Next Article in Journal
The Effect of Lure Position and Vegetation on the Performance of YATLORf Traps in the Monitoring of Click Beetles (Agriotes spp., Coleoptera: Elateridae)
Previous Article in Journal
Granger Causality Analysis of Transient Calcium Dynamics in the Honey Bee Antennal Lobe Network
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Insects
Volume 14
Issue 6
10.3390/insects14060540
insects-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

In Silico Probiogenomic Characterization of Lactobacillus delbrueckii subsp. lactis A4 Strain Isolated from an Armenian Honeybee Gut

by
Inga Bazukyan
1,
Dimitrina Georgieva-Miteva
2,
Tsvetelina Velikova
3 and
Svetoslav G. Dimov
2,*
1
Faculty of Biology, Yerevan State University, Yerevan 0025, Armenia
2
Faculty of Biology, Sofia University St. Kliment Ohridski, 8 Dragan Tzankov Str., 1164 Sofia, Bulgaria
3
Medical Faculty, Sofia University St. Kliment Ohridski, 1 Kozyak Str., 1407 Sofia, Bulgaria
*
Author to whom correspondence should be addressed.
Insects 2023, 14(6), 540; https://doi.org/10.3390/insects14060540
Submission received: 29 May 2023 / Revised: 7 June 2023 / Accepted: 8 June 2023 / Published: 9 June 2023
(This article belongs to the Section Insect Societies and Sociality)
Browse Figure
Review Reports Versions Notes

Abstract

:

Simple Summary

Although L. delbrueckii strains have been isolated from different bee products and beehive niches to our knowledge, this is the first report of the isolation of the L. delbrueckii strain from the gut of a honeybee. Furthermore, the genomic analysis of the strain revealed two major aspects of its genomic constitution: (1) it possesses many genetic determinants attributing probiotic properties for the honeybees, and (2) it shows a reduction in size which is a typical adaptation of bacteria undergoing transformation to an endosymbiont. These two observations motivated us to hypothesize that in the case of L. delbrueckii subsp. lactis A4, we witness the first case of an L. delbrueckii strain evolution to a honeybee endosymbiont.

Abstract

A Lactobacillus delbrueckii ssp. lactis strain named A4, isolated from the gut of an Armenian honeybee, was subjected to a probiogenomic characterization because of its unusual origin. A whole-genome sequencing was performed, and the bioinformatic analysis of its genome revealed a reduction in the genome size and the number of the genes—a process typical for the adaptation to endosymbiotic conditions. Further analysis of the genome revealed that Lactobacillus delbrueckii ssp. lactis strain named A4 could play the role of a probiotic endosymbiont because of the presence of intact genetic sequences determining antioxidant properties, exopolysaccharides synthesis, adhesion properties, and biofilm formation, as well as an antagonistic activity against some pathogens which is not due to pH or bacteriocins production. Additionally, the genomic analysis revealed significant potential for stress tolerance, such as extreme pH, osmotic stress, and high temperature. To our knowledge, this is the first report of a potentially endosymbiotic Lactobacillus delbrueckii ssp. lactis strain adapted to and playing beneficial roles for its host.

Graphical Abstract

1. Introduction

It is not easy to estimate precisely the economic value of honeybees as pollinators of crops and other plants. However, in any case, worldwide, it ranges up to dozens of billions of USD annually [1]. One of the main factors affecting the honeybee’s welfare is the composition of their gastrointestinal microbiota, which is well characterized and comprises several phylotypes [2]. A crucial role in the honeybee’s welfare is played by several core species and genera such as Snodgrassella alvi, Gilliamella apicola, Frischella perrara, and Commensalibacter sp. but also members of the genus Bifidobacterium and the former genus Lactobacillus [3]. Their representatives play crucial roles in pollen degradation and sugar breakdown [4,5] but also in the protective biofilm formation [6], as well as playing a defensive role by stimulating the immune response [7].
Members of the former genus Lactobacillus (before their re-classification in 2020 [8]), being part of the core microbiota, among other things, such as the breakdown of plant sugars, are reported to stimulate the host immune system, to protect against pathogens [9]. However, they have also been reported recently to modulate host learning and memory behaviors via regulating tryptophan metabolism [10]. Some of the most often isolated strains belong to Lactiplantibacillus plantarum, Lactiplantibacillus pentosus, Limosilactobacillus fermentum, Lacticaseibacillus paracasei, and Levilactobacillus brevis [11,12].
Till now, Lactobacillus delbrueckii isolates have not been reported to be present within the honeybee intestinal microbiota but have been reported to be present within the bee bread in hives in the Anatolia region of Turkey [13]. This lack of information about the presence and the role of Lactobacillus delbrueckii within the gastrointestinal microbiota motivated the probiogenomic [14] study of Lactobacillus delbrueckii subsp. lactis strain A4 was isolated from a honeybee gut in 2017 from a worker bee in the town of Artashat in the Republic of Armenia. This strain was first selected because of its antagonistic activity against some pathogens, such as Escherichia coli and Salmonella typhimurium, as well as because of its ability to grow at a wide range of pH (4–9) and to assimilate arabinose as a carbon source, characteristics suggesting probiotic potential.

2. Materials and Methods

2.1. Sampling and Maintenance of the Strain

The sampling was carried out in an apiary situated in the Ararat province in the town of Artashat (40°24′18″N, 44°34′35″E). The strain A4 was isolated from the gut of a young worker honeybee sampled on 25.08.2017 by enrichment of 10% skim milk with the particles of the honeybee’s gut. First, 50 µL from the inoculated milk was mixed with 1 mL of sterile peptone water (PW). Next, 10-fold serial dilutions in PW were made up to 10–4, then 100 µL of the 10−2 and 10−3 dilutions were plated on Petri dishes with De Man, Rogosa, and Sharpe (MRS) 1.5% agar (Merck, Darmstadt, Germany). After incubation for 36–48 h at 30 °C, single colonies were picked up and inoculated in 3 mL MRS liquid broth and incubated at 30 °C for 24 h. After that period, ten isolates were plated on MRS 1.5% agar with a sterile loop by the agar streaking method and incubated at 30 °C for 24 h. This procedure was repeated thrice, and the cultures were microscopically monitored for contamination. Finally, stocks of skim milk were prepared from each isolate [15] and were kept at −70 °C.

2.2. DNA Isolation

Total DNA was isolated from 3mL 24-h liquid culture in MRS broth obtained by single colony inoculation. The DNA was extracted using the “Gram Plus & Yeast Genomic DNA Purification Kit” (EURx, Gdansk, Poland) according to the manufacturer’s instructions. The final elution was performed in 70 µL of the kit’s elution buffer. The quality of the isolated DNA was monitored on a 0.8% agarose gel in a TBE buffer system, while the concentration was determined on a Quantus™ fluorimeter (Promega, Madison, WI 53711 USA). The isolated DNA was further stored at −70 °C.

2.3. DNA Techniques

First, for preliminary species determination, total DNA isolated from the strain was sent to Macrogen (Seoul, Republic of Korea) for 16S ribosomal RNA gene sequencing by the Sanger chain termination method with the primers pair 27F/1492R [16] on the ABI 3730xl System. 50 µL of the isolated DNA was shipped on dry ice to BGI (Tai Po, N.T. Hong Kong) for whole-genome sequencing on the DNBseq platform [17] for DNB-SEQ PE100/PE150 sequencing with a generation of 1 Gb of data.

2.4. Bioinformatic Analyses

The forward and the reverse reads of the 16S rRNA gene sequencing were assembled with SeqMan™ II v.5.01 software (DNASTAR Inc., Madison, WI 53705, USA), and the assembled contig was uploaded to the GenBank of the NCBI. The results were analyzed online at the NCBI by the Nucleotide BLAST program (https://blast.ncbi.nlm.nih.gov/Blast.cgi, accessed on 1 February 2023) [18]. The quality of the raw data obtained from the DBBseq platform was monitored by the online tool FastQC v. 0.11.9 [19]. The contigs and scaffolds were assembled by the online assembler SPAdes v. 3.15.4 [20]. The quality of the assembled contigs was assessed by the online tool Quast v. 5.2.0 [21]. The contigs shorter than 200 bp were filtered by the Filter FASTA v. 1.9.1.0 [22] online software. The last four steps were performed on the Galaxy server (usegalaxy.eu, accessed on 3 February 2023). To confirm the species attribution, the average nucleotide identity of the assembled genome was evaluated with the ANI calculator [23] located on the EzBioCloud (ezbiocloud.net/tools/ani, accessed on 11 February 2023). The assembled contigs were uploaded to the GenBank of the NCBI. The annotation was performed by the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) (https://www.ncbi.nlm.nih.gov/genome/annotation_prok/, accessed on 11 February 2023) [24,25,26]. The initial genotypic characterization was performed on the Center for Genomic Epidemiology (genomicepidemiology.org/services/, accessed on 11 February 2023) using the following tools: PlasmidFinder 2.1 (database version: 18 January 2023) [27,28,29] for the presence of plasmid replicons; ResFinder 4.1 (database version: 24 May 2022 and ResFinderFG 2.0 (database version: 30 June 2022 [27,29,30,31] for the presence of antibiotics resistance genetic determinants; PathogenFinder 1.1 [27,32] for the presence of pathogenicity genetic determinants; and MobileElementFinder v. 1.0.3 (database version: 9 June 2020) [33] for identification of mobile genetic elements which could be related to antimicrobial resistance genes and virulence factors. The presence of bacteriocins’ genetic determinants was checked with the online tool BAGEL4 (http://bagel4.molgenrug.nl/, accessed on 11 February 2023) [34]. The presence of the genetic determinants determining potential probiotic properties was performed using the NCBI’s Sequence Set Browser (https://www.ncbi.nlm.nih.gov/Traces/wgs/, accessed on 15 February 2023), followed by BLAST analyses against the NCBI’s database.

3. Results

3.1. Genome Assembly, Annotation, and Characterization

The results from sequencing the 16S rRNA gene of isolate A4 are available at the NCBI’s GenBank under the accession number OP784418.1. From the raw data of the next-generation whole-genome shotgun sequencing, 145 contigs were assembled. The assembly statistics are summarized in Table 1. The assembled contigs of the draft genome are available at the NCBI’s GenBank under accession number JAQSVE000000000. Both BLAST of the 16S rRNA gene sequence and ANI of the assembled genome attributed the isolate to Lactobacillus delbrueckii ssp. lactis based on the 16S rRNA gene homology and the average nucleotide identity, respectively, compared with the NCBI databases. The results from the annotation are presented in Table 2. No plasmid origins of replication were detected by the PlasmidFinder tool. Neither were found genetic determinants for antibiotic resistance and factors of pathogenicity by the ResFinder, ResFinderFG, and PathogenFinder tools. The MobileElementFinder tool revealed the presence of an intact ISL6 insertion sequence belonging to the IS3 family (IS150 group) and seventeen 3′- or 5′- truncated mobile elements belonging to the ISLre2, ISL3, IS4, IS30, IS110, and IS256 families.

3.2. Genotypic Characterization of L. delbrueckii A4

The results of the BLAST analysis of the genes with the potential to determine probiotic properties are presented in Table 3. All the listed database hits have the same length with the query sequence, a query cover of 100%, and a percentage of identity of 100.00%.

4. Discussion

The analysis of the draft genome assembly performed with the Quast tool (Table 1) revealed that the assembly quality has the potential to guarantee correct analysis of L. delbrueckii ssp. lactis A4 genome, mainly because of the number of the contigs greater than 1000 bp, the N50, N90, L50, and L90 values. The calculated genome size based on all assembled contigs was 1,881,475 bp. This value was not significantly different from the values of the genome sizes calculated based on the total length and the contigs larger than 1000 bp, which once again confirmed the good quality of the assembly. The calculated GC content was 49.84%—a typical value for the species [35]. Surprisingly, when it was compared to other L. delbrueckii ssp. lactis strains, the A4 stood out with its clearly reduced genome size (Table 4).
More interesting results were obtained from the annotation of the genome, which revealed a reduction not only in the genome size but also in the number of genes. L. delbrueckii ssp. lactis A4 possesses the least total number of genes when compared with the other 14 randomly chosen strains of the same species, the least number of pseudogenes, and, with one exception, the least number of RNA coding genes. This tendency is also partially visible when comparing the numbers of the protein-coding genes; still, four other strains possess a lesser number.
It is quite difficult to speculate why L. delbrueckii ssp. lactis A4 possesses a reduced genome. However, considering its unusual origin (to our knowledge, L. delbrueckii was never reported to be isolated from a honeybee gut), the hypothesis of some specialization to the honeybee gut ecological niche could not be excluded. It is observed and documented that the symbiosis between insects and bacteria leads to mutual adaptations. One aspect of these symbiotic interactions is the reduction of the genome size of the symbiotic bacteria [36,37]. This phenomenon also occurs in honeybees, as documented for Apibacter sp. [38], and the obligate endosymbionts Gillamella apicola and Snodgrassella alvi [39]. The distinctiveness of the L. delbrueckii ssp. lactis A4, as an example of a strain undergoing such adaptation, is further supported by the fact that, till now, to our knowledge, this is the only representative of the species isolated from a honeybee gut microbiota. We believe that this hypothesis is also supported by the fact that the reduction in the number of the genes affects mainly the RNA coding genes and pseudogenes, but to a minor extent, the number of the protein-coding genes, and thus keeping its “metabolic potential” which in the case of the species L. delbrueckii which lacks virulence factors and pathogenicity determinants, is beneficial for the host.
Even though lactobacilli (in the broader meaning of the former big genus before its reclassification in 2020 [8]) are considered a part of the honeybees’ core endosymbionts [3], till now, L. delbrueckii strains have not been reported to be isolated from the honeybees. So, logically, if the hypothesis of the genome adaption is true, what would be the role of L. delbrueckii ssp. lactis A4 for the honeybees, so is it selectively maintained long enough for genomic changes to occur? A logical answer to this question is that it possesses some crucial probiotic properties for the host. So, the next step of this research was to make a probiogenomic characterization, a notion proposed by de Jesus et al. to describe the analysis of the genome for the presence of genetic determinants giving probiotic properties [14], which in honeybees could be mainly antioxidant properties, exopolysaccharides synthesis, stress tolerance, adhesion to the gut epithelium and biofilm formation, as well as antagonistic activity towards pathogens.
It is documented that in human and animal models, lactic acid bacteria (LAB) and particularly L. delbrueckii, exert a beneficial protective effect against reactive oxygen species (ROS) on the intestinal mucosa and epithelium [40]. The studies of some of the mechanisms determining these antioxidant activities in LAB and lactobacilli revealed that they involve the synthesis of several enzymes and proteins [41,42], as well as the synthesis of exopolysaccharides (EPS) [43]. Nineteen enzymes and proteins involved in the common oxidative stress resistance are listed in different Lactobacillus species [41]. Genetic determinants for 7 of them were found in L. delbrueckii ssp. lactis A4: NAD(P)H-dependent oxidoreductase, DNA starvation/stationary phase protection protein, DsbA family protein, NAD(P)/FAD-dependent oxidoreductase, thioredoxin family protein, thioredoxin-disulfide reductase and thioredoxin (Table 3). Oxidative resistance genes are rather scarce among lactobacilli [41], so finding such a number in only one strain should not be considered a coincidence.
Additional antioxidant activity can be attributed to the chelator activity of the EPS, which can sequester heavy metal ions [43]; however, they can exert additional beneficial properties on their own. Different gene products participate in exopolysaccharide synthesis [44,45,46]. Genetic determinants for 9 of them were found in L. delbrueckii ssp. lactis A4 genome: exopolysaccharide biosynthesis protein, NADP-dependent phosphogluconate dehydrogenase, phosphoglucosamine mutase, beta-phosphoglucomutase, UDP-glucose-hexose-1-phosphate uridylyltransferase, UTP-glucose-1-phosphate uridylyltransferase GalU, bifunctional UDP-N-acetylglucosamine, diphosphorylase/glucosamine-1-phosphate N-acetyltransferase GlmU, oligosaccharide flippase family protein and flippase (Table 3). They strongly suggest an ability of EPS synthesis, which in turn can contribute to the already mentioned protection from heavy metals, but also by interfering with the adhesion of pathogens, as well as by exerting immunomodulatory properties and helping the biofilm formation [46].
Despite being related to EPS production, adhesion, and biofilm formation, they depend on different genetic determinants [47,48,49,50]. Eight genes encoding SLAP domain-containing proteins, D-alanyl-lipoteichoic acid biosynthesis protein DltB, D-alanyl-lipoteichoic acid biosynthesis protein DltD, elongation factor Tu, type I glyceraldehyde-3-phosphate dehydrogenase and triose-phosphate isomerase were found within the L. delbrueckii ssp. lactis A4 genome (Table 3). They could determine good intestinal adhesion properties, which in turn can result in competitiveness with pathogens [48] but also in immunomodulating and anti-inflammatory effects, as shown in in vitro studies [47]. Additionally, the strain probably possesses the ability for biofilm formation because three genes determine the synthesis of AI-2E family transporters and cell wall metabolism sensor histidine kinase WalK, involved in signaling and quorum sensing mechanisms [49,51,52,53].
The inhibition of pathogens’ growth could also be considered a probiotic property. In general, this property is attributed to the synthesis of bacteriocins or low molecular weight acids, which decrease the pH to levels intolerable to most pathogenic species. However, this is obviously not the case for the A4 strain because of the lack of bacteriocin genetic determinants and because it expresses this ability in neutralized conditions. Two alternatives are the production of H2O2 and biogenic amines. Lactate oxidase and pyruvate oxidase are two enzymes whose activity can lead to the production of H2O2 [54,55], and their genetic determinants were found within the A4 genome. Furthermore, genes encoding eight enzymes participating in the amino acids metabolism, which could also be involved in biogenic amines synthesis [56], were found: pyridoxal-dependent decarboxylase, orotidine-5’-phosphate decarboxylase, carboxymuconolactone decarboxylase family protein, diphosphomevalonate decarboxylase, diaminopimelate decarboxylase, putative ornithine decarboxylase, and bifunctional phosphopantothenoylcysteine decarboxylase/phosphopantothenate-cysteine ligase CoaBC (Table 3). However, for the moment, we do not have enough data on which of those two mechanisms is involved in the inhibitory activity, if it is due to both of them or some other unknown mechanism.
Finally, a probiotic strain to survive and be selectively maintained within the gut microbiome, especially in the case of the honeybees, should be stress tolerant and resistant to acidic and alkaline pH, osmotic stress, and heat. Extreme pH values are found in the different parts of the honeybee’s gut, while the feeding could achieve osmotic stress with honey. In addition, the body temperature could sometimes rise dramatically during a flight on a warm sunny day. Many genes are involved in coping with these extrema [14], and some were found within the L. delbrueckii ssp. lactis A4 genome—those encoding orotidine-5’-phosphate decarboxylase, S-ribosylhomocysteine lyase, phosphopyruvate hydratase, several AAA family ATPases, Na + /H+ antiporter NhaC, two-component system regulatory protein YycI, nucleotide exchange factor GrpE, molecular chaperones DnaK and DnaJ, heat-inducible transcriptional repressor HrcA, chaperonin GroEL, oligoendopeptidase F, CTP synthase, glucosamine-6-phosphate deaminase, aquaporin family proteins, aldo/keto reductase, ATP-dependent Clp protease ATP-binding subunit ClpX and F0F1 ATP synthase subunit alpha, beta, gamma, epsilon, B and C (Table 3). These findings suggest strong stress tolerance against the listed factors.

5. Conclusions

The whole-genome sequencing of L. delbrueckii ssp. lactis A4 genome suggests that this strain is an unusual one, probably undergoing adaptation to the honeybee gut conditions and thus becoming part of the symbiont microflora—a process never documented before for this species. The probiogenomic bioinformatic analysis supports this hypothesis because it revealed genetic determinants for a different probiotic for the host properties. The strain is stress-tolerant—a prerequisite to survive and to be selectively maintained within the insect’s microbiota.

Author Contributions

Conceptualization, S.G.D. and I.B.; methodology, S.G.D.; software, S.G.D.; validation, S.G.D., D.G.-M. and I.B.; formal analysis, S.G.D., D.G.-M., T.V. and I.B.; investigation, S.G.D.; resources, I.B.; data curation, S.G.D.; writing—original draft preparation, S.G.D., T.V. and I.B.; writing—review and editing, S.G.D.; visualization, S.G.D.; supervision, I.B.; project administration, I.B.; funding acquisition, I.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science Committee of the Republic of Armenia, grant number 21T-2I019, as well as by the Yerevan State University in the frames of inner research projects for 2022.

Data Availability Statement

The results from sequencing the 16S rRNA gene of L. delbrueckii ssp. lactis A4 are available at GenBank under the accession number OP784418.1. The assembled contigs of the draft genome are available at the NCBI’s GenBank under accession number JAQSVE000000000.

Acknowledgments

D. Miteva and T. Velikova are grantees of the European Union-NextGenerationEU through the National Recovery and Resilience Plan of the Republic of Bulgaria, project No BG-RRP-2.004–0008-C01.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hein, L. The economic value of the pollination service, a review across scales. Open Ecol. J. 2009, 2, 74–82. [Google Scholar] [CrossRef]
  2. Martinson, V.G.; Danforth, B.N.; Minckley, R.L.; Rueppell, O.; Tingek, S.; Moran, N.A. A simple and distinctive microbiota associated with honey bees and bumble bees. Mol. Ecol. 2011, 20, 619–628. [Google Scholar] [CrossRef]
  3. Zheng, H.; Steele, M.I.; Leonard, S.P.; Motta, E.V.S.; Moran, N.A. Honey bees as models for gut microbiota research. Lab. Anim. 2018, 47, 317–325. [Google Scholar] [CrossRef]
  4. Ibière, C.; Hegarty, C.; Stephenson, H.; Whelan, P.; O’Toole, P.W. Gut and Whole-Body Microbiota of the Honey Bee Separate Thriving and Non-thriving Hives. Microb. Ecol. 2019, 78, 195–205. [Google Scholar] [CrossRef]
  5. Papp, M.; Békési, L.; Farkas, R.; Makrai, L.; Judge, M.F.; Maróti, G.; Tőzsér, D.; Solymosi, N. Natural diversity of the honey bee (Apis mellifera) gut bacteriome in various climatic and seasonal states. PLoS ONE 2022, 17, e0273844. [Google Scholar] [CrossRef]
  6. Horak, R.D.; Leonard, S.P.; Moran, N.A. Symbionts shape host innate immunity in honeybees. Proc. R. Soc. B Biol. Sci. 2020, 287, 20201184. [Google Scholar] [CrossRef]
  7. Emery, O.; Schmidt, K.; Engel, P. Immune system stimulation by the gut symbiont Frischella perrara in the honey bee (Apis mellifera). Mol. Ecol. 2017, 26, 2576–2590. [Google Scholar] [CrossRef] [PubMed]
  8. Zheng, J.; Wittouck, S.; Salvetti, E.; Franz, C.M.A.P.; Harris, H.M.B.; Mattarelli, P.; O’Toole, P.W.; Pot, B.; Vandamme, P.; Walter, J.; et al. A taxonomic note on the genus Lactobacillus: Description of 23 novel genera, emended description of the genus Lactobacillus Beijerinck 1901, and union of Lactobacillaceae and Leuconostocaceae. Int. J. Syst. Evol. Microbiol. 2020, 70, 2782–2858. [Google Scholar] [CrossRef] [PubMed]
  9. Lang, H.; Duan, H.; Wang, J.; Zhang, W.; Guo, J.; Zhang, X.; Hu, X.; Zheng, H. Specific Strains of Honeybee Gut Lactobacillus Stimulate Host Immune System to Protect against Pathogenic Hafnia alvei. Microbiol. Spectr. 2022, 10, e01896-21. [Google Scholar] [CrossRef] [PubMed]
  10. Zhang, Z.; Mu, X.; Cao, Q.; Shi, Y.; Hu, X.; Zheng, H. Honeybee gut Lactobacillus modulates host learning and memory behaviors via regulating tryptophan metabolism. Nat. Commun. 2022, 13, 2037. [Google Scholar] [CrossRef] [PubMed]
  11. Lashani, E.; Davoodabadi, A.; Soltan Dallal, M.M. Some probiotic properties of Lactobacillus species isolated from honey and their antimicrobial activity against foodborne pathogens. Vet. Res. Forum. 2020, 11, 121–126. [Google Scholar] [CrossRef] [PubMed]
  12. Tajabadi, N.; Mardan, M.; Saari, N.; Mustafa, S.; Bahreini, R.; Manap, M.Y.A. Identification of Lactobacillus plantarum, Lactobacillus pentosus and Lactobacillus fermentum from honey stomach of honeybee. Braz. J. Microbiol. 2013, 44, 717–722. [Google Scholar] [CrossRef] [Green Version]
  13. Kahraman-Ilıkkan, Ö. Bacterial Profile and Fatty Acid Composition of Anatolian Bee Bread Samples by Metataxonomic and Metabolomic Approach. Curr. Microbiol. 2023, 80, 90. [Google Scholar] [CrossRef] [PubMed]
  14. de Jesus, L.C.L.; Drumond, M.M.; Aburjaile, F.F.; Sousa, T.d.J.; Coelho-Rocha, N.D.; Profeta, R.; Brenig, B.; Mancha-Agresti, P.; Azevedo, V. Probiogenomics of Lactobacillus delbrueckii subsp. lactis CIDCA 133: In Silico, In Vitro, and In Vivo Approaches. Microorganisms 2021, 9, 829. [Google Scholar] [CrossRef]
  15. Cody, W.L.; Wilson, J.W.; Hendrixson, D.R.; McIver, K.S.; Hagman, K.E.; Ott, C.M.; Nickerson, C.A.; Schurr, M.J. Skim milk enhances the preservation of thawed −80 °C bacterial stocks. J. Microbiol. Methods 2008, 75, 135–138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Lane, D.J. 16S/23S rRNA sequencing. In Nucleic Acid Techniques in Bacterial Systematics; Stackebrandt, E., Goodfellow, M., Eds.; John Wiley & Sons: New York, NY, USA, 1991; pp. 115–175. [Google Scholar]
  17. Mak, S.S.T.; Gopalakrishnan, S.; Carøe, C.; Geng, C.; Liu, S.; Sinding, M.S.; Kuderna, L.F.K.; Zhang, W.; Fu, S.; Vieira, F.G.; et al. Comparative performance of the BGISEQ-500 vs. Illumina HiSeq2500 sequencing platforms for palaeogenomic sequencing. Gigascience 2017, 6, gix049. [Google Scholar] [CrossRef] [Green Version]
  18. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403–410. [Google Scholar] [CrossRef]
  19. Andrews, S. FastQC: A Quality Control Tool for High throughput Sequence Data. 2010. 2017. Available online: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 7 June 2023).
  20. Antipov, D.; Korobeynikov, A.; McLean, J.S.; Pevzner, P.A. hybridSPAdes: An algorithm for hybrid assembly of short and long reads. Bioinformatics 2015, 32, 1009–1015. [Google Scholar] [CrossRef] [Green Version]
  21. Mikheenko, A.; Prjibelski, A.; Saveliev, V.; Antipov, D.; Gurevich, A. Versatile genome assembly evaluation with QUAST-LG. Bioinformatics 2018, 34, i142–i150. [Google Scholar] [CrossRef] [Green Version]
  22. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [Green Version]
  23. Yoon, S.H.; Ha, S.M.; Lim, J.; Kwon, S.; Chun, J. A large-scale evaluation of algorithms to calculate average nucleotide identity. Antonie Van Leeuwenhoek 2017, 110, 1281–1286. [Google Scholar] [CrossRef] [PubMed]
  24. Haft, D.H.; DiCuccio, M.; Badretdin, A.; Brover, V.; Chetvernin, V.; O’Neill, K.; Li, W.; Chitsaz, F.; Derbyshire, M.K.; Gonzales, N.R.; et al. RefSeq: An update on prokaryotic genome annotation and curation. Nucleic Acids Res. 2017, 46, D851–D860. [Google Scholar] [CrossRef] [PubMed]
  25. Li, W.; O’Neill, K.R.; Haft, D.H.; DiCuccio, M.; Chetvernin, V.; Badretdin, A.; Coulouris, G.; Chitsaz, F.; Derbyshire, M.K.; Durkin, A.S.; et al. RefSeq: Expanding the Prokaryotic Genome Annotation Pipeline reach with protein family model curation. Nucleic Acids Res. 2020, 49, D1020–D1028. [Google Scholar] [CrossRef]
  26. Tatusova, T.; DiCuccio, M.; Badretdin, A.; Chetvernin, V.; Nawrocki, E.P.; Zaslavsky, L.; Lomsadze, A.; Pruitt, K.D.; Borodovsky, M.; Ostell, J. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016, 44, 6614–6624. [Google Scholar] [CrossRef]
  27. Camacho, C.; Coulouris, G.; Avagyan, V.; Ma, N.; Papadopoulos, J.; Bealer, K.; Madden, T.L. BLAST+: Architecture and applications. BMC Bioinform. 2009, 10, 421. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. 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, 3895–3903. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Clausen, P.T.L.C.; Aarestrup, F.M.; Lund, O. Rapid and precise alignment of raw reads against redundant databases with KMA. BMC Bioinform. 2018, 19, 307. [Google Scholar] [CrossRef]
  30. Bortolaia, V.; Kaas, R.S.; Ruppe, E.; Roberts, M.C.; Schwarz, S.; Cattoir, V.; Philippon, A.; Allesoe, R.L.; Rebelo, A.R.; Florensa, A.F.; et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J. Antimicrob. Chemother. 2020, 75, 3491–3500. [Google Scholar] [CrossRef]
  31. Zankari, E.; Allesøe, R.; Joensen, K.G.; Cavaco, L.M.; Lund, O.; Aarestrup, F.M. PointFinder: A novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens. J. Antimicrob. Chemother. 2017, 72, 2764–2768. [Google Scholar] [CrossRef] [Green Version]
  32. Cosentino, S.; Voldby Larsen, M.; Møller Aarestrup, F.; Lund, O. PathogenFinder—Distinguishing Friend from Foe Using Bacterial Whole Genome Sequence Data. PLoS ONE 2013, 8, e77302. [Google Scholar] [CrossRef]
  33. Johansson, M.H.K.; Bortolaia, V.; Tansirichaiya, S.; Aarestrup, F.M.; Roberts, A.P.; Petersen, T.N. Detection of mobile genetic elements associated with antibiotic resistance in Salmonella enterica using a newly developed web tool: MobileElementFinder. J. Antimicrob. Chemother. 2020, 76, 101–109. [Google Scholar] [CrossRef]
  34. van Heel, A.J.; de Jong, A.; Song, C.; Viel, J.H.; Kok, J.; Kuipers, O.P. BAGEL4: A user-friendly web server to thoroughly mine RiPPs and bacteriocins. Nucleic Acids Res. 2018, 46, W278–W281. [Google Scholar] [CrossRef]
  35. Baek, M.-G.; Kim, K.W.; Yi, H. Subspecies-level genome comparison of Lactobacillus delbrueckii. Sci. Rep. 2023, 13, 3171. [Google Scholar] [CrossRef] [PubMed]
  36. Gupta, A.; Nair, S. Dynamics of Insect–Microbiome Interaction Influence Host and Microbial Symbiont. Front. Microbiol. 2020, 11, 1357. [Google Scholar] [CrossRef]
  37. Alonso, D.P.; Mancini, M.V.; Damiani, C.; Cappelli, A.; Ricci, I.; Alvarez, M.V.N.; Bandi, C.; Ribolla, P.E.M.; Favia, G. Genome Reduction in the Mosquito Symbiont Asaia. Genome Biol. Evol. 2018, 11, 1–10. [Google Scholar] [CrossRef] [Green Version]
  38. Zhang, W.; Zhang, X.; Su, Q.; Tang, M.; Zheng, H.; Zhou, X. Genomic features underlying the evolutionary transitions of Apibacter to honey bee gut symbionts. Insect Sci. 2022, 29, 259–275. [Google Scholar] [CrossRef]
  39. Kwong, W.K.; Engel, P.; Koch, H.; Moran, N.A. Genomics and host specialization of honey bee and bumble bee gut symbionts. Proc. Natl. Acad. Sci. USA 2014, 111, 11509–11514. [Google Scholar] [CrossRef] [Green Version]
  40. Chen, F.; Chen, J.; Chen, Q.; Yang, L.; Yin, J.; Li, Y.; Huang, X. Lactobacillus delbrueckii Protected Intestinal Integrity, Alleviated Intestinal Oxidative Damage, and Activated Toll-Like Receptor–Bruton’s Tyrosine Kinase–Nuclear Factor Erythroid 2-Related Factor 2 Pathway in Weaned Piglets Challenged with Lipopolysaccharide. Antioxidants 2021, 10, 468. [Google Scholar] [PubMed]
  41. Kong, Y.; Olejar, K.J.; On, S.L.W.; Chelikani, V. The Potential of Lactobacillus spp. for Modulating Oxidative Stress in the Gastrointestinal Tract. Antioxidants 2020, 9, 610. [Google Scholar] [CrossRef]
  42. Wang, Y.; Wu, Y.; Wang, Y.; Xu, H.; Mei, X.; Yu, D.; Wang, Y.; Li, W. Antioxidant Properties of Probiotic Bacteria. Nutrients 2017, 9, 521. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Min, W.-H.; Fang, X.-B.; Wu, T.; Fang, L.; Liu, C.-L.; Wang, J. Characterization and antioxidant activity of an acidic exopolysaccharide from Lactobacillus plantarum JLAU103. J. Biosci. Bioeng. 2019, 127, 758–766. [Google Scholar] [CrossRef] [PubMed]
  44. Lamothe, G.; Jolly, L.; Mollet, B.; Stingele, F. Genetic and biochemical characterization of exopolysaccharide biosynthesis by Lactobacillus delbrueckii subsp. bulgaricus. Arch. Microbiol. 2002, 178, 218–228. [Google Scholar] [CrossRef] [PubMed]
  45. Nguyen, P.T.; Nguyen, T.T.; Bui, D.C.; Hong, P.T.; Hoang, Q.K.; Nguyen, H.T. Exopolysaccharide production by lactic acid bacteria: The manipulation of environmental stresses for industrial applications. AIMS Microbiol. 2020, 6, 451–469. [Google Scholar] [CrossRef]
  46. Riaz Rajoka, M.S.; Wu, Y.; Mehwish, H.M.; Bansal, M.; Zhao, L. Lactobacillus exopolysaccharides: New perspectives on engineering strategies, physiochemical functions, and immunomodulatory effects on host health. Trends Food Sci. Technol. 2020, 103, 36–48. [Google Scholar] [CrossRef]
  47. Archer, A.C.; Kurrey, N.K.; Halami, P.M. In vitro adhesion and anti-inflammatory properties of native Lactobacillus fermentum and Lactobacillus delbrueckii spp. J. Appl. Microbiol. 2018, 125, 243–256. [Google Scholar] [CrossRef]
  48. Ramiah, K.; van Reenen, C.A.; Dicks, L.M.T. Surface-bound proteins of Lactobacillus plantarum 423 that contribute to adhesion of Caco-2 cells and their role in competitive exclusion and displacement of Clostridium sporogenes and Enterococcus faecalis. Res. Microbiol. 2008, 159, 470–475. [Google Scholar] [CrossRef]
  49. Lebeer, S.; Keersmaecker, S.C.J.D.; Verhoeven, T.L.A.; Fadda, A.A.; Marchal, K.; Vanderleyden, J. Functional Analysis of luxS in the Probiotic Strain Lactobacillus rhamnosus GG Reveals a Central Metabolic Role Important for Growth and Biofilm Formation. J. Bacteriol. 2007, 189, 860–871. [Google Scholar] [CrossRef] [Green Version]
  50. Rezaei, Z.; Salari, A.; Khanzadi, S. Biofilm Formation and Antibacterial Properties of Lactobacillus Isolated from Indigenous Dairy Products. J. Food Qual. Hazards Control 2021, 8, 162–168. [Google Scholar] [CrossRef]
  51. Lim, S.-M.; Lee, N.-K.; Paik, H.-D. Antibacterial and anticavity activity of probiotic Lactobacillus plantarum 200661 isolated from fermented foods against Streptococcus mutans. LWT 2020, 118, 108840. [Google Scholar] [CrossRef]
  52. Pang, X.; Liu, C.; Lyu, P.; Zhang, S.; Liu, L.; Lu, J.; Ma, C.; Lv, J. Identification of Quorum Sensing Signal Molecule of Lactobacillus delbrueckii subsp. bulgaricus. J. Agric. Food Chem. 2016, 64, 9421–9427. [Google Scholar] [CrossRef]
  53. Terraf, M.C.L.; Juárez Tomás, M.S.; Nader-Macías, M.E.F.; Silva, C. Screening of biofilm formation by beneficial vaginal lactobacilli and influence of culture media components. J. Appl. Microbiol. 2012, 113, 1517–1529. [Google Scholar] [CrossRef] [PubMed]
  54. Cornacchione, L.P.; Hu, L.T. Hydrogen peroxide-producing pyruvate oxidase from Lactobacillus delbrueckii is catalytically activated by phosphotidylethanolamine. BMC Microbiol. 2020, 20, 128. [Google Scholar] [CrossRef] [PubMed]
  55. Villegas, E.; Gilliland, S.E. Hydrogen Peroxide Production by Lactobacillus delbrueckii Subsp. Lactis I at 5 °C. J. Food Sci. 1998, 63, 1070–1074. [Google Scholar] [CrossRef]
  56. Yazgan, H.; Kuley, E.; Güven Gökmen, T.; Regenstein, J.M.; Özogul, F. The antimicrobial properties and biogenic amine production of lactic acid bacteria isolated from various fermented food products. J. Food Process. Preserv. 2021, 45, e15085. [Google Scholar] [CrossRef]
Table
Table 1. Statistical results of L. delbrueckii ssp. lactis A4 genome’s assembly.
Table 1. Statistical results of L. delbrueckii ssp. lactis A4 genome’s assembly.
Statistics ParameterValue
Number of contigs114
Number of contigs (>0 bp)145
Number of contigs (>1000 bp)106
Largest contig114,871
Total length1,871,570
Total length (>0 bp)1,881,475
Total length (>1000 bp)1,866,007
N5032,562
N908504
auN41,377
L5017
L9061
GC (%)49.84
Mismatches
Number of N’s per 100 kbp0
Number of N’s0
Table
Table 2. Results from the annotation of the L. delbrueckii ssp. lactis A4 genome.
Table 2. Results from the annotation of the L. delbrueckii ssp. lactis A4 genome.
Genomic Annotation ParameterValue
Genes (total)1917
CDSs (total)1854
Genes (coding)1774
CDSs (with protein)1774
Genes (RNA)63
rRNAs1, 2, 2 (5S, 16S, 23S)
complete rRNAs1 (5S)
partial rRNAs2, 2 (16S, 23S)
tRNAs55
ncRNAs3
Pseudo Genes (total)80
CDSs (without protein)80
Pseudo Genes (ambiguous residues)0 of 80
Pseudo Genes (frameshifted)38 of 80
Pseudo Genes (incomplete)38 of 80
Pseudo Genes (internal stop)18 of 80
Pseudo Genes (multiple problems)12 of 80
CRISPR Arrays1
Table
Table 3. Results from the BLAST analysis of the gene products potentially determining probiotic properties of L. delbrueckii ssp. lactis A4.
Table 3. Results from the BLAST analysis of the gene products potentially determining probiotic properties of L. delbrueckii ssp. lactis A4.
Gene Products with the Potential to Determine Probiotic PropertiesGeneBank Accession NumberBLAST hit Accession NumberLength (Number of Amino Acids)Max Score/Total ScoreE Value
Exopolysaccharides synthesis
exopolysaccharide biosynthesis proteinMDD1332515.1WP_002879977.12585380.0
NADP-dependent phosphogluconate dehydrogenaseMDD1332818.1WP_003616396.14619540.0
phosphoglucosamine mutaseMDD1331320.1WP_003617321.14509200.0
beta-phosphoglucomutaseMDD1331831.1WP_003616652.12214402 × 10−155
UDP-glucose-hexose-1-phosphate uridylyltransferaseMDD1331739.1WP_273963523.148810070.0
UTP-glucose-1-phosphate uridylyltransferase GalUMDD1331545.1WP_231539737.13046220.0
bifunctional UDP-N-acetylglucosamine diphosphorylase/glucosamine-1-phosphate N-acetyltransferase GlmUMDD1332146.1WP_120490223.14619440.0
oligosaccharide flippase family proteinMDD1331971.1WP_016395806.14759570.0
oligosaccharide flippase family proteinMDD1332322.1WP_273964740.14779550.0
flippaseMDD1332932.1WP_273964869.14769670.0
Adhesion
SLAP domain-containing proteinMDD1332405.1WP_273964748.153610820.0
SLAP domain-containing proteinMDD1332404.1WP_120490196.13997940.0
SLAP domain-containing proteinMDD1331713.1WP_191669459.11783626 × 10−126
D-alanyl-lipoteichoic acid biosynthesis protein DltBMDD1332807.1WP_003613651.14108370.0
D-alanyl-lipoteichoic acid biosynthesis protein DltDMDD1332805.1WP_273964847.14288820.0
elongation factor TuMDD1331613.1WP_003617518.13967970.0
type I glyceraldehyde-3-phosphate dehydrogenaseMDD1331303.1WP_002879985.13387010.0
triose-phosphate isomeraseMDD1331305.1WP_002879988.12525180.0
H2O2 production
lactate oxidaseMDD1332690.1WP_231520111.14088470.0
pyruvate oxidaseMDD1332991.1WP_273964886.160712530.0
Antioxidant properties
NAD(P)H-dependent oxidoreductaseMDD1332108.1WP_002879531.11793669 × 10−128
NAD(P)H-dependent oxidoreductaseMDD1332107.1WP_231533908.11823711 × 10−129
DNA starvation/stationary phase protection proteinMDD1331912.1WP_002878317.11553211 × 10−110
DsbA family proteinMDD1331257.1WP_086356203.12144379 × 10−155
NAD(P)/FAD-dependent oxidoreductaseMDD1331908.1WP_013440359.14449140.0
NAD(P)/FAD-dependent oxidoreductaseMDD1331702.1WP_236155003.14439050.0
thioredoxin family proteinMDD1332832.1WP_002879808.11062175 × 10−71
thioredoxin-disulfide reductaseMDD1331288.1WP_002879964.13106330.0
thioredoxinMDD1332291.1WP_016396729.11032131 × 10−69
Biofilm formation
AI-2E family transporterMDD1332686.1WP_016396259.13577210.0
AI-2E family transporterMDD1331996.1WP_273964107.13777480.0
AI-2E family transporterMDD1331266.1WP_130137291.13937940.0
cell wall metabolism sensor histidine kinase WalKMDD1332604.1WP_041811526.163112940.0
Biogenic amines production
pyridoxal-dependent decarboxylaseMDD1332960.1WP_260267444.11082277 × 10−75
orotidine-5’-phosphate decarboxylaseMDD1332839.1WP_013439932.12404993 × 10−178
carboxymuconolactone decarboxylase family proteinMDD1332744.1GHN63519.11062191 × 10−71
diphosphomevalonate decarboxylaseMDD1332354.1WP_016396687.13196610.0
diaminopimelate decarboxylaseMDD1332065.1WP_231540913.14369000.0
putative ornithine decarboxylaseMDD1331554.1WP_231540563.169514390.0
bifunctional phosphopantothenoylcysteine decarboxylase/phosphopantothenate-cysteine ligase CoaBCMDD1331448.1WP_138463443.13998010.0
Stresstolerance
orotidine-5’-phosphate decarboxylaseMDD1332839.1WP_013439932.12404993 × 10−178
S-ribosylhomocysteine lyaseMDD1332544.1WP_002879583.11593291 × 10−113
phosphopyruvate hydrataseMDD1332002.1WP_002879863.14258730.0
AAA family ATPaseMDD1332910.1WP_273964864.150710390.0
AAA family ATPaseMDD1332713.1WP_231534278.180816000.0
AAA family ATPaseMDD1332527.1WP_002879481.11803702 × 10−129
AAA family ATPaseMDD1332101.1WP_130137762.12595230.0
AAA family ATPaseMDD1331784.1WP_273963672.156011460.0
AAA family ATPaseMDD1331771.1WP_273963644.13116510.0
AAA family ATPaseMDD1331533.1WP_035162369.173114910.0
Na + /H+ antiporter NhaCMDD1332823.1WP_013440093.14598990.0
two-component system regulatory protein YycIMDD1332602.1WP_013438940.12665400.0
nucleotide exchange factor GrpEMDD1331986.1WP_130137528.11993924 × 10−137
molecular chaperone DnaKMDD1331987.1WP_236161911.161412320.0
molecular chaperone DnaJMDD1331988.1WP_130137527.13797750.0
heat-inducible transcriptional repressor HrcAMDD1331985.1WP_191669578.13477070.0
chaperonin GroELMDD1332210.1WP_120490554.153710700.0
oligoendopeptidase FMDD1332691.1WP_273964826.160012390.0
CTP synthaseMDD1332152.1WP_130183263.153911150.0
glucosamine-6-phosphate deaminaseMDD1331907.1WP_013440358.12344812 × 10−171
aquaporin family proteinMDD1332650.1WP_013440193.12344541 × 10−160
aquaporin family proteinMDD1332638.1WP_273964812.12404885 × 10−174
aldo/keto reductaseMDD1332535.1WP_273964794.12855860.0
ATP-dependent Clp protease ATP-binding subunit ClpXMDD1331611.1WP_003617523.14178450.0
F0F1 ATP synthase subunit gammaMDD1331368.1WP_035161843.13206560.0
F0F1 ATP synthase subunit epsilonMDD1331370.1WP_130137331.11462938 × 10−100
F0F1 ATP synthase subunit BMDD1331365.1WP_130137329.11683315 × 10−114
F0F1 ATP synthase subunit CMDD1331364.1WP_130137328.1741409 × 10−42
F0F1 ATP synthase subunit alphaMDD1331367.1WP_002880068.150310150.0
F0F1 ATP synthase subunit betaMDD1331369.1WP_013439311.14799650.0
Table
Table 4. Some genome properties of L. delbrueckii ssp. lactis A4 compared to other strains belonging to this species.
Table 4. Some genome properties of L. delbrueckii ssp. lactis A4 compared to other strains belonging to this species.
L. delbrueckii ssp. lactis StrainOriginGenBank Accession NumberGenome SizeNumber of GenesNumber of Protein-Coding GenesRNA Coding GenesPseudogenes
A4Honeybee gutJAQSVE000000000.11,881,475191717746380
KCTC 3034n/aCP023139.12,237,60822511978124233
NCIMB 702468dairy productJAJNTF000000000.11,871,7132036171813196
DSM 20076dairy productJAJNTW000000000.11,894,64020161720134162
NCIMB 702465dairy productJAJNTI000000000.11,957,69820821781116185
FAM 24850dairy productJAJNTA000000000.11,939,49620761774115187
CIRM BIA 1374dairy productJAJNTY000000000.11,918,40820601754100206
NCIMB 700280dairy productJAJNTN0100000001,979,89721071808120179
CIDCA 133raw milkCP065513.12,127,7852165194212895
FAM 21784human microbiomeVBSS00000000.12,005,19121121853108151
NWC_2_2fermented foodCP031023.12,269,17923311934121276
CRL581cheese starterATBQ00000000.11,909,8932025175088187
MAG_rmk202_ldelstarter cultureCP046131.12,166,76521631868125170
KCTC 3035n/aCP018156.11,972,7352006178812395
NBIMCC 8250n/aJAJQWQ000000000.11,931,2092009180958142
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bazukyan, I.; Georgieva-Miteva, D.; Velikova, T.; Dimov, S.G. In Silico Probiogenomic Characterization of Lactobacillus delbrueckii subsp. lactis A4 Strain Isolated from an Armenian Honeybee Gut. Insects 2023, 14, 540. https://doi.org/10.3390/insects14060540

AMA Style

Bazukyan I, Georgieva-Miteva D, Velikova T, Dimov SG. In Silico Probiogenomic Characterization of Lactobacillus delbrueckii subsp. lactis A4 Strain Isolated from an Armenian Honeybee Gut. Insects. 2023; 14(6):540. https://doi.org/10.3390/insects14060540

Chicago/Turabian Style

Bazukyan, Inga, Dimitrina Georgieva-Miteva, Tsvetelina Velikova, and Svetoslav G. Dimov. 2023. "In Silico Probiogenomic Characterization of Lactobacillus delbrueckii subsp. lactis A4 Strain Isolated from an Armenian Honeybee Gut" Insects 14, no. 6: 540. https://doi.org/10.3390/insects14060540

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop