Genomics Inform Search


Genomics Inform > Volume 17(4); 2019 > Article
Sulthana, Lakshmi, and Madempudi: High-quality draft genome and characterization of commercially potent probiotic Lactobacillus strains


Lactobacillus acidophilus UBLA-34, L. paracasei UBLPC-35, L. plantarum UBLP-40, and L. reuteri UBLRU-87 were isolated from different varieties of fermented foods. To determine the probiotic safety at the strain level, the whole genome of the respective strains was sequenced, assembled, and characterized. Both the core-genome and pan-genome phylogeny showed that L. reuteri was closest to L. plantarum than to L. acidophilus, which was closest to L. paracasei. The genomic analysis of all the strains confirmed the absence of genes encoding putative virulence factors, antibiotic resistance, and the plasmids.


Lactobacillus are a group of Gram-positive, rod-shaped, microaerophilic, non-spore-forming, lactic acid–producing bacteria [1], they are the natural and significant inhabitants of gastrointestinal tract of humans, as well as they are known to constitute a major part of the oral and vaginal microbiome [2-5]. Lactobacillus are the most common probiotics found in fermented food products, and the awareness of probiotic benefits is evolving more quickly. Commercially available Lactobacillus probiotic strains help to restore the microbiota of imbalanced gut caused due to antibiotic treatments; however, the pathogenicity and efficacy of potential probiotics have to be assessed for safety. Here, we report the whole genome sequence of commercially potent probiotic Lactobacillus strains: Lactobacillus acidophilus UBLA-34, Lactobacillus paracasei UBLPC-35, Lactobacillus plantarum UBLP-40, and Lactobacillus reuteri UBLRU-87.
Lactobacillus strains were isolated from serially diluted fermented foods under anaerobic conditions at 37℃ using MRS (deMan, Rogosa, and Sharpe) agar, the pure isolated colonies were cultured using MRS broth, the cells were harvested for DNA isolation with the phenol-chloroform extraction method, followed by 16S rRNA gene amplification (using the primers 27F and 1429R) [6], the strains were confirmed by PCR amplicons sequencing and phylogenetic analysis. High molecular weight genomic DNA of the identified strains was isolated by the above-described method, DNA fragments of 300- to 400-bp size were generated by ultrasonication, fragmented DNA was used to prepare a paired-end sequencing library with a Nextera DNA Flex Library preparation kit (Illumina, San Diego, CA, USA) and sequencing was performed on an Illumina NextSeq 500 System (Illumina).
A total of 2,735,462 (420× genome coverage), 2,213,461 (218× genome coverage), 2,337,040 (214× genome coverage), and 1,641,982 (270× genome coverage) paired-end reads were generated for L. acidophilus UBLA-34, L. paracasei UBLPC-35, L. plantarum UBLP-40, and L. reuteri UBLRU-87, respectively. The reads were quality filtered based on the Phred score using NGS QC Toolkit to remove low-quality sequences [7]. The quality-filtered paired-end reads were assembled to high-quality draft genomes (Table 1) by employing de novo genome assembler SPAdes version 3.11.1 [8] and the scaffolder SSPACE-standard version 3.0 [9].
The genomes were annotated using RAST [10] and the NCBI’s Prokaryotic Genomes Annotation Pipeline (PGAP) [11]. The genes were predicted and translated through the Prokaryotic Dynamic Programming Gene-finding Algorithm (Prodigal) program [12], following pathway identification with the Kyoto Encyclopedia of Genes and Genomes Automatic Annotation Server (KAAS) [13] (Table 2).
Pan-genomic analysis of Lactobacillus strains was performed to determine the conserved core and variable genes (Table 3) [14], the estimated pan-genome size was 6,487, and the parameter ‘b’ was calculated to be 0.794494 (Fig. 1), which confirms that the pan-genome is open. The highest number of new genes which contributed to the pan-genome was observed for L. plantarum UBLP-40 (Table 3). The highest part of the core genome of Lactobacillus genus was composed of genes related to metabolism, the second-highest contributing genes were related to information storage and processing, whereas the unique and accessory genes contained more amount of poorly characterized genes in comparison to core genome (Fig. 2). The phylogeny of core and pan-genome showed that L. reuteri shares the relatedness with L. plantarum, whereas L. paracasei is closest to L. acidophilus (Fig. 3).
All the four genomes of Lactobacillus strains were screened to determine the presence of genes encoding for putative virulence factors such as hemolysin BL, non-hemolytic enterotoxin NHE, enterotoxin T, cytotoxin T, and cereulide [15], antibiotic resistance [16], and plasmids [17]. None of the genomes (UBLA-34, UBLPC-35, UBLP-40, and UBLRU-87) showed the presence of putative virulence factor or antibiotic resistance encoding genes or plasmids or any antibiotic-resistant genes containing plasmids. Secondary metabolite producing gene cluster detection was performed for all the Lactobacillus strains, based on the hidden Markov model profiling of metabolite producing genes [18].

Lactobacillus acidophilus UBLA-34

RiPP biosynthetic gene cluster was found in scaffold number 6 (location: 53,280–66,324 nt) consisting of seven genes encoding gassericin. The homologous gene cluster was mined from Lactobacillus gasseri LA327, gassericin T gene cluster Lactobacillus gasseri LA158 gassericin T gene cluster, Lactobacillus gasseri EV1461 gassericin E gene cluster with a 33% similarity (Fig. 4).

Lactobacillus paracasei UBLPC-35

Two bacteriocin biosynthetic gene clusters were found in scaffold number 1 (location: 21,360–44,300 nt and 85,659–97,824 nt), there was no significant similarity found with the known gene clusters.

Lactobacillus plantarum UBLP-40

First bacteriocin biosynthetic gene cluster was found in scaffold number 7 (location: 101,210–113,360 nt), whereas terpene biosynthetic gene cluster was found in scaffold number 12 (location: 77,136–92,747 nt), there was no significant similarity found with the known gene clusters.

Lactobacillus reuteri UBLRU-87

No secondary metabolite producing gene cluster was found.

Data Availability

The raw sequence reads have been submitted to the NCBI SRA and the whole-genome shotgun project has been deposited in DDBJ/EMBL/GenBank under the following accession numbers: Lactobacillus acidophilus UBLA-34: SRR7958229, RBHY00000000: the version described in this paper is version RBHY01000000, Lactobacillus paracasei UBLPC-35: SRR8382560, RCFI00000000: the version described in this paper is version RCFI01000000, Lactobacillus plantarum UBLP-40: SRR8382543, RDEY00000000, the version described in this paper is version RDEY01000000, Lactobacillus reuteri UBLRU-87: SRR8382542, RIAU00000000, the version described in this paper is version RIAU01000000.


Authors’ Contribution

Conceptualization: SGL. Data curation: AS. Formal analysis: AS. Funding acquisition: RSM. Methodology: AS. Writing - original draft: AS. Writing - review & editing: AS.

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.


We acknowledge the staff at Sandor Lifesciences Pvt. Ltd. for their services.

Fig. 1.
The pan and core genome plot of Lactobacillus strains (total gene families represented by black color, core gene families are denoted by pink color).
Fig. 2.
Cluster of orthologous groups (COG) distribution of the core, accessory and unique genes.
Fig. 3.
Core-Pan genome phylogeny.
Fig. 4.
Bacteriocin gene clusters homologous to Lactobacillus acidophilus UBLA-34 (biosynthetic genes presented in red, regulatory genes in green and transport-related genes in blue color).
Table 1.
Genome characteristics
Strain Genome size (bp) No. of scaffolds Largest scaffold size (bp) N50 (bp) GC (%)
UBLA-34 1,951,037 34 669,777 167,656 34.6
UBLPC-35 3,038,799 11 2,520,091 2,520,091 46.02
UBLP-40 3,265,595 47 528,446 245,973 44.49
UBLRU-87 1,821,307 21 1,763,886 1,763,886 38.55
Table 2.
Genome annotation
Subsystem feature counts UBLA-34 UBLPC-35 UBLP-40 UBLRU-87
Cofactors, vitamins, prosthetic groups, pigments 45 56 106 82
Cell wall and capsule 28 47 60 38
Potassium metabolism 5 3 7 5
Membrane transport 42 49 53 19
Iron acquisition and metabolism 4 7 5 5
RNA metabolism 31 35 39 35
Nucleosides and nucleotides 78 83 88 82
Protein metabolism 122 132 136 130
Cell division and cell cycle 4 5 4 5
Regulation and cell signaling 23 34 29 10
Secondary metabolism 1 4 4 1
DNA metabolism 47 74 56 49
Fatty acids, lipids, and isoprenoids 23 47 35 46
Nitrogen metabolism 0 4 9 9
Dormancy and sporulation 5 6 6 5
Respiration 12 28 16 15
Stress response 5 46 20 8
Amino acids and derivatives 91 122 196 110
Sulfur metabolism 4 5 3 3
Phosphorus metabolism 15 28 33 28
Carbohydrates 124 233 240 115
Coding sequences 1,897 3,156 3,214 1,832
No. of RNAs 63 59 70 72
Table 3.
Pan-genome analysis
Strain No. of accessory genes No. of unique genes No. of exclusively absent genes No. of core genes
UBLA-34 364 1,119 118 308
UBLPC-35 484 1,577 105 308
UBLP-40 746 1,792 12 308
UBLRU-87 513 787 64 308


1. Ahrne S, Nobaek S, Jeppsson B, Adlerberth I, Wold AE, Molin G. The normal Lactobacillus flora of healthy human rectal and oral mucosa. J Appl Microbiol 1998;85:88–94.
crossref pmid
2. Ma B, Forney LJ, Ravel J. Vaginal microbiome: rethinking health and disease. Annu Rev Microbiol 2012;66:371–389.
crossref pmid pmc
3. Fettweis JM, Brooks JP, Serrano MG, Sheth NU, Girerd PH, Edwards DJ, et al. Differences in vaginal microbiome in African American women versus women of European ancestry. Microbiology 2014;160:2272–2282.
crossref pmid pmc
4. Salas-Jara MJ, Ilabaca A, Vega M, Garcia A. Biofilm forming Lactobacillus: new challenges for the development of probiotics. Microorganisms 2016;4:E35.
5. Owen OW. A study of bacterial counts (lactobacilli) in saliva related to orthodontic appliances; a preliminary report. Am J Orthod 1949;35:672–678.
crossref pmid
6. Karthik M, Bhavan PS, Seenivasan V, Asaikkutti A, Muralisankar T, Mahendran R. Dietary supplementation of Lactobacillus fermentum for improving the survival, growth and nutritional profiles of the prawn Macrobrachium rosenbergii, and 16S rDNA based identification of its establishment. Scholars Rep 2018;3:38–62.

7. Patel RK, Jain M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS One 2012;7:e30619.
crossref pmid pmc
8. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 2012;19:455–477.
crossref pmid pmc
9. Hunt M, Newbold C, Berriman M, Otto TD. A comprehensive evaluation of assembly scaffolding tools. Genome Biol 2014;15:R42.
crossref pmid pmc
10. Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, et al. The RAST Server: rapid annotations using subsystems technology. BMC Genomics 2008;9:75.
crossref pmid pmc
11. Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res 2016;44:6614–6624.
crossref pmid pmc pdf
12. Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010;11:119.
crossref pmid pmc pdf
13. Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 2007;35:W182–W185.
crossref pmid pmc pdf
14. Chaudhari NM, Gupta VK, Dutta C. BPGA- an ultra-fast pan-genome analysis pipeline. Sci Rep 2016;6:24373.
crossref pmid pmc pdf
15. Chen L, Zheng D, Liu B, Yang J, Jin Q. VFDB 2016: hierarchical and refined dataset for big data analysis: 10 years on. Nucleic Acids Res 2016;44:D694–D697.
crossref pmid pdf
16. Liu B, Pop M. ARDB: Antibiotic Resistance Genes Database. Nucleic Acids Res 2009;37:D443–D447.
crossref pmid pdf
17. Carattoli A, Zankari E, Garcia-Fernandez A, Voldby Larsen M, Lund O, Villa L, et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother 2014;58:3895–3903.
crossref pmid pmc
18. Blin K, Wolf T, Chevrette MG, Lu X, Schwalen CJ, Kautsar SA, et al. antiSMASH 4.0-improvements in chemistry prediction and gene cluster boundary identification. Nucleic Acids Res 2017;45:W36–W41.
crossref pmid pmc pdf
Share :
Facebook Twitter Linked In Google+
METRICS Graph View
  • 1 Crossref
  • 0 Scopus
  • 6,066 View
  • 147 Download
Related articles in GNI


Browse all articles >

Editorial Office
Room No. 806, 193 Mallijae-ro, Jung-gu, Seoul 04501, Korea
Tel: +82-2-558-9394    Fax: +82-2-558-9434    E-mail:                

Copyright © 2024 by Korea Genome Organization.

Developed in M2PI

Close layer
prev next