Acidiphilium angustum (NCCB 86036, KLB, ATCC 35903)

Name: Acidiphilium angustum (NCCB 86036, KLB, ATCC 35903)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 168531

Type strain

Strain Designation KLB

Culture col. no. NCCB 86036 KLB ATCC 35903

NCBI tax ID(s) 1408418 523

Links

Acidiphilium angustum KLB is a bacterium of the family Acetobacteraceae.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 45069

ATCC 35903,LMD 86.36,NCCB 86036

@ref 20215

Last LPSN update 2026-02-09 11:40:49

Domain Bacteria

Phylum Pseudomonadota

Class Alphaproteobacteria

Order Rhodospirillales

Family Acetobacteraceae

Genus Acidiphilium

Species Acidiphilium angustum

Full scientific name Acidiphilium angustum Wichlacz et al. 1986

Morphology

Cell morphology

@ref	Gram stain	Confidence	Motility
125438	negative	97.833
125439		90

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
125439	obligate aerobe	99.5

Isolation, sampling and environmental information

Taxonmaps

69479

Global distribution of 16S sequence D30772 (>99% sequence identity) for Acidiphilium from Microbeatlas

Aquatic Samples	137
Soil Samples	44
Animal Samples	33
Plant Samples	4
Total Samples	218

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
20215	1	Risk group

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
66792	ASM70158v1 assembly for Acidiphilium angustum ATCC 35903	scaffold	GCA_000701585	1408418.3	2561511102	1408418	37.66

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20215	Acidiphilium angustum gene for 16S ribosomal RNA	D30772	1413		523

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Acidiphilium angustum ATCC 35903
NCBI: GCA_000701585

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	yes	54.60	no
125439	motility	BacteriaNetⓘ	no	90.00	no
125439	gram_stain	BacteriaNetⓘ	negative	78.30	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate aerobe	99.50	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Acidiphilium angustum ATCC 35903 ATCC 35903
NCBI: GCA_000701585.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	97.83	no
125438	anaerobic	anaerobicⓘ	no	89.72	no
125438	aerobic	aerobicⓘ	yes	81.38	no
125438	spore-forming	spore-formingⓘ	no	88.05	no
125438	thermophilic	thermophileⓘ	no	95.20	no
125438	flagellated	motile2+ⓘ	yes	70.06	no

Literature

Topic	Title	Authors	Journal	DOI	Year
	Permanent draft genome sequence of Acidiphilium sp. JA12-A1.	Ullrich SR, Poehlein A, Voget S, Hoppert M, Daniel R, Leimbach A, Tischler JS, Schlomann M, Muhling M.	Stand Genomic Sci	10.1186/s40793-015-0040-y	2015
Phylogeny	Insights into Adaptive Mechanisms of Extreme Acidophiles Based on Quorum Sensing/Quenching-Related Proteins.	Huang S, Liu X, Yang W, Ma L, Li H, Liu R, Qiu J, Li Y.	mSystems	10.1128/msystems.01491-21	2022
	Ferric Uptake Regulator Provides a New Strategy for Acidophile Adaptation to Acidic Ecosystems.	Chen XK, Li XY, Ha YF, Lin JQ, Liu XM, Pang X, Lin JQ, Chen LX.	Appl Environ Microbiol	10.1128/aem.00268-20	2020
Phylogeny	Genome analysis suggests the bacterial family Acetobacteraceae is a source of undiscovered specialized metabolites.	Guzman J, Vilcinskas A.	Antonie Van Leeuwenhoek	10.1007/s10482-021-01676-7	2022
Genetics	Comparative genomics of the proteostasis network in extreme acidophiles.	Izquierdo-Fiallo K, Munoz-Villagran C, Orellana O, Sjoberg R, Levican G.	PLoS One	10.1371/journal.pone.0291164	2023
Genetics	Insights into the Metabolism and Evolution of the Genus Acidiphilium, a Typical Acidophile in Acid Mine Drainage.	Li L, Liu Z, Zhang M, Meng D, Liu X, Wang P, Li X, Jiang Z, Zhong S, Jiang C, Yin H.	mSystems	10.1128/msystems.00867-20	2020
Enzymology	Evaluation of a fluorescent lectin-based staining technique for some acidophilic mining bacteria.	Fife DJ, Bruhn DF, Miller KS, Stoner DL.	Appl Environ Microbiol	10.1128/aem.66.5.2208-2210.2000	2000
Genetics	Omics on bioleaching: current and future impacts.	Martinez P, Vera M, Bobadilla-Fazzini RA.	Appl Microbiol Biotechnol	10.1007/s00253-015-6903-8	2015
Phylogeny	Evolutionary relationships among sulfur- and iron-oxidizing eubacteria.	Lane DJ, Harrison AP, Stahl D, Pace B, Giovannoni SJ, Olsen GJ, Pace NR.	J Bacteriol	10.1128/jb.174.1.269-278.1992	1992
Genetics	Analysis of 1,000+ Type-Strain Genomes Substantially Improves Taxonomic Classification of Alphaproteobacteria.	Hordt A, Lopez MG, Meier-Kolthoff JP, Schleuning M, Weinhold LM, Tindall BJ, Gronow S, Kyrpides NC, Woyke T, Goker M.	Front Microbiol	10.3389/fmicb.2020.00468	2020
	Direct 5S rRNA Assay for Monitoring Mixed-Culture Bioprocesses.	Stoner DL, Browning CK, Bulmer DK, Ward TE, Macdonell MT.	Appl Environ Microbiol	10.1128/aem.62.6.1969-1976.1996	1996
Enzymology	Coffea arabica L., a new host plant for Acetobacter diazotrophicus, and isolation of other nitrogen-fixing acetobacteria.	Jimenez-Salgado T, Fuentes-Ramirez LE, Tapia-Hernandez A, Mascarua-Esparza MA, Martinez-Romero E, Caballero-Mellado J.	Appl Environ Microbiol	10.1128/aem.63.9.3676-3683.1997	1997
	Growth of Thiobacillus ferrooxidans on Formic Acid.	Pronk JT, Meijer WM, Hazeu W, van Dijken JP, Bos P, Kuenen JG.	Appl Environ Microbiol	10.1128/aem.57.7.2057-2062.1991	1991

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 )
#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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Acidiphilium angustum (NCCB 86036, KLB, ATCC 35903)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Physiology and metabolism

Oxygen tolerance

Isolation, sampling and environmental information

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_000701585

16S sequences

Genome-based predictions

Predictions

Literature

Literature

References

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