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. 2019 Feb 15;9(3):84. doi: 10.1007/s13205-019-1578-y

Complete genome sequence of acetate-producing Klebsiella pneumoniae L5-2 isolated from infant feces

Yong-Soo Park 1,4,#, Jisu Kang 1,3,#, Won-Hyong Chung 1, Mi Young Lim 1, Myung-Ji Seo 2, Young-Do Nam 1,3, Jung-Hoon Yoon 4,#, Dong-Ho Seo 1,✉,#
PMCID: PMC6375823  PMID: 30800595

Abstract

Acetate is an important metabolite in infants as it can affect metabolism as well as immune and inflammatory responses. However, there have been no studies on acetate production by Klebsiella pneumoniae isolated from infant feces. In this study, we isolated a K. pneumoniae strain, L5-2, from infant feces, and we found it produces acetate. The genome of L5-2 consisted of a 5,237,123-bp single chromosome and a 139,211-bp single plasmid. The G + C content was 57.27%. By whole-genome analysis of K. pneumoniae L5-2, we identified seven genes related to acetate production (poxA, pta, eutD, ackA, eutP, eutQ, and adhE). We confirmed acetate production by K. pneumoniae L5-2 by ion chromatography. The aldehyde/alcohol dehydrogenase (adhE) activity of K. pneumoniae L5-2 was significantly higher than that of the K. pneumoniae subsp. ozaenae ATCC 11296. Thus, the acetate-producing ability of K. pneumoniae L5-2 was influenced by the adhE gene. In addition, K. pneumoniae L5-2 had significantly less virulence factor-encoding genes than other K. pneumoniae strains isolated from humans. In conclusion, K. pneumoniae L5-2 isolated from infant feces has less virulence factors and higher adhE activity than other K. pneumoniae strains.

Electronic supplementary material

The online version of this article (10.1007/s13205-019-1578-y) contains supplementary material, which is available to authorized users.

Keywords: Klebsiella pneumoniae, Infant, Gut microbiota, Whole-genome sequencing, Short chain fatty acid, Acetate

Introduction

The gut microbiota comprises approximately 1000 species and 3.3 million genes, which is 150 times the number of genes in the human genome (Zhu et al. 2010). Although the importance of the gut microbiota and microbial metabolites in the maintenance of host health has been demonstrated (Ríos-Coviàn et al. 2016), the genetic and functional diversity of the gut microbiota is not fully elucidated (Li et al. 2014). Infant gut microbial communities are easily influenced by various factors, such as the environment, disease, and diet, and are thus more dynamic than those of adults (Matamoros et al. 2013). Klebsiella species are commonly found in soils, plants, insects, and human patients (Downie et al. 2013; Jiang et al. 2016). Recently, a study indicated that Klebsiella species are present in the gut of healthy infants, with Klebsiella pneumoniae being a dominant species (Gómez et al. 2016).

Short-chain fatty acids (SCFAs), also known as volatile fatty acids, are the major metabolites produced by gut microbiota (Ríos-Coviàn et al. 2016; Rooks and Garrett 2016) through anaerobic fermentation of dietary fibers or complex polysaccharides (Flint et al. 2008; Trompette et al. 2014). SCFAs can alter the gastrointestinal environment and modulate inflammation and energy balance through interaction with host factors (Read and Holmes 2017). SCFAs produced by the decomposition of prebiotics have been shown to promote the growth of microorganisms beneficial to infant health, such as Bifidobacteria and Lactobacillus (Groer et al. 2014). Acetate, propionate, and butyrate are representative SCFAs (White 2017), and an increase in acetate levels affects appetite regulation through homeostatic mechanisms (Frost et al. 2014) along with metabolism, immunity, and inflammation through interaction with G-protein-coupled receptor 43 (Maslowski et al. 2009).

We previously isolated Shigella flexneri, Citrobacter youngae, Enterococcus gallinarum, Enterococcus avium, Enterococcus raffinosus, and K. pneumoniae from infant feces (unpublished). Although K. pneumoniae isolated from the natural environment is known to produce acetate as a metabolite (Biebl et al. 1998), it is unclear whether the same is true for K. pneumoniae isolated from the gut of infants. To address this question and to identify a number of genes involved in acetate production, we performed whole-genome sequencing of K. pneumoniae L5-2 from infant feces and comparative genome analysis using a K. pneumoniae subsp. ozaenae ATCC 11296.

Materials and methods

Isolation of K. pneumoniae

Klebsiella pneumoniae L5-2 isolated from Korean infant feces was cultured in 1 l of selective LC medium composed of 10.0 g peptone, 4.0 g meat extract, 3.0 g sodium acetate, 1.0 g KH2PO4, 1.0 g yeast extract, 1.0 g ammonium citrate, 1.0 g skim milk, 0.1 g MgSO4, 0.05 g MnSO4, 1.0 ml of Tween-80 (Sigma-Aldrich, St. Louis, MO, USA), and 15 g bacteriological agar (Junsei Chemical Co., Tokyo, Japan) (Ravula and Shah 1998). The pH was adjusted to 7.0 ± 0.2, and the bacteria were anaerobically incubated at 37 °C for 48 h. K. pneumoniae subsp. ozaenae ATCC 11296 (Leibniz Institute DSMZ, Braunschweig, Germany) was used as a representative type strain for comparative analyses (Fierer et al. 2005). Strains L5-2 and ATCC 11296 were preserved in 20% glycerol at − 80 °C (Kitamoto et al. 2002).

Microbial growth measurement

Strains L5-2 and ATCC 11296 were cultured in 0.5% glucose-supplemented tryptic soy agar composed of 17.0 g/L pancreatic digest of casein, 3.0 g/L papaic digest of soybean, 2.5 g/L dextrose, 5.0 g/L sodium chloride, 2.5 g/L dipotassium phosphate (BD Biosciences, Franklin Lakes, NJ, USA), and 15 g/L bacteriological agar. The two strains were incubated in tryptic soy broth with glucose at 37 °C in a shaking incubator for 24 h. Microbial growth was determined by measuring the OD600nm in a 96-well plate using a SpectraMax i3 spectrophotometer (Molecular Devices, Sunnyvale, CA, USA). The experiment was repeated three times.

Analysis of acetate production

One milliliter of each culture was collected at 2-h intervals from 0 to 24 h. The samples were centrifuged at 15,500×g for 1 min using a Microfuge 20R (Beckman Coulter, Brea, CA, USA). One milliliter of the supernatant was filtered through a 0.45-µm syringe filter (GVS, Zola Predosa, Italy) and was transferred to a vial for analysis by ion chromatography (IC) (Metrohm, Herisau, Switzerland). The run time for each sample was set to 40 min (Lu et al. 2010). Integrations were automated. A Metrosep organic acid 250/7.8 column (Metrohm) was used. The eluent was made up to 1 l with sulfuric acid (0.5 mM) (99.999%; Sigma-Aldrich, St. Louis, MO, USA) and was used after filtering (500-ml bottle top filter, 0.22 µm; Corning Inc., Corning, NY, USA) and degassing (Powersonic 510; Hwashin, Yeongcheon, Korea). The liquid flow rate was set to 0.5 ml/min. IC pressure during analysis was maintained between 540 and 550 psi, and the instrument temperature was set to 30 °C. Sample analysis was started after 5 min of pump priming and 1 h of stabilization. The acetate concentration was calculated based on peak height because the unknown peak was not completely separated from acetate in IC analysis (Supplementary Fig. S1). The calibration curve for acetate concentration was of quadratic curve type, H (peak height) = 3.245 + 4.054 × Q (acetate amount) − 0.016 × Q2, the relative standard deviation was 6.554%, and the correlation coefficient was 0.999.

Genome sequencing, assembly, and annotation

Genomic DNA was extracted from strain L5-2 using a QIAmp DNA Mini kit (Qiagen, Hilden, Germany), and DNA quantity and quality were determined with a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and a Qubit 2.0 fluorometer (Life Technologies, Carlsbad, CA, USA).

Whole-genome sequencing was performed using the PacBio RS II platform. A 10-kb DNA library was constructed and sequenced by single-molecule real-time sequencing technology using P6-C4 chemistry (Pacific Biosciences, Menlo Park CA, USA). Sequence reads were assembled using HGAP 4.0 (Pacific Biosciences) with 5.3 Mb as the genome size option. Functional gene annotation was performed with the National Center for Biotechnology Information (NCBI) Prokaryotic Genome Annotation Pipeline (PGAAP) (Tatusova et al. 2016). Genes involved in acetate production were identified based on PGAAP annotation, and acetate metabolism pathways were predicted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (Kanehisa et al. 2017). DNA plotter (Carver et al. 2009) was used to generate a genome map and examine genome topology.

Phylogenetic and comparative genomic analyses

Acetate production-related genes identified in K. pneumoniae L5-2 were validated by comparisons to the K. pneumoniae subsp. ozaenae ATCC 11296 genome. Phylogenetic analysis was performed with MEGA 6, and a tree was constructed by the neighbor-joining method with 1000 bootstraps (Tamura et al. 2013). Average nucleotide identity (ANI) values were calculated to evaluate evolutionary distance using pyani version 0.2.7; option “-m ANIb” (Pritchard et al. 2016). The EzBioCloud, an online taxon search tool, was used to identify the 16S rRNA gene sequences of L5-2 and ATCC 11296 (Yoon et al. 2017). Complete or draft genome sequences of ATCC 11296, ATCC 13884, HS11286, TH1, and W14 were downloaded from the NCBI genome database [http://www.ncbi.nlm.nih.gov/genome/; accession nos. GCF_000826585 (ATCC 11,296); GCF_000163455 (ATCC13884); GCF_000240185 (HS11286); GCF_001676825 (TH1); GCF_001646625 (W14)].

Functional classification and identification of genes encoding virulence factors

Coding genes were aligned to the Clusters of Orthologous Groups (COG) database using BLASTP (E value cut-off: 1e−5) and were classified into COG categories (Galperin et al. 2015). Genes encoding virulence factors were searched in the pathogenic bacteria database (Chen et al. 2005, 2016) with the following criteria: minimum protein identity, 80%; minimum coverage, 80%; and minimum alignment length, 50 bp.

Aldehyde dehydrogenase activity assay

Aldehyde dehydrogenase activity was analyzed using the Aldehyde Dehydrogenase Activity Colorimetric Assay kit (Sigma-Aldrich, St. Louis, MO, USA) according to manufacturer’s instructions. Briefly, cell lysates were incubated with 50 µl of lysis buffer, 5 mM NAD+, and 5 mM propionaldehyde as a substrate in a 96-well plate at 37 °C. The initial absorbance at 340 nm was measured immediately using a SpectraMax i3 plate reader (Molecular Devices, Sunnyvale, CA, USA). The rate of change in absorbance at 340 nm was measured in triplicates over 30 min. A control reaction in which the substrate was not added was included to monitor the endogenous rate of NAD+ reduction. Aldehyde dehydrogenase activity was expressed in nmol/108 cells/min.

Results and discussion

Genome comparison

Sequence analysis of the 16S rRNA gene sequence of L5-2 was carried out using the NCBI and ExBioCloud databases (Kim et al. 2012). K. pneumoniae subsp. ozaenae showed the highest similarity to L5-2 (99.79%) in the EzBioCloud database (Table 1). Thus, we selected K. pneumoniae subsp. ozaenae ATCC 11296 as the type strain for comparative analyses. A phylogenetic tree constructed on the basis of 16S rRNA gene sequences confirmed the evolutionary relatedness of L5-2 and K. pneumoniae subsp. ozaenae ATCC 11296 (Fig. 1a). ANI values ranged from 98.8 to 99% (Fig. 1b).

Table 1.

Similarities based on 16S rRNA gene sequence were calculated using EzBioCloud and NCBI database

Strain EzBioCloud NCBI
Similarity (%) Completeness (%) Identity (%) Query cover (%)
Klebsiella pneumoniae subsp. ozaenae ATCC 11296 99.79 99.3 99 93
Klebsiella pneumoniae subsp. rhinoscleromatis ATCC 13884 99.59 100 99 92
Klebsiella pneumoniae W14 98.09 93.8 – –
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–, not detected

Fig. 1.

Fig. 1

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Phylogenetic tree of K. pneumoniae strains based on 16S rRNA gene sequences (a) and average nucleotide identity (b)

The complete genome sequence of K. pneumoniae L5-2 was obtained with 170-fold coverage. The total genome size was 5,376,334 bp, and the GC content was 57.27%. There were two contigs: a single circular chromosome (5,237,123 bp) and a single circular plasmid (139,211 bp) (Table 2; Fig. 2). In total, 5341 genes were assigned, including 5067 protein-coding genes, 155 pseudogenes, and 124 RNA genes (25 rRNA, 87 tRNA, and 12 non-coding RNA). The assembly level of ATCC 11296 was scaffold genome representation.

Table 2.

Genome summary of L5-2 and ATCC 11296

Characteristic L5-2 ATCC 11296
Genome
 Accession CP025683 GCA_000826585.2
 Assembly level Complete Scaffold
Sequence category 1 Chromosome, 1 plasmid 23 Scaffolds
Total size 5,376,334 4,944,477
GC content 57.27 57.26
Annotation
Genes 5341 5014
CDS 5217 4937
 Coding genes 5062 4433
 Pseudogenes 155 504
RNAs 124 77
 rRNAs (5S, 16S, 23S) 25 (9, 8, 8) 3 (1, 1, 1)
 tRNAs 87 64
 ncRNAs 12 10
Repeat region 0 2
CRISPRs 0 2
misc_feature 0 0
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CDS coding sequence, CRISPR clustered regularly interspaced short palindromic repeats, ncRNA non-coding RNA, rRNA ribosomal RNA, tRNA transfer RNA

Fig. 2.

Fig. 2

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Complete circular genome map of K. pneumoniae L5-2. a Chromosome. b Plasmid. Track 1 (light blue), forward coding sequence (CDS); track 2 (blue), reverse CDS, track 3 (light purple), rRNA (5S, 16S, and 23S); track 4 (green), tRNA; track 5 (red), clustered regularly interspaced short palindromic repeats (CRISPRs); track 6 (light green and purple), GC content; and track 7 (light green and purple), GC skew

Comparison of genomic features and functional classification

Protein-coding genes were functionally classified based on COG definitions. L5-2 and ATCC 11286 were assigned 25 out of 26 COG functional codes, excluding “Nuclear structure (Y)” (Table 3). In total, 5091 and 4676 genes in L5-2 and ATCC 11296, respectively, were assigned COG codes, whereas 636 and 838 genes, respectively, were unassigned. Eighteen COG categories of L5-2, including “Signal transduction mechanisms (T)”, “Extracellular structures (W)”, “Intracellular trafficking, secretion, and vesicular transport (U)”, and “Secondary metabolites biosynthesis, transport, and catabolism (Q)” showed a greater abundance of functional genes than ATCC 11286. ATCC 11296 was previously reported as having a total assembly gap length of 19,227 bp, 23 scaffolds, and 128 contigs (https://www.ncbi.nlm.nih.gov/assembly/GCF_000826585.2); this made it difficult to compare L5-2 at the complete genome level and ATCC 11296 at the scaffold level in the COG analysis.

Table 3.

Cluster of orthologous group (GOG) functional categories

CODE COG functional description L5-2 ATCC 11,296
J Translation, ribosomal structure, and biogenesis 272 272
A RNA processing and modification 1 1
K Transcription 488 421
L Replication, recombination, and repair 150 175
B Chromatin structure and dynamics 1 1
D Cell cycle control, cell division, and chromosome partitioning 51 52
Y Nuclear structure 0 0
V Defense mechanisms 122 110
T Signal transduction mechanisms 238 195
M Cell wall/membrane/envelope biogenesis 278 256
N Cell motility 78 50
Z Cytoskeleton 1 1
W Extracellular structures 49 27
U Intracellular trafficking, secretion, and vesicular transport 77 63
O Posttranslational modification, protein turnover, and chaperones 196 165
X Mobilome: prophages and transposons 42 128
C Energy production and conversion 292 262
G Carbohydrate transport and metabolism 573 512
E Amino acid transport and metabolism 508 468
F Nucleotide transport and metabolism 100 96
H Coenzyme transport and metabolism 242 235
I Lipid transport and metabolism 160 127
P Inorganic ion transport and metabolism 354 326
Q Secondary metabolites biosynthesis, transport, and catabolism 136 111
R General function prediction only 431 388
S Function unknown 251 234
− NA 636 838
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Genes and pathways involved in acetate production

Genes related to acetate production and metabolism were searched based on previous studies (Lama et al. 2017; Garsin 2010) and using the KEGG database (Fig. 3) (Kanehisa and Goto 2000). We focused on genes encoding four enzymes involved in acetate production, i.e., pyruvate dehydrogenase (poxA), phosphate acetyltransferase (pta and eutD), acetate kinase (ackA, eutP, and eutQ), and aldehyde/alcohol dehydrogenase (adhE) (Table 4 and Supplementary Table S1). L5-2 and ATCC 11296 harbored one poxA gene each; the gene product regulates pyruvate oxidase (poxB), which directly converts pyruvate to acetate in glycolysis (Wolfe 2005). A comparison of the growth curves revealed that L5-2 consistently grew better than ATCC 11296 (Fig. 4a). Therefore, we predicted that acetate production by L5-2 would be higher than that by ATCC 11296. Acetate was confirmed to the only SCFA produced by K. pneumoniae by IC analysis (Fig. S1). However, this was partially contradicted by the IC results: acetate production was higher in L5-2 than in ATCC 11296 up to 10 h, whereas the reverse was observed thereafter (Fig. 4b). This may be because L5-2 has an additional copy of adhE. Initially (< 10 h), L5-2 produces acetate using glucose, whereas later, it might produce ethanol from acetate by adhE. Therefore, it can be assumed that ATCC 11296 has higher acetate production than L5-2 after 10 h of culture. Previous study showed that increased ethanol production through metabolism reduces acetate production (Yazdani and Gonzalez 2008). Our results suggest that the copy number of adhE involved in the conversion of acetaldehyde to ethanol affects acetate production. Moreover, the intracellular adhE activity in L5-2 (103.33 ± 4.95 U) was significantly higher than that of ATCC 11296 (1.33 ± 0.19 U) (Fig. 5).

Fig. 3.

Fig. 3

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Acetate metabolic pathway of K. pneumoniae and metabolic pathway from pyruvate to acetate by fermentation in K. pneumoniae. Six genes (poxA, pta, eutD, ackA, eutP, and eutQ) involved in acetate production are shown in bold letters; one gene (adhE) involved in ethanol production is highlighted in bold red letters

Table 4.

Acetate production-related genes

Gene ID L5-2 ATCC 11296 HS11286 ATCC 13884 W14 TH1
Pyruvate dehydrogenase (poxA) 1 1a 1 1 1 1
Phosphate acetyltransferase (pta and eutD) 2 2 2 2 2 2
Acetate kinase protein (ackA, eutP, and eutQ) 4 4 6 4a 6 6
Aldehyde/alcohol dehydrogenase (adhE) 2 1 4 5 6 6
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L5-2, K. pneumoniae L5-2; HS 11286, K. pneumoniae subsp. pneumoniae HS11286; TH1, K. pneumoniae TH1; W14, K. pneumoniae W14; ATCC 11296, K. pneumoniae subsp. ozaenae ATCC 11296; ATCC 13884, K. pneumoniae subsp. rhinoscleromatis ATCC 13884

aPseudogene

Fig. 4.

Fig. 4

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Growth curves and acetate production by K. pneumoniae. a Comparison of growth curves of K. pneumoniae L5-2 and K. pneumoniae subsp. ozaenae ATCC 11296 from 0 to 24 h of culture. b Comparison of acetate production in K. pneumoniae L5-2 and K. pneumoniae subsp. ozaenae ATCC 11296 from 0 to 24 h of culture

Fig. 5.

Fig. 5

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Aldehyde dehydrogenase activity of K. pneumoniae L5-2 and K. pneumoniae subsp. ozaenae ATCC 11296

The poxA gene of ATCC 11296 is a confirmed pseudogene and may be affected by chromosome-level scaffolding (Table 4). Except for adhE, gene copy numbers were similar between the two strains. adhE encodes a bifunctional alcohol and aldehyde dehydrogenase that is important for the generation of ethanol from acetyl-CoA (Lo et al. 2015); a previous study showed that acetate production of adhE+ mutants (Caldicellulosiruptor bescii and Clostridium thermocellum) was decreased (Scully and Orlygsson 2017; Biswas et al. 2014). These findings also explain why L5-2 produces less acetate than ATCC 11296. L5-2 has fewer adhE genes than K. pneumoniae strains isolated from humans. L5-2 might have the highest acetate production among K. pneumoniae from humans, suggesting that it might create a beneficial environment in the infant’s intestines.

Comparison of virulence factors

Klebsiella pneumoniae causes hospital-acquired infections and accounts for a large number of cases of hepatitis, pneumonia, sepsis, and soft tissue infections (Podschun and Ullmann 1998). We found that L5-2 has fewer genes encoding virulence factors than did ATCC 11296; for instance, aerobactin (VF0123), yersiniabactin (VF0136), and aerobactin (VF0229) were found only in ATCC 11296 (Supplementary Table S2). However, five genes encoding type 1 fimbriae (VF0221) and six encoding Escherichia coli common pilus (ECP; VF0404) were found in the L5-2 genome. These differences can be explained by the source of each strain: ATCC 11296 was isolated from soil, whereas L5-2 was isolated from infant feces. Interestingly, L5-2 has very few virulence factors compared to other K. pneumoniae strains from humans (Table 5). This suggests that L5-2 does not act as a pathogen in infancy.

Table 5.

Number of virulence factor-encoding genes in various strains

Virulence gene ID L5-2 ATCC 11296 HS11286 ATCC 13884 TH1 W14
Type 1 fimbriae (VF0221) 5 0 5 4 5 5
LOS (CVF494) 1 1 1 1 1 1
OmpA (VF0236) 1 1 1 1 1 1
Enterobactin (VF0228) 8 8 8 0 8 8
Enterobactin (IA019) 1 1 1 0 1 1
ECP (VF0404) 6 0 6 6 6 6
Aerobactin (VF0123) 0 2 0 2 0 0
Yersiniabactin (VF0136) 0 2 11 12 11 11
Aerobactin (VF0229) 0 3 0 3 0 0
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L5-2, K. pneumoniae L5-2; HS 11286, K. pneumoniae subsp. pneumoniae HS11286; TH1, K. pneumoniae TH1; W14, K. pneumoniae W14; ATCC 11296, K. pneumoniae subsp. ozaenae ATCC 11296; ATCC 13884, K. pneumoniae subsp. rhinoscleromatis ATCC 13884

Type 1 fimbriae mediate and promote the adhesion of bacterial cells to the host (Khanal et al. 2015; Vizcarra et al. 2016). ECP also is an adherence factor (Rendón et al. 2007). The fact that genes encoding these factors are present in the K. pneumoniae L5-2 genome suggests an evolutionary adaptation that allows the microorganism to adhere to host intestinal epithelial cells (Table 5).

Acetate produced by gut microbiota has a variety of beneficial health effects. K. pneumoniae L5-2 isolated from infant’s intestine was confirmed to produce acetate through fermentation. In addition, we identified several genes that may be involved in acetate production and metabolism. Our results provide a useful resource for future investigations into the role of the gut microbiota in infant health.

Nucleotide sequence accession number

The complete genome sequence has been deposited in GenBank under accession numbers CP025683-CP025684.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (XLSX 25 KB) (25.5KB, xlsx)
Supplementary material 2 (DOCX 245 KB) (245.7KB, docx)

Acknowledgements

This research was supported by Main Research Programs (E0170602-02) of the Korea Food Research Institute funded by the Ministry of Science and Information and Communications Technologies.

Author contributions

D-HS designed and coordinated all the experiments; Y-SP performed the experiments, analyzed the genome data and wrote manuscript; SK performed the sequence assembly and genomic analysis and wrote manuscript; W-HC and MYL performed gene prediction and gene annotation; M-JS and Y-DN checked and edited the manuscript. J-HY measured aldehyde dehydrogenase activity. All authors have read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest in the publication.

Contributor Information

Jung-Hoon Yoon, Phone: 82-31-290-7800, Email: jhyoon69@skku.edu.

Dong-Ho Seo, Phone: 82-63-219-9385, Email: sdh83@kfri.re.kr.

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