Desulfovibrio inopinatus (DSM 10711, HHQ 20)

Name: Desulfovibrio inopinatus (DSM 10711, HHQ 20)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 4134

Type strain

Culture col. no. DSM 10711 HHQ 20

NCBI tax ID(s) 1121453 102109

Special Collections DSMZ

History DSMZ <- W. Reichenbecher, Universität Konstanz, Fakultät für Biologie, Konstanz, Germany; HHQ 20 <- W. Reichenbecher {1994}

Links

Desulfovibrio inopinatus DSM 10711 is an anaerobe bacterium that was isolated from marine sediment.

anaerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 48862

DSM 10711

@ref 20215

Last LPSN update 2026-05-29 08:25:52

Domain Bacteria

Phylum Thermodesulfobacteriota

Class Desulfovibrionia

Order Desulfovibrionales

Family Desulfovibrionaceae

Genus Desulfovibrio

Species Desulfovibrio inopinatus

Full scientific name Desulfovibrio inopinatus Reichenbecher and Schink 1999

Synonyms (1)

"Alteridesulfovibrio inopinatus"

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	97.594

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
4050	DESULFOBACTER MEDIUM (DSMZ Medium 193)		Medium recipe at MediaDive	Name: DESULFOBACTER MEDIUM (DSMZ Medium 193; with strain-specific modifications) Composition: NaCl 6.97906 g/l Na2SO4 2.99102 g/l Na-pyruvate 2.49252 g/l Na2CO3 1.49551 g/l MgCl2 x 6 H2O 1.29611 g/l KCl 0.498505 g/l Yeast extract 0.498504 g/l Na2S x 9 H2O 0.398804 g/l NH4Cl 0.299102 g/l KH2PO4 0.199402 g/l CaCl2 x 2 H2O 0.149552 g/l HCl 0.00249252 g/l FeCl2 x 4 H2O 0.00149551 g/l Sodium resazurin 0.000498505 g/l NaOH 0.000498504 g/l CoCl2 x 6 H2O 0.000189432 g/l Pyridoxine hydrochloride 9.97009e-05 g/l MnCl2 x 4 H2O 9.97009e-05 g/l ZnCl2 6.97906e-05 g/l (DL)-alpha-Lipoic acid 4.98504e-05 g/l p-Aminobenzoic acid 4.98504e-05 g/l Riboflavin 4.98504e-05 g/l Calcium D-(+)-pantothenate 4.98504e-05 g/l Thiamine HCl 4.98504e-05 g/l Nicotinic acid 4.98504e-05 g/l Na2MoO4 x 2 H2O 3.58923e-05 g/l NiCl2 x 6 H2O 2.39282e-05 g/l Folic acid 1.99402e-05 g/l Biotin 1.99402e-05 g/l H3BO3 5.98205e-06 g/l Na2WO4 x 2 H2O 3.98804e-06 g/l Na2SeO3 x 5 H2O 2.99103e-06 g/l CuCl2 x 2 H2O 1.99402e-06 g/l Vitamin B12 9.97009e-07 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
4050	positive	growth	30

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
4050	anaerobe
125439	facultative anaerobe	91.219

Spore formation

@ref	Spore formation	Confidence
125439		99.917

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Aquatic	#Marine
#Environmental	#Aquatic	#Sediment

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
4050	marine sediment	Venice	Italy	ITA	Europe

Taxonmaps

69479

Global distribution of 16S sequence AF177276 (>99% sequence identity) for Desulfovibrio inopinatus subclade from Microbeatlas

Aquatic Samples	66
Soil Samples	17
Animal Samples	5
Plant Samples	1
Total Samples	89

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
4050	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	ASM42930v1 assembly for Desulfovibrio inopinatus DSM 10711	scaffold	GCA_000429305	1121453.3	2524614631	1121453	62.99

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
4050	Desulfovibrio inopinatus 16S ribosomal RNA gene, partial sequence	AF177276	1484		102109

GC content

@ref	GC-content (mol%)	Method
4050	49.7	high performance liquid chromatography (HPLC)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: ASM42930v1 assembly for Desulfovibrio inopinatus DSM 10711
NCBI: GCA_000429305

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	facultative anaerobe	91.22	no
125439	gram_stain	BacteriaNetⓘ	negative	97.59	no
125439	motility	BacteriaNetⓘ	yes	44.99	no
125439	spore_formation	BacteriaNetⓘ	no	99.92	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Desulfovibrio inopinatus DSM 10711 DSM 10711
NCBI: GCA_000429305.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	89.04	no
125438	anaerobic	anaerobicⓘ	yes	81.44	yes
125438	aerobic	aerobicⓘ	no	80.85	no
125438	spore-forming	spore-formingⓘ	no	70.92	no
125438	thermophilic	thermophileⓘ	no	83.64	yes
125438	flagellated	motile2+ⓘ	yes	77.61	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Novel Insights on Extracellular Electron Transfer Networks in the Desulfovibrionaceae Family: Unveiling the Potential Significance of Horizontal Gene Transfer.	Gonzalez V, Abarca-Hurtado J, Arancibia A, Claverias F, Guevara MR, Orellana R.	Microorganisms	10.3390/microorganisms12091796	2024
Genetics	Bioinformatics Investigations of Universal Stress Proteins from Mercury-Methylating Desulfovibrionaceae.	Isokpehi RD, McInnis DS, Destefano AM, Johnson GS, Walker AD, Hall YA, Mapp BW, Johnson MO, Simmons SS.	Microorganisms	10.3390/microorganisms9081780	2021
	Unique Patterns and Biogeochemical Relevance of Two-Component Sensing in Marine Bacteria.	Held NA, McIlvin MR, Moran DM, Laub MT, Saito MA.	mSystems	10.1128/msystems.00317-18	2019
Metabolism	A distinct pathway for tetrahymanol synthesis in bacteria.	Banta AB, Wei JH, Welander PV.	Proc Natl Acad Sci U S A	10.1073/pnas.1511482112	2015
Metabolism	Development and Validation of Broad-Range Qualitative and Clade-Specific Quantitative Molecular Probes for Assessing Mercury Methylation in the Environment.	Christensen GA, Wymore AM, King AJ, Podar M, Hurt RA, Santillan EU, Soren A, Brandt CC, Brown SD, Palumbo AV, Wall JD, Gilmour CC, Elias DA.	Appl Environ Microbiol	10.1128/aem.01271-16	2016
	Structure-function studies of Vibrio cholerae quorum-sensing receptor CqsR signal recognition.	Guarnaccia AM, Steenhaut AD, Olenic SD, Na J, Perez LJ, Ng WL, Neiditch MB.	PLoS Pathog	10.1371/journal.ppat.1013447	2025
Metabolism	Hydroxyhydroquinone reductase, the initial enzyme involved in the degradation of hydroxyhydroquinone (1,2,4-trihydroxybenzene) by Desulfovibrio inopinatus.	Reichenbecher W, Philipp B, Suter MJ, Schink B.	Arch Microbiol	10.1007/s002039900130	2000
	Mercury Methylation Genes Identified across Diverse Anaerobic Microbial Guilds in a Eutrophic Sulfate-Enriched Lake.	Peterson BD, McDaniel EA, Schmidt AG, Lepak RF, Janssen SE, Tran PQ, Marick RA, Ogorek JM, DeWild JF, Krabbenhoft DP, McMahon KD.	Environ Sci Technol	10.1021/acs.est.0c05435	2020
	Influence of Phosphorus and Cell Geometry on the Fractionation of Sulfur Isotopes by Several Species of Desulfovibrio during Microbial Sulfate Reduction.	Zaarur S, Wang DT, Ono S, Bosak T.	Front Microbiol	10.3389/fmicb.2017.00890	2017
	Global prevalence and distribution of genes and microorganisms involved in mercury methylation.	Podar M, Gilmour CC, Brandt CC, Soren A, Brown SD, Crable BR, Palumbo AV, Somenahally AC, Elias DA.	Sci Adv	10.1126/sciadv.1500675	2015
Metabolism	Desulfovibrio inopinatus, sp. nov., a new sulfate-reducing bacterium that degrades hydroxyhydroquinone.	Reichenbecher W, Schink B	Arch Microbiol	10.1007/s002030050507	1997

References

#4050	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 10711
#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) .
#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 )

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Desulfovibrio inopinatus (DSM 10711, HHQ 20)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Oxygen tolerance

Spore formation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_000429305

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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