Inhibition of Three Potato Pathogens by Phenazine-Producing Pseudomonas spp. Is Associated with Multiple Biocontrol-Related Traits

Plant-beneficial phenazine-producing Pseudomonas spp. are effective biocontrol agents, thanks to the broad-spectrum antibiotic activity of the phenazine antibiotics they produce. These bacteria have received considerable attention over the last 20 years, but most studies have focused only on the ability of a few genotypes to inhibit the growth of a limited number of plant pathogens.

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Small molecule inhibitors of trans-translation have broad-spectrum antibiotic activity

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1302816110 ◽

2013 ◽

Vol 110 (25) ◽

pp. 10282-10287 ◽

Cited By ~ 46

Author(s):

N. S. Ramadoss ◽

J. N. Alumasa ◽

L. Cheng ◽

Y. Wang ◽

S. Li ◽

...

Keyword(s):

Small Molecule ◽

Broad Spectrum ◽

Small Molecule Inhibitors ◽

Antibiotic Activity ◽

Broad Spectrum Antibiotic

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Isolation and Characterization of Streptomyces spp. from Soil Showing Broad Spectrum Antibiotic Activity

The Korean Journal of Microbiology ◽

10.7845/kjm.2012.053 ◽

2012 ◽

Vol 48 (4) ◽

pp. 270-274 ◽

Cited By ~ 2

Author(s):

Sewook Park ◽

Taeok Bae ◽

Seung Bum Kim

Keyword(s):

Broad Spectrum ◽

Antibiotic Activity ◽

Broad Spectrum Antibiotic ◽

Streptomyces Spp ◽

Isolation And Characterization

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Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques

Pharmaceuticals ◽

10.3390/ph13090204 ◽

2020 ◽

Vol 13 (9) ◽

pp. 204

Author(s):

Rodrigo A. Nava Lara ◽

Jesús A. Beltrán ◽

Carlos A. Brizuela ◽

Gabriel Del Rio

Keyword(s):

Machine Learning ◽

Broad Spectrum ◽

Human Microbiome ◽

Antibiotic Activity ◽

Machine Learning Techniques ◽

Learning Approach ◽

Broad Spectrum Antibiotic ◽

Approach Training ◽

Learning Techniques ◽

Machine Learning Approach

Polypharmacologic human-targeted antimicrobials (polyHAM) are potentially useful in the treatment of complex human diseases where the microbiome is important (e.g., diabetes, hypertension). We previously reported a machine-learning approach to identify polyHAM from FDA-approved human targeted drugs using a heterologous approach (training with peptides and non-peptide compounds). Here we discover that polyHAM are more likely to be found among antimicrobials displaying a broad-spectrum antibiotic activity and that topological, but not chemical features, are most informative to classify this activity. A heterologous machine-learning approach was trained with broad-spectrum antimicrobials and tested with human metabolites; these metabolites were labeled as antimicrobials or non-antimicrobials based on a naïve text-mining approach. Human metabolites are not commonly recognized as antimicrobials yet circulate in the human body where microbes are found and our heterologous model was able to classify those with antimicrobial activity. These results provide the basis to develop applications aimed to design human diets that purposely alter metabolic compounds proportions as a way to control human microbiome.

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