Skip to main content
Download PDF

Comparative genomics of Leuconostoc lactis strains isolated from human gastrointestinal system and fermented foods microbiomes

BMC Genomic Data volume 23, Article number: 61 (2022) Cite this article

Abstract

Background

Leuconostoc lactis forms a crucial member of the genus Leuconostoc and has been widely used in the fermentation industry to convert raw material into acidified and flavored products in dairy and plant-based food systems. Since the ecological niches that strains of Ln. lactis being isolated from were truly diverse such as the human gut, dairy, and plant environments, comparative genome analysis studies are needed to better understand the strain differences from a metabolic adaptation point of view across diverse sources of origin. We compared eight Ln. lactis strains of 1.2.28, aa_0143, BIOML-A1, CBA3625, LN19, LN24, WIKIM21, and WiKim40 using bioinformatics to elucidate genomic level characteristics of each strain for better utilization of this species in a broad range of applications in food industry.

Results

Phylogenomic analysis of twenty-nine Ln. lactis strains resulted in nine clades. Whole-genome sequence analysis was performed on eight Ln. lactis strains representing human gastrointestinal tract and fermented foods microbiomes. The findings of the present study are based on comparative genome analysis against the reference Ln. lactis CBA3625 genome. Overall, a ~ 41% of all CDS were conserved between all strains. When the coding sequences were assigned to a function, mobile genetic elements, mainly insertion sequences were carried by all eight strains. All strains except LN24 and WiKim40 harbor at least one intact putative prophage region, and two of the strains contained CRISPR-Cas system. All strains encoded Lactococcin 972 bacteriocin biosynthesis gene clusters except for CBA3625.

Conclusions

The findings in the present study put forth new perspectives on genomics of Ln. lactis via complete genome sequence based comparative analysis and further determination of genomic characteristics. The outcomes of this work could potentially pave the way for developing elements for future strain engineering applications.

Peer Review reports

Background

Genus Leuconostoc (Ln) is comprised of 17 species of Ln. mesenteroides (divided into Ln. mesenteroides, Ln. dextranicum, and Ln. cremoris), Ln. pseudomesenteroides, Ln. citreum, Ln. gelidum, Ln. carnosum, Ln. kimchii, Ln. fallax, Ln. inhae, Ln. palmae, Ln. miyukkimchii, Ln. rapi, Ln. falkenbergense, Ln. holzapfelii, Ln. litchii, Ln. suionicum, Ln. garlicum, Ln. lactis [1]. As of 2017 the species suionicum has been designated, previously considered subspecies of Ln. mesenteroides [2].

Ln. lactis is a lactic acid bacterium (LAB) that naturally exists in diverse ecological environments and is commonly pertain to food fermentations. The isolation sources of this species are various environments, including cheese, whey, cucumber fermentation brine, kimchi, and the human gut [3, 4].

Ln. lactis is a gram ( +), catalase (-), cocci, facultative anaerobic, heterofermentative, non-motile, non-spore forming LAB carrying intrinsic vancomycin resistance [5, 6]. Certain Ln. lactis strains are able to produce buttery flavor metabolites for example diacetyl and acetoin at low pH. Thus, they could be utilized in fermented dairy foods [7, 8]. Moreover, some Leuconostoc strains could convert carbohydrates such as sucrose to dextran exopolysaccharide [9]. Due to the heterofermentative lifestyle of Ln. lactis, it produces equimolar of lactate, ethanol, and carbon dioxide upon fermenting a mole hexose sugar (glucose and galactose), in the absence of an external electron acceptor, through pentose phosphate pathway (PPP) also known as 6-phosphogluconate/phosphoketolase pathway [10]. However, when an external electron acceptor such as acetaldehyde or pyruvate is available in the microenvironment, this organism could convert sugars primarily into lactate, acetate and CO2 to maintain redox balance by reoxidizing NADH to NAD+ which was reduced to NADH from NAD+ in the upper half of PPP. This conversion into acetate produces one additional ATP thus it is more productive for the cell compared to ethanol route also called salvage shunt [5, 11].

The LAB is reported to show high adaptation to specific microbiological niches and carry smaller genomes as opposed to other bacteria because of reducing genome which is the consequence of their effort to maintain only the required number of crucial genes necessary for micro-niche specific survivability. Even though Ln. lactis genome is proportionally small; it has to maintain the capability of rapid and continuous evolution with its essential ecosystem via horizontal gene transfer of plasmids or transduction by phage infectivity [12,13,14]. Moreover, in order to maintain its viability and grow in a changing and highly specific ecosystem, LAB has to balance the maintenance of a strong immune system against bacteriophages, transmissible DNA elements, exogenous plasmids or transposases [14,15,16].

Several Leuconostoc species such as carnosum and mesenteroides have been evaluated by comparative genomic analysis [2, 17]. Although isolation source of Ln. lactis is reportedly diverse, metabolic potentials of Ln. lactis strains have not been subjected to extensive genomic research. Therefore, information on the species population dynamics and genomic diversity in various ecological systems such as kimchi, fermented cucumber brine, human gut or dairy is scarce if available at all [4]. To our knowledge, the present work is the first in-depth comparative study of Ln. lactis genomics and diversity in the human gastrointestinal tract and fermented foods microbiomes.

Result

General genome features

Whole-genome sequence statistics of thirty-three Ln. lactis strains extracted from NCBI Genbank [18] are shown in Table 1.

Table 1 Whole-genome sequence statistics of thirty-tree Ln. lactis strains
Full size table

The Leuconstoc lactis strains studied in the present study were comparatively evaluated using genomic analysis. Twenty-nine strains including the type-strain CBA3625 (Table 1) were chosen for comparative genomic analysis based on phosphoglucomutase gene (Fig. 1). A phylogenetic analysis of 29 strains carrying complete phosphoglucomutase gene was performed based on the nucleotide sequence alignment (Fig. 1). Four strains were eliminated because of truncated or absence of gene of interest. The phylogenetic tree revealed the formation of nine distinct clades. Human gastrointestinal isolates of BIOML-A1 and aa_0143 share the first clade, whereas dairy isolates of LN19, LN24, and kimchi isolates of WIKIM21, WiKim40, and CBA3622 share the second clade. The third clade was composed of green onion and kimchi isolates of SBC001 and CCK940, respectively. NBRC 12455, MSK.22.141, and MSK.22.137 share the same clade however, the isolation sources were not found. Stool isolate of 1001262B_160229_C9, cucumber fermentation brine isolate of 1.2.28, kimchi isolate of KACC 91922, and UBA6751 (isolation source is not available) are located on the fourth clade to seventh clade, respectively. The isolation source of the last two clade members was not available except CBA3625, which was isolated from kimchi (Fig. 1). Interestingly, the kimchi isolate of Ln. lactis CBA3625 was part of the eighth clade containing the strains of UBA5570 and UBA5566 isolated from wood and metal, respectively.

Fig. 1
figure 1

Hierarchical clustering tree of multiple sequence comparison based on phosphoglucomutase gene

Full size image

Comparative genomics of Ln. lactis

Next, we selected eight strains to conduct whole-genome nucleotide sequence comparisons. The genomes were chosen for further analysis were: BIOML-A1 (fecal sample), aa_0143 (stool), LN24 (dairy), LN19 (dairy), WiKim40 (kimchi), WIKIM21 (kimchi), 1.2.28 (cucumber fermentation brine), and CBA3625 (kimchi). Genomes of these strains were picked as a representative set of phylogenies shown in Fig. 1 and are highlighted in red. These strains were isolated from either fermented foods or the human gastrointestinal tract and range in size from 1.71 Mb to 1.79 Mb. The GC-content of each individual strain ranges between 42.9% and 43.4%. The OrthoANI and 16S rDNA sequence-based phylogenetic trees are shown in Fig. 2. The whole-genome analysis of eight strains was carried out using BRIG (Fig. 3), whole-genome sequence-based phylogenetic tree (Figure S1) and progressive Mauve (Figure S2).

Fig. 2
figure 2

A Average OrthoANI nucleotide sequence based phylogenetic tree. B 16S rDNA nucleotide sequence based phylogenetic tree of eight Leuconostoc lactis strains

Full size image
Fig. 3
figure 3

Whole-genome based BLAST comparison of eight Ln. lactis strains against reference strain CBA3625

Full size image

Average OrthoANI nucleotide sequence based phylogenetic tree generated four clades. The member of the clade-one consists of WIKIM21 only. LN24 and LN19 comprised the second clade. WiKim 40, BIOML-A1, and aa_0143 form the third clade. The last clade members were CBA3625 and 1.2.28 (Fig. 2A). The phylogenetic tree of all strains clustered based on 16S rDNA shows two distinct clusters (Fig. 2B). Dairy originated Ln. lactis LN19 and LN24 form a separate clade from the remaining strains located on the second cluster from bottom to up (Fig. 2B).

Notably, 1.2.28, WiKim40, and BIOML-A1 share the highest sequence identity against the reference genome CBA3625 (Fig. 2A). Similar results were also seen in the whole genome sequence based phylogenetic tree that the closest neighbors to CBA3625 were 1.2.28, WiKim40, and BIOML-A1 (Figure S1).

Figure 3 shows whole-genome based BLAST comparison of all strains against reference strain CBA3625. BRIG image shows alignment of eight Ln. lactis strains and their GC content and GC skews. Four regions lacking significant coverage were identified as putative prophages. The first two pronounced gaps on the genome alignment identified as putative prophage I and III were between 83.8 Kb — 104.5 Kb, and 131.4 Kb — 168.8 Kb, respectively. The largest gap seen at 1.03 Mb — 1.07 Mb was marked as putative prophage V. The last gap positioned between 1.12 Mb — 1.14 Mb was identified as putative prophage VI (Fig. 3, Table 2).

Table 2 Prophages predicted in eight Leuconostoc lactis strains using PHASTER
Full size table

For the characterization of genomic conservation between all isolates related to pan- and core genomes, overall coding potential (i.e. pangenome) was determined. It was observed that 40.7% of entire genes are conserved within 95% BLASTP identity (Fig. 4A). Of the 2994 total CDS, 1217 were shared by entire eight strains, which represent the core genome. The accessory genome also called the non-core genome, contained 1777 total CDS, perhaps determining fundamental differences of phenotypic traits across different strains [19].

Fig. 4
figure 4

(A) Distributions of coding sequences found in Ln. lactis pan-genome: Core genes (green), shell genes (blue), cloud genes (red) in chromosome. (B) Estimation of the pan-genome (blue) and the core genome (red) of eight Ln. lactis strains by including genomes one by one. R programming [20] language and ggplot2 [21] package was used to plot the graphics

Full size image

Interestingly, a considerable number of sequences without function prediction (hypothetical genes) was found across all Ln. lactis strains ranging from 34 to 39% (37% on average). These Ln. lactis genomes are potential candidates for further functional annotation studies.

Four distinct clusters emerged after clustering by gene absence/presence matrix (Fig. 5). Cluster 1 only consists of aa_0143 showing the highest percent identity, with cluster 2 composed of LN19 and LN24. Cluster 3 consists of only WIKIM21 and reveals the highest percent identity with BIOML-A1 and WiKim40. There are two sub-clusters within the last cluster which contains 1.2.28 and CBA3625, and WiKim40 and BIOML-A1, respectively.

Fig. 5
figure 5

Phylogenetic tree based on gene absence-presence and gene cluster matrix comparing the similarity between putative coding sequences. R programming language was used to create the heatmap [20]

Full size image

Figure 6 shows the upSet plot of the number of shared orthogroups of each strain and the number of shared orthogroups among the strains with bar charts. The number of shared orthogroups in all strains was 1369. LN19 and LN24 have the highest number of shared orthogroups among all strains tested. Next, aa_0143 and BIOML-A1 share the 46 orthogroups. aa_0143, BIOML-A1, and WiKim40 share 43 orthogroups.

Fig. 6
figure 6

The upSet plot shows the number of orthogroups of each strain and the number of shared orthogroups among the strains with bar charts. UpSetR [22]package in R programming language [20] was used to draw the figure

Full size image

Functional characteristics of Ln. lactis

Heatmap representation of CAZymes revealed five distinct clades. The amount of GH found was similar within the first and fifth clades (from bottom to up). The concentration of the GT was also found to be similar across the first and fifth clades. However, the highest number of GT family CAZymes does exist in clades three and four. All strains carried a similar amount of CE, except CBA3625, which had the largest number of CE family CAZymes (Fig. 7). The aa_0143 and BIOML-A1 are very similar in the number of enzymes they carry (Fig. 7). Interestingly, WIKIM21 shared the same clade with LN19 and LN24.

Fig. 7
figure 7

Heatmap of CAZymes distribution and clustering across eight Ln. lactis genomes. The color gradient from lighter to darker colours represents the abundance of CAZymes found in each genome. GH: Glycoside hydrolase, GT: Glycosyltransferase, CE: Carbohydrate esterase, AA: Auxiliary activity, CBM: Carbohydrate binding module. R programming language (version 4.1.1) [20] was used to draw the heatmap

Full size image

The core and pangenomes were annotated using Prokka and assigned to functional categories in KAAS. As expected, the largest pangenome categories include CDS with functions associated with carbohydrate metabolism, amino acid metabolism, membrane transport, translation, and vitamins and cofactors metabolism. Functional genome groups, including the lowest number of CDS fall into organismal systems. The highest number of genes accumulated in carbohydrate metabolism were amino- and nucleotide sugar, pyruvate, glycolysis, starch and sucrose metabolism. Major functional genes associated with lipid metabolism are pertained to fatty acid biosynthesis, glycerophospholipid, and glycerolipid metabolism. Amino acid metabolism mainly consists of cysteine and methionine, alanine, aspartate and glutamate metabolism, and phenylalanine, tyrosine and tryptophan biosynthesis. The highest standard deviation bars were achieved in histidine metabolism, and phenylalanine, tyrosine and tryptophan biosynthesis. The lowest number of genes in amino acid metabolism relates to tryptophan metabolism and lysine degradation (Fig. 8).

Fig. 8
figure 8

KEGG functional annotations a) detailed representation of functional classes belonging to six main functional categories b) subcategories of carbohydrate, lipid, and amino acid metabolism. Functional categories: A Metabolism, B Genetic Information Processing, C Environmental Information Processing, D Cellular Processes, E Organismal Systems, F Human Diseases. R programming language [20] and ggplot2 [21] package were used to create the images based on KAAS-KEGG number of functional categories

Full size image

Mobile genetic elements

Spacers and repeats from entire CRISPR loci were identified using CRISPRviz. Among all strains screened, only aa_0143 and BIOML-A1 harbor a single spacer. The spacer alignment revealed no identical spacer sequences across two strains showing a robust confirmation of evolutionary heterology (Fig. 9B). A similar heterology was also seen in repeat sequence alignments of BIOML-A1 and aa_0143 (Fig. 9A).

Fig. 9
figure 9

Visual representation and alignment of the repeat (A) and spacer (B) contents for each identified CRISPR locus. Each diamond corresponds to a CRISPR repeat, while each colored square corresponds to a CRISPR spacer. Unique color combinations show distinct nucleotide composition

Full size image

Table 2 shows intact and questionable prophage regions in genomes of all Ln. lactis strains screened. Prophage analysis results from PHASTER show that six strains (1.2.28, aa_0143, BIOML-A1, CBA3625, LN19, WIKIM21) have intact prophages; three strains (LN19, LN24, and WiKim40) have questionable prophage regions at their genomes. The size of intact and questionable prophages range between 20.8 Kb — 47.4 Kb (34.9 Kb on average) and 9.6 Kb —32.8 Kb (16.5 Kb on average), respectively.

A total of three plasmids were discovered in BIOML-A1, LN19, and LN24, with the former harboring repUS2 and the latter two strain containing rep31 (Table 3). The size of plasmids ranges from 0.66 kb to 1.15 kb. The minimum percent identity of predicted plasmids is 98.8%.

Table 3 Putative plasmids and genomic locations across three Ln. lactis strains (Strains not shown in the table were not predicted to carry plasmid)
Full size table

Bacteriocins

Analysis of Ln. lactis genomes with BAGEL4 showed a single type of bacteriocin “Lactoccoccin 972” exists in all genomes except CBA3625, which indicates potential antimicrobial characteristics of Ln. lactis strains. All results taken from BAGEL4 are checked with NCBI protein BLAST to validate bacteriocins.

Weblogo results show that amino acid sequences of Lactococcin 972 discovered in the Ln. lactis genomes in the present study were similar. A strong MNKFKKK motif is identified in N-terminus of Lactococcin 972 (Fig. 10).

Fig. 10
figure 10

The amino acid sequence logo of Lactococcin 972

Full size image

Discussion

In the present study, we genomically evaluated the Ln. lactis species and focused on eight strains representing the human gastrointestinal tract and fermented foods microbiomes. GC content is typical for low GC LAB. The proportion of hypothetical genes indicates that there is still more to uncover about Leuconostoc lactis. After extracting the genome of Ln. lactis, we conducted a global phylogeny of twenty-nine genomes (Fig. 1). This analysis predicted a remarkable diversity between Ln. lactis strains. Nine distinct clades were determined.

Interestingly, WiKim40 was isolated from kimchi, and its clade members were isolated from human feces (Figure S1). This perhaps indicates that Ln. lactis enters the gut microbiome through food sources. The genome analysis clearly segregated leuconostocs by species, subspecies, and allowed intra-species and intra-strain differentiations [4]. Generally, dairy isolates of LN19 and LN24, kimchi isolates of CBA3625, WIKIM21, WiKim40, human gastrointestinal isolates of aa_0143 and BIOML-A1, and cucumber fermentation brine isolate of 1.2.28 exist in closely related clades (Fig. 2). Only dairy-associated strains lack the arabinose metabolism genes such as araA, while the rest of the strains harbored that gene, which perhaps relates to the fact that no arabinose sugar exists in dairy environments. Apart from aa_0143, BIOML-A1, LN19, and LN24 remaining four strains were isolated from fermented plant materials where arabinose sugar is part of the composition. From an evolutionary perspective, this shows that repetitive subculturing of LN19 and LN24 in dairy caused the gene loss or gene decay of araA due to lack of this sugar in the milk microenvironment (Fig. 11). Similar results were also reported for Lactobacillus (L.) casei supragenome that the strain isolated from milk vs silage material had a different carbohydrate fermentation profile [23]. For example, the dairy strain could utilize lactose but not inulin, whereas silage isolate is lactose negative but inulin positive. We also found that bglF gene encoding glucose/b-glucoside family PTS transporter EIICBA did exist in all strains except cucumber fermentation brine isolate of 1.2.28. However, the crr gene encoding glucose/b-glucoside family PTS transporter EIIA did exist in all strains except for WIKIM21, WiKim40, and 1.2.28. The celC gene encoding cellobiose-diacetyl chitobiose family PTS transporter EIIA was not found in any strains tested (Fig. 11). In a study comparing 17 Ln. carnosum strains isolated from meat reported the presence of celA, celB, and celC genes [17], which implies intra-species diversity within Leuconostoc genus.

Fig. 11
figure 11

Putative genetic potential of eight Ln. lactis strains for carbohydrate metabolism based on predicted transporters or enzymes' presence (green) or absence (white)

Full size image

Obst et al. (1995) [24] reported that Leuconostoc strains isolated from dairy are reported to acquire plasmid-encoded LacLM through horizontal gene transfer to adapt milk microenvironment. Interestingly, all eight strains tested in the present work were found to be carrying lacS and lacZ genes regardless of their isolation source. Only dairy-associated strains of LN19 and LN24 harbored cit operon composed of citC, citD, citE, citF, and citG (Fig. 11). The lack of citrate uptake and utilization related genes in non-dairy associated Ln. lactis strains show evidence of prolonged degenerative evolution, perhaps due to a long period of proliferation in non-dairy niches [4] where no citrate exists.

Choline transport was predicted to be existing only in LN19 and LN24. These strains perhaps utilize choline, which is available in a wide range of milk products [25], to combat osmotic stress conditions such as high salt-in-moisture content of cheese. However, all strains were predicted to carry betaine transport genes that are functional in osmoprotectant activity [26].

The absence of genes encoding antibiotic resistance perhaps relates to the specificity of Ln. lactis for dairy and plant-based fermentation matrices secluded this species under limited selective pressure in such a microenvironment as antibiotic usage in plant, and dairy production is restricted. Safety of Ln. lactis is a critical phenomenon given that a considerable number of bacteria belong to this species which are ingested as foods for example cheese and kimchi. Another critical safety factor is biogenic amine production by decarboxylase genes which were also not found in dairy-associated strains where health concern is more pronounced than plant materials [17].

Ln. lactis shows evidence of prolonged evolutionary degeneration, perhaps due to long and repetitive periods of proliferation in milk and fermented plant materials such as kimchi and cucumber fermentation. Dairy-associated Ln. lactis strains appeared to be evolved alongside L. helveticus and L. sanfranciscensis. IS3 family showing significant sequence alignment in kimchi, fecal material, dairy material, and cucumber fermentation brine isolates indicating this IS element belonged to Weissella cibaria and was likely imported via horizontal gene transfer (Table S1). Dairy isolates perhaps evolved with L. helveticus because it is heavily utilized as adjunct and starter culture in the dairy industry for flavor and acid development in cheese [27]. LN19 and LN24 were predicted to carry the IS30 family proposing this IS element was received from L. helveticus (Table S1).

Interestingly, both WIKIM21 and WiKim40 contained IS elements predicted to be originating from L. helveticus which indicates high adaptability to grow in various micro-niches due to its capability to ferment a broad range of carbohydrates and it was also isolated from plant materials [28].

We did not come across any study describing bacteriocin biosynthesis in Ln. lactis strains. In the present study, Lactococcin 972, a homodimeric bacteriocin that targets lactoccal strains, was found in seven Ln. lactis strains. It was isolated from Lactococcus lactis subsp. lactis IPLA 972 in 1996, and unlike other bacteriocins, Lactococcin 972 does not primarily target cell membrane [29]. The bacteriocin synthesis potential of Ln. lactis is reported for the first time in the present study. Therefore, the screening for unique antimicrobials needs further studies due to diverse microbial ecosystems occupied by Ln. lactis strains and abundance of CDS without any function assigned.

It was reported that Lactococcin 972 shows a narrow and specific antimicrobial spectrum similar to Lactococcin Q, a dipeptide bacteriocin biosynthesized by Lactococcus lactis QU 4, which possesses antagonistic activity only against Lactococcus lactis strains [30]. This might be a competitive inhibition strategy that Ln. lactis developed to perhaps inhibit Lactococcus lactis where they usually coexist together, especially in dairy applications where Lactococcus lactis starter cultures are heavily utilized [31]. This could be explained by competitive exclusion, where two strains competing for the same nutrient cannot stably coexist; thus, a competitive strain always dominates its competitor and forces evolutionary modification and shifts to another niche or extinction [32].

In all strains screened, CRISPR-Cas system was found in aa_0143 and BIOML-A1, which have human fecal material origin. Cheese dead vats (i.e. slow or no milk acidification) cause huge economical loss for dairy industry due to bacteriophage infection of starter cultures such as Lactococcus lactis, Streptococcus thermophilus, and Ln. lactis [33]. The knowledge on Ln. lactis’ CRISPR-Cas could be further explored in fermented dairy foods biotechnology to protect and reduce bacteriophage infection of Ln. lactis dairy starter cultures (LN19 and LN24) for preventing economic loss in industry and conferring robust bioprocesses. The CRISPR-Cas in Ln. lactis strains described in the present study should further encounter functional assessment for investigating their utilization in microbial engineering against bacteriophage resistance to confer phage immunity to starter cultures.

Conclusion

This study aimed to boost the available fundamental knowledge on Leuconostoc lactis, a microorganism that plays an important role in industrial food fermentations. Global phylogeny on twenty-nine Ln. lactis strains revealed a great deal of diversity. A comparative whole-genome sequence analysis was performed on eight strains representing the human gastrointestinal tract and fermented foods. Comparative genome analysis showed all strains possess mobile genomic elements, namely insertion sequences (IS). CRISPR-Cas system was discovered in each of aa_0143 and BIOML-A1. All strains except CBA3625, LN 24, and WiKim40 harbor at least one intact putative prophage region. Apart from CBA3625, all strains encode genes functional for putative Lactococcin 972 biosynthesis. Metabolic differences according to strain isolation source were found between dairy and non-dairy (plant material) associated strains. For instance, plant-associated strains could utilize plant-based sugar arabinose, whereas dairy strains could not. However, dairy-associated strains were able to metabolize citrate though plant isolates could not, which perhaps relates to the loss of citrate uptake and utilization genes part of evolutionary adaptation due to repetitive growth of strains in plant fermentations where no citrate exists. We hope to contribute to setting the pipeline for future research and convey feasible data for better quality industrial biomanufacturing via dilating comparative genomic characterization of Ln. lactis.

Methods

We conducted a global phylogeny of Ln. lactis using 29 available genomes in NCBI based on the glycolysis gene “phosphoglucomutase”, which confers a high degree of granularity [34]. No phosphoglucomutase gene was available in four out of thirty-three Ln. lactis strains deposited to NCBI. Upon extracting the phosphoglucomutase gene sequence, nucleotide sequences were aligned using MUSCLE [35]. Trees were then constructed using RaxML (GTR, bootstrapping using 100 replicates). Phylogenetic tree of the RaxML result was drawn with Interactive Tree of Life online tool [36]. Next, we run whole-genome based comparisons by using BRIG and Mauve [37] with the following genomes: 1.2.28, aa_0143, BIOML-A1, CBA3625, LN19, LN24, WIKIM21, and WiKim40. These genomes were chosen because of their closed genome status or as selected representatives of distinctive phylogenetic clades from Fig. 1 representing human gastrointestinal tract or fermented foods microbiomes. Whole genomes of eight chosen Ln. lactis imported from the database of NCBI Genbank [18]. They are available with the following accession numbers: GCA_018993775.1, GCA_004167235.1, GCA_009678855.1, GCA_007954605.1, GCA_002092595.1, GCA_002092695.1, GCA_001411775.1, GCA_001698145.1. Genomes of those Ln. lactis strains were first merged into a single contig using AWK application and annotated with Prokka (version 1.14.5) [38] with the following flags: –kingdom Bacteria. The core- and pan-genomes were analyzed using Roary (version 3.13.0) by feeding Prokka results to Roary [39] with the following flags: –e –n –v –r.

Genomes were clustered, and phylogenetic trees based on whole-genome sequence and 16S rDNA were created on TYGS with default settings (https://tygs.dsmz.de) [40]. The calculation of orthologous average nucleotide identity values (OrthoANI) was performed by OrthoANI tool v0.93.1 [41]. All genomes were aligned and visualized with BLAST Ring Image Generator (BRIG) against CBA3625 as the reference genome [42]. BRIG image was created with the following options: upper percent identity threshold of 90%, lower percent identity threshold of 70%, and ring size of 30. In addition to genome alignments, GC content, GC skew, and prophage regions were mapped on the BRIG image.

Carbohydrate active enzyme (CAZyme) related genes were identified with the CAZy database (v10) in dbCAN server (https://bcb.unl.edu/dbCAN2/index.php) [43] by HMMER version 3.3.32 [44] according to suggested protocol of dbCAN. Results of the CAZYme analysis were classified based on the suggested threshold minimum 0.35 coverage and E-value 1e-15 by Oliviera et al. (2022) [45]. Then, Ln. lactis strains were classified based on the number of CAZYmes they harbored in their genomes. Functional annotation of the genomes and distribution of metabolic pathways were performed with KEGG Automatic Annotation Server (KAAS) by selecting prokaryotes as a representative set and bi-directional best hit (BBH) as the assignment method [46]. KEGG Mapper tool was utilized to identify the number of genes associated with functional classes and metabolic pathways [47, 48].

CRISPRviz [49] tool was used to identify, align, and visualize CRISPR loci containing spacers and repeats. In order to identify plasmids and their region in genomes, PlasmidFinder (version 2.0.1) was utilized [50, 51]. The discovery of potential bacteriocins and bacteriocin expressing regions in genomes was performed using BAGEL4 [52]. The potential bacteriocins were analyzed with NCBI Protein BLAST [53] to double-check BAGEL4 results. Then, sequence logo of confirmed bacteriocin was performed with WebLogo tool [54, 55]. Prophage regions located on genomes were identified with Phage Search Tool Enhanced Release (PHASTER) [56]. ISfinder tool [57] was utilized to identify insertion sequences in genomes. Antimicrobial resistance genes were screened by Comprehensive Antibiotic Resistance Database (CARD), a web-based tool [58].

Availability of data and materials

The genomes analyzed in the current study are available in the NCBI GenBank repository under the following accession numbers: GCA_018993775.1 (1.2.28), GCA_015551285.1 (1001262B_160229_C9), GCA_004167235.1 (aa_0143), GCA_009678855.1 (BIOML-A1), GCA_007954625.1 (CBA3622), GCA_007954605.1 (CBA3625), GCA_007954665.1 (CBA3626), GCA_002287365.1 (CCK940), GCA_019656035.1 (JCM 6123), GCA_014651235.1 (JCM 6123), GCA_000709265.1 (KACC 91922), GCA_000185085.2 (KCTC 3528), GCA_000179875.1 (KCTC 3773), GCA_002092595.1 (LN19), GCA_002092695.1 (LN24), GCA_020708945.1 (MSK.22.137), GCA_020708975.1 (MSK.22.141), GCA_006539105.1 (NBRC 12455), GCA_014050705.1 (SBC001), GCA_002386625.1 (UBA4605), GCA_002386555.1 (UBA4610), GCA_002425565.1 (UBA5566), GCA_002425485.1 (UBA5570), GCA_002420925.1 (UBA5657), GCA_002453615.1 (UBA6751), GCA_003521925.1 (UBA8466), GCA_003529125.1 (UBA8811), GCA_001411775.1 (WIKIM21), GCA_001698145.1 (WiKim40).

References

  1. Genome. National Library of Medicine (US), National Center for Biotechnology Information, Bethesda. 2004 https://www.ncbi.nlm.nih.gov/genome/. Accessed 11 Feb 2022.

  2. Jeon HH, Kim KH, Chun BH, Ryu BH, Han NS, Jeon CO. A proposal of Leuconostoc mesenteroides subsp. jonggajibkimchii subsp. nov. and reclassification of Leuconostoc mesenteroides subsp suionicum (Gu et al., 2012) as Leuconostoc suionicum sp. nov. based on complete genome sequences. Int J Syst Evol Microbiol. 2017;67:2225–30.

    Article  CAS  PubMed  Google Scholar 

  3. Chen I-MA, Chu K, Palaniappan K, Pillay M, Ratner A, Huang J, Huntemann M, Varghese N, White JR, Seshadri R, et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47:D666–77.

    Article  CAS  PubMed  Google Scholar 

  4. Frantzen CA, Kot W, Pedersen TB, Ardö YM, Broadbent JR, Neve H, Hansen LH, Dal Bello F, Østlie HM, Kleppen HP, et al. Genomic Characterization of Dairy Associated Leuconostoc Species and Diversity of Leuconostocs in Undefined Mixed Mesophilic Starter Cultures. Front Microbiol. 2017;8:132.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Holland R, Liu S-Q. Lactic Acid Bacteria | Leuconostoc spp. In: Fuquay JW, editor. Encyclopedia of Dairy Sciences (Second Edition). Second Edition. San Diego: Academic Press; 2011. p. 138–42.

  6. Kim S-A, Bae J-H, Seong H, Han NS. Development of Leuconostoc lactis–Specific Quantitative PCR and its Application for Identification and Enumeration in Fermented Foods. Food Anal Methods. 2020;13:992–9.

    Article  Google Scholar 

  7. Cicotello J, Wolf IV, D’Angelo L, Guglielmotti DM, Quiberoni A, Suárez VB. Response of Leuconostoc strains against technological stress factors: Growth performance and volatile profiles. Food Microbiol. 2018;73:362–70.

    Article  PubMed  Google Scholar 

  8. Cogan TM, Fitzgerald RJ, Doonan S. Acetolactate synthase of Leuconostoc lactis and its regulation of acetoin production. J Dairy Res. 1984;51:597–604.

    Article  CAS  Google Scholar 

  9. Saravanan C, Shetty PKH. Isolation and characterization of exopolysaccharide from Leuconostoc lactis KC117496 isolated from idli batter. Int J Biol Macromol. 2016;90:100–6.

    Article  CAS  PubMed  Google Scholar 

  10. Axelsson L. Lactic Acid Bacteria: Classification and Physiology. In: Lactic acid bacteria: microbiology and functional aspects. 2nd edition. New York: Marcel Dekker; 1998.

  11. Axelsson L. Lactic Acid Bacteria: Classification and Physiology. In: Salminen S, von Wright A, Ouwehand A, editors. Lactic Acid Bacteria. 3rd edition. Boca Raton: CRC Press; 2004.

  12. Heinl S, Grabherr R. Systems biology of robustness and flexibility: Lactobacillus buchneri-A show case. J Biotechnol. 2017;257:61–9.

    Article  CAS  PubMed  Google Scholar 

  13. Makarova K, Slesarev A, Wolf Y, Sorokin A, Mirkin B, Koonin E, Pavlov A, Pavlova N, Karamychev V, Polouchine N, et al. Comparative genomics of the lactic acid bacteria. Proc Natl Acad Sci U S A. 2006;103:15611–6.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Nethery MA, Henriksen ED, Daughtry KV, Johanningsmeier SD, Barrangou R. Comparative genomics of eight Lactobacillus buchneri strains isolated from food spoilage. BMC Genomics. 2019;20:902.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Copley SD. Toward a Systems Biology Perspective on Enzyme Evolution. J Biol Chem. 2012;287:3–10.

    Article  CAS  PubMed  Google Scholar 

  16. Reams AB, Roth JR. Mechanisms of Gene Duplication and Amplification. Cold Spring Harb Perspect Biol. 2015;7:a016592.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Candeliere F, Raimondi S, Spampinato G, Tay MYF, Amaretti A, Schlundt J, Rossi M. Comparative Genomics of Leuconostoc carnosum. Front Microbiol. 2021;11:605127.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2016;44:D67–72.

    Article  CAS  PubMed  Google Scholar 

  19. Daughtry KV, Johanningsmeier SD, Sanozky-Dawes R, Klaenhammer TR, Barrangou R. Phenotypic and genotypic diversity of Lactobacillus buchneri strains isolated from spoiled, fermented cucumber. Int J Food Microbiol. 2018;280:46–56.

    Article  CAS  PubMed  Google Scholar 

  20. R Core Team: R. A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2021.

    Google Scholar 

  21. Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag; 2016.

    Book  Google Scholar 

  22. Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics. 2017;33:2938–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Broadbent JR, Neeno-Eckwall EC, Stahl B, Tandee K, Cai H, Morovic W, Horvath P, Heidenreich J, Perna NT, Barrangou R, et al. Analysis of the Lactobacillus casei supragenome and its influence in species evolution and lifestyle adaptation. BMC Genomics. 2012;13:533.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Obst M, Meding ER, Vogel RF, Hammes WP. Two genes encoding the ß-galactosidase of Lactobacillus sake. Microbiology. 1995;141:3059–66.

    Article  CAS  PubMed  Google Scholar 

  25. Richard C, Lewis ED, Zhao Y-Y, Justice, Asomaning J, Jacobs RL, Field CJ, Curtis JM. Measurement of the total choline content in 48 commercial dairy products or dairy alternatives. J Food Compost Anal. 2016;45:1-8. https://doi.org/10.1016/j.jfca.2015.09.009.

  26. Kappes RM, Kempf B, Kneip S, Boch J, Gade J, Meier-Wagner J, Bremer E. Two evolutionarily closely related ABC transporters mediate the uptake of choline for synthesis of the osmoprotectant glycine betaine in Bacillus subtilis. Mol Microbiol. 1999;32:203–16.

    Article  CAS  PubMed  Google Scholar 

  27. Broadbent JR, Hughes JE, Welker DL, Tompkins TA, Steele JL. Complete Genome Sequence for Lactobacillus helveticus CNRZ 32, an Industrial Cheese Starter and Cheese Flavor Adjunct. Genome Announc. 2013;1:e00590-e613.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Dimitrov Z, Michaylova M, Mincova S. Characterization of Lactobacillus helveticus strains isolated from Bulgarian yoghurt, cheese, plants and human faecal samples by sodium dodecilsulfate polyacrylamide gel electrophoresis of cell-wall proteins, ribotyping and pulsed field gel fingerprinting. Int Dairy J. 2005;15:998–1005.

    Article  CAS  Google Scholar 

  29. Martinez B, Suarez JE, Rodriguez A. Lactococcin 972: a homodimeric lactococcal bacteriocin whose primary target is not the plasma membrane. Microbiology. 1996;142:2393–8.

    Article  CAS  PubMed  Google Scholar 

  30. Zendo T, Koga S, Shigeri Y, Nakayama J, Sonomoto K. Lactococcin Q, a Novel Two-Peptide Bacteriocin Produced by Lactococcus lactis QU 4. Appl Environ Microbiol. 2006;72:3383–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Li W, Ren M, Duo L, Li J, Wang S, Sun Y, Li M, Ren W, Hou Q, Yu J, et al. Fermentation Characteristics of Lactococcus lactis subsp. lactis Isolated From Naturally Fermented Dairy Products and Screening of Potential Starter Isolates. Front Microbiol. 2020;11:1794.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Chaucheyras-Durand F, Durand H. Probiotics in animal nutrition and health. Benef Microbes. 2010;1:3–9.

    Article  CAS  PubMed  Google Scholar 

  33. Donnelly CW, editor. Cheese and microbes. Washington, DC: ASM Press; 2014.

  34. Brandt K, Barrangou R. Using glycolysis enzyme sequences to inform Lactobacillus phylogeny. Microb Genom. 2018;4(6):e000187.

    PubMed Central  Google Scholar 

  35. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Darling AE, Mau B, Perna NT. progressiveMauve: Multiple Genome Alignment with Gene Gain. Loss and Rearrangement PLoS One. 2010;5:e11147.

    Article  PubMed  CAS  Google Scholar 

  38. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.

    Article  CAS  PubMed  Google Scholar 

  39. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, Fookes M, Falush D, Keane JA, Parkhill J. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31:3691–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Meier-Kolthoff JP, Göker M. TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy. Nat Commun. 2019;10:2182.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Lee I, Ouk Kim Y, Park S-C, Chun J. OrthoANI: An improved algorithm and software for calculating average nucleotide identity. Int J Syst Evol Microbiol. 2016;66:1100–3.

    Article  CAS  PubMed  Google Scholar 

  42. Alikhan N-F, Petty NK, Ben Zakour NL, Beatson SA. BLAST Ring Image Generator (BRIG): simple prokaryote genome comparisons. BMC Genomics. 2011;12:402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, Busk PK, Xu Y, Yin Y. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Potter SC, Luciani A, Eddy SR, Park Y, Lopez R, Finn RD. HMMER web server: 2018 update. Nucleic Acids Res. 2018;46:W200–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Oliveira FS, da Silva Rodrigues R, de Carvalho AF, Nero LA. Genomic Analyses of Pediococcus pentosaceus ST65ACC, a Bacteriocinogenic Strain Isolated from Artisanal Raw-Milk Cheese. Probiotics & Antimicro Prot. 2022. https://doi.org/10.1007/s12602-021-09894-1.

  46. Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007;35:W182-185.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kanehisa M, Sato Y, Kawashima M. KEGG mapping tools for uncovering hidden features in biological data. Protein Sci. 2022;31:47–53.

    Article  CAS  PubMed  Google Scholar 

  48. Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29:28–35.

    Article  CAS  PubMed  Google Scholar 

  49. Nethery MA, Barrangou R. CRISPR Visualizer: rapid identification and visualization of CRISPR loci via an automated high-throughput processing pipeline. RNA Biol. 2019;16:577–84.

    Article  PubMed  Google Scholar 

  50. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Carattoli A, Zankari E, García-Fernández A, Voldby Larsen M, Lund O, Villa L, Møller Aarestrup F, Hasman H. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother. 2014;58:3895–903.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. van Heel AJ, de Jong A, Song C, Viel JH, Kok J, Kuipers OP. BAGEL4: a user-friendly web server to thoroughly mine RiPPs and bacteriocins. Nucleic Acids Res. 2018;46:W278–81.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.

    Article  CAS  PubMed  Google Scholar 

  54. Crooks GE, Hon G, Chandonia J-M, Brenner SE. WebLogo: A Sequence Logo Generator. Genome Res. 2004;14:1188–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Schneider TD, Stephens RM. Sequence logos: a new way to display consensus sequences. Nucl Acids Res. 1990;18:6097–100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, Wishart DS. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44:W16–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Siguier P, Perochon J, Lestrade L, Mahillon J, Chandler M. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res. 2006;34:D32-36.

    Article  CAS  PubMed  Google Scholar 

  58. Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, Huynh W, Nguyen A-LV, Cheng AA, Liu S, et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 2020;2020(48):D517–25.

    Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

The funding for this research was partially provided by Abdullah Gul University.

Author information

Authors and Affiliations

  1. BioEngineering Department, Faculty of Life and Natural Sciences, Abdullah Gul University, Kayseri, TR, Turkey

    Ismail Gumustop & Fatih Ortakci

Authors
  1. Ismail Gumustop
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fatih Ortakci
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Comparative genome analysis and the writing of the manuscript were conducted by FO and IG. The study was conceived by FO. All listed authors have read and approved the final manuscript.

Corresponding author

Correspondence to Fatih Ortakci.

Ethics declarations

Ethics approval and consent to participate

All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Fig. S1. Phylogenetic tree of eight Leuconostoc lactis strains based on whole genome sequences. Fig. S2. Whole genome comparison of Mauve alignments of eight Ln. lactis whole genomes. NCBI accession numbers of the given strains can be found in Table 1. Table S1. Putative IS elements of eight Ln. lactis strains

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gumustop, I., Ortakci, F. Comparative genomics of Leuconostoc lactis strains isolated from human gastrointestinal system and fermented foods microbiomes. BMC Genom Data 23, 61 (2022). https://doi.org/10.1186/s12863-022-01074-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12863-022-01074-6

Keywords

BMC Genomic Data

ISSN: 2730-6844

Contact us