Genome analysis of triple phages that curtails MDR E. coli with ML based host receptor prediction and its evaluation

Infections by multidrug resistant bacteria (MDR) are becoming increasingly difficult to treat and alternative approaches like phage therapy, which is unhindered by drug resistance, are urgently needed to tackle MDR bacterial infections. During phage therapy phage cocktails targeting different receptors are likely to be more effective than monophages. In the present study, phages targeting carbapenem resistant clinical isolate of E. coli U1007 was isolated from Ganges River (U1G), Cooum River (CR) and Hospital waste water (M). Capsid architecture discerned using TEM identified the phage families as Podoviridae for U1G, Myoviridae for CR and Siphoviridae for M phage. Genome sequencing showed the phage genomes varied in size U1G (73,275 bp) CR (45,236 bp) and M (45,294 bp). All three genomes lacked genes encoding tRNA sequence, antibiotic resistant or virulent genes. A machine learning (ML) based multi-class classification model using Random Forest, Logistic Regression, and Decision Tree were employed to predict the host receptor targeted by receptor binding protein of 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. OmpC was validated as receptor for U1G by physiological experiments. In vivo intramuscular infection study in zebrafish showed that cocktail of dual phages (U1G + M) along with colsitin resulted in a significant 3.5 log decline in cell counts. Our study highlights the potential of ML tool to predict host receptor and proves the utility of phage cocktail to restrict E. coli U1007 in vivo.

"Urinary tract infection: Conventional testing to developing Technologies"

Urinary tract infections (UTIs) present an escalating global health concern, precipitating increased hospitalizations and antibiotic utilization, thereby fostering the emergence of antimicrobial resistance. Current diagnostic modalities exhibit protracted timelines and substantial financial burdens, necessitating specialized infrastructures. Addressing these impediments mandates the development of a precise diagnostic paradigm to expedite identification and augment antibiotic stewardship. The application of biosensors, recognized for their transformative efficacy, emerges as a promising resolution. Recent strides in biosensor technologies have introduced pioneering methodologies, yielding pertinent biosensors and integrated systems with significant implications for point-of-care applications. This review delves into historical perspectives, furnishing a comprehensive delineation of advancements in UTI diagnostics, disease etiology, and biomarkers, underscoring the potential merits of these innovations for optimizing patient care.

A dual-sensing strategy for the early diagnosis of urinary tract infections via detecting biofilm cellulose using aromatic amino acid-capped Au and Ag nanoparticles

Currently, urinary tract infection (UTI) diagnosis focuses on planktonic cell detection rather than biofilm detection, but the facile identification of UPEC bacterial biofilms is crucial in UTI diagnosis as the biofilm formed by bacteria is the causative agent of recurrent and chronic UTIs. Therefore, in this work, we developed a simple, cost-effective, colorimetric, and electrochemical-based strategy for the detection of cellulose in urine. Cellulose, a biofilm matrix component, was detected using tyrosine-capped gold and silver nanoparticles through a visible colorimetric change with a decrease in the absorbance intensity and a decrease in current response in the case of cyclic voltammetry. The sensor displayed a linear detection range of 10-70 mg mL-1 for colorimetry and 10-300 μg mL-1 for cyclic voltammetry with a good selectivity of <2.8% and a recovery rate of 95-100% in real-time sample analysis. Moreover, the binding affinity of cellulose with tyrosine was investigated using molecular docking studies to validate the sensing mechanism. We anticipate that our work will aid clinicians in the implementation of rapid, cost-effective, and definitive diagnosis of UTIs.