Small Things Matter: The 11.6-kDa TraB Protein is Crucial for Antibiotic Resistance Transfer Among Enterococci

Berger, Tamara M Ismael; Michaelis, Claudia; Probst, Ines; Sagmeister, Theo; Petrowitsch, Lukas; Puchner, Sandra; Pavkov‐Keller, Tea; Gesslbauer, Bernd; Grohmann, Elisabeth; Keller, Walter

doi:10.3389/fmolb.2022.867136

Cited by 2 publications

(13 citation statements)

References 62 publications

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Section: Discussionmentioning

confidence: 99%

“…By annotating the five typical features (CCATCCGGATGG, GAGGGGCCTGGA, GGCCGGCTGTTG, GTAAGAAGGAAC and TACTCAGCGTCC) to the machine-learning model, 30 candidate imipenem-related genes were identified. Four genes (KPHS_06130, KPHS_19620, KPHS_37760 and KPHS_p200900) were reported as antibiotic-related genes (mdtM [39], rne [40], dedA [41,42] and traB [43]) through searching relevant literatures. KPHS_06130 encodes multidrug resistance protein (mdtM).…”

Section: Discussionmentioning

confidence: 99%

“…KPHS_p200990 encodes conjugal transfer pilus assembly protein ( traB ). traB plays an important role in horizontal dissemination of diverse factors, such as antibiotic resistance genes [43]. Eight potential genes (KPHS_03210, KPHS_15230, KPHS_18270, KPHS_22130, KPHS_34080, KPHS_37730, KPHS_49960, KPHS_p101210) encode hypothetical protein of unknown function, which can be preferentially tested for their correlation with antibiotic resistance in subsequent experiments.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of genome-wide imipenem resistance features in Klebsiella pneumoniae using machine learning

et al. 2023

Journal of Medical Microbiology

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Introduction. The resistance rate of Klebsiella pneumoniae ( K. pneumoniae ) to imipenem is increasing year by year, and the imipenem resistance mechanism of K. pneumoniae is complex. Therefore, it is urgent to develop new strategies to explore the resistance mechanism of imipenem for its effective and accurate use in clinical practice. Hypothesis/Gap sStatement. Machine learning could identify resistance features and biological process that influence microbial resistance from whole-genome sequencing (WGS) data. Aims. This work aimed to predict imipenem resistance genetic features in K. pneumoniae from whole-genome k-mer features, and analyse their function for understanding its resistance mechanism. Methods. This study analysed WGS data of K. pneumoniae combined with resistance phenotype for imipenem, and established K. pneumoniae to imipenem genotype-phenotype model to predict resistance features using chi-squared test and random forest. An external clinical dataset was used to verify prediction power of resistance features. The potential genes were identified through alignment the resistance features with the K. pneumoniae reference genome using blastn, the functions of potential genes were further analysed to explore its resistance-related signalling pathways with GO and KEGG analysis, the resistance sequence patterns were screened using streme software. Finally, the resistance features were combined and modelled through four machine-learning algorithms (logistic regression, SVM, GBDT and XGBoost) to evaluate their phenotype prediction ability. Results. A total of 16 670 imipenem resistance features were predicted from genotype-phenotype model. The 30 potential genes were identified by annotating the resistance features and corresponded to known antibiotic-related genes (mdtM, dedA, rne, etc.). GO and KEGG pathway analyses indicated the possible association of imipenem resistance with metabolism process and cell membrane. CRYCAGCDN and CGRDAAAN were found from the imipenem resistance features, which were widely presented in the reported β-lactam resistance genes (bla SHV, bla CTX-M, bla TEM, etc.), and YCYAGCMCAST with metabolic functions (organic substance metabolic process, nitrogen compound metabolic process and cellular metabolic process) was identified from the top 50 resistance features. The 25 resistance genes in the training dataset included 19 genes in the external dataset, which verified the accuracy of prediction. The area under curve values of logistics regression, SVM, GBDT and XGBoost were 0.965, 0.966, 0.969 and 0.969, respectively, indicating that the imipenem resistance features have a strong prediction power. Conclusion. Machine-learning methods could effectively predict the imipenem resistance feature in K. pneumoniae , and provide resistance sequence profiles for predicting resistance phenotype and exploring potential resistance mechanisms. It provides an important insight into the potential therapeutic strategies of K. pneumoniae resistance to imipenem, and speed up the application of machine learning in routine diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Prediction of genome-wide imipenem resistance features in Klebsiella pneumoniae using machine learning

et al. 2023

Journal of Medical Microbiology

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“…The sequence of the traN variants was verified by Sanger sequencing. For biparental mating assays, E. faecalis JH2-2 (pIP501∆ traN ) was transformed by electroporation with one of the seven pEU327-RBS- traN variants as described in Berger et al (2022) .…”

Section: Methodsmentioning

confidence: 99%

“…The biparental mating assays were performed as described previously ( Berger et al, 2022 ). They were carried out in duplicates and repeated three times.…”

Section: Methodsmentioning

confidence: 99%

Effect of TraN key residues involved in DNA binding on pIP501 transfer rates in Enterococcus faecalis

Michaelis,

Berger,

Kuhlmann

et al. 2024

Front. Mol. Biosci.

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Conjugation is a major mechanism that facilitates the exchange of antibiotic resistance genes among bacteria. The broad-host-range Inc18 plasmid pIP501 harbors 15 genes that encode for a type IV secretion system (T4SS). It is a membrane-spanning multiprotein complex formed between conjugating donor and recipient cells. The penultimate gene of the pIP501 operon encodes for the cytosolic monomeric protein TraN. This acts as a transcriptional regulator by binding upstream of the operon promotor, partially overlapping with the origin of transfer. Additionally, TraN regulates traN and traO expression by binding upstream of the PtraNO promoter. This study investigates the impact of nine TraN amino acids involved in binding to pIP501 DNA through site-directed mutagenesis by exchanging one to three residues by alanine. For three traN variants, complementation of the pIP501∆traN knockout resulted in an increase of the transfer rate by more than 1.5 orders of magnitude compared to complementation of the mutant with native traN. Microscale thermophoresis (MST) was used to assess the binding affinities of three TraN double-substituted variants and one triple-substituted variant to its cognate pIP501 double-stranded DNA. The MST data strongly correlated with the transfer rates obtained by biparental mating assays in Enterococcus faecalis. The TraN variants TraN_R23A-N24A-Q28A, TraN_H82A-R86A, and TraN_G100A-K101A not only exhibited significantly lower DNA binding affinities but also, upon complementation of the pIP501∆traN knockout, resulted in the highest pIP501 transfer rates. This confirms the important role of the TraN residues R23, N24, Q28, H82, R86, G100, and K101 in downregulating pIP501 transfer. Although TraN is not part of the mating pair formation complex, TraE, TraF, TraH, TraJ, TraK, and TraM were coeluted with TraN in a pull-down. Moreover, TraN homologs are present not only in Inc18 plasmids but also in RepA_N and Rep_3 family plasmids, which are frequently found in enterococci, streptococci, and staphylococci. This points to a widespread role of this repressor in conjugative plasmid transfer among Firmicutes.

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Small Things Matter: The 11.6-kDa TraB Protein is Crucial for Antibiotic Resistance Transfer Among Enterococci

Cited by 2 publications

References 62 publications

Prediction of genome-wide imipenem resistance features in Klebsiella pneumoniae using machine learning

Prediction of genome-wide imipenem resistance features in Klebsiella pneumoniae using machine learning

Effect of TraN key residues involved in DNA binding on pIP501 transfer rates in Enterococcus faecalis

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