Biology subjects

Khezri, A.

Publications and source records attributed to Khezri, A..

3 recordsLinked to original sources

bioRxiv2025

Optimizing Sample Preparation for Direct Nanopore Sequencing to Enable Rapid Pathogen and Antimicrobial Resistance Profiling in Bovine Mastitis

Long-read metagenomic sequencing allows for the rapid, culture-independent, and accurate identification of causative pathogens and antimicrobial resistance (AMR) profiles, supporting precise antibiotic use and reducing the spread of resistance. However, its application to mastitis milk is challenging due to the complex milk matrix, low bacterial count, and high somatic cell content. This study primarily aimed to further optimize our previously developed direct sequencing protocol for milk samples from mastitis cases. Additional optimizations included combining centrifugation, gradient centrifugation, and fat fraction treatment with Tween 20 and citric acid. Subsequently, four DNA extraction kits (Blood and Tissue, Molysis Complete5, HostZero, and SPINeasy Host depletion) were evaluated for their ability to remove host DNA and enrich bacterial DNA for long-read sequencing with Oxford Nanopore technologies. qPCR was used to quantify bacterial and bovine DNA, allowing comparison of host depletion efficiency among the kits. Our results show that simple centrifugation effectively concentrates bacterial cells, removing the need for chemical treatments. The HostZero kit consistently produced higher DNA yields, better DNA integrity, and more effective host DNA depletion. Using nanopore sequencing, both Gram-positive and Gram-negative mastitis pathogens, along with their AMR genes, were successfully detected. Overall, this study underscores the importance of an effective DNA extraction method for the direct sequencing of mastitis milk samples. Additionally, our findings support the potential of direct metagenomic sequencing as a rapid, culture-free approach for identifying mastitis pathogens and their resistance profiles.

microbiology↗

bioRxiv2023

SERS nanowire chip and machine learning enabled instant identification and classification of clinically relevant wild-type and antibiotic resistant bacteria at species and strain level

The world health organization considers antimicrobial resistance (AMR) to be a critical global public health problem. Conventional culture-based methods that are used to detect and identify bacterial infection are slow. Thus, there is a growing need for the development of robust, cost-effective, and fast diagnostic solutions for the identification of pathogens. Surface-enhanced Raman spectroscopy (SERS) can be used to identify target analytes with sensitivity down to the single-molecule level. Here, we developed a SERS chip by optimizing the entire fabrication pipeline of the metal-assisted chemical etching (MACE) method. The MACE approach offers a large-scale, densely packed silver (Ag) nanostructure on top of silicon nanowires (Si-NWs) with a large aspect ratio that significantly enhances the Raman signal due to localised surface plasmonic enhancement. The optimised SERS chips exhibited sensitivity down to 10-12 M concentration of R6G molecule and detected reproducible Raman spectra of bacteria down to a concentration of 100 colony forming units (CFU)/ml, which is a thousand times lower than the clinical threshold of bacterial infections like UTI (105 CFU/ml). A Siamese neural network model was used to classify SERS Raman spectra from bacteria specimens. The trained model identified 12 different bacterial species, including those which are causative agents for tuberculosis and urinary tract infection (UTI). Next, the SERS chips and another Siamese neural network model were used to differentiate antibiotic-resistant strains from susceptible strains of E. coli. The enhancement offered by SERS chip enabled acquisitions of Raman spectra of bacteria directly in the synthetic urine by spiking the sample with only 103 CFU/ml E. coli. Thus, the present study lays the ground for the identification and quantification of bacteria on SERS chips, thereby offering a potential future use for rapid, reproducible, label-free, and low limit detection of clinical pathogens.

biophysics↗

bioRxiv2022

Highly sensitive quantitative phase microscopy and deep learning complement whole genome sequencing for rapid detection of infection and antimicrobial resistance

The current state-of-the-art infection and antimicrobial resistance diagnostics (AMR) is based mainly on culture-based methods with a detection time of 48-96 hours. Slow diagnoses lead to adverse patient outcomes that directly correlate with the time taken to administer optimal antimicrobials. Mortality risk doubles with a 24-hour delay in providing appropriate antibiotics in cases of bacteremia. Therefore, it is essential to develop novel methods that can promptly and accurately diagnose microbial infections at both species and strain levels in clinical settings. Here, we demonstrate that the complimentary use of label-free optical assay with whole-genome sequencing (WGS) can enable high-speed culture-free diagnosis of infection and AMR. Our assay is based on microscopy methods exploiting label-free, highly sensitive quantitative phase microscopy (QPM) followed by deep convolutional neural networks (DCNNs) based classification. We benchmarked our proposed workflow on 21 clinical isolates from four WHO priority pathogens (Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, and Acinetobacter baumannii) that were antibiotic susceptibility testing (AST) phenotyped, and their antimicrobial resistance (AMR) profile was determined by WGS. The proposed optical assay was in good agreement with the WGS characterization. Highly accurate classification based on the gram staining (100% for gram-negative and 83.4% for gram-positive), species (98.6%), and resistant/susceptible type (96.4%), as well as at the individual strain level (100% accurate in predicting 19 out of the 21 strains). These results demonstrate the potential of the QPM assay as a rapid and first-stage tool for species, presence, and absence of AMR, and strain-level classification, which WGS can follow up for confirmation of the pathogen ID and the characterization of the AMR profile and susceptibility antibiotic. Taken together, all this information is of high clinical importance. Such a workflow could potentially facilitate efficient antimicrobial stewardship and prevent the spread of AMR.

microbiology↗