Genomic insights into antibiotic resistance and mobilome of lactic acid bacteria and bifidobacteria
Open accessAbstract
sp. (bifidobacteria) can carry antimicrobial resistance genes (ARGs), yet data on resistance mechanisms in these bacteria are limited. The aim of our study was to identify the underlying genetic mechanisms of phenotypic resistance in 103 LAB and bifidobacteria using whole-genome sequencing. Sequencing data not only confirmed the presence of 36 acquired ARGs in genomes of 18 strains, but also revealed wide dissemination of intrinsic ARGs. The presence of acquired ARGs on known and novel mobile genetic elements raises the possibility of their horizontal spread. In addition, our data suggest that mutations may be a common mechanism of resistance. Several novel candidate resistance mechanisms were uncovered, providing a basis for further in vitro studies. Overall, 1,314 minimum inhibitory concentrations matched with genotypes in 92.4% of the cases; however, prediction of phenotype based on genotypic data was only partially efficient, especially with respect to aminoglycosides and chloramphenicol. Our study sheds light on resistance mechanisms and their transferability potential in LAB and bifidobacteria, which will be useful for risk assessment analysis.
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Probiotic research is increasingly using computational technologies including whole-genome sequencing, in silico functional annotation, comparative genomic analysis, pathway reconstruction, and AI-based predictive models to support analysis and selection of specific strains.
“Probiotic research is increasingly using computational technologies, including whole-genome sequencing, in silico functional annotation, comparative genomic analysis, pathway reconstruction, and AI-based predictive models, to support the analysis and selection of specific strains [132,133,151].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [133]
The Introduction (page unavailable) says whole-genome sequencing became a requirement for characterising certain food-chain microorganisms in 2018 and notes subsequent studies of resistance genes in probiotic-related bacteria. Materials and Methods (Whole-genome sequencing and assembly; Sequence analysis, pages unavailable) documents sequencing, annotation, and comparative genomic analyses of strains. The paper does not substantiate the full list of technologies: the cited passages do not describe pathway reconstruction or AI-based predictive models for strain selection. (LLM, conf=0.87)
Experimental — accuracy not yet measuredWhole-genome sequencing-based genomic screening allows in silico detection of antibiotic resistance genes, virulence-associated factors, and mobile genetic elements, providing a basis for modern probiotic safety evaluation.
“Whole-genome sequencing-based genomic screening allows for the in silico detection of antibiotic resistance genes, virulence-associated factors, and mobile genetic elements, thus providing a basis for modern probiotic safety evaluation [20,133,155,156].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [133]
The Introduction (page not provided) links whole-genome analysis of antibiotic resistance genes and mobile genetic elements to the safety of probiotics, and the Sequence analysis methods describe genomic screening for those genes. The cited passages do not establish detection of virulence-associated factors, so that part of the attributed claim is unsupported. (LLM, conf=0.88)
Experimental — accuracy not yet measuredComputational tools such as whole genome sequencing and bioinformatics analysis are increasingly included in probiotic regulatory dossiers as supporting evidence for strain characterization and safety, provided methods are transparent, reproducible, and interpretable alongside phenotypic data.
“Computational tools such as whole genome sequencing and bioinformatics analysis are increasingly being included in probiotic regulatory dossiers as supporting evidence for strain characterization and safety, provided that the methods are transparent, reproducible, and interpretable alongside phenotypic data [31,132,133].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [133]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredComputational approaches can result in false positives and false negatives due to annotation thresholds, database bias, fragmented assemblies, or model overfitting.
“These approaches can result in false positives and false negatives due to factors such as annotation thresholds, database bias, fragmented assemblies, or model overfitting, highlighting the need for careful validation against phenotypic data and careful interpretation when assessing the safety and efficacy of probiotics [67,132,133].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [133]
The Results (Phenotype–genotype agreement; page unavailable) report imperfect prediction from genomic data, and the Discussion says that detection methods and similarity cut-offs may explain discrepancies. The paper does not attribute false positives or false negatives to database bias, fragmented assemblies, or model overfitting. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAbsence of ARG identified by WGS does not imply absence of resistance mechanisms in probiotic lactic acid bacteria, as intrinsic or non-canonical resistance determinants may remain undetected due to database and methodological limitations.
“The absence of ARG identified by WGS does not imply the absence of resistance mechanisms in probiotic lactic acid bacteria, as intrinsic or non-canonical resistance determinants may remain undetected due to database and methodological limitations, highlighting the need for cautious interpretation of safety assessments based on genomic data [132,133,134].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [133]
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Tušar, T., Žerdoner, K., Matijašić, B. B., Paveljšek, D., Benedik, E., Bratanič, B., Mis, N. F., & Rogelj, I. (2014) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 12 · see citing works
Kondo, J., & Koganei, M. (2019) · Molecules · doi:10.3390/molecules25010159 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Current Concepts in Probiotic Safety and Efficacy
Churin, A. A., Sokolyanskaya, L. O., Lukina, A. P., & Karnachuk, O. V. (2026) · Nutrients · doi:10.3390/nu18040696 · Cited by 31 · see citing works
“…as whole genome sequencing and bioinformatics analysis are increasingly being included in probiotic regulatory dossiers as supporting evidence for strain characterization and safety, provided that the methods are transparent, reproducible, and interpretable alongside phenotypic data [31,132,133].”
InfluentialExploring the resistome of probiotics, starter cultures, and cheeses via metagenomic analysis
Rozman, V., Stopnišek, N., Lorbeg, P. M., Obermajer, T., Kušar, D., & Matijašić, B. B. (2025) · Food Control · doi:10.1016/j.foodcont.2025.111173 · Cited by 12 · see citing works
“MGEs were detected in the genetic environment of the predicted ARGs (5 coding sequences upstream and downstream) using a custom MGE database (285,059 MGE genes) (Rozman et al., 2022; Rozman Mohar Lorbeg, Treven et al., 2023).”
Self-citationExploring the genomic traits of infant-associated microbiota members from a Zimbabwean cohort
Mudhluli, T. E., Kujawska, M., Mueller, J., Felsl, A., Truppel, B.-A., Hall, L. J., Chitsike, I., Gomo, E., & Zhou, D. T. (2024) · BMC Genomics · doi:10.1186/s12864-024-10618-2 · Cited by 3 · see citing works
- Authors
- Rozman, V., Mohar Lorbeg, P., Treven, P., Accetto, T., Janežič, S., Rupnik, M., & Bogovič Matijašić, B.
- Affiliations
- University of Ljubljana (SI)
- University of Ljubljana
- University of Maribor (SI)
- Corresponding author
- Rozman, Vita
- Venue
- Life Science Alliance
- Publisher
- Life Science Alliance, LLC
- Published
- 2023-02-13
- Volume
- 6
- Issue
- 4
- Pages
- e202201637
- ISSN
- 2575-1077 (ISSN-L)
- PMC
- PMC9930590
- OpenAlex
- W4320486124
- Cited by
- 36
- References
- 87
- Citations by year
- 2026: 12 · 2025: 16 · 2024: 6 · 2023: 2
- FWCI
- 3.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Life Science Alliance (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Maribor digital library (University of Maribor) (submittedVersion, cc-by)
- Repository of the University of Ljubljana (University of Ljubljana) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Javna Agencija za Raziskovalno Dejavnost RS (6316-1/2017-273, 603-1/2017-13, J4-1769, P4-0097)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Medicine › Antibiotic Resistance in Bacteria
- Life Sciences › Agricultural and Biological Sciences › Food Science › Probiotics and Fermented Foods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- antibiotic resistance, antimicrobial resistance genes, lactic acid bacteria, Bifidobacterium, mobile genetic elements, horizontal gene transfer, resistance mechanisms, phenotypic resistance, minimum inhibitory concentration, aminoglycosides, chloramphenicol, foodborne bacteria
- MeSH terms
- Anti-Bacterial Agents, Bifidobacterium, Drug Resistance, Microbial, Genomics, Lactobacillales
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