How many strains of staphylococcus aureus are there

Our studies also indicate that genetic change in S. Recombination seems to be of a lesser importance as well. Apparently, the population structure of S. The question is whether either one of these groups is more virulent or has a higher colonizer capacity than the others. Clinical impact could possibly be defined by overall genotype grouping, but the impact of individual genes should not be underestimated. Genes giving rise to resistance to antimicrobial agents are important in this respect, since the possibility to overcome therapy gives a microorganism a selective advantage in the hospital setting where antibiotics are used in large amounts.

Strains from impetigo patients were enriched for the occurrence of the gene encoding exfoliative toxin B Koning et al. This gene encodes a proteinase that essentially degrades the protein desmoglein, an anchor-like protein responsible for skin integrity.

The involvement of such a gene in an exfoliating disease such as impetigo is comprehensible. Furthermore, strains enriched for the gene encoding the Panton Valentin leukocidin, an important staphylococcal toxin, were primarily involved in deep skin and soft tissue infections.

What remains is the important question of whether certain S. Feil et al. In contrast, sub-clusters of strains with differential degrees of pathogenicity were observed in the study by Melles et al. Furthermore, two sub-clusters in major cluster I also showed overrepresentation of bacteremia-associated isolates. Expansion of multi-resistant clones or clones associated with skin disease impetigo were observed as well. We showed clearly that some clones are more virulent than others, although, given the appropriate clinical conditions, each and every strain of S.

It is interesting to note that recent data from our laboratory also show the same tendency in veterinary clinical isolates of S.

Strains associated with mastitis in larger farm animals, which are putative pathogens, fell within the minor AFLP cluster IVa again. This suggests that several of the traits leading to infections in animals or humans are shared. Again, individual virulence factors contribute to the observed species- or syndrome-specificity. The collagen-specific MSCRAMM, for instance, was hardly present among human invasive isolates, whereas the toxic shock syndrome toxin was more frequently encountered among strains causing disease in animals.

AFLP-defined clonality has now been related to differential pathogenicity of S. The fact that we used a large number of strains and a genetic typing strategy not focusing on only common genes may have helped significantly in reaching the current conclusions.

Minimum inhibitory concentration MIC was determined against 8 antimicrobials. All isolates of S. Of the 32 methicillin-resistant S. Of the strains, PFGE analysis grouped the strains into 77 clusters.

MLST classified the strains into 33 sequence types ST and eight clonal complexes CCs of which 12 were singletons, and two belong to new allelic profiles. Isolates showed 46 spa -types that included two new spa-types designated as t and t MRSA and methicillin-susceptible S.

The study thus suggests that S. These findings necessitate the continuous surveillance of multidrug-resistant and virulent S. Staphylococcus aureus commensal to human skin and mucous membranes could cause nosocomial Lindsay and Holden, and systemic infections Jarraud et al. The isolation of methicillin-resistant S.

Godebo et al. Several studies have shown the presence of toxin genes among MRSA. However, hla gene was present in all isolates Shukla et al. However, pvl gene positive methicillin-susceptible S.

MSSA belonging to ST and spa -type isolated from community-acquired pneumonia in young patients carried the virulence genes cna and bbp and pvl Baranovich et al. The role of virulence genes in S. Dhawan et al. However, not a single technique alone could discriminate the bacteria because of differences in the degree of typeability, reproducibility, and discriminatory power Tenover et al.

Overall analysis of different typing techniques can provide information on diversity of the isolates that can be useful for outbreak investigations. In India, S. In this study, our aim was to determine the antibiotic susceptibility pattern, virulence profiles, and genomic diversity among MRSA and MSSA isolated from patients with a variety of infections, including ocular diseases and collected from different parts of India from to Genetic, serotype, and phenotypic data were used to determine whether isolates from a variety of infections had similar characteristics.

Also, five isolates were from the conjunctiva of the asymptomatic healthy volunteers LV Prasad Eye Institute, Bhubaneswar.

We conducted the study following the guidelines mentioned in the Declaration of Helsinki. The amplification of the S. We used S. Then centrifuged the culture and 0. Distributed an aliquot 0. After that, 0. Visual inspection judged the coagulation of plasma after 2, 4, 24, and 48 h and accordingly, strains were typed based on results obtained with staphylocoagulase reaction showing coagulation inhibition.

Minimum inhibitory concentrations MICs of oxacillin, chloramphenicol, vancomycin, tetracycline, gentamicin, erythromycin, clindamycin, and trimethoprim were determined by broth microdilution methodology as recommended by the CLSI breakpoints. PCR was used to detect the presence of collagen adhesion cna and extracellular fibrinogen binding protein efb among S.

PCR amplification was carried out to determine the presence of agr alleles using group-specific primers as described by Gilot et al. Pulsed-field gel electrophoresis of S.

The dendrogram of similarity showing the clustering of the isolates according to banding patterns was generated with Bionumerics software, version 7.

The internal fragments of seven housekeeping genes, viz. The nucleotide sequences were aligned using Mega 5. After manually comparing with reported alleles, STs were assigned accordingly. Sequencing was performed in biological duplicates to confirm the presence of novel alleles. The advanced cluster analysis was performed to define the clonal complexes CCs by using Bionumerics software, version 7. The similarity in at least six alleles grouped isolates of S.

The central ST of each separation was used to designate a CC. PCR amplified the polymorphic X region of Staphylococcus protein A spa gene following the conditions mentioned earlier Nelson et al. Amplified products were purified, and both strands were sequenced using an ABI sequencer model Life Technologies, Marsiling, Singapore at the sequencing facility of the Institute of Life Sciences Bhubaneswar, India.

All the isolates of S. Thirty-one of One of the methicillin-resistant strains of S. Among isolates, 43 Of the 31 MRSA isolates, two 6. Serotyping classified S. Nine of the 24 Table 1. Antibiotic resistance patterns and presence of antibiotic resistance genes in Staphylococcus aureus isolates from different parts of India.

One hundred two of the S. All the strains were susceptible to vancomycin when tested by broth microdilution assay. Thirty-one isolates of S. Ninety-five isolates of S. Twenty isolates carried all the three genes tested; however, 83 isolates were positive for cat: pC and cat: pC and 37 isolates for cat: pC and cat: pC genes, respectively Table 1.

One of the isolates sensitive to chloramphenicol was negative by PCR for all three genes. In contrast, 15 strains of S. Twenty-nine isolates were phenotypically resistant to tetracycline of which 29 isolates were positive for tetK , 25 for tetL , and 28 for tetM genes. Twenty-five isolates carried all the three genes tested; however, three strains carried tetK and tetM genes and one isolate tetL and tetM genes.

In contrast, 76 isolates sensitive to tetracycline were positive for the tetM gene, 66 for tetL , and 29 for tetK genes. Among them, 27 isolates carried all the three genes, six had tetK and tetM , and 39 strains had tetL and tetM genes, respectively. One isolate sensitive to tetracycline was negative by PCR for all three genes tested Table 1. Of the 91 isolates of S. Twenty-eight isolates carried all the erythromycin resistance genes, namely, msrA , ermA , and ermC.

Fifty-one isolates were positive for two genes, of which 30 isolates carried msrA and ermC genes, and 21 strains had ermA and ermC genes. Besides, 12 isolates were positive for a single gene of which five isolates carried the ermC gene, and seven isolates had msrA gene. In contrast, two of the 10 erythromycin sensitive isolates carried msrA and ermC genes, four strains possess msrA and ermC genes, and three isolates had the ermC gene.

Of the 64 isolates carrying the mphC gene, 22 isolates were phenotypically resistant to clindamycin Table 1. None of the 17 strains showing sensitivity to erythromycin carried any of the erythromycin resistance genes. One of the resistant isolate not carrying any of the erythromycin resistant genes is likely to be mediated by an as-yet-unknown mechanism. Similarly, 74 isolates were resistant to trimethoprim of which 45 isolates were positive for dfrA , dfrB , and dfrG genes, 27 strains for dfrB and dfrG genes, and one isolate each for dfrB and dfrG genes, respectively.

In contrast, 34 isolates sensitive to trimethoprim were also positive for dfrA , dfrB , and dfrG genes; however, one strain was positive for the dfrG gene Table 1. Ninety of Seventy eight of 90 The remaining isolates did not carry any of the genes tested.

Although the different models from the various databases reflect the same strain, the models have distinct diversities.

This can be explained by the differences in the reconstruction process. How the model is curated seems to play a pivotal role for the final model and its model instances. Thus, the reconstruction method needs to be chosen carefully, and manual or semi-automated additions might be required. With the vast amount of different strain-specific S.

Table 2 gives an overview about the main features of the S. The features were assigned based on the strengths of the different models or model collections after the model improvement steps. If one is interested in simulatable models, the table guides the reader to the corresponding models.

By combining different required features, the selection can be tailored. If one needs, e. High MEMOTE scores indicate a high degree of annotations, which facilitates the re-usability and comparability of a model.

A predictive value score was calculated based on the model analysis regarding their growth capabilities and the presence of experimental data. If a model was not simulatable, it received a predictive value score of 0. Otherwise, a score of 1 was added. For growth capabilities in one environment, a score of 1 was added; for growth in multiple environments, 2 was added. For every experimental verification procedure, such as growth verifications, auxotrophies, compliance with physiological data, or other experiments, a score of 1 was added.

The prediction of essential genes was not included in this score, as this analysis was only conducted for two models. By this scheme, the model i YS had the highest predictive value score of 7, followed by i MH and some models by Bosi et al.

As the models from Lee et al. Models with high predictive value score and high MEMOTE score are recommended for further use, while models with low predictive value score might need further refinement and experimental verification before usage. This table does not contain strain-specific information. Including the information from Figs. The analyses show that despite genomic and genetic similarities, GEMs of related strains are not necessarily similar to each other.

This accounts for both models of the same strain curated by different research groups and to related strains curated by the same group. Despite it is the same strain, the GEMs are quite different in their reaction content. This observation might have several reasons.

The first, and probably most striking, reason is the incompleteness of the models. As high-quality genome-scale metabolic reconstructions require manual curation and evaluation , and many models introduced in this review were created automatically or semi-automatically, some models might lack general or strain-specific reactions.

This lack of required reactions is also visible when optimizing the flux distributions of the models. For multiple models, no growth could be simulated in FBA, not even in full medium. This was especially the case for the automatically curated models from the Path2Models project and the semi-automatically curated models from Lee et al. But also some of the semi-automatically curated models from Bosi et al. Thus, a connection between automated or semi-automated curation and the functionality of the models seems to exist.

However, automated or semi-automated curation does not necessarily result in poor growth prediction, especially when the basis for the semi- automated processes underwent significant manual curation.

The other models from Bosi et al. Furthermore, some of the S. For a strain-specific model, these additional genes need to be incorporated into the GEM as well. Especially the metabolic and transporter genes are relevant for the strain-specific model. The plasmid of the S. These two genes are, e. As explained previously, the challenge lies within the different reaction and metabolite identifiers. In this review, we additionally tried to annotate the GEMs further to simplify the comparison of models with differing identifiers.

However, only approximately one third of all reactions and metabolites are annotated with identifiers of external databases. It is still challenging to find all cross-references for a particular metabolite or reaction in a specific database.

For that reason, we additionally evaluated the gene content of the strain-specific models, as most models contained identifiers from the KEGG database. The gene identifiers from other databases were mapped to the KEGG identifiers. Again, a bias is introduced when identifiers are mapped between databases: On the one hand, not all identifiers can be resolved in the other database.

This makes an automated mapping of several hundred identifiers infeasible. Extensive manual labor would be necessary to map these identifiers. The usage of consistent identifiers that comply with the database scheme and additional annotations is highly recommended and would simplify the re-usability, translatability, and comparability of models This observation is even more explicit when comparing the models by Lee et al.

However, the models are not equal, as the two groups used different approaches for the curation of the models. Missing reactions and strain-specific genes might also affect the growth behavior of a strain-specific model on a given medium. Only the model i MH showed growth on all tested media.

Additional growth experiments for specific S. This is also reflected in the predictive value score, which was assigned to the models. Especially for models with a low predictive value score, additional experiments would help determine and also increase the predictive value of the model.

Except for the models from Bosi et al. The models from the Path2Models project and Lee et al. The low score for the models from Lee et al.

Although the models from Bosi et al. They based their pipeline on a manually refined model and verified their predictions with experimental data.

More experimental data accompany more knowledge. The latest model, i YS has the highest predictive value score, was manually curated, and extensively experimentally validated. The result of such a time- and labor-intensive work is a GEM with a high predictive value and a strong recommendation for future usage.

In this review, all currently available genome-scale metabolic models GEMs of Staphylococcus aureus were presented and evaluated. It serves as guide for the different available reconstructions in various databases, using differing metabolite and reaction identifiers.

Some models originally comprise a large number of reactions, metabolites, and genes, after undergoing several manual curation steps and extensive annotating. Other models have a vast amount of reactions and metabolites, such as the reconstructions of the Path2Models project.

Such models could, e. Based on the information regarding availability, model format, MEMOTE score, growth behavior, used database identifiers, predictive value, and similarities between models, together with a previously defined research question, the appropriate genome-scale reconstruction can be chosen from the vast amount of available GEMs.

Another approach would be to use the strengths of every reconstruction and incorporate it into merged or combined models, which increase the correctness and the predictive value of a strain-specific model.

Despite the vast amount of presented models in this review, there is no suitable model for every S. Standardization of all models would be desirable but is currently not feasible with the available tools without extensive manual labor for hundreds of identifiers. No omics data was incorporated into many of the published GEMs so far.

Information about transcription profiles, for example, can help to refine metabolic reconstructions to better reflect the metabolic state of an organism in a defined environment. The incorporation of omics data can thus increase the predictive value of genome-based metabolic reconstructions However, with the help of the already available reconstructions and further information, strain-specific models could be created or extended.

Information from literature, merging of strain-specific models, and manual curation steps could further improve the predictive value of simulations and analyses of metabolic features of S.

Having predictive GEMs can eventually lead to the identification of novel targets for antimicrobial therapies in the fight against antibiotic resistant strains of S. The availability of all models, including the improved models, is listed in the supplementary Table S 1. All the necessary scripts and resources for model modifications and improvements are available in a git repository at github. Sakr, A. Staphylococcus aureus nasal colonization: an update on mechanisms, epidemiology, risk factors, and subsequent infections.

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Natl Acad. USA , E—9 Generation of genome-scale metabolic reconstructions for members of the human gut microbiota. Noronha, A. The virtual metabolic human database: integrating human and gut microbiome metabolism with nutrition and disease. Ebrahim, A. Lieven, C. Kanehisa, M. New approach for understanding genome variations in KEGG. Peterson, J. Folliculitis Folliculitis Folliculitis and skin abscesses are pus-filled pockets in the skin resulting from bacterial infection.

A hair root follicle is infected, causing a slightly painful, tiny pimple at the base of a hair. Impetigo Impetigo and Ecthyma Impetigo is a superficial skin infection, caused by Staphylococcus aureus, Streptococcus pyogenes, or both, that leads to the formation of scabby, yellow-crusted sores and, sometimes, small Impetigo may itch or hurt. Abscesses Skin abscesses Folliculitis and skin abscesses are pus-filled pockets in the skin resulting from bacterial infection.

Cellulitis Cellulitis Cellulitis is a spreading bacterial infection of the skin and the tissues immediately beneath the skin. This infection is most often caused by streptococci or staphylococci.

Redness, pain, and Cellulitis spreads, causing pain and redness. Toxic epidermal necrolysis Stevens-Johnson Syndrome SJS and Toxic Epidermal Necrolysis TEN Stevens-Johnson syndrome and toxic epidermal necrolysis are two forms of the same life-threatening skin disease that cause rash, skin peeling, and sores on the mucous membranes. See also Introduction Both lead to large-scale peeling of skin. Breast infections mastitis Breast Infection A breast infection mastitis can occur after delivery postpartum infection , usually during the first 6 weeks and almost always in women who are breastfeeding.

If the baby is not positioned The area around the nipple is red and painful. The bacteria may then infect the nursing infant. Pneumonia often causes a high fever, shortness of breath, and a cough with sputum that may be tinged with blood.

Lung abscesses Abscess in the Lungs A lung abscess is a pus-filled cavity in the lung surrounded by inflamed tissue and caused by an infection.

A lung abscess is usually caused by bacteria that normally live in the mouth and are They sometimes enlarge and involve the membranes around the lungs and sometimes cause pus to collect called an empyema Types of fluid Pleural effusion is the abnormal accumulation of fluid in the pleural space the area between the two layers of the thin membrane that covers the lungs.

Fluid can accumulate in the pleural These problems make breathing even more difficult. Bloodstream infection is a common cause of death in people with severe burns. Symptoms typically include a persistent high fever and sometimes shock. Osteomyelitis causes chills, fever, and bone pain. The skin and soft tissues over the infected bone become red and swollen, and fluid may accumulate in nearby joints. Other infections require samples of blood or infected fluids, which are sent to a laboratory to grow culture , identify, and test the bacteria.

Laboratory results confirm the diagnosis and determine which antibiotics can kill the staphylococci called susceptibility testing Testing of a Microorganism's Susceptibility and Sensitivity to Antimicrobial Drugs Infectious diseases are caused by microorganisms, such as bacteria, viruses, fungi, and parasites.

Doctors suspect an infection based on the person's symptoms, physical examination results, If a doctor suspects osteomyelitis, x-rays, computed tomography CT , magnetic resonance imaging MRI , radionuclide bone scanning Radionuclide Scanning In radionuclide scanning, radionuclides are used to produce images. A radionuclide is a radioactive form of an element, which means it is an unstable atom that becomes more stable by releasing These tests can show where the damage is and help determine how severe it is.

Bone biopsy is done to obtain a sample for testing. The sample may be removed with a needle or during surgery. People can help prevent the spread of these bacteria by always thoroughly washing their hands with soap and water or applying an alcohol-based hand sanitizer.

Some doctors recommend applying the antibiotic mupirocin inside the nostrils to eliminate staphylococci from the nose. However, because overusing mupirocin can lead to mupirocin resistance, this antibiotic is used only when people are likely to get an infection. For example, it is given to people before certain operations or to people who live in a household in which the skin infection is spreading.

If carriers of staphylococci need to have certain types of surgery, they are often treated with an antibiotic before the surgery. In some health care facilities, people are routinely screened for MRSA when they are admitted.