Coprobacter secundus (DSM 28864, VKM B-2857, JCM 33897)

Name: Coprobacter secundus (DSM 28864, VKM B-2857, JCM 33897)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 140126

Type strain

Strain Designation 177

Culture col. no. DSM 28864 VKM B-2857 JCM 33897 177

NCBI tax ID(s) 1501392 2843171

Special Collections DSMZ JCM

History DSMZ <- V. Shcherbakova, Institute of Biochemistry and Physiology of Microorganisms, Russian Academy of Science, Pushchino, Russia <- A.N. Shkoporov and B.A. Efimov; 177 JCM <-- DSM 28864 <-- V. Shcherbakova; Skryabin Inst. of Biochem. & Physiol. of Microorg., RAS, Russia <-- A. N. Shkoporov and B. A. Efimov; Russian Natl. Res. Med. Univ., Russia; 177.

Links

Coprobacter secundus 177 is a bacterium that was isolated from human faeces.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 403723

JCM 33897,VKM B-2857,DSM 28864

@ref 20215

Last LPSN update 2026-05-29 08:39:39

Domain Bacteria

Phylum Bacteroidota

Class Bacteroidia

Order Bacteroidales

Family Barnesiellaceae

Genus Coprobacter

Species Coprobacter secundus

Full scientific name Coprobacter secundus Shkoporov et al. 2015

Synonyms (1)

"Gabonia massiliensis"

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	98.732

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
42733	PYG MEDIUM (MODIFIED) (DSMZ Medium 104)		Medium recipe at MediaDive	Name: PYG MEDIUM (modified) (DSMZ Medium 104) Composition: Yeast extract 10.0 g/l Peptone 5.0 g/l Trypticase peptone 5.0 g/l Beef extract 5.0 g/l Glucose 5.0 g/l L-Cysteine HCl x H2O 0.5 g/l NaHCO3 0.4 g/l NaCl 0.08 g/l K2HPO4 0.04 g/l KH2PO4 0.04 g/l MgSO4 x 7 H2O 0.02 g/l CaCl2 x 2 H2O 0.01 g/l Hemin 0.005 g/l Ethanol 0.0038 g/l Resazurin 0.001 g/l Tween 80 Vitamin K1 NaOH Distilled water

Culture temp

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

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	anaerobe	94.856

Spore formation

@ref	Spore formation	Confidence
125439		99.611

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
42733	-	-	+	+	-	+	-	-	-	+	-	-	-	-	-	-	+	-	-	+	-	-	-	-	+	-	-	-	-

Isolation, sampling and environmental information

Isolation source categories

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

Isolation

@ref	Sample type	Host species	Country	Country ISO 3 Code	Continent
42733	human faeces	Homo sapiens	Russia	RUS	Europe
67770	Stool of an 18-year-old healthy Russian female	Homo sapiens

Taxonmaps

69479

Global distribution of 16S sequence KJ572412 (>99% sequence identity) for Bacteroidales from Microbeatlas

Aquatic Samples	186
Soil Samples	38
Animal Samples	12866
Plant Samples	9
Total Samples	13099

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
42733	1	Risk group (German classification)

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM80310v2 assembly for Coprobacter secundus 177	contig	GCA_000803105	1572646.3	2630968585	1501392	72.35

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67770	Coprobacter secundus strain 177 16S ribosomal RNA gene, partial sequence	KJ572412	1290		1501392

GC content

@ref	GC-content (mol%)	Method
42733	37.8	sequence analysis
67770	37.8	genome sequence analysis

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM80310v2 assembly for Coprobacter secundus 177
NCBI: GCA_000803105

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	gram_stain	BacteriaNetⓘ	negative	98.73	no
125439	oxygen_tolerance	BacteriaNetⓘ	anaerobe	94.86	no
125439	motility	BacteriaNetⓘ	no	64.23	no
125439	spore_formation	BacteriaNetⓘ	no	99.61	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Investigating the causal role of the gut microbiome in Kawasaki disease: mediating effects of immune cells.	Fan Y, Zhang S, Guo F.	Clin Rheumatol	10.1007/s10067-025-07687-3	2025
	The Causal Relationship Between Gut and Skin Microbiota and Chronic Obstructive Pulmonary Disease:A Bidirectional Two-Sample Mendelian Randomization Analysis.	Luo Z, Liao G, Meng M, Huang X, Liu X, Wen W, Yue T, Yu W, Wang C, Jiang Y.	Int J Chron Obstruct Pulmon Dis	10.2147/copd.s494289	2025
Pathogenicity	3-O-Acetyl-11-Keto-beta-Boswellic Acid Suppresses Colitis-Associated Colorectal Cancer by Inhibiting the NF-Kb Signaling Pathway and Remodeling Gut Microbiota.	Xu F, Li W, Zheng XJ, Hao Y, Yang YH, Yang H, Zhang S, Cao WX, Li XX, Zhang X, Du GH, Ji TF, Wang JH.	Oncol Res	10.32604/or.2025.062386	2025
	Association of Gut Dysbiosis with Disease Phenotype and Treatment in Systemic Lupus Erythematosus.	Medina-Martinez I, Gil-Gutierrez R, Garcia-Garcia J, de la Hera-Fernandez FJ, Navarrete-Navarrete N, Zamora-Pasadas M, Ortego-Centeno N, Callejas-Rubio JL, Garcia-Garcia F, Galvez-Peralta J, Rodriguez-Nogales A, Correa-Rodriguez M, Rueda-Medina B.	Med Sci (Basel)	10.3390/medsci13030151	2025
	Gut microbiota influences lung cancer risk through circulating cytokines-Insights from a Mendelian randomization study.	Shen W, Hou R, Zhang J, Liu C, Zhang C, Liu X.	Medicine (Baltimore)	10.1097/md.0000000000044897	2025
Phylogeny	Polyphenol-mediated microbiome modulation in STEMI patients: a pilot study	Issilbayeva A, Sergazy S, Zhashkeyev A, Gulyayev A, Kozhakhmetov S, Shulgau Z, Nurgaziyev M, Nurgaziyeva A, Zhetkenev S, Mukhanbetzhanov N, Jarmukhanov Z, Mukhanbetzhanova Z, Vinogradova E, Zhumadilov Z, Kushugulova A, Aljofan M.	Front Med (Lausanne)		2025
	A powerful machine learning approach to identify interactions of differentially abundant gut microbial subsets in patients with metastatic and non-metastatic pancreatic cancer.	Villani A, Fontana A, Panebianco C, Ferro C, Copetti M, Pavlovic R, Drago D, Fiorentini C, Terracciano F, Bazzocchi F, Canistro G, Pisati F, Maiello E, Latiano TP, Perri F, Pazienza V.	Gut Microbes	10.1080/19490976.2024.2375483	2024
Genetics	Population-based metagenomics analysis reveals altered gut microbiome in sarcopenia: data from the Xiangya Sarcopenia Study.	Wang Y, Zhang Y, Lane NE, Wu J, Yang T, Li J, He H, Wei J, Zeng C, Lei G.	J Cachexia Sarcopenia Muscle	10.1002/jcsm.13037	2022
Metabolism	The dynamics of the gut microbiota in prediabetes during a four-year follow-up among European patients-an IMI-DIRECT prospective study.	Lyu L, Fan Y, Vogt JK, Clos-Garcia M, Bonnefond A, Pedersen HK, Dutta A, Koivula R, Sharma S, Allin KH, Brorsson C, Cederberg H, Chabanova E, De Masi F, Dermitzakis E, Elders PJ, Blom MT, Hollander M, Eriksen R, Forgie I, Frost G, Giordano GN, Grallert H, Haid M, Hansen TH, Jablonka B, Kokkola T, Mahajan A, Mari A, McDonald TJ, Musholt PB, Pavo I, Prehn C, Ridderstrale M, Ruetten H, Hart LM', Schwenk JM, Stankevic E, Thomsen HS, Vangipurapu J, Vestergaard H, Vinuela A, Walker M, Hansen T, Linneberg A, Nielsen HB, Brunak S, McCarthy MI, Froguel P, Adamski J, Franks PW, Laakso M, Beulens JWJ, Pearson E, Pedersen O.	Genome Med	10.1186/s13073-025-01508-7	2025
	Metagenomic sequencing reveals altered gut microbial compositions and gene functions in patients with non-segmental vitiligo.	Luan M, Niu M, Yang P, Han D, Zhang Y, Li W, He Q, Zhao Y, Mao B, Chen J, Mou K, Li P.	BMC Microbiol	10.1186/s12866-023-03020-7	2023
	Fecal short-chain fatty acids vary by sex and amyloid status.	Kuehn JF, Zhang Q, Heston MB, Kang JW, Harding SJ, Davenport-Sis NJ, Peter DC, Kerby RL, Vemuganti V, Schiffmann EC, Tallon MM, Harpt J, Hajra A, Wheeler JL, Shankar S, Mickol A, Zemberi J, Chow H, Zhang E, Clements E, Noughani H, Forst A, Everitt G, Kollmorgen G, Quijano-Rubio C, Christian BT, Carlsson CM, Johnson SC, Asthana S, Zetterberg H, Blennow K, Ulland TK, Rey FE, Bendlin BB.	Alzheimers Dement	10.1002/alz.70877	2025
	Microbial diversity in the vaginal microbiota and its link to pregnancy outcomes.	Baud A, Hillion KH, Plainvert C, Tessier V, Tazi A, Mandelbrot L, Poyart C, Kennedy SP.	Sci Rep	10.1038/s41598-023-36126-z	2023
	Prevotella copri alleviates sarcopenia via attenuating muscle mass loss and function decline.	Liu X, Wu J, Tang J, Xu Z, Zhou B, Liu Y, Hu F, Zhang G, Cheng R, Xia X, Chen Y, Wu H, Wang D, Yue J, Dong B, Fu J, Yu H, Dong B.	J Cachexia Sarcopenia Muscle	10.1002/jcsm.13313	2023
Genetics	Unveiling Candida albicans intestinal carriage in healthy volunteers: the role of micro- and mycobiota, diet, host genetics and immune response.	Delavy M, Sertour N, Patin E, Le Chatelier E, Cole N, Dubois F, Xie Z, Saint-Andre V, Manichanh C, Walker AW, Quintana-Murci L, Duffy D, d'Enfert C, Bougnoux ME, Consortium MI.	Gut Microbes	10.1080/19490976.2023.2287618	2023
	Effects of alpha-glyceryl monolaurate on growth, immune function, volatile fatty acids, and gut microbiota in broiler chickens.	Lan J, Chen G, Cao G, Tang J, Li Q, Zhang B, Yang C.	Poult Sci	10.1016/j.psj.2020.11.052	2021
Genetics	Longitudinal fecal microbiome and metabolite data demonstrate rapid shifts and subsequent stabilization after an abrupt dietary change in healthy adult dogs.	Lin CY, Jha AR, Oba PM, Yotis SM, Shmalberg J, Honaker RW, Swanson KS.	Anim Microbiome	10.1186/s42523-022-00194-9	2022
Metabolism	Quercetin metabolism by fecal microbiota from healthy elderly human subjects.	Tamura M, Hoshi C, Kobori M, Takahashi S, Tomita J, Nishimura M, Nishihira J.	PLoS One	10.1371/journal.pone.0188271	2017
	Effects of novel microecologics combined with traditional Chinese medicine and probiotics on growth performance and health of broilers.	Gao J, Wang R, Liu J, Wang W, Chen Y, Cai W.	Poult Sci	10.1016/j.psj.2021.101412	2022
	Disease-associated gut microbiome and metabolome changes in patients with chronic obstructive pulmonary disease.	Bowerman KL, Rehman SF, Vaughan A, Lachner N, Budden KF, Kim RY, Wood DLA, Gellatly SL, Shukla SD, Wood LG, Yang IA, Wark PA, Hugenholtz P, Hansbro PM.	Nat Commun	10.1038/s41467-020-19701-0	2020
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
	Allocoprobacillus halotolerans gen. nov., sp. nov and Coprobacter tertius sp. nov., isolated from human gut microbiota.	Teng NMY, Kiu R, Evans R, Baker DJ, Zenner C, Robinson SD, Hall LJ.	Int J Syst Evol Microbiol	10.1099/ijsem.0.005950	2023
Phylogeny	Coprobacter secundus subsp. similis subsp. nov. and Solibaculum mannosilyticum gen. nov., sp. nov., isolated from human feces.	Sakamoto M, Ikeyama N, Toyoda A, Murakami T, Mori H, Morohoshi S, Kunihiro T, Iino T, Ohkuma M.	Microbiol Immunol	10.1111/1348-0421.12886	2021
Phylogeny	Alistipes inops sp. nov. and Coprobacter secundus sp. nov., isolated from human faeces.	Shkoporov AN, Chaplin AV, Khokhlova EV, Shcherbakova VA, Motuzova OV, Bozhenko VK, Kafarskaia LI, Efimov BA	Int J Syst Evol Microbiol	10.1099/ijsem.0.000617	2015

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 )
#42733	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 28864
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#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 .
#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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Coprobacter secundus (DSM 28864, VKM B-2857, JCM 33897)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

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_000803105

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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