Secondary Logo

Journal Logo

Review Article

A narrative review of single-nucleotide polymorphism detection methods and their application in studies of Staphylococcus aureus

Jian, Ying; Li, Min

Author Information
Journal of Bio-X Research: March 2021 - Volume 4 - Issue 1 - p 1-9
doi: 10.1097/JBR.0000000000000071
  • Open

Abstract

Introduction

Staphylococcus aureus (S. aureus) is both a biological pathogen and a symbiotic bacterium, and approximately 30% of the healthy non-institutionalized population is colonized with these bacteria.[1] The China Antimicrobial Surveillance Network (CHINET) recently reported that S. aureus accounted for approximately 9.3% of all clinical infections in China in 2019, making it the third-ranked of all pathogens and the first-ranked gram-positive bacterium (www.chinets.com/Data/AntibioticDrugFast). S. aureus has been deemed responsible for 11% of infections in European children, ranking second only to coagulase-negative staphylococci in a recent study.[2]

S. aureus can cause metastatic or complicated infections, including bacteremia, pneumonia, osteoarticular infections, endocarditis, and skin and soft tissue infections.[3] Methicillin-resistant S. aureus (MRSA) has played a crucial role in S. aureus infections, despite the fact that its contribution to bacteremia infections varies throughout the world and has generally fallen in the past decades.[1] MRSA is associated with poorer clinical outcomes compared with methicillin-sensitive S. aureus (MSSA).[4] The past decades have witnessed a worldwide epidemic of community-associated MRSA and multidrug-resistant S. aureus (MDRSA). In contrast to healthcare-associated MRSA infections, community-associated MRSA infections can occur in healthy individuals, suggesting that community-associated MRSA strains have enhanced virulence compared with traditional healthcare-associated MRSA strains.[5] Likewise, infections of MDRSA with co-resistance to tigecycline, methicillin, vancomycin, and linezolid are intractable because of the extremely limited clinical treatment options.[6] In short, S. aureus infections are of great clinical significance, and importance should be attached to their prevention, detection, and treatment.

A single-nucleotide polymorphism (SNP), or single-nucleotide variation (SNV), is one of the most common heritable variations. A SNP is a DNA sequence polymorphism caused by the alteration of a single nucleotide in a specific position at the genomic level.[7] The single-nucleotide base alteration can be a transition, transversion, insertion, or deletion. In the genome, widely distributed and highly conserved SNPs can be mapped and modeled as genetic markers with high resolution.[8] Furthermore, the amino acid sequence of a protein can be modified by sequence variations in the coding regions, which sometimes influence the functional or structural features of relevant proteins. SNPs are valuable indicators of clinical diagnosis and prognosis, and can be applied to investigate inter-individual differences in aspects such as antimicrobial resistance, the evolution of molecular epidemiology, or the outbreak and transmission of S. aureus.[9,10]

Studies focusing on SNP analysis can be broadly divided into two categories: first, the detection of unknown SNPs, which is primarily used to increase the marker density of genetic maps and search for genetic markers of targeted characteristics; and second, the screening for known SNPs in a population, which involves genotyping SNPs at certain known positions in a sample.[11] The present review mainly focuses on mainstream detection methods and the significance of the practical application of SNPs to S. aureus research.

Database search strategy

Literature retrieval was electronically implemented using PubMed database. The following combinations of key words were used to initially select the articles to be evaluated: single-nucleotide polymorphism and detection; single-nucleotide polymorphism and whole genome sequencing; single-nucleotide polymorphism and PCR; single-nucleotide polymorphism and S. aureus. The authors screened the titles, the abstracts, and the full texts to find those literatures that were potentially suitable; and necessary articles related to these literatures were also searched to understand the topic more fully. Most of the selected studies (85% of all references) were published from 2010 to 2019. Two ancient publications from 1993 were included on account of its relevance in detection methods of single-nucleotide polymorphism.

Detection methods for single nucleotide polymorphisms

Although single-nucleotide-relevant alterations are more difficult to detect than numerous ones, multiple methods can be performed to identify mutations/SNPs in S. aureus genomes. Specifically, distinguishing single nucleotide alterations with high sensitivity and specificity is important for evaluating these methods.[12] Common methods for detecting SNPs include DNA genome sequencing methods (eg, whole-genome sequencing (WGS) and targeted gene sequencing)[13] and PCR-based methods (especially using real-time PCR platforms),[14] and additional detection methods include mass spectrometry,[15] SNaPshot,[16] microchip methods,[17] and denaturing high-performance liquid chromatography (DHPLC)[18] (Table 1).

Table 1 - Different SNP detection methods: characteristics, strengths, and limitations
Group Methods Characteristics Strengths Limitations
Whole genome sequencing NGS Steps include DNA extraction, library preparation, target enrichment and sequencing; bioinformatic analysis of raw data, variant call file and SNP calling; multiple sequencing platforms and bioinformatics tools can be used in SNP analysis Provides both de-novo sequencing and resequencing of known genomes; high throughput Limited analytic sensitivity; limited analysis of genome areas; limitations in interpreting novel or rare mutations; limitations in integration of genomic information into patient's actual clinical status
PCR-based methods ASPCR (TaqMan) Design of appropriate and specific probes/primers for the SNP/mutation is the crucial step; based on energy transfer of fluorescence of indicator and quencher dyes fluorescence Relatively high throughput; relatively high precision; Sensitivity is influenced by the activity of probe used to detect PCR products
HRM analysis Variation in fluorescence indicates the shift of Tm as it is altered by the differences in nucleotide sequence, GC content and amplicon length, and change of Tm would be visible in melting curve analysis Can be performed to detect both known and unknown SNPs; relatively high-throughput and fast speed Requires a previous PCR amplification step to increase the number of target DNA molecules containing SNPs
Digital PCR DNA template primarily diluted in 96-well plates, making 2 wells cover one template molecule; diluted template is employed by PCR amplification Able to determine variants presented in each allele or both presented in only one allele Limited sensitivity by its limited capacity to analyze wells
COLD-PCR Able to enhance amplification efficiency of one template by optimized denaturing temperature, heteroduplexes will be preferentially denatured over the wild-type homoduplexes High sensitivity; able to detect known and unknown SNPs Different approaches of COLD-PCR might bring differences in the results
Mass spectrometry MALDI-TOF-MS 3-Hydroxypicolinic acid acts as a superior matrix in applying MALDI-TOF-MS DNA analysis; enables numerous SNPs to be genotyped in only a single experiment Multiplexing analysis, wide coverage of detection sites, high throughput and rapid speed, high accuracy Costly mass spectrometer is required; multiple sample preparation steps
SNP array SNP array Probes designed specific to the targeted genome, and can be hybridized with DNA sample for confirming the specific allele of SNP High throughput, considerable automated procedure and reasonable price; simple data analysis step Faces challenge in detecting SNP in polyploid and complex genomes; can only genotype known SNP locations
SNaPshot SNaPshot®(Applied Biosystems) Commercial mini-sequencing method based on the mechanism of single base extension; bioinformatic software is needed; multi-step process and each assay runs separately Enables multiplexing of SNPs in a single assay (up to 30–50 SNPs) comparatively; relatively high sensitivity Relatively high risk of contamination and transfer error
DHPLC DHPLC Utilizes reusable matrix primarily composed of alkylated nonporous poly beads and reaches the result with no need for visible gel electrophoresis High throughput, rapid analysis speed, high sensitivity and reasonable cost; can distinguish rare SNP Cannot detect homozygous mutations directly; cannot determine concrete mutation type
ASPCR = allele-specific PCR, COLD-PCR = co-amplification at lower denaturing temperature PCR, DHPLC = denaturing high-performance liquid chromatography, HRM = high-resolution melting curve, MALDI-TOF-MS = matrix-assisted laser desorption ionization time-of-flight mass spectrometry, NGS = next-generation sequencing, SNP = single-nucleotide polymorphism.

WGS

WGS (primarily next-generation sequencing [NGS]) has led to a revolutionary advance in genetic research. Highly automated, fluorescence-based, and many orders of magnitude faster than the Sanger sequencing of 40 years ago, NGS provides genetic information with high throughput.[9] With an increasingly reasonable price, NGS is becoming more and more widely used in the detection of SNPs, and the technology is still improving.[13] Moreover, NGS has hastened the process of WGS tremendously, making it possible to obtain full sequencing data in only a few days.[19]

NGS can provide consumer de novo sequencing, as well as perform resequencing of known target genomes.[13] Importantly, NGS can also sequence multiple individuals simultaneously and extend the genomic information to a species or population. Moreover, WGS is advantageous for detecting genetic variants such as SNPs, small insertions/deletions, structural variations, and copy number variations, which can reveal individual differences in comparison to a reference genome.[20]

The capability of NGS analysis mainly depends on having reliable manufacturers and bioinformatic tools. There are several major sequencing manufacturers with systems for NGS, including Illumina (Solexa) and Thermo Fisher (Ion Torrent); the pre-commercial QIAGEN (GeneReader) and Roche (Genia); and the post-commercial Roche (454 GS FLX), Helicos BioSciences (Heliscope), and Thermo Fisher (SOLiD).[21] Of these systems, the Illumina sequencing platform is currently the most widely used for SNP detection. Likewise, excellent alignment and variant calling algorithms require the highest quality of bioinformatic tools or software. A pipeline that has been tested and recommended by Mielczarek et al[13] consists of the Burrows-Wheeler Aligner (BWA), SAMtools, and Genome Analysis Toolkit (GATK) packages. Data obtained using NGS platforms can also be applied to genome-wide association studies (GWAS).[22]

The general procedures for clinical NGS include DNA extraction, library preparation, target enrichment, and sequencing. The resulting raw sequencing data reads must undergo various steps, including demultiplexing, quality control, mapping the reads to the reference genome (also called resequencing), variant identification, and annotation. Ultimately, a variant call file can be generated with the aforementioned procedure. When a consistent difference is exhibited in multiple reads, a SNP can be called.[23]

PCR-based SNP detection methods

It is difficult to envisage current molecular biology research without PCR. PCR-based methods, especially real-time PCR, are widely applied to detect SNPs and mutations. In real-time PCR instruments, PCR-amplified products undergo gel electrophoresis followed by DNA-binding fluorescent dye staining to confirm the end products. RT-PCR can monitor the DNA amplification of each cycle, allowing a visual readout of PCR amplification kinetics, and provides platforms to measure the quality and quantity of PCR products.[14] Generally, PCR-based methods for the detection of SNPs/mutations can be divided into two types:

  • 1) primers matched with substituted nucleotides or oligonucleotides, to clamp or block the non-targeted template, are used to match mutant-allele-directed specific or polymorphic analysis; or
  • 2) melting curve analysis, using hybridization probes, hydrolysis probes, or double-stranded DNA-binding fluorescent dyes, combined with real-time PCR techniques.[14]

Allele-specific PCR (ASPCR) is a sequence-specific amplification method using PCR, and is also known as mismatch amplification mutation assay. In this method, an amplification-refractory mutation system can be used to detect known SNPs/mutations.[24] Primers or probes used in ASPCR are specific for the SNP/mutation under detection, and a PCR amplicon is needed to identify this SNP/mutation. The design of appropriate probes/primers is a crucial step of ASPCR.[25] TaqMan probe-based ASPCR for SNP detection has been commercialized for many years, and serves as a basic method to detect SNPs/mutations. This method is appropriate for low-throughput applications.[26] It is based on the energy transfer of fluorescence, in which the proximity of the indicator and quencher dyes in the intact probes reduces the indicator dye fluorescence. When the probes are mismatched with the template, the difference in annealing temperature varies, influencing the degradation of the probe. These changes are reflected in fluorescence, suggesting the detection of homozygous or heterozygous conditions with mutant or wild-type alleles.[27] TaqMan probe-based ASPCR results are influenced by the activity of the probe used to detect the PCR products, while the probe degradation amount is affected by the concentration of the probe, the initial number of target molecules, and the number of cycles.

Melting curve analysis, especially high-resolution melting curve (HRM) analysis, is also widely used to detect SNPs/mutations. HRM analysis can be performed to detect both known and unknown SNPs. Based on variations in the fluorescence of DNA-binding dye, through the conversion of double-stranded DNA to single-stranded DNA with a change in temperature, HRM analysis can detect the existence of SNPs/mutations and confirm the nucleotide substitution type.[28,29] Melting temperature (Tm) is an elementary thermodynamic characteristic of DNA that can be altered by differences in nucleotide sequence, GC content, and amplicon length. A change in fluorescence indicates a shift in Tm that is visible in the melting curve analysis.[30,31] Four classes of SNPs can be differentiated by HRM analysis, because these four kinds of SNPs have different Tm changes. G/A and C/T base exchanges comprise the first class of SNP, while G/T and C/A form class two. Classes three and four consist of C/G and A/T base exchanges, respectively. Among the four classes of SNPs, class one and two base exchanges can be clearly genotyped by HRM because of their high Tm differences, of 0.5°C. Class three base exchange induces a Tm difference of 0.4°C, while a difference of 0.3°C is produced by class four base exchange.[29]

Digital PCR and co-amplification at lower denaturing temperature PCR (COLD-PCR) are important for detecting SNPs/mutations. In digital PCR (also called realistic single-molecule PCR), a DNA template is typically diluted in 96-well plates, where two wells cover one template molecule, on average. Thereafter, this diluted template is later used for PCR amplification in nested PCR. To distinguish wild-type or mutant sequences, two molecular beacons are added to the reaction mixture before PCR amplification, and the two beacons are labeled with different fluorescent dyes. Without cloning the PCR products in advance, digital PCR is able to determine whether the variants are present in each allele or in only one allele, which is different from other methods.[32–35] Nevertheless, digital PCR is limited in its capacity to analyze wells, which impacts the sensitivity of this method. In contrast, the COLD-PCR method is named for its ability to enhance the amplification efficiency of one template by optimizing the denaturing temperature. In COLD-PCR, a critical denaturation temperature (Tc, at which mutation-containing DNA is preferentially melted over wild type) should be used that is lower than the melting temperature (Tm). Furthermore, wild-type and mutant allele heteroduplexes are preferentially denatured over wild-type homoduplexes, enabling the heteroduplexes to be amplified several times more than the homoduplexes.[36,37] There are a number of modified COLD-PCR methods, including improved and complete enrichment COLD-PCR and temperature-tolerant COLD-PCR, which have different approaches and should be differentially applied depending on the case.

Other SNP detection methods

Mass-spectrometry-based methods

Mass spectrometry, especially matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), has been very useful to analyze genetic variation in the post-WGS generation. SNP genotyping was the first application developed on this platform.[38] It was not until 3-hydroxypicolinic acid was reported as a superior matrix for the application of DNA analysis that MALDI-TOF-MS began to play a powerful role in reliable SNP detection. This technique has developed substantially over the past decades.[39] Matrix-assisted laser desorption ionization is very fast, particularly when accompanied by time-of-flight analysis, and is useful for analyzing DNA mixtures. In addition, MALDI-TOF-MS enables numerous SNPs to be genotyped in a single multiplexed experiment.[40–42] Moreover, mass spectrometry depends on the immediate mass measurement of products, resulting in easier interpretation when multiplexed sites are compared, in comparison with other DNA analysis strategies that are characterized by radioactive or fluorescent reporters.

SNP array analysis

SNP arrays are a high-throughput method with a considerably automated procedure. They are becoming more readily available and increasingly applicable, with a reasonable price. SNP arrays can be used in genetic studies including GWAS, genomic selection, gene mapping, population structure analysis, and linkage map construction.[43–47] SNP arrays are a type of DNA microarray that are designed with probes specific to the targeted genome. The probes are hybridized with a DNA sample to confirm the specific allele of the SNP.[48] Data from SNP arrays are not as complicated to analyze as those from WGS methods, although both require a background in bioinformatics.[49] However, SNP arrays face challenges in detecting SNPs in polyploid and complex genomes, and they can only genotype known SNP locations, which requires prior genomic information.[50]

SNaPshot analysis

SNaPshot® (Applied Biosystems) is a commercial mini-sequencing method based on the mechanism of single-base extension.[16] Multiplex PCR is used to amplify the DNA template, to generate targeted amplicons that might contain the focused SNP. This is followed by a purification procedure to degrade unincorporated dNTPs and unbound primers. The 3’-end of the SNaPshot primer binds the focused SNP and is lengthened by DNA polymerase, while the oligonucleotide tails (ie, CT or poly C) can be incorporated into the 5’-end of the detection primer, which aids in the detection of single-base extension products by capillary electrophoresis. These features enable SNaPshot to multiplex up to 30 to 40 SNPs in a single assay.[51,52] Bioinformatic software, such as GeneMapper (Applied Biosystems), is also needed to analyze the genetic data. Moreover, some assays have provided evidence of high sensitivity, with the full profiles of a few SNPs from microscale DNA samples in SNaPshot analysis.[53] However, as a multi-step process, each SNaPshot assay runs separately. The samples are therefore subject to multiple tube tests, which increases the risk of transfer and contamination errors.[54]

DHPLC

DHPLC has been widely used in SNP detection and analysis in the post-WGS generation. It uses a reusable matrix that is primarily composed of alkylated nonporous poly beads (styrene-divinylbenzene), meaning that results can be obtained without the need for visible gel electrophoresis.[55] DHPLC has become a favored method based on its high throughput, rapid analysis (using ultraviolet absorbance detection), high sensitivity, and reasonable cost.[56] Rare nucleotide polymorphisms can be distinguished because DHPLC is able to distinguish genetic signal from low background noise. Heteroduplex fractions, such as variant alleles, can be concentrated by DHPLC, and this is followed by direct sequencing or cloning plus sequencing to characterize the detected variation. However, DHPLC is limited because it cannot determine concrete mutation types or directly detect homozygous mutations. As a consequence, DHPLC has been largely replaced by other detection methods.

Application of SNPs in studies of S. aureus

Epidemiological outbreak and transmission analysis of S. aureus infections

S. aureus outbreak and transmission investigations have been previously carried out using multilocus sequence typing (MLST) analyses.[10] However, with the rapid development of WGS technology, and especially with the lower costs and time required for NGS methods, whole-genome SNP analysis is now performed more widely and more continuously for the investigation of S. aureus outbreaks and transmission. The number of SNPs that differ between isolates is regularly used to describe whether or not the isolates belong to the same outbreak, although the specific cut-off point remains in dispute: Golubchik et al[57] determined that a discrepancy of more than 40 SNPs between isolates can distinguish two outbreaks, while Uhlemann et al[58] defined 23 SNPs as the maximum distance between isolates that are from the same epidemiological outbreak. Various other studies of within-host SNP diversity and MRSA transmission have defined the cut-off value as 40 to 60 pairwise core genome SNPs for a recent transmission event (Hamming distance).[59,60]

Transmission in households and other small groups

S. aureus can be transferred in households because family members are in close contact with one another. Alam et al[61] reported a transmission event of USA300, the dominant community-associated MRSA clone in the United States, in households. A WGS study was conducted of 146 USA300 MRSA clones that were collected from households in Los Angeles and Chicago from 2008 to 2010. The clones were investigated using SNP analysis and phylogenetic tree analysis. The authors co Angeles concluded that there was little genetic variation between Chicago families (mean number of SNPs = 12) and Los families (mean number of SNPs = 17.6), suggesting the continuous transmission of USA300 among individuals within families. Additionally, Weterings et al[62] reported a transfer of methicillin resistance from an MRSA clone, which caused an infection in three patients and a medical worker. Based on WGS, MLST, and SNP analysis, little difference was found between this MSSA isolate and the MRSA isolate, with a genetic distance of 44 SNPs.

As well as household members, military members in the same unit are also in close contact. Millar et al[63] linked phylogenetic, clinical, epidemiological, and genomic data to assess interclass and intraclass transmission in military recruits. They identified 2719 SNPs in 80 USA300 strains isolated from 74 recruits, of which the overall SNV median between isolates was 173. Additionally, the intraclass median SNV was more than 140, while the interclass median was less than 40, suggesting that interclass isolates were closely related but intraclass strains were not.

Hospital outbreaks of S. aureus infections

Hospital-acquired infections have an important role in S. aureus outbreaks and transmission. Tong et al[64] studied the genetic differences between 79 MRSA isolates from 46 individual patients and five medical workers in a hospital. A mean difference of 6.7 SNPs was detected, and it was observed that MRSA spread in this care unit with a SNP accumulation rate of 9.1E-6 per site, per year. Furthermore, Kong et al[65] reported a putative outbreak of MRSA that affected 12 individuals in a single department. WGS and GATK SNP analysis were performed on 20 isolates of MRSA, and a maximum of 16 SNPs were identified in the outbreak clones compared internally, whereas the outbreak clones were distinct from the two non-outbreak clones with at least 122 and 997 SNPs in each.[65] Moreover, two outbreaks of MSSA in two neonatal wards in Belgium were investigated by Roisin et al.[66] The isolates between the two outbreaks differed by at least 78 SNPs, while they differed by 35 SNPs within the first outbreak and by six SNPs within the second outbreak.

Epidemic MRSA-15 (EMRSA-15) was the epidemic MRSA clonotype in Europe. Holmes et al[67] conducted a high-resolution epidemiological analysis based on whole-genome SNP assays of EMRSA-15. To identify discriminatory SNPs, the authors sequenced 17 EMRSA-15 strains that were chosen to represent the phenotypic diversity and genotypic breadth of this clonotype in Scotland.[67] Pulsed-field gel electrophoresis and staphylococcal protein A typing were performed first. A total of 904 SNPs (723 strain-specific SNPs) were found in only one of the 17 isolates, while 181 SNPs were detected in two or more strains. The authors concluded that SNP-based assays of EMRSA-15 isolates are a discriminatory, quick, and reproducible (intra-assay coefficients of variability were less than 25%) technique for tracing hospital outbreaks.

Severe S. aureus infections and acute outbreaks and transmission sometimes occur in intensive care units. An investigation demonstrated 34 S. aureus transmission events in one 10-bed intensive care unit over 10 months, of which the authors considered that 29 events were extremely related (fewer than 25 SNPs detected between isolates), while the remaining 5 events were probably interrelated (fewer than 50 SNPs between isolates).[68]

Community and wider patterns of spread

The global pandemic outbreak and transmission of S. aureus involves a very large quantity of isolates, but centralizes on single or several specific clone types. Bartels et al[69] monitored MRSA transmission in Denmark in 2013, and focused on ST6-t304, CC22, and ST80 clonotypes. For ST6-t304, epidemiologically closely related outbreak isolates differed by 1–10 SNPs, while strains that were obviously from different outbreaks differed by 67–200 SNPs. For CC22, isolates within families had a genetic variation of 0–14 SNPs, compared with 69–1207 SNPs in epidemiologically dissimilar outbreaks. For ST80, strains isolated from epidemiologically related and unrelated outbreaks had a difference of 0–10 SNPs and 130–365 SNPs, respectively.

The global transmission of S. aureus infections might be the hardest to reconstruct, although it may be possible to determine the route via SNP analysis. A global transmission of ST8 clonotype community-acquired S. aureus was reconstructed by Strauss et al.[70] They determined that ST8 possibly emerged in the mid-19th century in Central Europe, and in all isolates under investigation from Africa, they identified five nonsynonymous SNPs that might separate these strains from other isolates in other continents. Uhlemann et al[71] reported the international spread of ST398 clonotype MSSA through human migration. This study included strains isolated from the same individual, or from within households, within networks, or in the community, and determined that the medians of pairwise SNP distances for these four groups was 8, 13, 25, and 82, respectively.

Transmission and evolution of antimicrobial resistance genes

Vancomycin, linezolid, and daptomycin are the most widely used antimicrobials for the treatment of MRSA infections.[72,73] However, with the extensive use of these antibiotics, more and more cases of antimicrobial resistance have been reported. Mutations of resistance genes occur when S. aureus isolates are under environmental stress, and these resistance genes can then be transferred from resistant isolates to susceptible isolates through phages or plasmids.

In recent years, the most extensively studied SNPs for antimicrobial resistance have been those that lead to daptomycin resistance after therapy for MRSA infections. Recent studies have demonstrated that daptomycin non-susceptible isolates are generated through the stepwise acquisition of SNPs in a certain number of genes, the foremost of which are rpoB, clpX, and mprF.[74–82] Isolates with mutant rpoB exhibit diverse phenotypes, such as reduced expression of virulence traits, increased cell wall thickness, induced expression of Spx (a stress-associated transcriptional regulator), and poor growth.[74] SNPs identified in daptomycin-resistant isolates are located in specific regions of the mprF gene, and appear to be gain-of-function mutations that are hypothesized to decrease susceptibility to Ca2+-complexed daptomycin by a charge-repulsive mechanism.[74,79]ClpX is a conserved gene in daptomycin non-susceptible S. aureus, and mutations result in degraded protein expression but promoted protein folding and interactions. Moreover, the downregulation of clpX markedly reduces the virulence of S. aureus, demonstrating that clpX is crucial for S. aureus infections.[74]

Vancomycin resistance in S. aureus isolates is a very severe clinical challenge, because vancomycin is currently recommended as a therapy for most MRSA infections.[73] Alam et al[83] reported an analysis in 2014 of vancomycin-sensitive S. aureus and vancomycin-intermediated S. aureus (VISA), in which isolates with a minimum inhibitory concentration (MIC) of 4 to 8 mg/L were defined as VISA, although the latest Clinical & Laboratory Standards Institute guidelines now define vancomycin-resistant S. aureus as having an MIC ≥ 4 mg/L. WGS and SNP/insertion/deletion analysis were performed by the authors, followed by GWAS, to determine the acquisition of vancomycin-resistance genes in these VISA isolates. Compared with the S. aureus reference isolate N315 sequence, 55 977 high-quality SNPs were detected in all vancomycin-sensitive S. aureus and VISA isolates that were studied, whereas only one very significant association (P = 8.78E−8) was found at H481 in the rpoB gene in connection with vancomycin MIC elevation.[83] A vancomycin- and daptomycin-resistant MRSA isolate has been studied by Yamaguchi et al[76] and was compared with a vancomycin- and daptomycin-susceptible clone isolated from the same patient. However, in the initial course of an aortic and anterior mediastinal abscess infection, and before antimicrobial treatment, four SNPs were uncovered in this resistant isolate. These SNPs were in capB (G137, 669A), rpoB (C561,676T), lytN (A1209,832G), and mprF (T1346,904A). Likewise, Katayama et al[84] reported the prevalence of a continuous, slow increase of vancomycin MICs.

Linezolid is another important treatment option for MRSA infections; thus, the challenge of linezolid resistance must be taken seriously. A case of linezolid resistance after three 15-day linezolid medication regimens in a patient with cystic fibrosis was reported by Rouard et al.[85] Thirteen MRSA isolates were collected in this patient during the 5-year duration of his infections, and both 23S rRNA Sanger sequencing and WGS were carried out on these isolates. A SNP of G2576T in 5 rrl (23S rRNA gene) copies was linked to linezolid resistance, while whole-genome SNP analysis indicated that, compared with the first linezolid-susceptible strain that was isolated in this patient, a minimum of 28 and a maximum of 58 SNPs were detected in linezolid-resistant isolates. Furthermore, Iguchi et al[86] conducted 23S rRNA sequencing of several linezolid-resistant and linezolid-susceptible isolates and performed WGS of a resistant strain, accompanied with SNP calling and SNP phylogenetic trees. Using both 23S rRNA sequencing and WGS, fewer SNPs were related to linezolid resistance compared with the previous study by Rouard et al.[85]

Other applications of SNPs that are relevant to S. aureus

SNP analysis can be used to compare S. aureus with other bacteria. Generally, a conserved core genome and a mobile accessory genome comprise the bacterial genomes, while genetic diversity of the conserved core genome is mainly caused by the allocation of SNPs. Moran Losada et al[87] reported a sequence variation by three-base periodicity in the conserved core genome. They analyzed the SNPs of 41 S. aureus (AT-rich) and 20 Pseudomonas aeruginosa (GC-rich) strains. A total of 136,258 SNPs and 113,172 SNPs were identified in the S. aureus and P. aeruginosa genome data, respectively, and the distribution of SNPs in the core genome of S. aureus and P. aeruginosa exhibited a three-base periodicity. Additionally, they demonstrated that this three-base periodicity in P. aeruginosa was more likely to be attributed to transitions rather than to transversions. Chen et al[88] used WGS with GWAS analysis to study the interactions between S. aureus and Escherichia coli (E. coli). They co-cultured 36 pair-wise E. coli and S. aureus strains and monitored the growth of each isolate. In these co-cultured strains, 162 SNPs that significantly affected growth rate were detected compared with the initial strains, and there were 85 SNPs in the co-cultured S. aureus strains. Moreover, 706 and 129 SNPs were significantly associated with changes in bacterial numbers in E. coli and S. aureus, respectively, suggesting that WGS with GWAS analysis can be used to study specific interactions between bacteria.

Livestock-associated MRSA might be epidemiologically linked to the MRSA that affects humans, and SNP analysis can provide genetic evidence for this association. Harrison et al[89] sequenced 46 ST22 MRSA strains from dogs and cats (companion animals that are in close contact with humans) and compared these with human isolates from the ST22 MRSA linage. The substitution rate in the core genome under this model was about 1.47 × 10−6 per nucleotide site, per year, which was not significantly different to that of human isolates. Phylogenomic analyses revealed that these animal isolates were interspersed throughout the EMRSA-15 pandemic clade and clustered with human isolates from the United Kingdom, suggesting that MRSA in companion animals may cause human infections under some circumstances. Moreover, Larsen et al[90] investigated the epidemiology and evolution of a novel livestock-associated MRSA strain that can infect and colonize human beings, even without animal contact. The authors compared this isolate with a collection of S. aureus CC9/CC398 isolates from humans, animals, and retail foods, and the genetic evidence suggested that poultry meat was the probable source of this livestock-associated MRSA infection. In addition, core genome MLST and SNP analyses were used by Slott Jensen et al[91] to investigate a hospital outbreak of livestock-associated MRSA CC398. Five strains that were isolated from five patients in the outbreak were analyzed, along with 17 epidemiologically unrelated CC398 MRSA isolates. The five patient isolates were clustered, with 2 SNPs, while core genome MLST classified these 5 isolates into the same type, and eventually separated them from the other epidemiologically unrelated isolates.

SNP analysis has further applications in studying the molecular biology of S. aureus colonization and infection. Lilje et al[92] conducted WGS with SNP analysis to assess whether there was a difference between bacteremia and infective endocarditis in S. aureus bloodstream isolates, but failed to distinguish bacteremia from endocarditis on the basis of existing genetic evidence. In addition, Goyal et al[93] determined the short-term (1–3 months) and mid-term (36 months) genomic evolution of S. aureus in artificially colonized volunteers and natural carriers by employing MLST and SNP analysis with WGS techniques.

The application of SNP analysis is very important when we aim to determine genetic associations in new studies. The current review is limited because this technique is growing quickly, especially with the extensive use of WGS.

Limitations

This review described common methodologies for detecting SNPs instantly, while ideal SNP detecting methods must rely on a combination of advances in biochemistry, engineering and analytical software. For instance, the third-generation-sequencing has been employed in SNP analysis in many scenarios as its increasingly sophisticated technology and pyramidally accuracy. By improving some specific gene fragments or integrating existing methods, large-scale high-precision SNP detection methods can be established. The physical and chemical properties of SNPs and their derivatives, such as optical, electrical and magnetic properties, can be used to explore new methods. It is hoped that an ideal SNP detection method will be developed. Moreover, application of SNP analysis on S. aureus investigation is not limited in the area we described in this review. With the development of detection methodology, technical platform and mathematical algorithm, SNP analysis will be applied to a broader field of S. aureus research, and provide a better basis for the study of drug resistance, virulence variation, and clinical infection prevention and control of S. aureus.

Conclusion

As the third generation of genetic markers, SNPs are a hotspot in current research on S. aureus. Methodologies for detecting SNPs are diverse; they were previously dominated by PCR-based methods, but are currently dominated by WGS analysis because NGS is becoming more sensitive to genetic variations and more reasonably priced. SNP analysis can be applied in epidemiological outbreak and transmission analysis of S. aureus infection, including for household transmission, community and hospital infections, and even national and global outbreaks. The evolution and transmission of antimicrobial resistance genes of S. aureus isolates can also be analyzed by SNPs. In the case of genetic analysis of S. aureus, SNP analysis can be performed to obtain valuable data. For example, SNPs have been detected to analyze the interactions of S. aureus with other bacteria, as well as to establish the links between S. aureus associated with humans and livestock.

Acknowledgments

None.

Author contributions

YJ and ML conceived the manuscript. YJ participated in literature retrieval, manuscript drafting and writing of the main part in the manuscript. ML reviewed and modified the manuscript. Both authors approved the final version of the paper.

Financial support

None.

Conflicts of interest

The authors declare that they have no conflicts of interest.

References

[1]. Tong SY, Davis JS, Eichenberger E, et al. Staphylococcus aureus infections: epidemiology, pathophysiology, clinical manifestations, and management. Clin Microbiol Rev 2015;28:603–661.
[2]. Zingg W, Hopkins S, Gayet-Ageron A, et al. Health-care-associated infections in neonates, children, and adolescents: an analysis of paediatric data from the European Centre for Disease Prevention and Control point-prevalence survey. Lancet Infect Dis 2017;17:381–389.
[3]. Turner NA, Sharma-Kuinkel BK, Maskarinec SA, et al. Methicillin-resistant Staphylococcus aureus: an overview of basic and clinical research. Nat Rev Microbiol 2019;17:203–218.
[4]. Gould IM. Is vancomycin redundant for serious staphylococcal infection? Int J Antimicrob Agents 2010;36 (Suppl 2):S55–S57.
[5]. DeLeo FR, Otto M, Kreiswirth BN, et al. Community-associated meticillin-resistant Staphylococcus aureus. Lancet 2010;375:1557–1568.
[6]. Assis LM, Nedeljković M, Dessen A. New strategies for targeting and treatment of multi-drug resistant Staphylococcus aureus. Drug Resist Updat 2017;31:1–14.
[7]. Kim S, Misra A. SNP genotyping: technologies and biomedical applications. Annu Rev Biomed Eng 2007;9:289–320.
[8]. Jorde LB, Watkins WS, Bamshad MJ, et al. The distribution of human genetic diversity: a comparison of mitochondrial, autosomal, and Y-chromosome data. Am J Hum Genet 2000;66:979–988.
[9]. Humphreys H, Coleman DC. Contribution of whole-genome sequencing to understanding of the epidemiology and control of meticillin-resistant Staphylococcus aureus. J Hosp Infect 2019;102:189–199.
[10]. Manara S, Pasolli E, Dolce D, et al. Whole-genome epidemiology, characterisation, and phylogenetic reconstruction of Staphylococcus aureus strains in a paediatric hospital. Genome Med 2018;10:82.
[11]. Piekoszewska-Ziętek P, Turska-Szybka A, Olczak-Kowalczyk D. Single nucleotide polymorphism in the aetiology of caries: systematic literature review. Caries Res 2017;51:425–435.
[12]. Simola DF, Kim J. Sniper: improved SNP discovery by multiply mapping deep sequenced reads. Genome Biol 2011;12:R55.
[13]. Mielczarek M, Szyda J. Review of alignment and SNP calling algorithms for next-generation sequencing data. J Appl Genet 2016;57:71–79.
[14]. Matsuda K. PCR-based detection methods for single-nucleotide polymorphism or mutation: real-time PCR and its substantial contribution toward technological refinement. Adv Clin Chem 2017;80:45–72.
[15]. Millis MP. Medium-throughput SNP genotyping using mass spectrometry: multiplex SNP genotyping using the iPLEX Gold assay. Methods Mol Biol 2011;700:61–76.
[16]. Mehta B, Daniel R, Phillips C, et al. Forensically relevant SNaPshot((R)) assays for human DNA SNP analysis: a review. Int J Legal Med 2017;131:21–37.
[17]. Deulvot C, Charrel H, Marty A, et al. Highly-multiplexed SNP genotyping for genetic mapping and germplasm diversity studies in pea. BMC genomics 2010;11:468.
[18]. Ding C, Jin S. High-throughput methods for SNP genotyping. Methods Mol Biol 2009;578:245–254.
[19]. Goodwin S, McPherson JD, McCombie WR. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 2016;17:333–351.
[20]. Hua YP, Zhou T, Liao Q, et al. Genomics-assisted identification and characterization of the genetic variants underlying differential nitrogen use efficiencies in allotetraploid rapeseed genotypes. G3 (Bethesda) 2018;8:2757–2771.
[21]. Levy SE, Myers RM. Advancements in next-generation sequencing. Annu Rev Genomics Hum Genet 2016;17:95–115.
[22]. Xiao Y, Liu H, Wu L, et al. Genome-wide association studies in maize: praise and stargaze. Mol Plant 2017;10:359–374.
[23]. Yohe S, Thyagarajan B. Review of clinical next-generation sequencing. Arch Pathol Lab Med 2017;141:1544–1557.
[24]. Bergallo M, Gambarino S, Loiacono E, et al. Evaluation of IFN-( polymorphism+874 T/A in patients with recurrent tonsillitis by PCR real time mismatch amplification mutation assay (MAMA real time PCR). Cytokine 2015;71:278–282.
[25]. Rodríguez A, Rodríguez M, Córdoba JJ, et al. Design of primers and probes for quantitative real-time PCR methods. Methods Mol Biol 2015;1275:31–56.
[26]. Zhuo R, Hasing ME, Team of Molecular Diagnostics, et al. A single nucleotide polymorphism at the TaqMan probe-binding site impedes real-time reverse transcription-PCR-based detection of norovirus GII.4 Sydney. J Clin Microbiol 2015;53:3353–3354.
[27]. Sanchez JA, Abramowitz JD, Salk JJ, et al. Two-temperature LATE-PCR endpoint genotyping. BMC Biotechnol 2006;6:44.
[28]. Buddhakosai W, Sukmak M, Klinsawat W, et al. Discrimination of tiger using a novel high resolution melting (HRM) and multiplex SNP-specific HRM (MSS-HRM) technique. Forensic Sci Int Genet 2014;13:30–33.
[29]. Gomes S, Castro C, Barrias S, et al. Alternative SNP detection platforms, HRM and biosensors, for varietal identification in Vitis vinifera L. using F3H and LDOX genes. Sci Rep 2018;8:5850.
[30]. Ririe KM, Rasmussen RP, Wittwer CT. Product differentiation by analysis of DNA melting curves during the polymerase chain reaction. Anal Biochem 1997;245:154–160.
[31]. Mehta B, Daniel R, McNevin D. HRM and SNaPshot as alternative forensic SNP genotyping methods. Forensic Sci Med Pathol 2017;13:293–301.
[32]. Sreejith KR, Ooi CH, Jin J, et al. Digital polymerase chain reaction technology - recent advances and future perspectives. Lab Chip 2018;18:3717–3732.
[33]. Quan PL, Sauzade M, Brouzes E. dPCR: a technology review. Sensors (Basel) 2018;18:1271.
[34]. Vynck M, Trypsteen W, Thas O, et al. The future of digital polymerase chain reaction in virology. Mol Diagn Ther 2016;20:437–447.
[35]. Cao L, Cui X, Hu J, et al. Advances in digital polymerase chain reaction (dPCR) and its emerging biomedical applications. Biosens Bioelectron 2017;90:459–474.
[36]. Zuo Z, Jabbar KJ. COLD-PCR: applications and advantages. Methods Mol Biol 2016;1392:17–25.
[37]. Mauger F, How-Kit A, Tost J. COLD-PCR technologies in the area of personalized medicine: methodology and applications. Mol Diagn Ther 2017;21:269–283.
[38]. Tost J, Gut IG. Genotyping single nucleotide polymorphisms by mass spectrometry. Mass Spectrom Rev 2002;21:388–418.
[39]. Wu KJ, Steding A, Becker CH. Matrix-assisted laser desorption time-of-flight mass spectrometry of oligonucleotides using 3-hydroxypicolinic acid as an ultraviolet-sensitive matrix. Rapid Commun Mass Spectrom 1993;7:142–146.
[40]. Park JH, Jang H, Jung YK, et al. A mass spectrometry-based multiplex SNP genotyping by utilizing allele-specific ligation and strand displacement amplification. Biosens Bioelectron 2017;91:122–127.
[41]. Zhang J, Zhang J, Tao R, et al. Mass spectrometry-based SNP genotyping as a potential tool for ancestry inference and human identification in Chinese Han and Uygur populations. Sci Justice 2019;59:228–233.
[42]. Gut IG. DNA analysis by MALDI-TOF mass spectrometry. Hum Mutat 2004;23:437–441.
[43]. SR M, MH W, CW T, et al. Open access resources for genome-wide association mapping in rice. Nat Commun 2016;7:10532.
[44]. Clarke WE, Higgins EE, Plieske J, et al. A high-density SNP genotyping array for Brassica napus and its ancestral diploid species based on optimised selection of single-locus markers in the allotetraploid genome. Theor Appl Genet 2016;129:1887–1899.
[45]. Dalton-Morgan J, Hayward A, Alamery S, et al. A high-throughput SNP array in the amphidiploid species Brassica napus shows diversity in resistance genes. Funct Integr Genomics 2014;14:643–655.
[46]. Wang J, Chu S, Zhang H, et al. Development and application of a novel genome-wide SNP array reveals domestication history in soybean. Sci Rep 2016;6:20728.
[47]. Felcher KJ, Coombs JJ, Massa AN, et al. Integration of two diploid potato linkage maps with the potato genome sequence. PLoS One 2012;7:e36347.
[48]. LaFramboise T. Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances. Nucleic Acids Res 2009;37:4181–4193.
[49]. Clevenger J, Chavarro C, Pearl SA, et al. Single nucleotide polymorphism identification in polyploids: a review, example, and recommendations. Mol Plant 2015;8:831–846.
[50]. Wang S, Wong D, Forrest K, et al. Characterization of polyploid wheat genomic diversity using a high-density 90,000 single nucleotide polymorphism array. Plant Biotechnol J 2014;12:787–796.
[51]. Podini D, Vallone PM. SNP genotyping using multiplex single base primer extension assays. Methods Mol Biol 2009;578:379–391.
[52]. Phillips C, Salas A, Sanchez JJ, et al. Inferring ancestral origin using a single multiplex assay of ancestry-informative marker SNPs. Forensic Sci Int Genet 2007;1:273–280.
[53]. Walsh S, Lindenbergh A, Zuniga SB, et al. Developmental validation of the IrisPlex system: determination of blue and brown iris colour for forensic intelligence. Forensic Sci Int Genet 2011;5:464–471.
[54]. Daniel R, Santos C, Phillips C, et al. A SNaPshot of next generation sequencing for forensic SNP analysis. Forensic Sci Int Genet 2015;14:50–60.
[55]. Huber CG, Oefner PJ, Preuss E, Bonn GK. High-resolution liquid chromatography of DNA fragments on non-porous poly(styrene-divinylbenzene) particles. Nucleic Acids Res 1993;21:1061–1066.
[56]. Xiao W, Oefner PJ. Denaturing high-performance liquid chromatography: a review. Hum Mutat 2001;17:439–474.
[57]. Golubchik T, Batty EM, Miller RR, et al. Within-host evolution of Staphylococcus aureus during asymptomatic carriage. PLoS One 2013;8:e61319.
[58]. Uhlemann AC, Dordel J, Knox JR, et al. Molecular tracing of the emergence, diversification, and transmission of S. aureus sequence type 8 in a New York community. Proc Natl Acad Sci U S A 2014;111:6738–6743.
[59]. Mohammadi-Kambs M, Hölz K, Somoza MM, et al. Hamming distance as a concept in DNA molecular recognition. ACS Omega 2017;2:1302–1308.
[60]. Wang C, Kao WH, Hsiao CK. Using hamming distance as information for SNP-Sets clustering and testing in disease association studies. PloS One 2015;10:e0135918.
[61]. Alam MT, Read TD, Petit RA 3rd, et al. Transmission and microevolution of USA300 MRSA in U.S. households: evidence from whole-genome sequencing. mBio 2015;6:e00054.
[62]. Weterings V, Bosch T, Witteveen S, et al. Next-generation sequence analysis reveals transfer of methicillin resistance to a methicillin-susceptible staphylococcus aureus strain that subsequently caused a methicillin-resistant staphylococcus aureus outbreak: a descriptive study. J Clin Microbiol 2017;55:2808–2816.
[63]. Millar EV, Rice GK, Elassal EM, et al. Genomic characterization of USA300 methicillin-resistant staphylococcus aureus (MRSA) to evaluate intraclass transmission and recurrence of skin and soft tissue infection (SSTI) among high-risk military trainees. Clin Infect Dis 2017;65:461–468.
[64]. Tong SY, Holden MT, Nickerson EK, et al. Genome sequencing defines phylogeny and spread of methicillin-resistant Staphylococcus aureus in a high transmission setting. Genome Res 2015;25:111–118.
[65]. Kong Z, Zhao P, Liu H, et al. Whole-genome sequencing for the investigation of a hospital outbreak of MRSA in China. PLoS One 2016;11:e0149844.
[66]. Roisin S, Gaudin C, De Mendonça R, et al. Pan-genome multilocus sequence typing and outbreak-specific reference-based single nucleotide polymorphism analysis to resolve two concurrent Staphylococcus aureus outbreaks in neonatal services. Clin Microbiol Infect 2016;22:520–526.
[67]. Holmes A, McAllister G, McAdam PR, et al. Genome-wide single nucleotide polymorphism-based assay for high-resolution epidemiological analysis of the methicillin-resistant Staphylococcus aureus hospital clone EMRSA-15. Clin Microbiol Infect 2014;20:O124–131.
[68]. Dancer SJ, Adams CE, Smith J, et al. Tracking Staphylococcus aureus in the intensive care unit using whole-genome sequencing. J Hosp Infect 2019;103:13–20.
[69]. Bartels MD, Larner-Svensson H, Meiniche H, et al. Monitoring meticillin resistant Staphylococcus aureus and its spread in Copenhagen, Denmark, 2013, through routine whole genome sequencing. Euro Surveill 2015;20:21112.
[70]. Strauß L, Stegger M, Akpaka PE, et al. Origin, evolution, and global transmission of community-acquired Staphylococcus aureus ST8. Proc Natl Acad Sci U S A 2017;114:E10596–10604.
[71]. Uhlemann AC, McAdam PR, Sullivan SB, et al. Evolutionary dynamics of pandemic methicillin-sensitive Staphylococcus aureus ST398 and its international spread via routes of human migration. mBio 2017;8:e01375–01416.
[72]. Hassoun A, Linden PK, Friedman B. Incidence, prevalence, and management of MRSA bacteremia across patient populations-a review of recent developments in MRSA management and treatment. Crit Care 2017;21:211.
[73]. Paiva JA, Eggimann P. Treatment of severe MRSA infections: current practice and further development. Intensive Care Med 2017;43:233–236.
[74]. Baek KT, Thogersen L, Mogenssen RG, et al. Stepwise decrease in daptomycin susceptibility in clinical Staphylococcus aureus isolates associated with an initial mutation in rpoB and a compensatory inactivation of the clpX gene. Antimicrob Agents Chemother 2015;59:6983–6991.
[75]. Lindsay JA. Transmission of antimicrobial resistance in resource-poor healthcare. Trends Microbiol 2015;23:69–70.
[76]. Yamaguchi T, Suzuki S, Okamura S, et al. Evolution and single-nucleotide polymorphisms in methicillin-resistant Staphylococcus aureus strains with reduced susceptibility to vancomycin and daptomycin, based on determination of the complete genome. Antimicrob Agents Chemother 2015;59:3585–3587.
[77]. Capone A, Cafiso V, Campanile F, et al. In vivo development of daptomycin resistance in vancomycin-susceptible methicillin-resistant Staphylococcus aureus severe infections previously treated with glycopeptides. Eur J Clin Microbiol Infect Dis 2016;35:625–631.
[78]. Kang KM, Mishra NN, Park KT, et al. Phenotypic and genotypic correlates of daptomycin-resistant methicillin-susceptible Staphylococcus aureus clinical isolates. J Microbiol 2017;55:153–159.
[79]. Ernst CM, Slavetinsky CJ, Kuhn S, et al. Gain-of-function mutations in the phospholipid flippase MprF confer specific daptomycin resistance. mBio 2018;9:e01659–01718.
[80]. Okado JB, Avaca-Crusca JS, Oliveira AL, et al. Daptomycin and vancomycin heteroresistance revealed among CC5-SCCmecII MRSA clone and in vitro evaluation of treatment alternatives. J Glob Antimicrob Resist 2018;14:209–216.
[81]. Ernst CM, Peschel A. MprF-mediated daptomycin resistance. Int J Med Microbiol 2019;309:359–363.
[82]. Prosperi M, Salemi M, Azarian T, et al. Unexpected predictors of antibiotic resistance in housekeeping genes of Staphylococcus aureus. ACM-BCB 2019;2019:259–268.
[83]. Alam MT, Petit RA 3rd, Crispell EK, et al. Dissecting vancomycin-intermediate resistance in staphylococcus aureus using genome-wide association. Genome Biol Evol 2014;6:1174–1185.
[84]. Katayama Y, Azechi T, Miyazaki M, et al. Prevalence of slow-growth vancomycin nonsusceptibility in methicillin-resistant Staphylococcus aureus. Antimicrob Agents Chemother 2017;61:e00452–00517.
[85]. Rouard C, Garnier F, Leraut J, et al. Emergence and within-host genetic evolution of methicillin-resistant Staphylococcus aureus resistant to linezolid in a cystic fibrosis patient. Antimicrob Agents Chemother 2018;62:e00720–00818.
[86]. Iguchi S, Mizutani T, Hiramatsu K, et al. Rapid acquisition of linezolid resistance in methicillin-resistant Staphylococcus aureus: role of hypermutation and homologous recombination. PLoS One 2016;11:e0155512.
[87]. Morán Losada P, Fischer S, Chouvarine P, et al. Three-base periodicity of sites of sequence variation in Pseudomonas aeruginosa and Staphylococcus aureus core genomes. FEBS Lett 2016;590:3538–3543.
[88]. Chen N, Zhu J, Ye M, et al. Interactions between Escherichia coli and Staphylococcus aureus determined by genome-wide association analysis. Wei Sheng Wu Xue Bao 2017;57:526–538.
[89]. Harrison EM, Weinert LA, Holden MT, et al. A shared population of epidemic methicillin-resistant Staphylococcus aureus 15 circulates in humans and companion animals. mBio 2014;13 5:e00985–001013.
[90]. Larsen J, Stegger M, Andersen PS, et al. Evidence for human adaptation and foodborne transmission of livestock-associated methicillin-resistant Staphylococcus aureus. Clin Infect Dis 2016;63:1349–1352.
[91]. Slott Jensen ML, Nielsine Skov M, Pries Kristiansen H, et al. Core genome multi-locus sequence typing as an essential tool in a high-cost livestock-associated meticillin-resistant Staphylococcus aureus CC398 hospital outbreak. J Hosp Infect 2020;104:574–581.
[92]. Lilje B, Rasmussen RV, Dahl A, et al. Whole-genome sequencing of bloodstream Staphylococcus aureus isolates does not distinguish bacteraemia from endocarditis. Microb Genom 2017;3:e000138.
[93]. Goyal M, Javerliat F, Palmieri M, et al. Genomic evolution of Staphylococcus aureus during artificial and natural colonization of the human nose. Front Microbiol 2019;10:1525.
Keywords:

real-time PCR, single-nucleotide polymorphism, Staphylococcus aureus, transmission, whole-genome sequencing

Copyright © 2021 The Chinese Medical Association, Published by Wolters Kluwer Health, Inc. under the CCBY-NC-ND license.