Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Laref N

PROVIDER: S-EPMC9756238 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Laref Nora N Belkheir Khadidja K

Journal, genetic engineering & biotechnology 20221216 1

<h4>Background</h4>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.<h4>Method</h4>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 ...[more]

PMID: 36525129

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Project description:BackgroundUsefulness of next-generation sequencing (NGS) in assessing bacteria associated with oral squamous cell carcinoma (OSCC) has been undermined by inability to classify reads to the species level.ObjectiveThe purpose of this study was to develop a robust algorithm for species-level classification of NGS reads from oral samples and to pilot test it for profiling bacteria within OSCC tissues.MethodsBacterial 16S V1-V3 libraries were prepared from three OSCC DNA samples and sequenced using 454's FLX chemistry. High-quality, well-aligned, and non-chimeric reads ≥350 bp were classified using a novel, multi-stage algorithm that involves matching reads to reference sequences in revised versions of the Human Oral Microbiome Database (HOMD), HOMD extended (HOMDEXT), and Greengene Gold (GGG) at alignment coverage and percentage identity ≥98%, followed by assignment to species level based on top hit reference sequences. Priority was given to hits in HOMD, then HOMDEXT and finally GGG. Unmatched reads were subject to operational taxonomic unit analysis.ResultsNearly, 92.8% of the reads were matched to updated-HOMD 13.2, 1.83% to trusted-HOMDEXT, and 1.36% to modified-GGG. Of all matched reads, 99.6% were classified to species level. A total of 228 species-level taxa were identified, representing 11 phyla; the most abundant were Proteobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Actinobacteria. Thirty-five species-level taxa were detected in all samples. On average, Prevotella oris, Neisseria flava, Neisseria flavescens/subflava, Fusobacterium nucleatum ss polymorphum, Aggregatibacter segnis, Streptococcus mitis, and Fusobacterium periodontium were the most abundant. Bacteroides fragilis, a species rarely isolated from the oral cavity, was detected in two samples.ConclusionThis multi-stage algorithm maximizes the fraction of reads classified to the species level while ensuring reliable classification by giving priority to the human, oral reference set. Applying the algorithm to OSCC samples revealed high diversity. In addition to oral taxa, a number of human, non-oral taxa were also identified, some of which are rarely detected in the oral cavity.

Dataset Information

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

Background

Method

Results

Conclusion

Publications

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

Similar Datasets

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