Hallella seregens (DSM 28061, ATCC 51272, CCUG 33639)

Name: Hallella seregens (DSM 28061, ATCC 51272, CCUG 33639)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 24344

Type strain

Culture col. no. DSM 28061 ATCC 51272 CCUG 33639 CIP 107046 VPI D238B-15

NCBI tax ID(s) 1336250 52229

Special Collections DSMZ CCUG CIP

History <- CCUG <- ATCC <- L. V. Moore; VPI D238B-15 CIP <- 2001, CCUG <- 1994, ATCC <- L.V. Moore: strain VPI D238B-15

Links

Hallella seregens DSM 28061 is an anaerobe, Gram-negative, rod-shaped bacterium that was isolated from gingival crevice of periodontally healthy patient.

Gram-negative rod-shaped anaerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 42891

ATCC 51272,CCUG 33639,CIP 107046,DSM 28061

@ref 20215

Last LPSN update 2026-02-09 11:23:57

Domain Bacteria

Phylum Bacteroidota

Class Bacteroidia

Order Bacteroidales

Family Prevotellaceae

Genus Hallella

Species Hallella seregens

Full scientific name Hallella seregens Moore and Moore 1994

Morphology

Cell morphology

@ref	Gram stain	Cell shape	Confidence
125439	negative		98.1
125438	negative		94.437
122568	negative	rod-shaped

Colony morphology

21085

Incubation period2-3 days

Culture and growth conditions

Culture medium

@ref	Name	Medium link	Composition
21085	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
36022	MEDIUM 20 - for Anaerobic bacteria		Agar (15.000 g);Glucose (5.000 g);Yeast extract (20.000 g);Tryptone (30.000 g);Cysteine hydrochloride (0.500 g);distilled water (1000.000 ml);Hemin solution -M00149 (25.000 ml)
122568	CIP Medium 20	Medium recipe at CIP

Culture temp

@ref	Growth	Type	Temperature (°C)
21085	positive	growth	37
36022	positive	growth	37
51651	positive	growth	37

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
21085	anaerobe
51651	anaerobe
125438	anaerobe	93.527
125439	anaerobe	98.6

Spore formation

@ref	Spore formation	Confidence
125439		99.7
125438		90.488

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
68367	17057 ChEBI	cellobiose	-	builds acid from	from API 20A
68367	17634 ChEBI	D-glucose	+	builds acid from	from API 20A
68367	16899 ChEBI	D-mannitol	-	builds acid from	from API 20A
68367	16024 ChEBI	D-mannose	+	builds acid from	from API 20A
68367	65327 ChEBI	D-xylose	+	builds acid from	from API 20A
68367	4853 ChEBI	esculin	-	hydrolysis	from API 20A
68367	5291 ChEBI	gelatin	-	hydrolysis	from API 20A
68367	17754 ChEBI	glycerol	-	builds acid from	from API 20A
68367	30849 ChEBI	L-arabinose	+	builds acid from	from API 20A
68380	29985 ChEBI	L-glutamate	-	degradation	from API rID32A
68367	62345 ChEBI	L-rhamnose	+	builds acid from	from API 20A
68367	17716 ChEBI	lactose	+	builds acid from	from API 20A
68367	17306 ChEBI	maltose	+	builds acid from	from API 20A
68367	6731 ChEBI	melezitose	-	builds acid from	from API 20A
68380	16634 ChEBI	raffinose	-	fermentation	from API rID32A
68367	16634 ChEBI	raffinose	+	builds acid from	from API 20A
68367	17814 ChEBI	salicin	-	builds acid from	from API 20A
68367	30911 ChEBI	sorbitol	-	builds acid from	from API 20A
68367	17992 ChEBI	sucrose	+	builds acid from	from API 20A
68367	27082 ChEBI	trehalose	-	builds acid from	from API 20A
68380	27897 ChEBI	tryptophan	-	energy source	from API rID32A
68367	27897 ChEBI	tryptophan	-	energy source	from API 20A
68367	16199 ChEBI	urea	-	hydrolysis	from API 20A

Metabolite production

@ref	Chebi-ID	Metabolite	Production
68380	35581 ChEBI	indole		from API rID32A
68367	35581 ChEBI	indole		from API 20A

Metabolite tests

@ref	Chebi-ID	Metabolite	Indole test
68380	35581 ChEBI	indole	-	from API rID32A
68367	35581 ChEBI	indole	-	from API 20A

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	beta-galactosidase	+	3.2.1.23	from API rID32A
68380	beta-Galactosidase 6-phosphate	-		from API rID32A
68367	beta-glucosidase	-	3.2.1.21	from API 20A
68380	beta-glucuronidase	-	3.2.1.31	from API rID32A
68367	gelatinase	-		from API 20A
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
68367	urease	-	3.5.1.5	from API 20A

Fatty acid profile

Metadata FA analysis

@ref

51651

Fatty acid	Percentage	ECL
C16:0 3OH	15.3	17.52
C16:0	13.9	16
C18:2 ω6,9c/C18:0 ANTE	13.1	17.724
C14:0 ISO	12.7	13.618
C14:0	6.7	14
Unidentified	5	17.793
C16:0 iso	3.8	15.626
C18:1 ω9c	3.7	17.769
C15:0 ANTEISO	3.6	14.711
C18:0	2.9	18
Unidentified	2.5	17.256
Unidentified	2.3	18.227
C17:1 ω5c ISO	2.2	16.461
C15:0	1.7	15
C16:0 iso 3OH	1.7	17.145
C16:1 ω7c	1.4	15.819
C16:1 ω7c alcohol	0.9	15.386
Unidentified	0.9	15.936
Unidentified	0.8	19.171
Unidentified	0.7	18.705
C14:0 3OH/C16:1 ISO I	0.6	15.485
Unidentified	0.6	14.309
C13:0 2OH	0.6	14.191
C17:0 3OH	0.5	18.535
Unidentified	0.5	19.696
C13:1 at 12-13	0.5	12.931
Unidentified	0.4	14.946
C13:0 3OH/C15:1 i I/H	0.4	14.469
C12:0	0.4	12

API 20A

@ref	IND	URE	GLU	MAN	LAC	SAC	MAL	SAL	XYL	ARA	GEL	ESC	GLY	CEL	MNE	MLZ	RAF	SOR	RHA	TRE	CAT	Spores presentSPOR	GramGRAM	Morphology coccus="+" rod="-"COCC
21085	-	-	+	-	+	+	+	-	+	+	-	-	-	-	+	-	+	-	+	-	not determinedn.d.	not determinedn.d.	not determinedn.d.	not determinedn.d.

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
51651	-	-	+	+	-	+	-	+	-	+	-	-	-	-	-	-	+	-	-	+	-	-	-	-	+	-	-	-	-
21085	+	+	+	+	-	+	+/-	+	-	+	+	-	-	-	+	-	+	-	-	+	-	-	-	-	+	-	-	-	-

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Infection	#Patient	-
#Host Body-Site	#Oral cavity and airways	#Gingiva

Isolation

@ref	Sample type	Country	Country ISO 3 Code	Continent	Isolation date
21085	gingival crevice of periodontally healthy patient
51651	Gingival crevice of periodontally healthy patient
122568	Gingival crevice of periodontally healthy patient	United States of America	USA	North America	1994

Taxonmaps

69479

Global distribution of 16S sequence X81877 (>99% sequence identity) for Prevotella from Microbeatlas

Aquatic Samples	247
Soil Samples	230
Animal Samples	17748
Plant Samples	99
Total Samples	18324

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
21085	2	Risk group (German classification)
122568	1	Risk group (French classification)

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
124043	ASM4243143v1 assembly for Hallella seregens ATCC 51272	contig	GCA_042431435			1336250	75.22
66792	ASM51854v1 assembly for Hallella seregens ATCC 51272	scaffold	GCA_000518545	1336250.3	2545555854	1336250	65.71

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
21085	H.seregens 16S rRNA gene	X81877	1463		1336250

GC content

@ref	GC-content (mol%)	Method
21085	56.0	sequence analysis

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Hallella seregens ATCC 51272
NCBI: GCA_000518545

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	99.70	no
125439	motility	BacteriaNetⓘ	yes	51.70	no
125439	gram_stain	BacteriaNetⓘ	negative	98.10	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	98.60	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Hallella seregens ATCC 51272 ATCC 51272
NCBI: GCA_000518545.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	94.44	no
125438	anaerobic	anaerobicⓘ	yes	93.53	yes
125438	aerobic	aerobicⓘ	no	94.31	yes
125438	spore-forming	spore-formingⓘ	no	90.49	no
125438	thermophilic	thermophileⓘ	no	93.72	no
125438	flagellated	motile2+ⓘ	no	88.50	no

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Distribution of Bacterial alpha1,3-Galactosyltransferase Genes in the Human Gut Microbiome.	Montassier E, Al-Ghalith GA, Mathe C, Le Bastard Q, Douillard V, Garnier A, Guimon R, Raimondeau B, Touchefeu Y, Duchalais E, Vince N, Limou S, Gourraud PA, Laplaud DA, Nicot AB, Soulillou JP, Berthelot L.	Front Immunol	10.3389/fimmu.2019.03000	2019
Genetics	Analysis of 1,000 Type-Strain Genomes Improves Taxonomic Classification of Bacteroidetes.	Garcia-Lopez M, Meier-Kolthoff JP, Tindall BJ, Gronow S, Woyke T, Kyrpides NC, Hahnke RL, Goker M.	Front Microbiol	10.3389/fmicb.2019.02083	2019
	Engineering Saccharomyces Cerevisiae With Novel Functional Xylose Isomerases From Rumen Microbiota for Enhanced Biofuel Production.	Vargas BO, Carazzolle MF, Galhardo JP, Jose J, de Souza BC, Correia JBL, Santos JRD, Pereira GAG, de de Mello FDSB.	Biotechnol J	10.1002/biot.70050	2025
Genetics	Exploring the molecular intersection for hypertension, hyperlipidemia and their comorbid conditions through multi-omics approaches.	Li W, Zhou D, Ji Y, Tian H, Meng N, Li J, Guo N, He X, Dao M, Jin X.	Front Cardiovasc Med	10.3389/fcvm.2025.1593688	2025
	Alterations in lung and gut microbiota reduce diversity in patients with nontuberculous mycobacterial pulmonary disease.	Choi JY, Shim B, Park Y, Kang YA.	Korean J Intern Med	10.3904/kjim.2023.097	2023
Genetics	Monensin Alters the Functional and Metabolomic Profile of Rumen Microbiota in Beef Cattle.	Ogunade I, Schweickart H, Andries K, Lay J, Adeyemi J.	Animals (Basel)	10.3390/ani8110211	2018
	Effect of a Hop Extract Standardized in 8-Prenylnaringenin on Bone Health and Gut Microbiome in Postmenopausal Women with Osteopenia: A One-Year Randomized, Double-Blind, Placebo-Controlled Trial.	Lecomte M, Tomassi D, Rizzoli R, Tenon M, Berton T, Harney S, Fanca-Berthon P.	Nutrients	10.3390/nu15122688	2023
	Big data mining, rational modification, and ancestral sequence reconstruction inferred multiple xylose isomerases for biorefinery.	Chen S, Xu Z, Ding B, Zhang Y, Liu S, Cai C, Li M, Dale BE, Jin M.	Sci Adv	10.1126/sciadv.add8835	2023
Phylogeny	16S rRNA gene similarities indicate that Hallella seregens (Moore and Moore) and Mitsuokella dentalis (Haapsalo et al.) are genealogically highly related and are members of the genus Prevotella: emended description of the genus Prevotella (Shah and Collins) and description of Prevotella dentalis comb. nov.	Willems A, Collins MD.	Int J Syst Bacteriol	10.1099/00207713-45-4-832	1995
Phylogeny	Recently described clinically important anaerobic bacteria: taxonomic aspects and update.	Jousimies-Somer H.	Clin Infect Dis	10.1086/516227	1997
Phylogeny	Genome-Based Taxonomic Classification of Bacteroidetes.	Hahnke RL, Meier-Kolthoff JP, Garcia-Lopez M, Mukherjee S, Huntemann M, Ivanova NN, Woyke T, Kyrpides NC, Klenk HP, Goker M.	Front Microbiol	10.3389/fmicb.2016.02003	2016
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
Phylogeny	Oribaculum catoniae gen. nov., sp. nov.; Catonella morbi gen. nov., sp. nov.; Hallella seregens gen. nov., sp. nov.; Johnsonella ignava gen. nov., sp. nov.; and Dialister pneumosintes gen. nov., comb. nov., nom. rev., Anaerobic gram-negative bacilli from the human gingival crevice.	Moore LV, Moore WE	Int J Syst Bacteriol	10.1099/00207713-44-2-187	1994

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 )
#21085	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 28061
#36022	; Curators of the CIP;
#51651	Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 33639
#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) .
#68367	Automatically annotated from API 20A .
#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 .
#122568	Collection of Institut Pasteur ; Curators of the CIP; CIP 107046
#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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Hallella seregens (DSM 28061, ATCC 51272, CCUG 33639)

Name and taxonomic classification

Morphology

Cell morphology

Colony morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Metabolite utilization

Metabolite production

Metabolite tests

Enzymes

Fatty acid profile

API 20A

API rID32A

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_042431435

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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