisikowski, Laura Navarro Suarez, Anthony P. Neumann, Garret Suen, Tomas de Wouters, Sascha Rohn, Ilias Lagkouvardos, Emma Allen-Vercoe, Cathrin Spröer, Boyke Bunk, Anja J. Taverne-Thiele, Marcel Giesbers, Jerry M. Wells, Klaus Neuhaus, Angelika Schnieke, Felipe Cava, Nicola Segata, Lothar Elling, Till Strowig, Jason M. Ridlon, Tobias A. M. Gulder, Jörg Overmann, Thomas Clavel: A collection of bacterial isolates from the pig intestine reveals functional and taxonomic diversity. Nat. Commun. 11: 2020 ( DOI 10.1038/s41467-020-19929-w )
#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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