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Staphylococcus aureus

Staphylococcus aureus is a gram-positive bacterium that is a common cause of skin and soft tissue infections, pneumonia, and bloodstream infections.
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»

1
Verification of Antimicrobial Resistance Genes
Verification of the databases was made by testing ResFinder with the 1862 GenBank files from which the genes were collected, to verify that the method would find all genes with ID = 100%.
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 (http://www.ncbi.nlm.nih.gov/genome). This resulted in 30 isolates, from 30 different species, containing 85 chromosome/plasmid sequences. All sequences were run through all databases in ResFinder with a selected threshold of ID = 98.00%.
Zankari E., Hasman H., Cosentino S., Vestergaard M., Rasmussen S., Lund O., Aarestrup F.M, & Larsen M.V. (2012). Identification of acquired antimicrobial resistance genes. Journal of Antimicrobial Chemotherapy, 67(11), 2640-2644.
Publication 2012
Antibiotics Chromosomes Escherichia coli Genes Genome Klebsiella pneumoniae Microbicides Pathogenicity Pharmaceutical Preparations Phenotype Plasmids Salmonella enterica Staphylococcus aureus Susceptibility, Disease Vibrio cholerae
2
Comparative Genomic Analysis Across Bacterial Pathogens
Analysis of the full set of PMEN1 sequences used the alignment from (26 (link)); 11 closely-related isolates were extracted as a subsample for comparison with the output of ClonalFrame. For the analysis of S. aureus ST239, 14 representatives from the South-East Asian clade were extracted from the larger alignment (49 (link)) for the equivalent comparative analysis. For the analysis of Helicobacter pylori, eight publically available complete genomes were selected from across the species that included both the most closely-related pair of isolates and the isolate most divergent from the rest of the sample, based on a previous analysis (50 (link)). These genomes were then aligned using progressiveMauve (51 (link)), generating a 1.8 Mb core genome alignment for analysis.
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.
Croucher N.J., Page A.J., Connor T.R., Delaney A.J., Keane J.A., Bentley S.D., Parkhill J, & Harris S.R. (2014). Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Research, 43(3), e15.
Publication 2014
Genome Helicobacter pylori Sequence Analysis Southeast Asian People Staphylococcus aureus Streptococcus pneumoniae
3
Microbial Resistance Gene Detection
MLST databases for Staphylococcus aureus, Streptococcus pneumoniae, Salmonella enterica, Escherichia coli, Enterococcus faecium, Listeria monocytogenes and Enterobacter cloaceae were downloaded from pubmlst.org using the getmlst.py script included with SRST2 (June 2014).
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 ].
Inouye M., Dashnow H., Raven L.A., Schultz M.B., Pope B.J., Tomita T., Zobel J, & Holt K.E. (2014). SRST2: Rapid genomic surveillance for public health and hospital microbiology labs. Genome Medicine, 6(11), 90.
Publication 2014
Alleles Enterobacter Enterococcus faecium Escherichia coli Genes Listeria monocytogenes Microbicides Oligonucleotide Primers Plasmids Replicon Salmonella enterica Staphylococcus aureus Streptococcus pneumoniae
4
Validation of ResFinder 4.0 for Antimicrobial Resistance Detection
ResFinder 4.0 was validated with datasets consisting of MIC values (BMD or Etest, Table 1) and WGS data (Illumina sequencing) of Escherichia coli, Salmonella spp., Campylobacter jejuni, E. faecium, E. faecalis and S. aureus of different origins (Table 1). These datasets represent a convenience sample. Phenotypic AST results were interpreted using the EUCAST epidemiological cut-off values (ECOFFs) to categorize isolates as WT (MIC ≤ECOFF) and non-WT (MIC >ECOFF) (www.eucast.org). Exceptions were: (i) one S. aureus dataset for which phenotypic AST was performed by disc diffusion and interpreted by EUCAST clinical breakpoints (Table 1); and (ii) one E. coli dataset that consisted of Illumina WGS data only and MIC values were available for the data provider but not for the ResFinder 4.0 developers, thus providing a blind test of the tool performance (Table 1). WGS data were obtained as raw reads and processed through a quality control (QC) pipeline as described here: https://bitbucket.org/genomicepidemiology/foodqcpipeline/. In brief, reads were trimmed using bbduk2 (https://jgi.doe.gov/data-and-tools/bbtools/) to a phred score of 20, reads less than 50 bp were discarded, adapters were trimmed away and a draft de novo assembly was created using SPAdes.21 (link) From the assemblies, contigs below 500 bp were discarded. The most important parameters that were used to assess quality of sequencing data were: number of bases left after trimming, N50, number of contigs and total size of assembly. QC parameters used as guidelines were: read depth of at least 25×, N50 of >30 000 bp and a limit on the number of contigs to <500.
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 1) have been deposited at NCBI (E. coli dataset from Germany: PRJNA616452; E. faecium dataset from Germany: PRJNA625631; E. faecium dataset from Belgium: PRJNA552025; S. aureus dataset from Belgium: PRJNA615176) and in the European Nucleotide Archive (S. aureus dataset from Denmark: PRJEB37586).
Bortolaia V., Kaas R.S., Ruppe E., Roberts M.C., Schwarz S., Cattoir V., Philippon A., Allesoe R.L., Rebelo A.R., Florensa A.F., Fagelhauer L., Chakraborty T., Neumann B., Werner G., Bender J.K., Stingl K., Nguyen M., Coppens J., Xavier B.B., Malhotra-Kumar S., Westh H., Pinholt M., Anjum M.F., Duggett N.A., Kempf I., Nykäsenoja S., Olkkola S., Wieczorek K., Amaro A., Clemente L., Mossong J., Losch S., Ragimbeau C., Lund O, & Aarestrup F.M. (2020). ResFinder 4.0 for predictions of phenotypes from genotypes. Journal of Antimicrobial Chemotherapy, 75(12), 3491-3500.
Publication 2020
Campylobacter jejuni Diffusion Epsilometer Test Escherichia coli Europeans Fibrinogen Genetic Diversity Genotype Microbicides Nucleotides Phenotype Reproduction R Factors Salmonella Staphylococcus aureus Visually Impaired Persons
5
Mock Community DNA Preparation
A single aliquot of the mock community was used throughout the sequencing effort analyzed in this study. This mock community represented 21 strains distributed among members of the Bacteria (n = 20) and Archaea (n = 1). Among the 20 bacterial sequences, there were 6 phyla, 10 classes, 12 orders, and 18 families and genera. The aliquot of mock community DNA was prepared by mixing genomic DNA from Acinetobacter baumanii (NC_009085), Actinomyces odontolyticus (DS264586), Bacillus cereus (AE017194), Bacteroides vulgatus (NC_009614), Clostridium beijerinckii (NC_009617), Deinococcus radiodurans (NC_001263), Enterococcus faecalis (NC_004668), Escherichia coli (NC_000913), Helicobacter pylori (NC_000915), Lactobacillus gasseri (NC_008530), Listeria monocytogenes (NC_003210), Neisseria meningitidis (NC_003112), Propionibacterium acnes (NC_006085), Pseudomonas aeruginosa (NC_002516), Rhodobacter sphaeroides (NC_007493, NC_007494), Staphylococcus aureus (NC_007793), Staphylococcus epidermidis (NC_004461), Streptococcus agalactiae (NC_004116), Streptococcus mutans (NC_004350), Streptococcus pneumoniae (NC_003028), and Methanobrevibacter smithii (NC_009515). Given the low homology between the three PCR primer pairs and the M. smithii 16S rRNA gene sequence, these sequences were rarely observed and have been omitted from the analysis of this study. The proportions of genomic DNAs added were calculated to have an equal number of 16S rRNA genes represented for each species; however, the original investigators did not verify the final relative abundances.
Schloss P.D., Gevers D, & Westcott S.L. (2011). Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies. PLoS ONE, 6(12), e27310.
Publication 2011
Acinetobacter Archaea Bacillus cereus Bacteria Bacteroides vulgatus Clostridium beijerinckii Deinococcus radiodurans DNA Enterococcus faecalis Escherichia coli Genes Genome Helicobacter pylori Lactobacillus gasseri Listeria monocytogenes Methanobrevibacter Neisseria meningitidis Oligonucleotide Primers Propionibacterium acnes Pseudomonas aeruginosa Rhodobacter sphaeroides Ribosomal RNA Genes RNA, Ribosomal, 16S Schaalia odontolytica Staphylococcus aureus Staphylococcus epidermidis Strains Streptococcus agalactiae Streptococcus mutans Streptococcus pneumoniae

Most recents protocols related to «Staphylococcus aureus»

1
Synergistic Antimicrobial Effect of Organic Acids and Surfactants
Not available on PMC !

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.

TABLE 3
FormulationABCD
IngredientsOn 100%On 100%On 100%On 100%
C1-8 Organic acids02.62.62.6
Hydrogen peroxide0.50.50.50
Sodium sarcosinate1.501.51.5
Ethanol5555
Sodium xylene0.30.30.30.3
sulfonate
WaterBal.Bal.Bal.Bal.
Micro Efficacy3.290.826.386.38
against S. aureus
(Log Reduction)

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.

US11871744B2. Synergistic disinfectant compositions having enhanced antimicrobial efficacy and stability, and methods of using the same (2024-01-16). Diversey, Inc. [US]. Inventors: Farida H. Tinwala [IN], Xiaobao Li [US], Decio R. Silva, Jr. [NL], Yogaraj Nabar [IN], Arnoud Ubald Maria Gengler [NL].
Patent 2024
Acids Alkanesulfonates Amino Acids Ethanol Lactic Acid Microbicides Peroxide, Hydrogen Salicylic Acid Sodium Chloride sodium lauroyl sarcosinate Sodium Sarcosinate Stabilizing Agents Staphylococcus aureus Surface-Active Agents Xylene
2
Bacterial Uptake of Radiotracer F-PABA
Not available on PMC !

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 (FIG. 1). In contrast, [18F]F-PABA was not taken up by Enterococcus faecalis. E. faecalis has a folate salvage pathway and can take up folate from the environment. Thus, folic acid biosynthesis is dispensable in this organism, which also explains why sulfonamides are not used to treat infection by E. faecalis. These studies suggest that F-PABA uptake depends on on the de novo biosynthesis of folate.

US11857352B2. Positron imaging tomography imaging agent composition and method for bacterial infection (2024-01-02). The Research Foundation for the State University of New York [US]. Inventors: Peter Tonge [US], Zhuo Zhang [US], Peter Smith-Jones [US], Li Liu [US], Hui Wang [US].
Patent 2024
4-Aminobenzoic Acid Anabolism Bacteria Cells Enterococcus faecalis Escherichia coli Folate Folic Acid Gram Negative Bacteria Infection Klebsiella pneumoniae Methicillin Methicillin-Resistant Pneumonia Staphylococcus aureus Sulfonamides
3
Antibacterial Activity Evaluation Protocol
The antibacterial
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
Sahin F., Camdal A., Demirel Sahin G., Ceylan A., Ruzi M, & Onses M.S. (2023). Disintegration and Machine-Learning-Assisted Identification of Bacteria on Antimicrobial and Plasmonic Ag–CuxO Nanostructures. ACS Applied Materials & Interfaces, 15(9), 11563-11574.
Publication 2023
Agar Anti-Bacterial Agents Bacteria Buffers Cells Escherichia coli Gossypium Gram-Positive Bacteria Gram Negative Bacteria Humidity Phosphates Staphylococcus aureus
4
Bacterial Species Identification via SERS Spectra
To identify different types of bacterial species from the collected
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.
Sahin F., Camdal A., Demirel Sahin G., Ceylan A., Ruzi M, & Onses M.S. (2023). Disintegration and Machine-Learning-Assisted Identification of Bacteria on Antimicrobial and Plasmonic Ag–CuxO Nanostructures. ACS Applied Materials & Interfaces, 15(9), 11563-11574.
Publication 2023
Acclimatization Bacillus subtilis Bacteria Bacterial Typing Cloning Vectors DNA Library Enterococcus faecalis Escherichia coli Python Staphylococcus aureus Streptococcus mutans
5
Antibacterial Activity Assessment of PTMC Films
The activated Staphylococcus aureus was transferred to a nutrient solution and incubated at 37°C for 24 h. The bacteria were then appropriately diluted with this nutrient solution. Subsequently, the Staphylococcus aureus bacterial solution was removed with a sterile cotton swab and spread evenly over the nutrient agar plates. Three parallel samples were taken at each time point. The solution was then incubated at a constant temperature for 24 h. The diameter of the inhibition ring was measured using a vernier caliper with an accuracy of 0.1 mm, and the area without visible bacterial growth was visually observed as the edge of the inhibition ring. Staphylococcus aureus was also incubated with the control pure PTMC films.
Zhang J., Zhang X., Li W., Guo J., Yang L, & Yan G. (2023). Poly (trimethylene carbonate)/doxycycline hydrochloride films in the treatment of Achilles tendon defect in rats. Frontiers in Bioengineering and Biotechnology, 11, 1135248.
Publication 2023
Agar Bacteria Gossypium Nutrients Psychological Inhibition Staphylococcus aureus Sterility, Reproductive

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1
Staphylococcus aureus by American Type Culture Collection
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Staphylococcus aureus is a bacterial strain available in the American Type Culture Collection (ATCC) product portfolio. It is a Gram-positive, spherical-shaped bacterium commonly found in the human nasal passages and on the skin. This strain is widely used in research and laboratory settings for various applications.
2
Escherichia coli by American Type Culture Collection
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Escherichia coli is a bacterium that is commonly used in laboratory settings. It serves as a model organism for microbiology and molecular biology research. Escherichia coli can be cultivated and studied to understand fundamental cellular processes and mechanisms.
3
Pseudomonas aeruginosa by American Type Culture Collection
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Pseudomonas aeruginosa is a bacterial strain available from the American Type Culture Collection (ATCC). It is a Gram-negative, aerobic bacterium commonly found in soil and water environments. This strain can be used for various research and testing purposes.
4
S aureus by American Type Culture Collection
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S. aureus is a strain of bacteria from the American Type Culture Collection (ATCC). It is a well-characterized laboratory strain commonly used for various microbiological applications.
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Enterococcus faecalis by American Type Culture Collection
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Klebsiella pneumoniae is a Gram-negative, non-spore-forming, encapsulated, lactose-fermenting, facultatively anaerobic, rod-shaped bacterium. It is a common inhabitant of the human gastrointestinal tract and can cause various types of infections, including pneumonia, urinary tract infections, and septicemia.
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Escherichia coli ATCC 25922 is a bacterial strain from the American Type Culture Collection. It is a commonly used reference strain for various microbiological and biochemical applications.

More about "Staphylococcus aureus"

Staphylococcus aureus (S. aureus) is a gram-positive bacterium that is a leading cause of skin, soft tissue, pneumonia, and bloodstream infections.
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.
PubCompare.ai is an AI-driven platform that can help optimize your S. aureus research by enabling you to easily locate the best protocols from literature, pre-prints, and patents, while comparing them to enhance reproducibility and accuracy.
Experiecne the power of PubCompare.ai for your Staphylococcus aureus studies.