Browse
Submit Data
Databases
API
Help

Dataset Information

8 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy.

ABSTRACT: In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health.

SUBMITTER: Nakar A

PROVIDER: S-EPMC8761712 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Detection of Sphingomyelin Clusters by Raman Spectroscopy.

Project description:Sphingomyelin (SM) is a major sphingolipid in mammalian cells that forms specific lipid domains in combination with cholesterol (Chol). Using molecular-dynamics simulation and density functional theory calculation, we identified a characteristic Raman band of SM at ?1643 cm(-1) as amide I of the SM cluster. Experimental results indicate that this band is sensitive to the hydration of SM and the presence of Chol. We showed that this amide I Raman band can be utilized to examine the membrane distribution of SM. Similarly to SM, ceramide phosphoethanolamine (CerPE) exhibited an amide I Raman band in almost the same region, although CerPE lacks three methyl groups in the phosphocholine moiety of SM. In contrast to SM, the amide I band of CerPE was not affected by Chol, suggesting the importance of the methyl groups of SM in the SM-Chol interaction.

| S-EPMC5018142 | biostudies-literature

Using Raman spectroscopy for early detection of resistance-breaking strains of tomato spotted wilt orthotospovirus in tomatoes.

Project description:Tomato spotted wilt (TSW) disease caused by tomato spotted wilt orthotospovirus (TSWV, Orthotospovirus tomatomaculae) poses a significant threat to specialty and staple crops worldwide by causing over a billion dollars in crop losses annually. Current strategies for TSWV diagnosis heavily rely on nucleic acid or protein-based techniques which require significant technical expertise, and are invasive, time-consuming, and expensive, thereby catalyzing the search for better alternatives. In this study, we explored the potential of Raman spectroscopy (RS) in early detection of TSW in a non-invasive and non-destructive manner. Specifically, we investigated whether RS could be used to detect strain specific TSW symptoms associated with four TSWV strains infecting three differentially resistant tomato cultivars. In the acquired spectra, we observed notable reductions in the intensity of vibrational peaks associated with carotenoids. Using high-performance liquid chromatography (HPLC), we confirmed that TSWV caused a substantial decrease in the concentration of lutein that was detected by RS. Finally, we demonstrated that Partial Least Squares-Discriminant Analysis (PLS-DA) could be used to differentiate strain-specific TSW symptoms across all tested cultivars. These results demonstrate that RS can be a promising solution for early diagnosis of TSW, enabling timely disease intervention and thereby mitigating crop losses inflicted by TSWV.

| S-EPMC10791937 | biostudies-literature

Altered antibiotic transport in OmpC mutants isolated from a series of clinical strains of multi-drug resistant E. coli.

Project description:Antibiotic-resistant bacteria, particularly gram negative species, present significant health care challenges. The permeation of antibiotics through the outer membrane is largely effected by the porin superfamily, changes in which contribute to antibiotic resistance. A series of antibiotic resistant E. coli isolates were obtained from a patient during serial treatment with various antibiotics. The sequence of OmpC changed at three positions during treatment giving rise to a total of four OmpC variants (denoted OmpC20, OmpC26, OmpC28 and OmpC33, in which OmpC20 was derived from the first clinical isolate). We demonstrate that expression of the OmpC K12 porin in the clinical isolates lowers the MIC, consistent with modified porin function contributing to drug resistance. By a range of assays we have established that the three mutations that occur between OmpC20 and OmpC33 modify transport of both small molecules and antibiotics across the outer membrane. This results in the modulation of resistance to antibiotics, particularly cefotaxime. Small ion unitary conductance measurements of the isolated porins do not show significant differences between isolates. Thus, resistance does not appear to arise from major changes in pore size. Crystal structures of all four OmpC clinical mutants and molecular dynamics simulations also show that the pore size is essentially unchanged. Molecular dynamics simulations suggest that perturbation of the transverse electrostatic field at the constriction zone reduces cefotaxime passage through the pore, consistent with laboratory and clinical data. This subtle modification of the transverse electric field is a very different source of resistance than occlusion of the pore or wholesale destruction of the transverse field and points to a new mechanism by which porins may modulate antibiotic passage through the outer membrane.

| S-EPMC3203869 | biostudies-literature

Designing dendronic-Raman markers for sensitive detection using surface-enhanced Raman spectroscopy.

Project description:Surface-Enhanced Raman Spectroscopy (SERS) is well-established as a tool for bio-diagnostics but is often limited by analyte sensitivity and the need for specialized substrates. Signal enhancement can be achieved by attaching multiple Raman markers to a single analyte. Dendronic frameworks with multiple Raman markers attached to the periphery offer an opportunity to examine this idea. In this article, dendrons with thiophenol groups on their periphery were synthesized and tested as a SERS analyte. For this study, simple gold nanoparticles (∼60 nm) were used as a substrate. A 102 fold enhancement in detection was observed upon going from a mono-thiophenol (MT) to a tetra-thiophenol (TT). Dendronic Raman markers increased the probability of SERS occurrence at lower concentrations when compared to a single Raman active molecule. This strategy extends the applicability of SERS, as these analyte molecules can be just mixed or drop-casted on any kind of SERS substrate.

| S-EPMC9071040 | biostudies-literature

Conformational fingerprinting of tau variants and strains by Raman spectroscopy.

Project description:Tauopathies are a group of disorders in which the deposition of abnormally folded tau protein accompanies neurodegeneration. The development of methods for detection and classification of pathological changes in protein conformation are desirable for understanding the factors that influence the structural polymorphism of aggregates in tauopathies. We have previously demonstrated the utility of Raman spectroscopy for the characterization and discrimination of different protein aggregates, including tau, based on their unique conformational signatures. Building on this, in the present study, we assess the utility of Raman spectroscopy for characterizing and distinguishing different conformers of the same protein which in the case of tau are unique tau strains generated in vitro. We now investigate the impact of aggregation environment, cofactors, post-translational modification and primary sequence on the Raman fingerprint of tau fibrils. Using quantitative conformational fingerprinting and multivariate statistical analysis, we found that the aggregation of tau in different buffer conditions resulted in the formation of distinct fibril strains. Unique spectral markers were identified for tau fibrils generated using heparin or RNA cofactors, as well as for phosphorylated tau. We also determined that the primary sequence of the tau monomer influenced the conformational signature of the resulting tau fibril, including 2N4R, 0N3R, K18 and P301S tau variants. These results highlight the conformational polymorphism of tau fibrils, which is reflected in the wide range of associated neurological disorders. Furthermore, the analyses presented in this study provide a benchmark for the Raman spectroscopic characterization of tau strains, which may shed light on how the aggregation environment, cofactors and post-translational modifications influence tau conformation in vivo in future studies.

| S-EPMC8330415 | biostudies-literature

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.

Project description:Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

| S-EPMC8446005 | biostudies-literature

IncF plasmid diversity in multi-drug resistant Escherichia coli strains from animals in China.

Project description:The purpose of this study was to characterize a collection of 103 multidrug resistance IncF plasmids recovered from Escherichia coli of food producing and companion animals between 2003 and 2012. A total of 103 incF plasmids were characterized using an established PCR-based IncF replicon sequence typing (RST) system to identify FII, FIA, and FIB (FAB) groups. Plasmids were also analyzed using-restriction fragment length polymorphism (RFLP). Antibiotic Resistance determinants bla CTX-M , plasmid-mediated quinolone resistance (PMQR) genes and rmtB and plasmid addiction systems (PAS) were identified by PCR screening. A total of 20 different RSTs from 103 IncF plasmids were identified. The groups F2 and F33 with the RST formulae A-: B- were the most frequently encountered types (63.1%). The antibiotic resistance genes (ARGs) bla CTX-M , rmtB, and oqxB were carried by 82, 37, and 34 IncF plasmids, respectively. Most of these plasmids carried more than one resistance gene (59.2%, 61/103). The IncF plasmids also had a high frequency of addiction systems (mean 2.54) and two antisense RNA-regulated systems (hok-sok and srnBC) and a protein antitoxin-regulated system (pemKI) were the most prevalent. Not surprisingly, RFLP profiles among the IncF plasmids were diverse even though some shared identical IncF-RSTs. This is the first extensive study of IncF plasmid-positive E. coli isolates from animals in China. Our results demonstrate that IncF is the most prevalent plasmid family in E. coli plasmids and they commonly carry multiple resistance determinants that render them resistant to different antibiotic classes simultaneously. IncF plasmids also harbor addiction systems, promoting their stability and maintenance in the bacterial host, under changing environmental conditions.

| S-EPMC4585273 | biostudies-literature

Surface Enhanced Raman Spectroscopy for Single Molecule Protein Detection.

Project description:A two-step process of protein detection at a single molecule level using SERS was developed as a proof-of-concept platform for medical diagnostics. First, a protein molecule was bound to a linker in the bulk solution and then this adduct was chemically reacted with the SERS substrate. Traut's Reagent (TR) was used to thiolate Bovine serum albumin (BSA) in solution followed by chemical cross linking to a gold surface through a sulfhydryl group. A Glycine-TR adduct was used as a control sample to identify the protein contribution to the SER spectra. Gold SERS substrates were manufactured by electrochemical deposition. Solutions at an ultralow concentration were used for attaching the TR adducts to the SERS substrate. Samples showed the typical behavior of a single molecule SERS including spectral fluctuations, blinking and Raman signal being generated from only selected points on the substrate. The fluctuating SER spectra were examined using Principle Component Analysis. This unsupervised statistics allowed for the selecting of spectral contribution from protein moiety indicating that the method was capable of detecting a single protein molecule. Thus we have demonstrated, that the developed two-step methodology has the potential as a new platform for medical diagnostics.

| S-EPMC6710251 | biostudies-literature

High prevalence of fluoroquinolone-resistant Escherichia coli strains isolated from urine clinical samples.

Project description:Background:Fluoroquinolone resistant Escherichia coli isolates have become an important challenge in healthcare settings in Iran. In this study, we have determined Fluoroquinolone resistant E. coli isolates (from both outpatients and inpatients) and evaluated mutations of gyrA and parC within the quinolone resistance-determining regions (QRDR) of these clinical isolates. Materials and methods:Clinical isolates were recovered from the urine sample of patients with urinary tract infections admitted at Alzahra hospital, Iran, between September and February 2013. We assessed antimicrobial susceptibility of all isolates and determined mutations in QRDR of gyrA and parC genes from 13 fluoroquinolone-resistant isolates by DNA sequencing. Results:A total of 135 E. coli strains were obtained from 135 patients (91 outpatients and 44 inpatients). The resistance rate of fluoroquinolones (Ciprofloxacin, Norfloxacin and Ofloxacin) among our strains was 45.2%. Two E. coli isolates were shown just a single mutation, but other isolates possessed 2-5 mutations in gyrA and parC genes. Mutations in the QRDR regions of gyrA were at positions Ser83 and Asp87 and parC at positions Ser80, Glu84, Gly78. Conclusions:Ciprofloxacin is the most common antimicrobial agent used for treating urinary tract infections (UTIs) in healthcare settings in Iran. Accumulation of different substitutions in the QRDR regions of gyrA and parC confers high-level resistance of fluoroquinolones in clinical isolates.

| S-EPMC6477561 | biostudies-literature

Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection.

Project description:Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer.

| S-EPMC6780219 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data