Insight into using a novel ultraviolet/peracetic acid combination disinfection process to simultaneously remove antibiotics and antibiotic resistance genes in wastewater: Mechanism and comparison with conventional processes

Abstract Background: The global increase in the incidence of non-tuberculosis mycobacterial infections is of increasing concern due their high levels of intrinsic antibiotic resistance. Although integrated viral genomes, called prophage, are linked to increased antibiotic resistance in some bacterial species, we know little of their role in mycobacterial drug resistance. Results: We present here for the first time evidence of increased antibiotic resistance and expression of intrinsic antibiotic resistance genes in a strain of Mycobacterium chelonae carrying prophage. Strains carrying the prophage McProf demonstrated increased resistance to amikacin. Resistance in these strains was further enhanced by exposure to sub-inhibitory concentrations of the antibiotic, acivicin, or by the presence of a second prophage, BPs. Increased expression of the virulence gene, whiB7, was observed in strains carrying both prophage, BPs and McProf, relative to strains carrying a single prophage or no prophages. Conclusions: This study provides evidence that prophage alter expression of important mycobacterial intrinsic antibiotic resistance genes and additionally offers insight into the role prophage may play in mycobacterial adaptation to stress.

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An insight into the exploration of proliferation of antibiotic resistance genes in high-fat diet induced obesity mice

Genomics ◽

10.1016/j.ygeno.2021.05.041 ◽

2021 ◽

Author(s):

Rui Wang ◽

Dawei Chen ◽

Fang Wang ◽

Xiuqin Fan ◽

Chaonan Fan ◽

...

Keyword(s):

Antibiotic Resistance ◽

Resistance Genes ◽

High Fat Diet ◽

Antibiotic Resistance Genes ◽

High Fat ◽

Diet Induced Obesity ◽

Insight Into

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Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome

mSystems ◽

10.1128/msystems.00123-17 ◽

2018 ◽

Vol 3 (1) ◽

Cited By ~ 23

Author(s):

Sumayah F. Rahman ◽

Matthew R. Olm ◽

Michael J. Morowitz ◽

Jillian F. Banfield

Keyword(s):

Machine Learning ◽

Antibiotic Resistance ◽

Resistance Genes ◽

Sequence Data ◽

Antibiotic Resistance Genes ◽

Machine Learning Model ◽

Infant Gut Microbiome ◽

Microbial Systems ◽

Gut Microbial Community ◽

Insight Into

The process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows unique insight into microbial systems. We apply this technique to investigate how the antibiotic resistance genes of bacteria affect their ability to flourish in the gut under various conditions. Our analysis reveals that strain-level selection in formula-fed infants drives enrichment of beta-lactamase genes in the gut resistome. Using genomes from metagenomes, we built a machine learning model to predict how organisms in the gut microbial community respond to perturbation by antibiotics. This may eventually have clinical applications.

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