Short sequence reads from 23 isolates of five different species, Escherichia coli, Klebsiella pneumoniae, Salmonella enterica, Staphylococcus aureus and Vibrio cholerae, were also submitted to ResFinder. All 23 isolates had been sequenced on the Illumina platform using paired-end reads. A ResFinder threshold of ID = 98.00% was selected, as previous tests of ResFinder had shown that a threshold lower than this gives too much noise (e.g. fragments of genes). Phenotypic antimicrobial susceptibility testing was determined as MIC determinations, as previously described.8 (link)With ‘(chromosome and plasmid)(multi-drug or antimicrobial or antibiotic)(resistant or resistance) pathogen’ as search criteria, one isolate from each species with completely sequenced and assembled, and chromosome and plasmid data were collected from the NCBI Genomes database (
Staphylococcus aureus
It is a leading cause of hospital-acquired (nosocomial) infections and can be difficult to treat due to the emergence of antibiotic-resistant strains, such as methicillin-resistant Staphylococcus aureus (MRSA).
Staphylococcus aureus is a commensal organism, meaning it can be found on the skin and in the nasal passages of healthy individuals without causing disease.
However, it can become pathogenic and cause a wide range of infections, from mild skin infections to life-threatening conditions.
Research on Staphylococcus aureus is crucial for developing effective prevention and treatment strategies to reduce the burden of these infections.
Most cited protocols related to «Staphylococcus aureus»
Short sequence reads from 23 isolates of five different species, Escherichia coli, Klebsiella pneumoniae, Salmonella enterica, Staphylococcus aureus and Vibrio cholerae, were also submitted to ResFinder. All 23 isolates had been sequenced on the Illumina platform using paired-end reads. A ResFinder threshold of ID = 98.00% was selected, as previous tests of ResFinder had shown that a threshold lower than this gives too much noise (e.g. fragments of genes). Phenotypic antimicrobial susceptibility testing was determined as MIC determinations, as previously described.8 (link)With ‘(chromosome and plasmid)(multi-drug or antimicrobial or antibiotic)(resistant or resistance) pathogen’ as search criteria, one isolate from each species with completely sequenced and assembled, and chromosome and plasmid data were collected from the NCBI Genomes database (
The resulting whole genome alignments were then analyzed using the default settings of Gubbins, except that the S. pneumoniae and S. aureus analyses were run until convergence. For S. pneumoniae and S. aureus, ClonalFrame (19 (link)) was also run using default settings, without estimating node ages, with a burn in chain length of 25 000 and a parameter estimation chain length of 25 000. For H. pylori, convergence was achieved when ClonalFrame was run without estimating node ages or theta, using a burn in chain length of 10 000 and a parameter estimation chain length of 10 000. Convergence was assessed through plotting the variation in parameter values over the course of the MCMC; these are shown in Supplementary Figures S4, S7 and S9.
Antimicrobial resistance gene detection was performed using the ARG-Annot database of acquired resistance genes [18 (link)]. Allele sequences (DNA) were downloaded in fasta format [43 ] (May, 2014). Sequences were clustered into gene groups with ≥80% identity using CD-hit [44 (link)] and the headers formatted for use with SRST2 using the scripts provided (cdhit_to_csv.py, csv_to_gene_db.py). A copy of the formatted sequence database used in this study is included in the SRST2 github repository [35 ].
Representative sequences for 18 plasmid replicons were extracted from GenBank using the accessions and primer sequences specified by Carattoli et al. [45 (link)]. A copy of the formatted sequence database used in this study is included in the SRST2 github repository [35 ].
WGS data (FASTQ) were used as input for ResFinder 4.0 using default parameters (≥80% identity over ≥60% of the length of the target gene) and also for SNP-based phylogenetic analysis as previously described22 (link) to verify the genetic diversity of the validation datasets. SNP analysis was not performed for the Salmonella spp. dataset whose diversity was already described previously.23 (link) The ResFinder 4.0 output was analysed to define AMR genotypes, i.e. patterns of resistance determinants observed for each antimicrobial, in each dataset.
Genotype–phenotype concordance was defined as presence or absence of a genetic determinant of resistance to a specific antimicrobial agent in non-WT (nWT) or WT isolates, respectively. Genotype–phenotype discordance was defined either as presence of a relevant AMR determinant in WT isolates or as absence of a relevant AMR determinant in nWT isolates. All discordances were individually analysed.
Sequence data that did not derive from previous studies (Table
Most recents protocols related to «Staphylococcus aureus»
Example 3
Table 3 showed the micro efficacy of the tested disinfectant formulations against S. aureus based on the EPA standard according to the OECD Quantitative Methods for Evaluating the Activity of Microbicides.
A very strong synergistic effect between C1-8 organic acids and amino acid based surfactant against S. aureus was observed in the disinfectant Formulation C, wherein the organic acids were a mixture of salicylic acid and lactic acid (at 0.4% weight and 2.2% weight, respectively, based on total weight of the formulation), the amino acid based surfactant was a sodium salt of N-lauroyl sarcosinate (hereinafter “Sodium sarcosinate”), and the stabilizing agent was ethanol. Formulation D showed that the high efficacy against S. aureus were achieved even without the use of hydrogen peroxide in the formulation.
Example 3
The ability of different bacterial species to take up [18F]F-PABA was studied. The radiotracer accumulated in both methicillin sensitive S. aureus (MSSA, Newman) and methicillin-resistant S. aureus (MRSA), as well as the Gram negative bacteria E. coli and Klebsiela pneumoniae.
In the case of MSSA we also demonstrated that heat-killed cells were unable to take up [18F]F-PABA (
activity of the samples was evaluated both qualitatively and quantitatively
against Gram-negative bacteria, E. coli (ATCC25922), and Gram-positive bacteria, S. aureus (ATCT25923). Specifically, bacterial suspensions at 0.5 McFarland
turbidity were prepared in a Mueller–Hinton broth. For qualitative
analysis, the AATCC 147 parallel streak method was adopted. This analysis
involved using a cotton swab that was dipped once into the prepared
bacterial suspension and spreading on solid agar medium in parallel
lines. The antibacterial activity of the samples (1 × 3 cm2) was evaluated qualitatively by measuring the inhibition
zone diameter after 24 h of incubation at 37 °C and 85% humidity.
The bactericidal activity of the surface was evaluated quantitatively
by following the AATCC 100 test protocol with a slight modification.
Here, a 100 μL of the prepared bacterial suspensions was cultivated
on the nanostructured surface. The samples were then kept in an incubator
at 37 °C and 85% humidity for 24 h. After the incubation, the
samples were immersed into 10 mL of PBS (phosphate buffer solution,
ClearBand) and washed by sonication for 10 min and vortexing for 1
min. Consequently, a 100 μL of this suspension was fetched and
spread on a solid agar plate using a glass Drigalski stick. After
24 h of incubation, the cell colonies formed on the agar plates were
counted and the antibacterial activity value of the surfaces was calculated
according to the following equation At is the average number of colonies obtained
from the fabricated nanostructures, while Ut is the average number
of colonies obtained from the control samples. In similar standards,
the critical threshold R value is recommended as
2, and if R ≥ 2, the material is considered
as antibacterial.34
SERS spectra, we used the common machine learning algorithms from
the open-source Python (3.8) library, Scikit-learn. To read, process,
and visualize the spectral data, we used python packages: NumPy, SciPy,
Matplotlib, and Seaborn.
To classify the five different bacteria
species, 1114 SERS spectra were recorded on the Ag–CuxO nanostructures. These include 157 for Bacillus subtilis (B. subtilis), 309 for Escherichia coli (E. coli), 155 for Enterococcus faecalis (E. faecalis), 343 for Staphylococcus aureus (S. aureus), and 150 for Streptococcus mutans (S. mutans). Specifically, the data
were first normalized using StandardScaler and then principal component
analysis (PCA) was applied on the transformed data. Machine learning
methods were used to distinguish bacteria. To facilitate the machine
learning-based identification for real-life adaptation, the spectral
data obtained from bacteria were used directly, without any pre-processing
such as background subtraction or smoothing. For each bacterial species,
approximately 66.7% of the spectral data were used as training data,
which was obtained by parsing it using the randomization parameter
(randomization coefficient = 40) of the split function from the Scikit-learn
library. These data were used to train classification algorithms like
support vector machines (SVM), k-nearest neighbors (KNN), and decision
tree. Finally, the remaining approximately 33.3% of the bacterial
spectra were used to test the accuracy of the system.
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More about "Staphylococcus aureus"
It is a common commensal organism, found on the skin and in the nasal passages of healthy individuals, but can become pathogenic and cause a wide range of infections, from mild to life-threatening.
S. aureus is a major concern in healthcare settings, where it can cause hospital-acquired (nosocomial) infections that are often difficult to treat due to the emergence of antibiotic-resistant strains, such as methicillin-resistant Staphylococcus aureus (MRSA).
Other common bacterial pathogens include Escherichia coli (E. coli), Pseudomonas aeruginosa, Enterococcus faecalis, Klebsiella pneumoniae, and Bacillus subtilis.
Research on S. aureus is crucial for developing effective prevention and treatment strategies to reduce the burden of these infections.
Techniques like microbial culturing, antibiotic susceptibility testing, and molecular characterization are used to study S. aureus and other bacteria.
Cell culture models, such as those using Escherichia coli ATCC 25922 or Candida albicans, can also provide valuable insights.
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