Aggregatilinea lenta (KCTC 15625, JCM 32065, MO-CFX2)

Name: Aggregatilinea lenta (KCTC 15625, JCM 32065, MO-CFX2)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 164183

Type strain

Strain Designation MO-CFX2

Culture col. no. KCTC 15625 JCM 32065 MO-CFX2

NCBI tax ID(s) 913108

Special Collections JCM KCTC Literature strains

History JCM <-- M. Miyazaki; JAMSTEC, Japan; MO-CFX2 <-- H. Imachi.

Links

Aggregatilinea lenta MO-CFX2 is a facultative anaerobe, Gram-negative, filament-shaped bacterium that was isolated from Marine subsurface sediment collected off the Shimokita Peninsula of Japan.

Gram-negative filament-shaped facultative anaerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 24731

KCTC 15625,JCM 32065

@ref 20215

Last LPSN update 2026-05-29 08:45:21

Domain Bacteria

Phylum Chloroflexota

Class Anaerolineae

Order Aggregatilineales

Family Aggregatilineaceae

Genus Aggregatilinea

Species Aggregatilinea lenta

Full scientific name Aggregatilinea lenta Nakahara et al. 2019

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Motility	Confidence
67846	negative	20-200 µm	0.5-0.6 µm	filament-shaped
125439	negative					96.345

Multicellular morphology

67846

Forms multicellular complexyes

Culture and growth conditions

Culture temp

@ref	Growth	Type	Temperature (°C)
67770	positive	growth	30
67846	positive	growth	20-37
67846	positive	optimum	30

Culture pH

@ref	Ability	Type	PH	PH range
67846	positive	optimum	6.5-7
67846	positive	growth	5.5-8.5	alkaliphile

Physiology and metabolism

Oxygen tolerance

67846

Oxygen tolerancefacultative anaerobe

Spore formation

@ref	Spore formation	Confidence
125439		99.367

Halophily

@ref	Salt	Growth	Tested relation	Concentration
67846	NaCl	positive	growth	0-30 g/L
67846	NaCl	positive	optimum	5 g/L

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
67846	30089 ChEBI	acetate	-	assimilation
67846		casamino acids	-	assimilation
67846	15824 ChEBI	D-fructose	-	assimilation
67846	12936 ChEBI	D-galactose	-	assimilation
67846	17634 ChEBI	D-glucose	-	assimilation
67846	16024 ChEBI	D-mannose	-	assimilation
67846	16988 ChEBI	D-ribose	-	assimilation
67846	65327 ChEBI	D-xylose	-	assimilation
67846	28262 ChEBI	dimethyl sulfoxide	-	assimilation
67846	30849 ChEBI	L-arabinose	-	assimilation
67846	17632 ChEBI	nitrate	-	assimilation
67846	16301 ChEBI	nitrite	-	assimilation
67846	17309 ChEBI	pectin	-	assimilation
67846	17272 ChEBI	propionate	-	assimilation
67846	28017 ChEBI	starch	-	assimilation
67846	30031 ChEBI	succinate	-	assimilation
67846	16189 ChEBI	sulfate	-	assimilation
67846	16094 ChEBI	thiosulfate	-	assimilation
67846		tryptone	-	assimilation
67846		yeast extract	+	growth

Isolation, sampling and environmental information

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent	Latitude	Longitude
67770	Marine subsurface sediment collected off the Shimokita Peninsula of Japan		Pacific Ocean
67846	marine subsurface sediment (1,180 m water depth)	Shimokita Peninsula, north-western Pacific Ocean	Japan	JPN	Asia	41.1773	142.201 41.1773/142.201

Interaction and safety

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67846	Grel_assembly01 assembly for Aggregatilinea lenta MO-CFX2	scaffold	GCA_003569045	913108.7	2967508864	913108	72.16

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
67846	Aggregatilinea lenta MO-CFX2 gene for 16S rRNA, partial sequence	AB598278	1428		913108

GC content

@ref	GC-content (mol%)	Method
67846	63.2	genome sequence analysis

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Grel_assembly01 assembly for Aggregatilinea lenta MO-CFX2
BV-BRC: 913108.7

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	aerobe	85.92	no
125439	gram_stain	BacteriaNetⓘ	negative	96.35	no
125439	motility	BacteriaNetⓘ	yes	68.28	no
125439	spore_formation	BacteriaNetⓘ	no	99.37	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: anaerobic bacterium MO-CFX2
PATRIC: 913108.7

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	56.82	yes
125438	anaerobic	anaerobicⓘ	no	80.56	yes
125438	aerobic	aerobicⓘ	no	52.65	yes
125438	spore-forming	spore-formingⓘ	no	51.89	no
125438	thermophilic	thermophileⓘ	no	75.05	yes
125438	flagellated	motile2+ⓘ	no	77.46	yes

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Aggregatilinea lenta gen. nov., sp. nov., a slow-growing, facultatively anaerobic bacterium isolated from subseafloor sediment, and proposal of the new order Aggregatilineales ord. nov. within the class Anaerolineae of the phylum Chloroflexi.	Nakahara N, Nobu MK, Takaki Y, Miyazaki M, Tasumi E, Sakai S, Ogawara M, Yoshida N, Tamaki H, Yamanaka Y, Katayama A, Yamaguchi T, Takai K, Imachi H	Int J Syst Evol Microbiol	10.1099/ijsem.0.003291	2019

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 )
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#67846	Nozomi Nakahara, Masaru K. Nobu, Yoshihiro Takaki, Masayuki Miyazaki, Eiji Tasumi, Sanae Sakai, Miyuki Ogawara, Naoko Yoshida, Hideyuki Tamaki, Yuko Yamanaka, Arata Katayama, Takashi Yamaguchi, Ken Takai, Hiroyuki Imachi: Aggregatilinea lenta gen. nov., sp. nov., a slow-growing, facultatively anaerobic bacterium isolated from subseafloor sediment, and proposal of the new order Aggregatilineales ord. nov. within the class Anaerolineae of the phylum Chloroflexi. IJSEM 69: 1185 - 1194 2019 ( DOI 10.1099/ijsem.0.003291 )
#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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Aggregatilinea lenta (KCTC 15625, JCM 32065, MO-CFX2)

Name and taxonomic classification

Morphology

Cell morphology

Multicellular morphology

Culture and growth conditions

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

Halophily

Metabolite utilization

Isolation, sampling and environmental information

Isolation

Interaction and safety

Sequence information

Genome sequences

Genome browser: GCA_003569045

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

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