Subdoligranulum variabile (DSM 15176, CCUG 47106, BI-114)

Name: Subdoligranulum variabile (DSM 15176, CCUG 47106, BI-114)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 17688

Type strain

Strain Designation BI-114

Culture col. no. DSM 15176 CCUG 47106 BI-114

NCBI tax ID(s) 411471 214851

Special Collections DSMZ CCUG

History <- K. Holmström, Bioneer A/S, Department of Molecular Characterization, Hørsholm, Denmark; BI-114 <- T. Möller; {2001}

Links

Subdoligranulum variabile BI-114 is an anaerobe bacterium that was isolated from human feces of a 47-year-old female.

anaerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 132723

CCUG 47106,DSM 15176,VTT E-062950

@ref 20215

Last LPSN update 2026-02-09 11:22:17

Domain Bacteria

Phylum Bacillota

Class Clostridia

Order Eubacteriales

Family Oscillospiraceae

Genus Subdoligranulum

Species Subdoligranulum variabile

Full scientific name Subdoligranulum variabile Holmstrøm et al. 2004

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	99

Colony morphology

57513

Incubation period3 days

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
5741	WILKINS-CHALGREN ANAEROBE BROTH (N2/CO2) (DSMZ Medium 339a)		Medium recipe at MediaDive	Name: WILKINS-CHALGREN ANAEROBE BROTH (N2/CO2) (DSMZ Medium 339a; with strain-specific modifications) Composition: dehydrated Wilkins-Chalgren medium 33.0 g/l L-Cysteine HCl 0.3 g/l Sodium resazurin 0.0005 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
5741	positive	growth	37
57513	positive	growth	37

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
5741	anaerobe
57513	anaerobe
125438	anaerobe	90.922
125439	obligate anaerobe	99.3

Spore formation

@ref	Spore formation	Confidence
125439		98.1

Pathway annotation

Computationally determined by

@ref	pathway	enzyme coverage	annotated reactions
66794	gluconeogenesis	100	8 of 8
66794	C4 and CAM-carbon fixation	100	8 of 8
66794	starch degradation	100	10 of 10
66794	cis-vaccenate biosynthesis	100	2 of 2
66794	adipate degradation	100	2 of 2
66794	L-lactaldehyde degradation	100	3 of 3
66794	coenzyme A metabolism	100	4 of 4
66794	CDP-diacylglycerol biosynthesis	100	2 of 2
66794	anapleurotic synthesis of oxalacetate	100	1 of 1
66794	folate polyglutamylation	100	1 of 1
66794	UDP-GlcNAc biosynthesis	100	3 of 3
66794	suberin monomers biosynthesis	100	2 of 2
66794	palmitate biosynthesis	90.91	20 of 22
66794	threonine metabolism	90	9 of 10
66794	chorismate metabolism	88.89	8 of 9
66794	valine metabolism	88.89	8 of 9
66794	aspartate and asparagine metabolism	88.89	8 of 9
66794	vitamin B1 metabolism	84.62	11 of 13
66794	metabolism of amino sugars and derivatives	80	4 of 5
66794	glycogen metabolism	80	4 of 5
66794	Entner Doudoroff pathway	80	8 of 10
66794	peptidoglycan biosynthesis	80	12 of 15
66794	cellulose degradation	80	4 of 5
66794	photosynthesis	78.57	11 of 14
66794	d-mannose degradation	77.78	7 of 9
66794	CO2 fixation in Crenarchaeota	77.78	7 of 9
66794	phenylalanine metabolism	76.92	10 of 13
66794	vitamin B12 metabolism	76.47	26 of 34
66794	glycogen biosynthesis	75	3 of 4
66794	CMP-KDO biosynthesis	75	3 of 4
66794	butanoate fermentation	75	3 of 4
66794	ppGpp biosynthesis	75	3 of 4
66794	acetate fermentation	75	3 of 4
66794	reductive acetyl coenzyme A pathway	71.43	5 of 7
66794	purine metabolism	68.09	64 of 94
66794	oxidative phosphorylation	67.03	61 of 91
66794	serine metabolism	66.67	6 of 9
66794	formaldehyde oxidation	66.67	2 of 3
66794	acetoin degradation	66.67	2 of 3
66794	pyrimidine metabolism	66.67	30 of 45
66794	glycolate and glyoxylate degradation	66.67	4 of 6
66794	molybdenum cofactor biosynthesis	66.67	6 of 9
66794	octane oxidation	66.67	2 of 3
66794	glycolysis	64.71	11 of 17
66794	pentose phosphate pathway	63.64	7 of 11
66794	d-xylose degradation	63.64	7 of 11
66794	metabolism of disaccharids	63.64	7 of 11
66794	isoleucine metabolism	62.5	5 of 8
66794	degradation of sugar alcohols	62.5	10 of 16
66794	dTDPLrhamnose biosynthesis	62.5	5 of 8
66794	NAD metabolism	61.11	11 of 18
66794	glutamate and glutamine metabolism	60.71	17 of 28
66794	hydrogen production	60	3 of 5
66794	alanine metabolism	58.62	17 of 29
66794	histidine metabolism	58.62	17 of 29
66794	propanol degradation	57.14	4 of 7
66794	tetrahydrofolate metabolism	57.14	8 of 14
66794	polyamine pathway	56.52	13 of 23
66794	degradation of hexoses	55.56	10 of 18
66794	degradation of sugar acids	52	13 of 25
66794	arginine metabolism	50	12 of 24
66794	cysteine metabolism	50	9 of 18
66794	ethanol fermentation	50	1 of 2
66794	non-pathway related	50	19 of 38
66794	ketogluconate metabolism	50	4 of 8
66794	sulfopterin metabolism	50	2 of 4
66794	selenocysteine biosynthesis	50	3 of 6
66794	phenylmercury acetate degradation	50	1 of 2
66794	myo-inositol biosynthesis	50	5 of 10
66794	quinate degradation	50	1 of 2
66794	ribulose monophosphate pathway	50	1 of 2
66794	aminopropanol phosphate biosynthesis	50	1 of 2
66794	lactate fermentation	50	2 of 4
66794	leucine metabolism	46.15	6 of 13
66794	urea cycle	46.15	6 of 13
66794	isoprenoid biosynthesis	46.15	12 of 26
66794	methionine metabolism	46.15	12 of 26
66794	vitamin B6 metabolism	45.45	5 of 11
66794	proline metabolism	45.45	5 of 11
66794	lysine metabolism	45.24	19 of 42
66794	lipid metabolism	45.16	14 of 31
66794	nitrate assimilation	44.44	4 of 9
66794	heme metabolism	42.86	6 of 14
66794	ubiquinone biosynthesis	42.86	3 of 7
66794	cardiolipin biosynthesis	42.86	3 of 7
66794	degradation of pentoses	42.86	12 of 28
66794	citric acid cycle	42.86	6 of 14
66794	tryptophan metabolism	42.11	16 of 38
66794	degradation of aromatic, nitrogen containing compounds	41.67	5 of 12
66794	arachidonate biosynthesis	40	2 of 5
66794	O-antigen biosynthesis	40	2 of 5
66794	methylglyoxal degradation	40	2 of 5
66794	factor 420 biosynthesis	40	2 of 5
66794	glycine metabolism	40	4 of 10
66794	bacilysin biosynthesis	40	2 of 5
66794	sulfate reduction	38.46	5 of 13
66794	6-hydroxymethyl-dihydropterin diphosphate biosynthesis	37.5	3 of 8
66794	tyrosine metabolism	35.71	5 of 14
66794	glutathione metabolism	35.71	5 of 14
66794	lipid A biosynthesis	33.33	3 of 9
66794	flavin biosynthesis	33.33	5 of 15
66794	3-phenylpropionate degradation	33.33	5 of 15
66794	acetyl CoA biosynthesis	33.33	1 of 3
66794	IAA biosynthesis	33.33	1 of 3
66794	sphingosine metabolism	33.33	2 of 6
66794	arachidonic acid metabolism	33.33	6 of 18
66794	ascorbate metabolism	31.82	7 of 22
66794	phenol degradation	30	6 of 20
66794	4-hydroxyphenylacetate degradation	30	3 of 10
66794	propionate fermentation	30	3 of 10
66794	coenzyme M biosynthesis	30	3 of 10
66794	benzoyl-CoA degradation	28.57	2 of 7
66794	dolichyl-diphosphooligosaccharide biosynthesis	27.27	3 of 11
66794	androgen and estrogen metabolism	25	4 of 16
66794	cyclohexanol degradation	25	1 of 4
66794	toluene degradation	25	1 of 4
66794	carnitine metabolism	25	2 of 8
66794	biotin biosynthesis	25	1 of 4
66794	4-hydroxymandelate degradation	22.22	2 of 9

Isolation, sampling and environmental information

Isolation source categories

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

Isolation

@ref	Sample type	Sampling date	Geographic location	Country	Country ISO 3 Code	Continent
5741	human feces of a 47-year-old female		Horsholm	Denmark	DNK	Europe
57513	Human feces,47-yr-old woman	2001-10-24	Hörsholm	Denmark	DNK	Europe

Taxonmaps

69479

Global distribution of 16S sequence AJ518869 (>99% sequence identity) for Gemmiger formicilis from Microbeatlas

Aquatic Samples	2746
Soil Samples	1289
Animal Samples	151032
Plant Samples	355
Total Samples	155422

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
5741	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	ASM2515257v1 assembly for Subdoligranulum variabile DSM 15176	complete	GCA_025152575	214851.67	8049194197	214851	98.59
66792	ASM15795v1 assembly for Subdoligranulum variabile DSM 15176	scaffold	GCA_000157955	411471.126	2562617078	411471	60.83

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
5741	Subdoligranulum variabile 16S rRNA gene, type strain BI 114T	AJ518869	1428		214851

GC content

5741

GC-content (mol%)52.2

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Subdoligranulum variabile strain DSM 15176
PATRIC: 214851.67

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	98.10	no
125439	motility	BacteriaNetⓘ	no	60.90	no
125439	gram_stain	BacteriaNetⓘ	negative	99.00	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate anaerobe	99.30	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Subdoligranulum variabile DSM 15176
NCBI: GCA_025152575.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	yes	67.01	no
125438	anaerobic	anaerobicⓘ	yes	90.92	no
125438	aerobic	aerobicⓘ	no	95.50	no
125438	spore-forming	spore-formingⓘ	no	60.44	no
125438	thermophilic	thermophileⓘ	no	90.70	yes
125438	flagellated	motile2+ⓘ	no	89.10	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Metabolism	Roles of human colonic bacteria in pectin utilization and associated cross-feeding networks revealed using synthetic co-cultures.	Solvang M, Farquharson FM, Horgan G, Pisano S, Holck J, Zeuner B, Russell WR, Louis P.	Microbiology (Reading)	10.1099/mic.0.001559	2025
	Gut Subdoligranulum variabile ameliorates rheumatoid arthritis by promoting TSG-6 synthesis from joint cells.	Li H, Dai J, Zhao C, Hu T, Zhao G, Wang Q, Zhang L.	Front Immunol	10.3389/fimmu.2024.1418717	2024
	Culturing of a complex gut microbial community in mucin-hydrogel carriers reveals strain- and gene-associated spatial organization.	Jin X, Yu FB, Yan J, Weakley AM, Dubinkina V, Meng X, Pollard KS.	Nat Commun	10.1038/s41467-023-39121-0	2023
Genetics	The estrobolome: Estrogen-metabolizing pathways of the gut microbiome and their relation to breast cancer.	Larnder AH, Manges AR, Murphy RA.	Int J Cancer	10.1002/ijc.35427	2025
	High barley intake in non-obese individuals is associated with high natto consumption and abundance of butyrate-producing bacteria in the gut: a cross-sectional study.	Maruyama S, Matsuoka T, Hosomi K, Park J, Murakami H, Miyachi M, Kawashima H, Mizuguchi K, Kobayashi T, Ooka T, Yamagata Z, Kunisawa J.	Front Nutr	10.3389/fnut.2024.1434150	2024
	Development of culture methods capable of culturing a wide range of predominant species of intestinal bacteria.	Hirano R, Nishita I, Nakai R, Bito A, Sasabe R, Kurihara S.	Front Cell Infect Microbiol	10.3389/fcimb.2023.1056866	2023
Metabolism	Vitamin Biosynthesis by Human Gut Butyrate-Producing Bacteria and Cross-Feeding in Synthetic Microbial Communities.	Soto-Martin EC, Warnke I, Farquharson FM, Christodoulou M, Horgan G, Derrien M, Faurie JM, Flint HJ, Duncan SH, Louis P.	mBio	10.1128/mbio.00886-20	2020
	Systemic metabolic depletion of gut microbiome undermines responsiveness to melanoma immunotherapy.	Zakharevich NV, Morozov MD, Kanaeva VA, Filippov MS, Zyubko TI, Ivanov AB, Ulyantsev VI, Klimina KM, Olekhnovich EI.	Life Sci Alliance	10.26508/lsa.202302480	2024
	New gene markers for classification and quantification of Faecalibacterium spp. in the human gut.	Tanno H, Chatel JM, Martin R, Mariat D, Sakamoto M, Yamazaki M, Salminen S, Gueimonde M, Endo A.	FEMS Microbiol Ecol	10.1093/femsec/fiad035	2023
	Microbiota therapy acts via a regulatory T cell MyD88/RORgammat pathway to suppress food allergy.	Abdel-Gadir A, Stephen-Victor E, Gerber GK, Noval Rivas M, Wang S, Harb H, Wang L, Li N, Crestani E, Spielman S, Secor W, Biehl H, DiBenedetto N, Dong X, Umetsu DT, Bry L, Rachid R, Chatila TA.	Nat Med	10.1038/s41591-019-0461-z	2019
	Dynamic metabolic interactions and trophic roles of human gut microbes identified using a minimal microbiome exhibiting ecological properties.	Shetty SA, Kostopoulos I, Geerlings SY, Smidt H, de Vos WM, Belzer C.	ISME J	10.1038/s41396-022-01255-2	2022
Genetics	Understanding the Representative Gut Microbiota Dysbiosis in Metformin-Treated Type 2 Diabetes Patients Using Genome-Scale Metabolic Modeling.	Rosario D, Benfeitas R, Bidkhori G, Zhang C, Uhlen M, Shoaie S, Mardinoglu A.	Front Physiol	10.3389/fphys.2018.00775	2018
	Assessment of Gram- and Viability-Staining Methods for Quantifying Bacterial Community Dynamics Using Flow Cytometry.	Duquenoy A, Bellais S, Gasc C, Schwintner C, Dore J, Thomas V.	Front Microbiol	10.3389/fmicb.2020.01469	2020
	Inter-species Metabolic Interactions in an In-vitro Minimal Human Gut Microbiome of Core Bacteria.	Shetty SA, Kuipers B, Atashgahi S, Aalvink S, Smidt H, de Vos WM.	NPJ Biofilms Microbiomes	10.1038/s41522-022-00275-2	2022
	Intermediate role of gut microbiota in vitamin B nutrition and its influences on human health.	Wan Z, Zheng J, Zhu Z, Sang L, Zhu J, Luo S, Zhao Y, Wang R, Zhang Y, Hao K, Chen L, Du J, Kan J, He H.	Front Nutr	10.3389/fnut.2022.1031502	2022
Phylogeny	Unique beta-Glucuronidase Locus in Gut Microbiomes of Crohn's Disease Patients and Unaffected First-Degree Relatives.	Gloux K, Anba-Mondoloni J.	PLoS One	10.1371/journal.pone.0148291	2016
Metabolism	A metagenomic beta-glucuronidase uncovers a core adaptive function of the human intestinal microbiome.	Gloux K, Berteau O, El Oumami H, Beguet F, Leclerc M, Dore J.	Proc Natl Acad Sci U S A	10.1073/pnas.1000066107	2011
Metabolism	Strain dropouts reveal interactions that govern the metabolic output of the gut microbiome.	Wang M, Osborn LJ, Jain S, Meng X, Weakley A, Yan J, Massey WJ, Varadharajan V, Horak A, Banerjee R, Allende DS, Chan ER, Hajjar AM, Wang Z, Dimas A, Zhao A, Nagashima K, Cheng AG, Higginbottom S, Hazen SL, Brown JM, Fischbach MA.	Cell	10.1016/j.cell.2023.05.037	2023
Metabolism	Glucuronides in the gut: Sugar-driven symbioses between microbe and host.	Pellock SJ, Redinbo MR.	J Biol Chem	10.1074/jbc.r116.767434	2017
Enzymology	Distinct and redundant functions of three homologs of RNase III in the cyanobacterium Synechococcus sp. strain PCC 7002.	Gordon GC, Cameron JC, Pfleger BF.	Nucleic Acids Res	10.1093/nar/gky041	2018
	Functional Comparison of Bacteria from the Human Gut and Closely Related Non-Gut Bacteria Reveals the Importance of Conjugation and a Paucity of Motility and Chemotaxis Functions in the Gut Environment.	Dobrijevic D, Abraham AL, Jamet A, Maguin E, van de Guchte M.	PLoS One	10.1371/journal.pone.0159030	2016
Phylogeny	Butyrate production in phylogenetically diverse Firmicutes isolated from the chicken caecum.	Eeckhaut V, Van Immerseel F, Croubels S, De Baere S, Haesebrouck F, Ducatelle R, Louis P, Vandamme P.	Microb Biotechnol	10.1111/j.1751-7915.2010.00244.x	2011
	Autometa: automated extraction of microbial genomes from individual shotgun metagenomes.	Miller IJ, Rees ER, Ross J, Miller I, Baxa J, Lopera J, Kerby RL, Rey FE, Kwan JC.	Nucleic Acids Res	10.1093/nar/gkz148	2019
Metabolism	Products of gut microbial Toll/interleukin-1 receptor domain NADase activities in gnotobiotic mice and Bangladeshi children with malnutrition.	Weagley JS, Zaydman M, Venkatesh S, Sasaki Y, Damaraju N, Yenkin A, Buchser W, Rodionov DA, Osterman A, Ahmed T, Barratt MJ, DiAntonio A, Milbrandt J, Gordon JI.	Cell Rep	10.1016/j.celrep.2022.110738	2022
	Extensive microbial and functional diversity within the chicken cecal microbiome.	Sergeant MJ, Constantinidou C, Cogan TA, Bedford MR, Penn CW, Pallen MJ.	PLoS One	10.1371/journal.pone.0091941	2014
Metabolism	Biological systems discovery in silico: radical S-adenosylmethionine protein families and their target peptides for posttranslational modification.	Haft DH, Basu MK.	J Bacteriol	10.1128/jb.00040-11	2011
	Design, construction, and in vivo augmentation of a complex gut microbiome.	Cheng AG, Ho PY, Aranda-Diaz A, Jain S, Yu FB, Meng X, Wang M, Iakiviak M, Nagashima K, Zhao A, Murugkar P, Patil A, Atabakhsh K, Weakley A, Yan J, Brumbaugh AR, Higginbottom S, Dimas A, Shiver AL, Deutschbauer A, Neff N, Sonnenburg JL, Huang KC, Fischbach MA.	Cell	10.1016/j.cell.2022.08.003	2022
Genetics	In vitro culture conditions for maintaining a complex population of human gastrointestinal tract microbiota.	Kim BS, Kim JN, Cerniglia CE.	J Biomed Biotechnol	10.1155/2011/838040	2011
Metabolism	Cytokine Response after Stimulation with Key Commensal Bacteria Differ in Post-Infectious Irritable Bowel Syndrome (PI-IBS) Patients Compared to Healthy Controls.	Sundin J, Rangel I, Repsilber D, Brummer RJ.	PLoS One	10.1371/journal.pone.0134836	2015
Metabolism	From correlation to causality: the case of Subdoligranulum.	Van Hul M, Le Roy T, Prifti E, Dao MC, Paquot A, Zucker JD, Delzenne NM, Muccioli G, Clement K, Cani PD	Gut Microbes	10.1080/19490976.2020.1849998	2020
Phylogeny	Subdoligranulum variabile gen. nov., sp. nov. from human feces.	Holmstrom K, Collins MD, Moller T, Falsen E, Lawson PA	Anaerobe	10.1016/j.anaerobe.2004.01.004	2004

References

#5741	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 15176
#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 )
#57513	Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 47106
#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) .
#66794	Antje Chang, Lisa Jeske, Sandra Ulbrich, Julia Hofmann, Julia Koblitz, Ida Schomburg, Meina Neumann-Schaal, Dieter Jahn, Dietmar Schomburg: BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49: D498 - D508 2020 ( DOI 10.1093/nar/gkaa1025 , PubMed 33211880 )
#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 )

Rate our new design

Content

About us

Subdoligranulum variabile (DSM 15176, CCUG 47106, BI-114)

Name and taxonomic classification

Morphology

Cell morphology

Colony morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Pathway annotation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_025152575

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

Help

Social Media