Background Though detection of transmission clusters of methicillin-resistant Staphylococcus aureus (MRSA) infections is a priority for infection control personnel in hospitals, the transmission dynamics of MRSA among hospitalized patients with bloodstream infections (BSIs) has not been thoroughly studied. Whole genome sequencing (WGS) of MRSA isolates for surveillance is valuable for detecting outbreaks in hospitals, but the bioinformatic approaches used are diverse and difficult to compare. Methods We combined short-read WGS with genotypic, phenotypic, and epidemiological characteristics of 106 MRSA BSI isolates collected for routine microbiological diagnosis from inpatients in two hospitals over 12 months. Clinical data and hospitalization history were abstracted from electronic medical records. We compared three genome sequence alignment strategies to assess similarity in cluster ascertainment. We conducted logistic regression to measure the probability of predicting prior hospital overlap between clustered patient isolates by the genetic distance of their isolates. Results While the three alignment approaches detected similar results, they showed some variation. A Gene-family-based alignment pipeline was most consistent across MRSA clonal complexes. We identified nine unique clusters of closely related BSI isolates. Most BSI were healthcare-associated and community-onset. Our logistic model showed that with 13 single nucleotide polymorphisms the likelihood that any two patients in a cluster had overlapped in a hospital was 50 percent. Conclusions Multiple clusters of closely related MRSA isolates can be identified using WGS among strains cultured from BSI in two hospitals. Genomic clustering of these infections suggest that transmission resulted from a mix of community spread and healthcare exposures long before BSI diagnosis.
Background: Though detection of transmission clusters of methicillin-resistant Staphylococcus aureus (MRSA) infections is a priority for infection control personnel in hospitals, the transmission dynamics of MRSA among hospitalized patients with bloodstream infections (BSIs) has not been thoroughly studied. Whole genome sequencing (WGS) of MRSA isolates for surveillance is valuable for detecting outbreaks in hospitals, but the bioinformatic approaches used are diverse and difficult to compare. Methods: We combined short-read WGS with genotypic, phenotypic, and epidemiological characteristics of 106 MRSA BSI isolates collected for routine microbiological diagnosis from inpatients in two hospitals over 12 months. Clinical data and hospitalization history were abstracted from electronic medical records. We compared three genome sequence alignment strategies to assess similarity in cluster ascertainment. We conducted logistic regression to measure the probability of predicting prior hospital overlap between clustered patient isolates by the genetic distance of their isolates. Results: While the three alignment approaches detected similar results, they showed some variation. A pangenome-based alignment method was most consistent across MRSA clonal complexes. We identified nine unique clusters of closely-related BSI isolates. Most BSI were healthcare-associated and community-onset. Our logistic model showed that with 13 single nucleotide polymorphisms the likelihood that any two patients in a cluster overlapped in a hospital was 50 percent. Conclusions: Multiple clusters of closely related MRSA isolates can be identified using WGS among strains cultured from BSI in two hospitals. Genomic clustering of these infections suggest that transmission resulted from a mix of community spread and healthcare exposures long before BSI diagnosis.
The most common approach to sampling the bacterial populations within an infected or colonised host is to sequence genomes from a single colony obtained from a culture plate. However, it is recognized that this method does not capture the genetic diversity in the population. An alternative is to sequence a mixture containing multiple colonies ("pool-seq"), but this has the disadvantage that it is a non-homogeneous sample, making it difficult to perform specific experiments. We compared differences in measures of genetic diversity between eight single-colony isolates (singles) and pool-seq on a set of 2286 S. aureus culture samples. The samples were obtained by swabbing three body sites on 85 human participants quarterly for a year, who initially presented with a methicillin-resistant S. aureus skin and soft-tissue infection (SSTI). We compared parameters such as sequence quality, contamination, allele frequency, nucleotide diversity and pangenome diversity in each pool to the corresponding singles. Comparing singles from the same culture plate, we found that 18% of sample collections contained mixtures of multiple Multilocus sequence types (MLSTs or STs). We showed that pool-seq data alone could predict the presence of multi-ST populations with 95% accuracy. We also showed that pool-seq could be used to estimate the number of polymorphic sites in the population. Additionally, we found that the pool may contain clinically relevant genes such as antimicrobial resistance markers that may be missed when only examining singles. These results highlight the potential advantage of analysing genome sequences of total populations obtained from clinical cultures rather than single colonies.
Background Methicillin-resistant S. aureus (MRSA) is a common antibiotic-resistant human pathogen that spreads from person to person. Asymptomatic host colonization precedes both skin and soft tissue infections (SSTI) and bloodstream infections (BSI), but it is not known how closely related MRSA strains are that cause infections at different body sites. Methods Using Illumina whole genome sequencing, we compared the strain types and genomes of MRSA isolates from 132 SSTIs and 145 sequential BSIs at 3 Philadelphia hospitals in 7/2018-1/2021. We investigated the epidemiological links and genomic clusters among isolates causing BSI and SSTIs. Results Abscesses were the most common type of SSTIs (65/132, 49%). Clonal complex (CC) 8 was the most identified CC among both SSTI strains (102/132, 77%) and BSI strains (73/145, 50%). While CC5 was more commonly found among BSIs than SSTIs (50/145, 34% vs. 19/132, 13%). Outbreak clusters with 15 or fewer SNPs were identified. Three clusters that contained only strains from SSTIs were all CC8, and the five clusters that only contained strains from BSIs had three CC5 clusters and two CC8 clusters. Among eight clusters that included at least one SSTI and one BSI strain, only one was composed of CC5 and the others were all CC8 strains. Within these eight clusters the SSTIs were from cellulitis infections (4/8), an abscess (3/8), or an infected ulcer (1/8). Conclusion We found that CC8 strains were the most common among both SSTIs and BSIs. CC5 strains, however, were more commonly found in BSIs than SSTIs, and among outbreak clusters in the BSI strains. Eight outbreak clusters were identified that linked strains causing SSTIs or BSIs, seven of the clusters contained only CC8 strains. While abscesses were the most common infection type to cause an SSTI, within the outbreak clusters containing strains from both SSTIs and BSIs, both cellulitis and abscess were equally identified. Disclosures Michael Z. David, MD PhD, Contrafect: Grant/Research Support|GSK: Advisor/Consultant|Johnson and Johnson: Advisor/Consultant.
Background MRSA BSIs have 15-50% mortality and are commonly diagnosed in US hospitals. However, the frequency of hospital transmission of MRSA causing BSI is unknown. Methods We performed Illumina shotgun whole genome sequencing (WGS) of 106 sequential MRSA isolates from different adults with a BSI at two Philadelphia academic hospitals in a single health system in July 2018-June 2019. We abstracted clinical data from the electronic medical record. Genomic data were analyzed preliminarily using the Staphopia Analysis Pipeline. Results Among 106 subjects, 51.9% were male, 47.2% were white, 46.2% were black, 23.6% were < 40 years of age, and mean age was 53.1 years (s.d. 17 years). One isolate had WGS data that were inadequate for analysis. Of 105 genomes, 52 were clonal cluster (CC) 8, 22 were sequence type (ST) 5, and 16 were ST105; the remaining 15 strains belonged to 8 other CCs. Of CC8 strains, 44 were USA300 and 6 were USA500. There were 6 clusters (i.e., < 35 SNP differences in the core genome) among the 105 isolates. Four clusters were CC5 and two were CC8 strains. One cluster of CC5 strains involved 3 subjects, and 5 clusters involved 2 subjects. One cluster of ST8/USA300 strains were separated by only 1 SNP (Fig a). This and two other clustered pairs were from subjects who had overlapping hospital stays. Two of these paired subjects had an overlap in the same unit while the third pair was in the hospital together on a number of occasions (total of 40 days overlap) but never simultaneously in the same unit. The other three clustered pairs did not have temporally overlapping hospital stays, suggesting transmission via a hospital reservoir. One of these three pairs had hospitalizations overlapping in time, one at each study hospital, before each of them had infections with the related MRSA strains. There was not a clear-cut clustering of SNP distances among the isolate genomes into transmission and non-transmission groups, with some pairs of patient isolates separated by 40-80 SNPs (Fig. b). Figure 1. Conclusion We were able to discern from WGS data alone that some MRSA BSIs in 2 hospitals were likely due to strains transmitted between patients. Universal WGS of BSI strains may detect MRSA outbreaks in real time, even in the absence of overlapping hospitalizations, and is an emerging strategy to detect healthcare transmission of MRSA. Disclosures Michael Z. David, MD PhD, GSK (Consultant)
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