Synergistic growth effect among bacteria recovered from root canal infections

Júnior, Gil Moreira; Sobrinho, Antônio Paulino Ribeiro; Bambirra, Bernardo Henrique Silva; Bambirra, Felipe Henrique Silva; Carvalho, Maria Auxiliadora Roque de; Farias, Luiz M.; Nicoli, Jacques R.; Moreira, E. S. A.

doi:10.1590/s1517-83822011000300017

Cited by 8 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…use of TWEEN), or by a combination of both (76,77). Moreover, some of the bacteria used in the experiment are reported not to have reached stationary phase yet after 24h of growth (78,79). Bacterial cells that are still in the exponential phase are known to have additional copy numbers of genes because they are actively dividing (80)(81)(82).…”

Section: Discussionmentioning

confidence: 99%

Quantifying synthetic bacterial community composition with flow cytometry: efficacy in mock communities and challenges in co-cultures

Mermans,

Chatzigiannidou,

Teughels

et al. 2024

Preprint

View full text Add to dashboard Cite

Determination of bacterial community composition in synthetic communities is critical for understanding microbial systems. The community composition is typically determined through bacterial plating or through PCR-based methods which can be labor-intensive, expensive or prone to bias. Simultaneously, flow cytometry has been suggested as a cheap and fast alternative. However, since the technique captures the phenotypic state of bacterial cells, accurate determination of community composition could be affected when bacteria are co-cultured. We investigated the performance of flow cytometry for quantifying oral synthetic communities and compared it to the performance of strain specific qPCR and 16S rRNA gene amplicon sequencing. Therefore, axenic cultures, mock communities and co-cultures of oral bacteria were prepared. Random forest classifiers trained on flow cytometry data of axenic cultures were used to determine the composition of the synthetic communities, as well as strain specific qPCR and 16S rRNA gene amplicon sequencing. Flow cytometry was shown to have a lower average root mean squared error and outperformed the PCR-based methods in even mock communities (flow cytometry: 0.11 ± 0.04; qPCR: 0.26 ± 0.09; amplicon sequencing: 0.15 ± 0.01). When bacteria were co-cultured, neither flow cytometry, strain specific qPCR and 16S rRNA gene amplicon sequencing resulted in similar community composition. Performance of flow cytometry was decreased compared to mock communities due to changing phenotypes. Finally, discrepancies between flow cytometry and strain specific qPCR were found. These findings highlight the challenges ahead for quantifying community composition in co-cultures by flow cytometry.

show abstract

Section: Discussionmentioning

confidence: 99%

Quantifying synthetic bacterial community composition with flow cytometry: efficacy in mock communities and challenges in co-cultures

Mermans,

Chatzigiannidou,

Teughels

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…damaged) after 3.5h of treatment (Supplementary 2). Propidium iodide has been demonstrated to be able to stain cells in the exponential growth phase (Shi et al 2007) and F. nucleatum has been shown to reach the stationary phase only after 48h (Moreira Júnior et al 2011). This could justify why increased membrane damage was only observed for a few antimicrobials with this strain.…”

Section: Discussionmentioning

confidence: 99%

Unlocking the Mechanism of Action: A Cost-Effective Flow Cytometry Approach for Accelerating Antimicrobial Drug Development

Mermans,

Baets,

García-Timermans

et al. 2023

Preprint

View full text Add to dashboard Cite

Antimicrobial resistance is one of the greatest challenges to global health. While the development of new antimicrobials can combat resistance, low profitability is reducing the number of new compounds brought to the market. Elucidating the mechanism of action is crucial for developing new antimicrobials. This process can become expensive as there are no universally applicable pipelines and scientific expertise in different fields is required. One way to determine the mechanism of action is the use of predictive modeling, as antimicrobials can be classified into limited groups.. We demonstrate a cost-effective flow cytometry approach for determining the mechanisms of action of new compounds. Cultures ofActinomyces viscosusandFusobacterium nucleatumwere treated with a range of antimicrobials and measured by flow cytometry. A Gaussian mixture mask was applied over the data to construct a phenotypic fingerprint. The fingerprints were used to train random forest classifiers, and classifiers were used to predict the mechanism of action of cephalothin. Significant statistical differences were found among the 10 different treatment groups. A pairwise comparison between treatment groups showed a statistical difference for 35 out of 45 pairs forActinomyces viscosusand 32 out of 45 pairs forFusobacterium nucleatumafter 3.5h of treatment. The best performing random forest classifier yielded a Matthews correlation coefficient of 0.92 and the mechanism of action of cephalothin could be successfully predicted. These findings suggest that flow cytometry can be a cheap and fast alternative for determining the mechanism of action of new antimicrobials.

show abstract