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A bacterial reporter panel for the detection and classification of antibiotic substances.

Melamed S, Lalush C, Elad T, Yagur-Kroll S, Belkin S, Pedahzur R - Microb Biotechnol (2012)

Bottom Line: The ever-growing use of pharmaceutical compounds, including antibacterial substances, poses a substantial pollution load on the environment.All of the tested antibiotics were detected by the panel, which displayed different response patterns for each substance.These unique responses were analysed by several algorithms that enabled clustering the compounds according to their functional properties, and allowed the classification of unknown antibiotic substances with a high degree of accuracy and confidence.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant and Environmental Sciences, The Alexander Silberman Institute of Life Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel.

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Related in: MedlinePlus

Clustering of the 11 antibiotics and additional 5 ‘unknown’ antibiotics by Spearman rank correlation coefficient based on the induction pattern of 12 reporter strains, following 5 h of exposure (the ‘unknown’ antibiotics are underlined and their branches are dotted).
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f3: Clustering of the 11 antibiotics and additional 5 ‘unknown’ antibiotics by Spearman rank correlation coefficient based on the induction pattern of 12 reporter strains, following 5 h of exposure (the ‘unknown’ antibiotics are underlined and their branches are dotted).

Mentions: Using the response characteristics of this 14‐member reporter panel, we have attempted to cluster the antibiotics into groups that display similar response patterns. By applying different combinations of distance metrics and linkage methods to the responses measured every hour during a 10 h exposure, we searched for the 12 reporters which provided the best clustering results. After 4 h of exposure, 622 desired clustering options were obtained, 80 after 5 h and 6 after 6 h. Based on the relevancy of the clustering method and on the distances between the antibiotics in the resulting tree, we have removed the zwf and emrA constructs and were left with a final 12‐member panel. A cluster tree of the antibiotics based on the selected 12 reporter strains, obtained by the use of a Spearman rank correlation coefficient as a distance metric and a weighted average distance as a linkage method (Arai et al., 1993; Tan et al., 2003), is shown in Fig. 3. The four protein synthesis interfering antibiotics (tetracycline, oxytetracycline, chloramphenicol and puromycin) clustered together, with the similarly structured tetracycline and oxytetracycline forming an independent but close branch. Ampicillin and amoxicillin, both β‐lactam antibiotics, were similarly grouped, as did the sulfonamides sulfamethoxazole and sulfadimethoxine. Within the limitations of our testing scheme, therefore, the clusters formed based on the bacterial responses corresponded very well to the antibiotics' known modes of action. Nalidixic acid, rifampin and colistin, each singly representing a different antibiotics group, formed an independent branch that is bound to expand once more data become available.


A bacterial reporter panel for the detection and classification of antibiotic substances.

Melamed S, Lalush C, Elad T, Yagur-Kroll S, Belkin S, Pedahzur R - Microb Biotechnol (2012)

Clustering of the 11 antibiotics and additional 5 ‘unknown’ antibiotics by Spearman rank correlation coefficient based on the induction pattern of 12 reporter strains, following 5 h of exposure (the ‘unknown’ antibiotics are underlined and their branches are dotted).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3815330&req=5

f3: Clustering of the 11 antibiotics and additional 5 ‘unknown’ antibiotics by Spearman rank correlation coefficient based on the induction pattern of 12 reporter strains, following 5 h of exposure (the ‘unknown’ antibiotics are underlined and their branches are dotted).
Mentions: Using the response characteristics of this 14‐member reporter panel, we have attempted to cluster the antibiotics into groups that display similar response patterns. By applying different combinations of distance metrics and linkage methods to the responses measured every hour during a 10 h exposure, we searched for the 12 reporters which provided the best clustering results. After 4 h of exposure, 622 desired clustering options were obtained, 80 after 5 h and 6 after 6 h. Based on the relevancy of the clustering method and on the distances between the antibiotics in the resulting tree, we have removed the zwf and emrA constructs and were left with a final 12‐member panel. A cluster tree of the antibiotics based on the selected 12 reporter strains, obtained by the use of a Spearman rank correlation coefficient as a distance metric and a weighted average distance as a linkage method (Arai et al., 1993; Tan et al., 2003), is shown in Fig. 3. The four protein synthesis interfering antibiotics (tetracycline, oxytetracycline, chloramphenicol and puromycin) clustered together, with the similarly structured tetracycline and oxytetracycline forming an independent but close branch. Ampicillin and amoxicillin, both β‐lactam antibiotics, were similarly grouped, as did the sulfonamides sulfamethoxazole and sulfadimethoxine. Within the limitations of our testing scheme, therefore, the clusters formed based on the bacterial responses corresponded very well to the antibiotics' known modes of action. Nalidixic acid, rifampin and colistin, each singly representing a different antibiotics group, formed an independent branch that is bound to expand once more data become available.

Bottom Line: The ever-growing use of pharmaceutical compounds, including antibacterial substances, poses a substantial pollution load on the environment.All of the tested antibiotics were detected by the panel, which displayed different response patterns for each substance.These unique responses were analysed by several algorithms that enabled clustering the compounds according to their functional properties, and allowed the classification of unknown antibiotic substances with a high degree of accuracy and confidence.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant and Environmental Sciences, The Alexander Silberman Institute of Life Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel.

Show MeSH
Related in: MedlinePlus