Open Access

Complete genome sequence of “Enterobacter lignolyticus” SCF1

  • Kristen M. DeAngelis1, 2Email author,
  • Patrik D’Haeseleer1, 3,
  • Dylan Chivian4, 5,
  • Julian L. Fortney1,
  • Jane Khudyakov2, 3,
  • Blake Simmons2, 6,
  • Hannah Woo1, 2,
  • Adam P. Arkin4, 5,
  • Karen Walston Davenport7,
  • Lynne Goodwin7,
  • Amy Chen8,
  • Natalia Ivanova8,
  • Nikos C. Kyrpides8,
  • Konstantinos Mavromatis8,
  • Tanja Woyke8 and
  • Terry C. Hazen1, 2
Standards in Genomic Sciences20115:5010069

https://doi.org/10.4056/sigs.2104875

Published: 15 October 2011

Abstract

In an effort to discover anaerobic bacteria capable of lignin degradation, we isolated “Enterobacter lignolyticus” SCF1 on minimal media with alkali lignin as the sole source of carbon. This organism was isolated anaerobically from tropical forest soils collected from the Short Cloud Forest site in the El Yunque National Forest in Puerto Rico, USA, part of the Luquillo Long-Term Ecological Research Station. At this site, the soils experience strong fluctuations in redox potential and are net methane producers. Because of its ability to grow on lignin anaerobically, we sequenced the genome. The genome of “E. lignolyticus” SCF1 is 4.81 Mbp with no detected plasmids, and includes a relatively small arsenal of lignocellulolytic carbohydrate active enzymes. Lignin degradation was observed in culture, and the genome revealed two putative laccases, a putative peroxidase, and a complete 4-hydroxyphenylacetate degradation pathway encoded in a single gene cluster.

Keywords

Anaerobic lignin degradation tropical forest soil isolate facultative anaerobe

Introduction

One of the biggest barriers to efficient lignocellulose deconstruction is the problem of lignin, both occluding the action of cellulases and as wasteful lignin by-products. Tropical forest soils are the sites of very high rates of decomposition, accompanied by very low and fluctuating redox potential conditions [1,2]. Because early stage decomposition is typically dominated by fungi and the free-radical generating oxidative enzymes phenol oxidase and peroxidase [3,4], we targeted anaerobic tropical forest soils with the idea that they would be dominated by bacterial rather than fungal decomposers. To discover organisms that were capable of breaking down lignin without the use of oxygen free radicals, we isolated “Enterobacter lignolyticus” SCF1 under anaerobic conditions using lignin as the sole carbon source. In addition to this, it has been observed to withstand high concentrations of ionic liquids [5], and thus was targeted for whole genome sequencing.

Organism information

E. lignolyticus” SCF1 was isolated from soil collected from the Short Cloud Forest site in the El Yunque experimental forest, part of the Luquillo Long-Term Ecological Research Station in Luquillo, Puerto Rico, USA (Table 1). Soils were diluted in water and inoculated into roll tubes containing MOD-CCMA media with alkali lignin as the source of carbon. MOD-CCMA media consists of 2.8 g L-1 NaCl, 0.1 g L-1 KCl, 27 mM MgCl2, 1 mM CaCl2, 1.25 mM NH4Cl, 9.76 g L-1 MES, 1.1 ml L-1 K2HPO4, 12.5 ml L-1 trace minerals [19,20], and 1 ml L-1 Thauer’s vitamins [21]. Tubes were incubated at room temperature for up to 12 weeks, at which point the colony was picked, grown in 10% tryptic soy broth (TSB), and characterized.
Table 1.

Classification and general features of “Enterobacter lignolyticus” SCF1

MIGS ID

Property

Term

Evidence code

 

Current classification

Domain Bacteria

TAS[6]

 

Phylum Proteobacteria

TAS[7]

 

Class Gammaproteobacteria

TAS[8,9]

 

Order Enterobacteriales

TAS[10]

 

Family Enterobacteriaceae

TAS[1113]

 

Genus Enterobacter

TAS[11,1316]

 

Species “Enterobacter lignolyticus

 
 

Strain SCF

 
 

Gram stain

negative

NAS

 

Cell shape

rod

IDA

 

Motility

motile via flagella

IDA

 

Sporulation

non-sporulating

IDA

 

Temperature range

Mesophile

 
 

Optimum temperature

30°C

 
 

Carbon source

glucose, xylose, others; see Table 8

IDA

MIGS-6

Habitat

Soil collected from a subtropical lower montane wet forest

TAS [17]

MIGS-6.3

Salinity

Can tolerate up to 0.75 M NaCl, 1 M KCl, 0.3 M NaOAc, 0.3 M KOAc. Growth in 10% trypticase soy broth is improved with 0.125 M NaCl

TAS [5]

MIGS-22

Oxygen

facultative aerobe; grows well under completely oxic and anoxic conditions

IDA

MIGS-15

Biotic relationship

free-living

IDA

MIGS-14

Pathogenicity

no

 

MIGS-4

Geographic location

Luquillo Experimental Forest, Puerto Rico

IDA

MIGS-5

Sample collection time

July 2009

IDA

MIGS-4.1

Latitude

18.268N

IDA

MIGS-4.2

Longitude

65.760 W

IDA

MIGS-4.3

Depth

10 cm

IDA

MIGS-4.4

Altitude

1027 msl

IDA

a) Evidence codes - IDA: Inferred from Direct Assay; TAS: Traceable Author Statement (i.e., a direct report exists in the literature); NAS: Non-traceable Author Statement (i.e., not directly observed for the living, isolated sample, but based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from the Gene Ontology project [18].

When grown on 10% TSB agar plates, SCF1 colonies are translucent white, slightly irregular in shape with wavy margins, and have a shiny smooth surface. SCF1 was determined to be a non-sporulating strain based on a Pasteurization test. To do this, a suspension of SCF1 cells was heated at 80°C for 10 minutes. 5µl of heated culture and non-heated control culture were both spotted onto 10% TSB agar and incubated for growth for 3 days at room temperature. The non-heated cells grew while the heated culture did not, indicating the absence of heat-resistant spores.

For initial genotyping and for validating the isolation, the small subunit ribosomal RNA gene was sequenced by Sanger sequencing using the universal primers 8F and 1492R [22].The 16S rRNA sequence places “Enterobacter lignolyticus” SCF1 in the family Enterobacteriaceae. However, 16S rRNA sequence is not sufficient to clearly define the evolutionary history of this region of the Gammaproteobacteria, and initially led to the incorrect classification of “E. lignolyticus” SCF1 as a member of the Enterobacter cloacae species. We have rectified its phylogenetic placement using the MicrobesOnline species tree [23], which is generated using 69 single-copy near-universal protein families [24] aligned by MUSCLE [25] with tree construction using FastTree-2 [26] (Figure 1).
Figure 1.

Phylogenetic tree highlighting the position of “Enterobacter lignolyticus” SCF1 relative to other type and non-type strains within the Enterobacteriaceae. Strains shown are those within the Enterobacteriaceae having corresponding NCBI genome project ids listed within [27]. The tree is based on a concatenated MUSCLE alignment [25] of 69 near-universal single-copy COGs (COGs 12, 13, 16, 18, 30, 41, 46, 48, 49, 52, 60, 72, 80, 81, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 102, 103, 104, 105, 124, 126, 127, 130, 143, 149, 150, 162, 164, 172, 184, 185, 186, 197, 198, 200, 201, 202, 215, 237, 244, 256, 284, 441, 442, 452, 461, 504, 519, 522, 525, 528, 532, 533, 540, 541, 552). The tree was constructed using FastTree-2 [26] using the JTT model of amino acid evolution [28]. FastTree-2 infers approximate maximum-likelihood phylogenetic placements and provides local support values based on the Shimodaira-Hasegawa test [29]. Solid circles represent local support values over 90% and open circles over 80%. Erwinia tasmaniensis was used as an outgroup.

Genome sequencing information

Genome project history

The genome was selected based on the ability of “E. lignolyticus” SCF1 to grow on and degrade lignin anaerobically. The genome sequence was completed on August 9, 2010, and presented for public access on 15 October 2010 by Genbank. Finishing was completed at Los Alamos National Laboratory. A summary of the project information is shown in Table 2, which also presents the project information and its association with MIGS version 2.0 compliance [30].
Table 2.

Project information

MIGS ID

Property

Term

MIGS-31

Finishing quality

Finished

MIGS-28

Libraries used

Illumina GAii shotgun, 454 Titanium Standard, and two 454 paired-end

MIGS-29

Sequencing platforms

Illumina, 454

MIGS-31.2

Fold coverage

40× for 454 and 469× for Illumina

MIGS-30

Assemblers

Newbler, Velvet, Phrap

MIGS-32

Gene calling method

Prodigal 1.4, GenePRIMP

 

Genbank ID

CP002272

 

Genbank Date of Release

October 15, 2010

 

GOLD ID

Gc01746

 

Project relevance

Anaerobic lignin, switchgrass decomposition

Growth conditions and DNA isolation

E. lignolyticus” SCF1 grows well aerobically and anaerobically, and was routinely cultivated aerobically in 10% tryptic soy broth (TSB) with shaking at 200 rpm at 30°C. DNA for sequencing was obtained using the Qiagen Genomic-tip kit and following the manufacturer’s instructions for the 500/g size extraction. Three column preparations were necessary to obtain 50 µg of high molecular weight DNA. The quantity and quality of the extraction were checked by gel electrophoresis using JGI standards.

Genome sequencing and assembly

The draft genome of “Enterobacter lignolyticus” SCF1 was generated at the DOE Joint Genome Institute (JGI) using a combination of Illumina [31] and 454 technologies [32]. For this genome we constructed and sequenced an Illumina GAii shotgun library which generated 50,578,565 reads totaling 3,844 Mb, a 454 Titanium standard library which generated 643,713 reads and two paired end 454 libraries with average insert sizes of 12517 +/− 3129 bp kb and 10286 +/− 2571 bp which generated 346,353 reads totaling 339.3 Mb of 454 data. All general aspects of library construction and sequencing performed at the JGI can be found at the JGI website [33].

The initial draft assembly contained 28 contigs in 1 scaffold. The 454 Titanium standard data and the 454 paired end data were assembled together with Newbler, version 2.3. The Newbler consensus sequences were computationally shredded into 2 kb overlapping fake reads (shreds). Illumina sequencing data was assembled with VELVET, version 0.7.63 [34], and the consensus sequences were computationally shredded into 1.5 kb overlapping fake reads (shreds). We integrated the 454 Newbler consensus shreds, the Illumina VELVET consensus shreds and the read pairs in the 454 paired end library using parallel phrap, version SPS - 4.24 (High Performance Software, LLC). The software Consed [3537] was used in the following finishing process. Illumina data was used to correct potential base errors and increase consensus quality using the software Polisher developed at JGI (Alla Lapidus, unpublished). Possible mis-assemblies were corrected using gapResolution (Cliff Han, unpublished), Dupfinisher [38], or sequencing cloned bridging PCR fragments with subcloning. Gaps between contigs were closed by editing in Consed, by PCR and by Bubble PCR (J-F Cheng, unpublished) primer walks. A total of 198 additional reactions were necessary to close gaps and to raise the quality of the finished sequence. The total size of the genome is 4,814,049 bp and the final assembly is based on 191.3 Mb of 454 draft data, which provided an average 40× coverage of the genome, and 2249.8 Mb of Illumina draft data, which provided an average 469× coverage of the genome; the coverage from different technologies is reported separately because they have different error patterns.

Genome annotation

Protein coding genes were identified using Prodigal [39] and tRNA, rRNA and other RNA genes using tRNAscan-SE [40], RNAmmer [41] and Rfam [42] as part of the ORNL genome annotation pipeline followed by a round of manual curation using the JGI GenePRIMP pipeline [43]. The predicted CDSs were translated and used to search the National Center for Biotechnology Information (NCBI) nonredundant database, UniProt, TIGR-Fam, Pfam, PRIAM, KEGG, COG, and InterPro databases. Additional gene prediction analysis and functional annotation were performed within the Integrated Microbial Genomes - Expert Review (IMG-ER) platform [44] using the JGI standard annotation pipeline [45,46].

Genome properties

The genome consists of a 4,814,049 bp circular chromosome with a GC content of 57.02% (Table 3 and Figure 2). Of the 4,556 genes predicted, 4,449 were protein-coding genes, and 107 RNAs; 50 pseudogenes were also identified. The majority of the protein-coding genes (85.8%) were assigned with a putative function while the remaining ones were annotated as hypothetical proteins. The distribution of genes into COGs functional categories is presented in Table 4, Table5 and Table 6.
Figure 2.

Graphical circular map of the genome. From outside to the center: Genes on forward strand (color by COG categories), Genes on reverse strand (color by COG categories), RNA genes (tRNAs green, rRNAs red, other RNAs black), GC content, GC skew.

Table 3.

Nucleotide content and gene count levels of the genome

Attribute

Value

% of Total

Genome size (bp)

4,814,049

100.00%

DNA coding region (bp)

4,312,328

89.58%

DNA G+C content (bp)

2,744,879

57.02%

Number of replicons

1

 

Extrachromosomal elements

0

 

Total genes

4,556

100.00%

RNA genes

107

2.35%

rRNA operons

7

 

Protein-coding genes

4,449

97.65%

Pseudo genes

50

1.10%

Genes with function prediction

3,909

85.80%

Genes in paralog clusters

823

18.06%

Genes assigned to COGs

3,743

82.16%

Genes assigned Pfam domains

3,995

87.69%

Genes with signal peptides

1,009

22.15%

Genes with transmembrane helices

1,108

24.32%

CRISPR-associated genes (CAS)

0

% of Total

Table 4.

Number of genes associated with the 25 general COG functional categories

Code

Value

%agea

Description

J

184

4.37

Translation

A

1

0.02

RNA processing and modification

K

360

8.54

Transcription

L

155

3.68

Replication, recombination and repair

B

0

0

Chromatin structure and dynamics

D

33

0.78

Cell cycle control, mitosis and meiosis

Y

0

0

Nuclear structure

V

48

1.14

Defense mechanisms

T

219

5.20

Signal transduction mechanisms

M

239

5.67

Cell wall/membrane biogenesis

N

138

3.27

Cell motility

Z

0

0

Cytoskeleton

W

1

0.02

Extracellular structures

U

150

3.56

Intracellular trafficking and secretion

O

140

3.32

Posttranslational modification, protein turnover, chaperones

C

275

6.52

Energy production and conversion

G

432

10.25

Carbohydrate transport and metabolism

E

415

9.85

Amino acid transport and metabolism

F

98

2.33

Nucleotide transport and metabolism

H

176

4.18

Coenzyme transport and metabolism

I

108

2.56

Lipid transport and metabolism

P

235

5.58

Inorganic ion transport and metabolism

Q

85

2.02

Secondary metabolites biosynthesis, transport and catabolism

R

409

9.70

General function prediction only

S

314

7.45

Function unknown

-

813

17.84

Not in COGs

a) The total is based on the total number of protein coding genes in the annotated genome.

Table 5.

Number of non-orthologous protein-coding genes found in “Enterobacter lignolyticus” SCF1 with respect to related genomes

Species

Number of distinct genes in “E. lignolyticus” SCF1

Enterobacter sp. 638

1,580

Enterobacter cancerogenus ATCC 35316

1,551*

Enterobacter cloacae ATCC 13047

2,891*

Klebsiella pneumoniae 342

1,389

Klebsiella pneumoniae MGH 78578

1,451

Klebsiella pneumoniae NTUH-K2044

1,424

Klebsiella variicola At-22

1,394

Citrobacter koseri ATCC BAA-895

1,507

Citrobacter rodentium ICC168

1,682

Escherichia coli K-12 MG1655

1,654

Salmonella enterica Typhi Ty2

1,811

Cronobacter turicensis z3032

1,875

Cronobactersakazakii ATCC BAA-894

1,918

Erwinia tasmaniensis Et1/99

2,392

Protein-coding genes distinct in “E. lignolyticus” SCF1 compared with all orthologous genes found in above genomes

643

* Based on incompletely annotated genome.

Table 6.

Number of genes not found in near-relatives associated with the 25 general COG functional categories*

Code

Value

Description

-

151

Hypothetical (no conserved gene family)

-

17

Transposase / Integrase (annotation-based)

-

80

Transport (annotation-based)

-

66

Signaling and Regulation

J

6

Translation

A

0

RNA processing and modification

K

51

Transcription

L

18

Replication, recombination and repair

B

0

Chromatin structure and dynamics

D

2

Cell cycle control, mitosis and meiosis

Y

0

Nuclear structure

V

7

Defense mechanisms

T

30

Signal transduction mechanisms

M

41

Cell wall/membrane biogenesis

N

20

Cell motility

Z

0

Cytoskeleton

W

1

Extracellular structures

U

22

Intracellular trafficking and secretion

O

9

Posttranslational modification, protein turnover, chaperones

C

20

Energy production and conversion

G

68

Carbohydrate transport and metabolism

E

28

Amino acid transport and metabolism

F

5

Nucleotide transport and metabolism

H

5

Coenzyme transport and metabolism

I

14

Lipid transport and metabolism

P

23

Inorganic ion transport and metabolism

Q

8

Secondary metabolites biosynthesis, transport and catabolism

R

43

General function prediction only

S

23

Function unknown

-

255

Not in COGs

* Number of genes from set of 643 genes not found in near-relatives associated with the 25 general COG functional categories and several annotation-based classifications. Note that counts do not sum to 643 genes as a given gene is sometimes classified in more than one COG functional category.

Lignocellulose degradation pathways

E. lignolyticus” SCF1 has a relatively small arsenal of lignocellulolytic carbohydrate active enzymes, including a single GH8 endoglucanase, and a GH3 beta-glucosidase, but no xylanase or beta-xylosidase. Table 7 provides a more complete list of lignocellulolytic enzymes. The genome also contains a large number of saccharide and oligosaccharide transporters, including several ribose ABC transporters, a xylose ABC transporter (Entcl_0174-0176), and multiple cellobiose PTS transporters (Entcl_1280, Entcl_2546-2548, Entcl_3764, Entcl_4171-4172).
Table 7.

Selection of lignocellulolytic carbohydrate active, lignin oxidative (LO) and lignin degrading auxiliary (LDA) enzymes [47,48]†.

Locus Tag

Family

Function

Entcl_0212

GH8

endoglucanase (EC 3.2.1.4)

Entcl_1570

GH3

beta-glucosidase (EC 3.2.1.21)

Entcl_0851

GH1

6-phospho-beta-glucosidase (EC 3.2.1.86)

Entcl_0991

GH1

6-phospho-beta-glucosidase (EC 3.2.1.86)

Entcl_1274

GH1

6-phospho-beta-glucosidase (EC 3.2.1.86)

Entcl_3004

GH1

6-phospho-beta-glucosidase (EC 3.2.1.86)

Entcl_3339

GH2

beta-galactosidase (EC 3.2.1.23)

Entcl_0624

GH2

beta-galactosidase (EC 3.2.1.23)

Entcl_2579

GH2

beta-mannosidase (EC 3.2.1.25)

Entcl_2687

GH3

beta-N-acetylhexosaminidase (EC 3.2.1.52)

Entcl_3271

GH4

alpha-galactosidase (EC 3.2.1.22)

Entcl_0170

GH13

alpha-amylase (EC 3.2.1.1)

Entcl_3416

GH13

alpha-glucosidase (EC 3.2.1.20)

Entcl_2926

GH18

chitinase (EC 3.2.1.14)

Entcl_2924

GH19

chitinase (EC 3.2.1.14)

Entcl_4037

GH35

beta-galactosidase (EC 3.2.1.23)

Entcl_3090

GH38

alpha-mannosidase (EC 3.2.1.24)

Entcl_0250

CE4

polysaccharide deacetylase (EC 3.5.-.-)

Entcl_3596

CE4

polysaccharide deacetylase (EC 3.5.-.-)

Entcl_3059

CE8

pectinesterase (EC 3.1.1.11)

Entcl_2112

LDA2

vanillyl-alcohol oxidase (EC 1.1.3.38)

Entcl_1569

LDA2

D-lactate dehydrogenase (EC 1.1.1.28)

Entcl_4187

LDA2

UDP-N-acetylmuramate dehydrogenase (EC 1.1.1.158)

Entcl_3603

LO1

putative laccase (EC 1.10.3.2)

Entcl_0735

LO1

putative laccase (EC 1.10.3.2)

Entcl_4301

LO2

catalase/peroxidase (EC 1.11.1.6, 1.11.1.7)

† Enzyme families are as per the CAZy and FOLy databases

The mechanisms for lignin degradation in bacteria are still poorly understood. Two multi-copper oxidases (putative laccases) and a putative peroxidase (see Table 7) may be involved in oxidative lignin degradation. We also found multiple glutathione S-transferase proteins, and it is possible that one or more of these may be involved in cleavage of beta-aryl ether linkages, as is the case with LigE/LigF in Sphingomonas paucimobilis [49]. However, “E. lignolyticus” SCF1 does not seem to posses the core protocatechuate and 3-O-methylgallate degradation pathways responsible for lignin catabolism in S. paucimobilis. Instead, lignin catabolism may proceed via homoprotocatechuate through the 4-hydroxyphenylacetate degradation pathway, encoded on a gene cluster conserved between other Enterobacter, Klebsiella, and some E. coli strains (Figures 3, 4).
Figure 3.

The entire 4-hydroxyphenylacetate degradation pathway is encoded in a single gene cluster HpaRGEDFHIXABC, including a divergently expressed regulator (HpaR), and a 4-hydroxyphenylacetate permease (HpaX).

Figure 4.

The 4-hydroxyphenylacetate degradation pathway via homoprotocatechuate (3,4-dihydroxyphenylacetate).

Lignin degradation

We have grown SCF1 in xylose minimal media with and without lignin, and measured both cell counts (by acridine orange direct counts) and lignin degradation (by change in absorbance at 280 nm) over time. Lignin degradation was substantial after two days (left), and significantly enhanced growth of cells in culture (right); data are expressed as mean with standard deviation (n=3, Figure 5). Further studies will explore the moieties of lignin used in anaerobic growth as well as explore growth on and utilization of other types of lignin.
Figure 5.

Anaerobic lignin degradation by “E. lignolyticus” SCF1 after 48 hours in culture, grown with xylose minimal media.

Phenotypic Microarray

We used the Biolog phenotypic microarray to test the range of growth conditions. For each of the eight plates in the array, “E. lignolyticus” SCF1 cells were grown up on 10% TSB agar plates, scraped off and resuspended in 20mM D-Glucose MOD-CCMA, adjusted to 0.187 OD, 1× concentrate of Biolog Dye Mix G added, and then inoculated. PM plates include two plates with different carbon sources (PM 1 and 2a), one plate of different simple nitrogen sources (PM 3b), one plates of phosphorous and sulfur sources (PM4A), one plate of nutritional supplements (PM5), and three plates of amino acid dipeptides as nitrogen sources (PM6, PM7, PM8). Carbon source, D-Glucose, was omitted from MOD-CCMA when used to inoculate PM1 and 2a. Similarly, NH4Cl, KH2PO4 and vitamins were omitted from 20mM D-Glucose MOD CCMA when inoculating plates containing nitrogen sources, phosphorus/sulfur sources, and nutrient supplements, respectively. On plates 6–8, the positive control is L-Glutamine. The phenotypic microarray revealed a number of carbon and nitrogen sources that resulted in four times the growth or more compared to the negative control based on duplicate runs (Table 8 and 9), as well as sulfur and phosphorous sources that improved growth by 10% or more (Tables 10 and 11). None of the dipeptides resulted in an increase in growth more than twice the background, and so are not reported here. Of the nutritional supplements tested in PM5, 2′-deoxyuridine and 2′-deoxyadenosine resulted in 10% growth improvement, while (5) 4-amino-imidazole-4(5)-carboxamide, Tween 20, Tween 40, Tween 60, and Tween 80 resulted in 20% growth improvement.
Table 8.

Carbon source by phenotypic array (PM 1 and 2a)

Chemical Name

KEGG

CAS

Ratio to background

D-Fructose

C00095

57-48-7

8.48

D-Sorbitol

C00794

50-70-4

8.36

N-Acetyl-D-Glucosamine

C03000

7512-17-6

8.30

D-Gluconic Acid

C00257

527-07-1

8.28

D-Trehalose

C01083

99-20-7

8.18

D-Mannose

C00159

3458-28-4

8.10

D-Xylose

C00181

58-86-6

8.09

a-D-Glucose

C00031

50-99-7

8.07

N-Acetyl-D-Mannosamine

C00645

7772-94-3

7.92

D-Mannitol

C00392

69-65-8

7.92

D-Galactose

C00124

59-23-4

7.92

D-Glucosaminic Acid

C03752

3646-68-2

7.85

D-Ribose

C00121

50-69-1

7.76

b-Methyl-D-Glucoside

 

709-50-2

7.70

D-Glucuronic Acid

C00191

14984-34-0

7.69

D-Glucosamine

C00329

66-84-2

7.68

D-Galactonic Acid-g-Lactone

C03383

2782-07-2

7.67

Maltose

C00208

69-79-4

7.62

2-Deoxy-D-Ribose

C01801

533-67-5

7.57

Glycerol

C00116

56-81-5

7.52

m-Hydroxyphenyl Acetic Acid

C05593

621-37-4

7.42

L-Arabinose

C00259

87-72-9

7.40

m-Inositol

C00137

87-89-8

7.39

L-Serine

C00065

56-45-1

7.38

3-Methylglucose

 

13224-94-7

7.36

Maltotriose

C01835

1109-28-0

7.30

D-Melibiose

C05402

585-99-9

7.25

L-Fucose

C01019

2438-80-4

7.25

D-Arabinose

C00216

10323-20-3

7.10

Hydroxy-L-Proline

C01015

51-35-4

7.08

2′-Deoxyadenosine

C00558

16373-93-6

7.02

L-Alanine

C00041

56-41-7

6.94

Tyramine

C00483

60-19-5

6.93

Gly-Pro

 

704-15-4

6.93

D-Galacturonic Acid

C00333

91510-62-2

6.91

L-Rhamnose

C00507

3615-41-6

6.86

p-Hydroxyphenyl Acetic Acid

C00642

156-38-7

6.83

Acetic Acid

C00033

127-09-3

6.81

L-Proline

C00148

147-85-3

6.80

Fumaric Acid

C00122

17013-01-3

6.80

D,L-Malic Acid

C00497

6915-15-7

6.75

D,L-Lactic acid

C01432

312-85-6

6.71

Dihydroxyacetone

C00184

96-26-4

6.69

Tween 20

C11624

9005-64-5

6.57

N-Acetyl-D-Galactosamine

 

14215-68-0

6.45

Inosine

C00294

58-63-9

6.45

Ala-Gly

 

687-69-4

6.43

L-Histidine

C00135

5934-29-2

6.37

D-Alanine

C00133

338-69-2

6.29

D-Fructose-6-Phosphate

C00085

26177-86-637250-85-4

6.25

L-Glutamine

C00064

56-85-9

6.08

Gly-Glu

 

7412-78-4

6.00

D-Cellobiose

C00185

528-50-7

5.98

D-Glucose-1-Phosphate

C00103

56401-20-8

5.95

D-Psicose

C06468

551-68-8

5.92

Citric Acid

C00158

6132-04-3

5.91

L-Glutamic Acid

C00025

6106-04-3

5.84

b-Methyl-D-Galactoside

C03619

1824-94-8

5.70

L-Aspartic Acid

C00049

3792-50-5

5.65

D-Serine

C00740

312-84-5

5.63

Methylpyruvate

 

600-22-6

5.62

Pyruvic Acid

C00022

113-24-6

5.56

Propionic Acid

C00163

137-40-6

5.48

Melibionic Acid

 

70803-54-2

5.43

D-Malic Acid

C00497

636-61-3

5.38

D-Aspartic Acid

C00402

1783-96-6

5.38

5-Keto-D-Gluconic Acid

C01062

91446-96-7

5.37

Succinic Acid

C00042

6106-21-4

5.35

Gly-Asp

C02871

 

5.28

D,L-a-Glycerol Phosphate

C00093

3325-00-6

5.26

Putrescine

C00134

333-93-7

5.14

Gentiobiose

C08240

554-91-6

5.00

D-Glucose-6-Phosphate

C00092

3671-99-6

4.90

a-Methyl-D-Galactoside

C03619

3396-99-4

4.84

Uridine

C00299

58-96-8

4.68

Bromosuccinic Acid

 

923-06-8

4.68

Thymidine

C00214

50-89-5

4.63

L-Asparagine

C00152

70-47-3

4.55

a-Hydroxybutyric Acid

C05984

19054-57-0

4.38

L-Malic Acid

C00149

138-09-0

4.34

L-Ornithine

C00077

3184-13-2

4.28

N-Acetyl-D-glucosaminitol

 

4271-28-7

4.23

L-Lyxose

C01508

1949-78-6

4.23

L-Threonine

C00188

72-19-5

4.21

g-Amino-N-Butyric Acid

C00334

56-12-2

4.19

Arbutin

C06186

497-76-7

4.17

Table 9.

Nitrogen sources by phenotypic array (PM 3b)

Chemical Name

KEGG

CAS

Ratio to background

Gly-Gln

 

13115-71-4

5.63

Gly-Asn

  

5.63

L-Cysteine

C00097

7048-04-6

5.29

Gly-Glu

 

7412-78-4

5.26

Ala-Gln

 

39537-23-0

4.92

Ala-Asp

C02871

20727-65-5

4.58

L-Aspartic Acid

C00049

3792-50-5

4.33

L-Glutamine

C00064

56-85-9

4.03

Table 10.

Phosphorous source by phenotypic array (PM 4a)

Chemical Name

KEGG

CAS

Ratio to background

O-Phospho-D-Serine

 

73913-63-0

1.42

Phospho-Glycolic Acid

C00988

 

1.28

Carbamyl Phosphate

C00416

72461-86-0

1.26

O-Phospho-L-Threonine

 

1114-81-4

1.25

Tripolyphosphate

C02466

 

1.24

O-Phospho-L-Serine

 

407-41-0

1.23

Cysteamine-S-Phosphate

 

3724-89-8

1.22

Cytidine 2′-Monophosphate

C03104

85-94-9

1.21

Guanosine 5′-Monophosphate

C00144

5550-12-9

1.21

Guanosine 3′-Monophosphate

C06193

 

1.20

Phosphoenol Pyruvate

C00074

5541-93-5

1.20

Cytidine 3′-Monophosphate

C05822

84-52-6

1.20

Cytidine 5′-Monophosphate

C00055

6757-06-8

1.20

Adenosine 2′,3′-Cyclic Monophosphate

 

37063-35-7

1.20

Phospho-L-Arginine

 

108321-86-4

1.20

Adenosine 3′-Monophosphate

C01367

84-21-9

1.20

Guanosine 2′,3′-Cyclic Monophosphate

 

15718-49-7

1.19

D-3-Phospho-Glyceric Acid

C00631

80731-10-8

1.19

Phosphate

C00009

10049-21-5

1.19

Guanosine 2′-Monophosphate

 

6027-83-4

1.19

Thiophosphate

 

10489-48-2

1.18

Thymidine 3′-Monophosphate

 

108320-91-8

1.18

Thymidine 5′-Monophosphate

C00364

33430-62-5

1.16

6-Phospho-Gluconic Acid

 

53411-70-4

1.16

Dithiophosphate

  

1.16

2-Aminoethyl Phosphonic Acid

C03557

2041-14-7

1.15

Phosphoryl Choline

C00588

4826-71-5

1.14

D,L-a-Glycerol Phosphate

C00093

3325-00-6

1.13

Trimetaphosphate

C02466

7785-84-4

1.13

Table 11.

Sulfur source by phenotypic array (PM 4a)

Chemical Name

KEGG

CAS

Ratio to background

L-Cysteine Sulfinic Acid

C00607

1115-65-7

1.24

Gly-Met

 

554-94-9

1.23

Tetramethylene Sulfone

 

126-33-0

1.21

L-Methionine

C00073

63-68-3

1.21

N-Acetyl-D,L-Methionine

C02712

71463-44-0

1.20

L-Methionine Sulfoxide

C02989

3226-65-1

1.19

Tetrathionate

C02084

13721-29-4

1.18

L-Cysteine

C00097

7048-04-6

1.17

Sulfate

C00059

7727-73-3

1.14

L-Djenkolic Acid

C08275

28052-93-9

1.14

Cys-Gly

 

19246-18-5

1.13

Conclusion

Close relatives of “Enterobacter lignolyticus” SCF1 were isolated seven independent times from Puerto Rico tropical forest soils, growing anaerobically with lignin or switchgrass as the sole carbon source, suggesting that it is relatively abundant in tropical forest soils and has broad capability for deconstruction of complex heteropolymers such as biofuel feedstocks. In a previous study, Enterobacter was one of four isolates from the poplar rhizosphere chosen for genomic sequencing because of its ability to improve the carbon sequestration ability of poplar trees when grown in poor soils [50].

Isolates from the Enterobacteriaceae are extremely genetically diverse despite the near identity of genotypic markers such as small subunit ribosomal (16S) RNA genes. Multi-locus sequence typing and comparative genomic hybridization show that the isolates seem to fall into two distinct clades: the first being more homogeneous and containing isolates found in hospitals, and the second being more diverse and found in a broader array of environments [51].

This organism was determined to grow aerobically and anaerobically, and when screening for enzyme activity, the enzymes isolated showed accelerated phenol oxidase and peroxidase enzyme activity under aerobic conditions. In addition, this organism is capable of growth in 8% ethyl-methylimidazolium chloride ([C2mim]Cl), an ionic liquid being studied for pre-treatment of feedstocks. This extremely high tolerance to ionic liquids is potentially quite useful for industrial biofuels production from feedstocks and the mechanism is currently under investigation.

Declarations

Acknowledgements

The work conducted in part by the U.S. Department of Energy Joint Genome Institute and in part by the Joint BioEnergy Institute (http://www.jbei.org) supported by the U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under Contract No. DE-AC02-05CH11231.

Authors’ Affiliations

(1)
Ecology Department, Lawrence Berkeley National Laboratory
(2)
Microbial Communities Group, Deconstruction Division, Joint BioEnergy Institute
(3)
Lawrence Livermore National Laboratory
(4)
Physical Biosciences Division, Lawrence Berkeley National Laboratory
(5)
Technologies Division, Joint BioEnergy Institute
(6)
Sandia National Lab
(7)
Los Alamos National Laboratory
(8)
Department of Energy Joint Genome Institute

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© The Author(s) 2011