Acidithiobacillus albertensis (DSM 14366, ATCC 35403)

Name: Acidithiobacillus albertensis (DSM 14366, ATCC 35403)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 128

Type strain

Culture col. no. DSM 14366 ATCC 35403

NCBI tax ID(s) 119978

Special Collections DSMZ

History <- ATCC; ATCC 35403 <- E. J. Laishley, University of Calgary, Canada; <- R. D. Bryant;

Links

Acidithiobacillus albertensis DSM 14366 is a bacterium that was isolated from acidic soil at sulfur stockpile.

genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 41059

ATCC 35403,DSM 14366

@ref 20215

Last LPSN update 2026-02-09 11:17:48

Domain Bacteria

Phylum Pseudomonadota

Class Acidithiobacillia

Order Acidithiobacillales

Family Acidithiobacillaceae

Genus Acidithiobacillus

Species Acidithiobacillus albertensis

Full scientific name Acidithiobacillus albertensis corrig. (Bryant et al. 1988) Kelly and Wood 2000

Synonyms (2)

Acidithiobacillus albertis
Thiobacillus albertis

Morphology

Cell morphology

@ref	Gram stain	Confidence
125439	negative	98.8
125438	negative	96.947

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
5322	ACIDITHIOBACILLUS THIOOXIDANS MEDIUM (DSMZ Medium 71)		Medium recipe at MediaDive	Name: ACIDITHIOBACILLUS THIOOXIDANS MEDIUM (DSMZ Medium 71; with strain-specific modifications) Composition: Na2S2O3 x 5 H2O 5.0 g/l KH2PO4 3.0 g/l (NH4)2SO4 3.0 g/l MgSO4 x 7 H2O 0.5 g/l CaCl2 x 2 H2O 0.25 g/l FeSO4 x 7 H2O 0.01 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
5322	positive	growth	30

Physiology and metabolism

Spore formation

@ref	Spore formation	Confidence
125439		99.4
125438		90.199

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Environmental	#Terrestrial	#Soil
#Condition	#Acidic	-
#Condition	#Sulfuric	-

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
5322	acidic soil at sulfur stockpile	Alberta, Fox Creek	Canada	CAN	North America

Taxonmaps

69479

Global distribution of 16S sequence KF537578 (>99% sequence identity) for Acidithiobacillus from Microbeatlas

Aquatic Samples	1330
Soil Samples	228
Animal Samples	187
Plant Samples	13
Total Samples	1758

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
5322	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	ASM193165v1 assembly for Acidithiobacillus albertensis DSM 14366	scaffold	GCA_001931655	119978.4	2751185777	119978	35.17

16S sequences

@ref	Description	Accession	Length	NCBI tax ID
20218	Acidithiobacillus albertensis culture-collection DSM:14366 16S ribosomal RNA gene, partial sequence; 16S-23S ribosomal RNA intergenic spacer, tRNA-Ile and tRNA-Ala genes, complete sequence; and 23S ribosomal RNA gene, partial sequence	KF537578	2243	119978
5322	Acidithiobacillus albertensis 16S rRNA gene, strain DSM 14366	AJ459804	1494	119978
124043	Acidithiobacillus albertensis strain DSM 14366 16S ribosomal RNA gene, partial sequence.	KX894725	1536	119978
124043	Acidithiobacillus albertensis strain ATCC 35403 16S ribosomal RNA gene, partial sequence.	KX894724	1352	119978

GC content

@ref	GC-content (mol%)	Method
5322	61.5	thermal denaturation, midpoint method (Tm)

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Acidithiobacillus albertensis DSM 14366
NCBI: GCA_001931655

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	99.40	no
125439	motility	BacteriaNetⓘ	yes	64.90	no
125439	gram_stain	BacteriaNetⓘ	negative	98.80	no
125439	oxygen_tolerance	BacteriaNetⓘ	facultative anaerobe	70.30	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Acidithiobacillus albertensis DSM 14366
NCBI: GCA_001931655.1

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.95	no
125438	anaerobic	anaerobicⓘ	no	84.08	no
125438	aerobic	aerobicⓘ	yes	62.00	no
125438	spore-forming	spore-formingⓘ	no	90.20	no
125438	thermophilic	thermophileⓘ	no	94.53	no
125438	flagellated	motile2+ⓘ	yes	67.23	no

Literature

Topic	Title	Authors	Journal	DOI	Year
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
Phylogeny	Phylogeny, Divergent Evolution, and Speciation of Sulfur-Oxidizing Acidithiobacillus Populations.	Zhang X, Liu X, Li L, Wei G, Zhang D, Liang Y, Miao B.	BMC Genomics	10.1186/s12864-019-5827-6	2019
	Insights into Systems for Iron-Sulfur Cluster Biosynthesis in Acidophilic Microorganisms.	Myriam P, Braulio P, Javiera RA, Claudia MV, Omar O, Renato C, Gloria L.	J Microbiol Biotechnol	10.4014/jmb.2206.06045	2022
	Effect of Introduction of Exogenous Strain Acidithiobacillus thiooxidans A01 on Structure and Function of Adsorbed and Planktonic Microbial Consortia During Bioleaching of Low-Grade Copper Sulfide.	Liu Y, Wang J, Hou H, Chen G, Liu H, Liu X, Shen L.	Front Microbiol	10.3389/fmicb.2019.03034	2019
	From Genes to Bioleaching: Unraveling Sulfur Metabolism in Acidithiobacillus Genus.	Ibanez A, Garrido-Chamorro S, Coque JJR, Barreiro C.	Genes (Basel)	10.3390/genes14091772	2023
Genetics	Comparative Genomic Analysis Reveals the Distribution, Organization, and Evolution of Metal Resistance Genes in the Genus Acidithiobacillus.	Li L, Liu Z, Meng D, Liu X, Li X, Zhang M, Tao J, Gu Y, Zhong S, Yin H.	Appl Environ Microbiol	10.1128/aem.02153-18	2019
Genetics	Genomic evolution of the class Acidithiobacillia: deep-branching Proteobacteria living in extreme acidic conditions.	Moya-Beltran A, Beard S, Rojas-Villalobos C, Issotta F, Gallardo Y, Ulloa R, Giaveno A, Degli Esposti M, Johnson DB, Quatrini R.	ISME J	10.1038/s41396-021-00995-x	2021
Metabolism	Biomarker panels for characterizing microbial community biofilm formation as composite molecular process.	Bosse M, Heuwieser A, Heinzel A, Lukas A, Oliveira G, Mayer B.	PLoS One	10.1371/journal.pone.0202032	2018
	Sulfur Oxidation in the Acidophilic Autotrophic Acidithiobacillus spp.	Wang R, Lin JQ, Liu XM, Pang X, Zhang CJ, Yang CL, Gao XY, Lin CM, Li YQ, Li Y, Lin JQ, Chen LX.	Front Microbiol	10.3389/fmicb.2018.03290	2018
	A persistent and diverse airway microbiota present during chronic obstructive pulmonary disease exacerbations.	Huang YJ, Kim E, Cox MJ, Brodie EL, Brown R, Wiener-Kronish JP, Lynch SV.	OMICS	10.1089/omi.2009.0100	2010
Genetics	Draft genome sequence of the type strain of the sulfur-oxidizing acidophile, Acidithiobacillus albertensis (DSM 14366).	Castro M, Moya-Beltran A, Covarrubias PC, Gonzalez M, Cardenas JP, Issotta F, Nunez H, Acuna LG, Encina G, Holmes DS, Johnson DB, Quatrini R	Stand Genomic Sci	10.1186/s40793-017-0282-y	2017

References

#5322	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 14366
#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 )
#20218	Verslyppe, B., De Smet, W., De Baets, B., De Vos, P., Dawyndt P.: StrainInfo introduces electronic passports for microorganisms.. Syst Appl Microbiol. 37: 42 - 50 2014 ( DOI 10.1016/j.syapm.2013.11.002 , PubMed 24321274 )
#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 .
#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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Acidithiobacillus albertensis (DSM 14366, ATCC 35403)

Name and taxonomic classification

Morphology

Cell morphology

Culture and growth conditions

Culture medium

Culture temp

Physiology and metabolism

Spore formation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_001931655

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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