Application of phage therapy: Synergistic effect of phage EcSw (ΦEcSw) and antibiotic combination towards antibiotic‐resistant
 Escherichia coli

Easwaran, Maheswaran; Zoysa, Mahanama De; Shin, Hyun‐Jin

doi:10.1111/tbed.13646

Cited by 24 publications

(5 citation statements)

References 49 publications

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“…As antibiotic resistant bacteria are becoming more prevalent, new strategies are needed to combat these bacterial infections. Recent experiments show that antibiotics and phages may synergistically target bacterial populations when used in tandem [4,5]. While some studies have revealed that two phage types can synergistically fight bacterial infections [4], we demonstrate that certain virus types that are naturally present in human hosts may inhibit bacterial infection control with antibiotics.…”

Section: Introductionmentioning

confidence: 61%

Phage-antibiotic synergy inhibited by temperate and chronic virus competition

Landa¹,

Mossman²,

Whitaker³

et al. 2021

Preprint

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As antibiotic resistance grows more frequent for common bacterial infections, alternative treatment strategies such as phage therapy have become more widely studied in the medical field. While many studies have explored the efficacy of antibiotics, phage therapy, or synergistic combinations of phages and antibiotics, the impact of virus competition on the efficacy of antibiotic treatment has not yet been considered. Here, we model the synergy between antibiotics and two viral types, temperate and chronic, in controlling bacterial infections. We demonstrate that while combinations of antibiotic and temperate viruses exhibit synergy, competition between temperate and chronic viruses inhibits bacterial control with antibiotics. In fact, our model reveals that antibiotic treatment may counterintuitively increase the bacterial load when a large fraction of the bacteria develop antibiotic-resistance.

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Section: Introductionmentioning

confidence: 61%

Phage-antibiotic synergy inhibited by temperate and chronic virus competition

Landa¹,

Mossman²,

Whitaker³

et al. 2021

Preprint

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“…The same occurs in colistin + U1G group, nevertheless, the bacteria during the attempt to develop phage resistance might partially lose resistance to colistin and hence the combination can achieve enhanced killing than when individually treated. Zebrafish model has been used to study the efficiency of bacteriophages in curtailing infections caused by P. aeruginosa, K. pneumoniae, E. coli and E. faecalis [39,[47][48][49]. Nevertheless, majority of studies has compared the effect of antibiotics and phage therapy and scarce reports are available that has studied the combination of antibiotic and phage therapy.…”

Section: Discussionmentioning

confidence: 99%

Genome Analysis of Bacteriophage (U1G) of Schitoviridae, Host Receptor Prediction using Machine Learning Tools and its Evaluation to Mitigate Colistin Resistant Clinical Isolate of Escherichia Coli In Vitro and In Vivo

Sundaramoorthy¹,

KU²,

JBB³

et al. 2023

Preprint

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The objective of the present study is to isolate phages targeting colistin resistant E. coli clinical isolates (U3790 and U1007), sequence and analyze the phage genome and use machine learning tools to predict host cell surface receptor and finally evaluate the efficiency of the phage in vitro and in vivo in a zebrafish model. Phage targeting colistin resistant U3790 could not be isolated possibly due to presence of capsule and intact prophages in genome of U3790 strain. Phage specific for E. coli U1007 was isolated from Ganges River (designated as U1G). The obtained phage was triple purified and enriched. U1G phages had a greater burst size of 195 PFU/cell and a short latent time of 25 min. TEM analysis showed that U1Gphage possessed a capsid of 70 nm in diameter with a shorter tail, which shows that U1G belongs to the family Podoviridae. Genome sequencing and analysis revealed that the size of the phage genome is 73275 bp with no tRNA sequence, antibiotic resistant or virulent genes. PHASTER annotation revealed the presence of phage RNA polymerase gene in the genome, which favors the classification of phage under a new family Schitoviridae. A machine learning (ML) based multi-class classification model using algorithms such as Random Forest, Logistic Regression, and Decision Tree were employed to predict the host receptor targeted by U1G phage and the best performing two algorithms predicted LPS O antigen as the host receptor targeted by the U1G receptor binding protein (RBP), the tail spike protein. The isolated phage was stable from pH 5.0 to 9.0 and upto 45°C. In vitro time kill assay showed an initial 5 log decline in CFU/ml of U1007 at 2 h in the presence of U1G followed by regrowth, Addition of colistin with U1G restricted the growth until 6 h, however it also resulted in a regrowth by 24 h. The phage did not pose any toxicity to zebrafish as evidenced by liver/brain enzyme profiles. In vivo intramuscular infection study showed that U1G and Col + U1G treatment caused a 0.8 log and 1.4 log decline, respectively underscoring its potential for use in phage cocktail therapy.

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“…It is likely that the bacteria during its attempt to develop phage resistance might partially lose resistance to colistin and hence the combination can achieve enhanced killing than when individually treated. Zebrafish model has been used to study the efficiency of bacteriophages in curtailing infections caused by P. aeruginosa, K. pneumoniae, E. coli and E. faecalis [53,[67][68][69] . Nevertheless, the majority of studies have compared the effect of antibiotics and phage therapy and very few reports have studied the combination of antibiotic and phage therapy.…”

Section: Discussionmentioning

confidence: 99%

Genome Analysis of Bacteriophage (U1G) of Schitoviridae, Host Receptor Prediction using Machine Learning Tools and its Evaluation to Mitigate Colistin Resistant Clinical Isolate of Escherichia Coli In Vitro and In Vivo

Sundaramoorthy,

KU,

Nair

et al. 2023

Preprint

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The objective of the present study is to isolate phages targeting multidrug resistant (MDR), extended spectrum beta lactamase (ESBL) positive clinical isolate of E. coli ( U1007), sequence and analyze the phage genome and use machine learning tools to predict host cell surface receptor and finally evaluate the efficiency of monophage and phage cocktail in vitro and in vivo in a zebrafish model. Phage specific for E. coli U1007 was isolated from Ganges River (designated as U1G), Cuoom River (designated as CR) and Hospital waste water (designated as M phage). The obtained phages were triple purified and enriched. U1G phages had a greater burst size of 124 PFU/cell and a latent time of 25 min.M phage had a burst size of 150 PFU/cell with a shortlatent time of 20min. Similarly CR phage has a short latent 20 min and a burst size of 115 PFU/cell. Based on capsid architecture, U1G phage resembles Podoviridae, CR phage is structurally similar to Myoviridae and M phage has morphology that resembles Siphoviridae. Genome sequencing and analysis revealed that the size of the U1G phage genome is 73275 bp Whereas that of CR phage and M phage are 45236 bp and 45294 bp, respectively. All three genomes were marked by the absence of genes encoding tRNA sequence, antibiotic resistant or virulent genes. A machine learning (ML) based multi-class classification model using algorithms such as Random Forest, Logistic Regression, and Decision Tree were employed to predict the host receptor targeted by all 3 phages and the best performing algorithm Random Forest predicted LPS O antigen , LamB or OmpC for U1G FhuA, OmpC for CR phage and FhuA, LamB, TonB or OmpF for the M phage as the host receptor targeted by the receptor binding protein (RBP) of the phages. In vitro time kill assay showed that treatment with monophages alone and along with colistin resulted in regrowth whereas phage combinations significantly reduced the regrowth and by 24h the phage cocktail along with colistin produced a significant 3 log declinein cell counts relative to the untreated control. In vivo intramuscular infection study in zebrafish showed that phages were non toxic and a cocktail of dual (U1G +M) phage along with colsitin resulted in a significant 3.5 log decline in cell counts whereas triple phage combination along with colistin resulted in 3 log decline in cell counts probably due to host receptor competition. Our study highlights the potential of phage cocktail therapy in mitigating MDR clinical isolate of E. coli in vitro and in vivo.

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Application of phage therapy: Synergistic effect of phage EcSw (ΦEcSw) and antibiotic combination towards antibiotic‐resistant Escherichia coli

Cited by 24 publications

References 49 publications

Phage-antibiotic synergy inhibited by temperate and chronic virus competition

Phage-antibiotic synergy inhibited by temperate and chronic virus competition

Genome Analysis of Bacteriophage (U1G) of <em>Schitoviridae</em>, Host Receptor Prediction using Machine Learning Tools and its Evaluation to Mitigate Colistin Resistant Clinical Isolate of<em> Escherichia Coli</em> <em>In Vitro</em> and <em>In Vivo</em>

Genome Analysis of Bacteriophage (U1G) of <em>Schitoviridae</em>, Host Receptor Prediction using Machine Learning Tools and its Evaluation to Mitigate Colistin Resistant Clinical Isolate of<em> Escherichia Coli</em> <em>In Vitro</em> and <em>In Vivo</em>

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