Application of 16S rRNA virtual RFLP for the discrimination of some closely taxonomic-related lactobacilli species

Background

Several species in Lactobacillaceae family were recognized as potential probiotic bacteria. In this group of lactic acid bacteria, species are taxonomically closed and usually share similar 16S rRNA gene, thus, instead of so their identification and discrimination are too difficult.

Method

In the present study, virtual restriction fragment length polymorphism (RFLP) is instead of was used as a tool to discriminate between the closely related species Lactiplantibacillus plantarum (L plantarum), Lactiplantibacillus paraplantarum (L paraplantarum), and Lactiplantibacillus pentosus (L pentosus); Latilactobacillus sakei (L sakei), Latilactobacillus curvatus(L curvatus), and Latilactobacillus graminis (L graminis); Lacticaseibacillus casei (L casei), Lacticaseibacillus paracasei (L paracasei), Lacticaseibacillus zeae, and Lacticaseibacillus rhamnosus; Lactobacillus gasseri (L gasseri) and Lactobacillus johnsonii (L johnsonii). In silico comparative analysis of 16S rRNA sequences digested by 280 restriction enzymes was performed in order to search the key enzymes which gives different profiles.

Results

Results revealed that L casei, L paracasei, L zeae, and Lb rhamnosus could be separated from each other on the basis of AlwI, BpuEI, BsgI, BsrDI, BstYI, EarI, MluCI, and NsPI RFLP. Results showed also that different RFLP patterns were obtained from L sakei, L graminis and L curvatus by using both AflI and NspI endonucleases (in separated restriction) and L plantarum, L paraplantarum, and L pentosus were distinguished each one from the other by MucI, NspI, and TspDTI PCR-RFLP. Lb gasseri and L johnsonii were also separated on the basis of Mse I, Taq I, and Dra I RFLP.

Conclusion

In this study, we proved that too closely related species could be separated in virtual analysis on basis of their 16S rRNA RFLP patterns using key restriction enzymes method.

Loại tài liệu:

Article - Bài báo

Tác giả:

Laref, Nora

Đề mục:

Journal of Genetic Engineering and Biotechnology

Nhà xuất bản:

Elsevier

Ngày xuất bản:

December 2022

Số trang/ tờ:

Định dạng:

pdf

Định danh tư liệu:

DOI: https://doi.org/10.1186/s43141-022-00448-8 | ISSN 1687-157X

Nguồn gốc:

Journal of Genetic Engineering and Biotechnology, Volume 20, Issue 1, December 2022, 167

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