The microbiome of the reproductive tract has been associated with (sub)fertility and it has been suggested that dysbiosis reduces success rates and pregnancy outcomes. The endometrial microbiome is of particular interest given the potential impact on the embryo implantation. To date, all endometrial microbiome studies have applied a metagenomics approach. A sequencing-based technique, however, has its limitations, more specifically in adequately exploring low-biomass settings, such as intra-uterine/endometrial samples. In this proof-of-concept study, we demonstrate the applicability of culturomics, a high-throughput culturing approach, to investigate the endometrial microbiome. Ten subfertile women undergoing diagnostic hysteroscopy and endometrial biopsy, as part of their routine work-up at Brussels IVF, were included after their informed consent. Biopsies were used to culture microbiota for up to 30 days in multiple aerobic and anaerobic conditions. Subsequent WASPLab®-assisted culturomics enabled a standardized methodology. Matrix-assisted laser desorption/ionization–time of flight mass spectrometry (MALDI-TOF MS) or 16S rRNA sequencing was applied to identify all of bacterial and fungal isolates. Eighty-three bacterial and two fungal species were identified. The detected species were in concordance with previously published metagenomics-based endometrial microbiota analyses as 77 (91%) of them belonged to previously described genera. Nevertheless, highlighting the added value of culturomics to identify most isolates at the species level, 53 (62.4%) of the identified species were described in the endometrial microbiota for the first time. This study shows the applicability and added value of WASPLab®-assisted culturomics to investigate the low biomass endometrial microbiome at a species level.
Whole genome sequencing (WGS) enables detailed characterization of bacteria at single nucleotide resolution. It provides data about acquired resistance genes and mutations leading to resistance. Although WGS is becoming an essential tool to predict resistance patterns accurately, comparing genotype to phenotype with WGS is still in its infancy. Additional data and validation are needed. In this retrospective study, we analysed 234 E. coli isolates from positive blood cultures using WGS as well as microdilution for 11 clinically relevant antibiotics, to compare the two techniques. We performed whole genome sequencing analyses on 234 blood culture isolates (genotype) to detect acquired antibiotic resistance. Minimal inhibitory concentrations (MIC) for E. coli were performed for amoxicillin, cefepime, cefotaxime, ceftazidime, meropenem, amoxicillin/clavulanic acid, piperacillin/tazobactam, amikacin, gentamicin, tobramycin, and ciprofloxacin, using the ISO 20776-1 standard broth microdilution method as recommended by EUCAST (phenotype). We then compared the two methods for statistical ‘agreement’. A perfect (100%) categorical agreement between genotype and phenotype was observed for gentamicin and meropenem. However, no resistance to meropenem was observed. A high categorical agreement (> 95%) was observed for amoxicillin, cefepime, cefotaxime, ceftazidime, amikacin, and tobramycin. A categorical agreement lower than 95% was observed for amoxicillin/clavulanic acid, piperacillin/tazobactam, and ciprofloxacin. Most discrepancies occurred in isolates with MICs within ± 1 doubling dilution of the breakpoint and 22.73% of the major errors were samples that tested phenotypically susceptible at higher antibiotic exposure and were therefore considered as ‘not resistant’. This study shows that WGS can be used as a valuable tool to predict phenotypic resistance against most of the clinically relevant antibiotics used for the treatment of E. coli bloodstream infections.
Sink drains as reservoirs of VIM-2 metallo-β-lactamase-producing Pseudomonas aeruginosa in a Belgian intensive care unit: relation to patients investigated by whole genome sequencing,
with details of the nature of the infringement. We will investigate the claim and if justified, we will take the appropriate steps.
Pathogenic E. coli strains can be classified into two major groups, based on the presence of specific virulence factors: extraintestinal pathogenic E. coli (ExPEC) and diarrheagenic E. coli (DEC). Several case reports describe that DEC can cause bloodstream infections in some rare cases. This mainly concerns a few specific sequence types that express virulence factors from both ExPEC and DEC. In this study, we retrospectively analysed 234 E. coli blood isolates with whole genome sequencing (WGS). WGS was performed on an Illumina NovaSeq6000. Genotyping was performed using BioNumerics software. The presence of genes was determined with a minimum percentage sequence identity (ID) threshold of 95% and a minimum length for sequence coverage of 95%. Three of the 234 (1.28%) isolates were defined as DEC, 182 (77.78%) as ExPEC, and 49 (20.94%) did not carry pathotype-associated virulence genes. We identified 112 different virulence genes, 48 O-antigens, and 28 H-antigens 82 STs, among the 234 analyzed isolates. ST131 and ST88 were related to healthcare-associated infections. This study provides insight into the prevalence of virulence factors in a large set of E. coli blood isolates from the UZ Brussel. It illustrates high diversity in virulence profiles and highlights the potential of DEC to carry virulence factors associated with extraintestinal infections, making it possible for unusual pathotypes to invade and survive in the bloodstream causing bacteraemia. Diarrheagenic strains causing bacteremia are rare and presently underreported, but modern sequencing techniques will better underscore their importance.
Background: Healthcare-associated SARS-CoV-2 infections need to be explored further. Our study is an analysis of hospital-acquired infections (HAIs) and ambulatory healthcare workers (aHCWs) with SARS-CoV-2 across the pandemic in a Belgian university hospital. Methods: We compared HAIs with community-associated infections (CAIs) to identify the factors associated with having an HAI. We then performed a genomic cluster analysis of HAIs and aHCWs. We used this alongside the European Centre for Disease Control (ECDC) case source classifications of an HAI. Results: Between March 2020 and March 2022, 269 patients had an HAI. A lower BMI, a worse frailty index, lower C-reactive protein (CRP), and a higher thrombocyte count as well as death and length of stay were significantly associated with having an HAI. Using those variables to predict HAIs versus CAIs, we obtained a positive predictive value (PPV) of 83.6% and a negative predictive value (NPV) of 82.2%; the area under the ROC was 0.89. Genomic cluster analyses and representations on epicurves and minimal spanning trees delivered further insights into HAI dynamics across different pandemic waves. The genomic data were also compared with the clinical ECDC definitions for HAIs; we found that 90.0% of the ‘definite’, 87.8% of the ‘probable’, and 70.3% of the ‘indeterminate’ HAIs belonged to one of the twenty-two COVID-19 genomic clusters we identified. Conclusions: We propose a novel prediction model for HAIs. In addition, we show that the management of nosocomial outbreaks will benefit from genome sequencing analyses.
It is generally accepted that microorganisms can colonize a non-pathological endometrium. However, in a clinical setting, endometrial samples are always collected by passing through the vaginal–cervical route. As such, the vaginal and cervical microbiomes can easily cross-contaminate endometrial samples, resulting in a biased representation of the endometrial microbiome. This makes it difficult to demonstrate that the endometrial microbiome is not merely a reflection of contamination originating from sampling. Therefore, we investigated to what extent the endometrial microbiome corresponds to that of the vagina, applying culturomics on paired vaginal and endometrial samples. Culturomics could give novel insights into the microbiome of the female genital tract, as it overcomes sequencing-related bias. Ten subfertile women undergoing diagnostic hysteroscopy and endometrial biopsy were included. An additional vaginal swab was taken from each participant right before hysteroscopy. Both endometrial biopsies and vaginal swabs were analyzed using our previously described WASPLab-assisted culturomics protocol. In total, 101 bacterial and two fungal species were identified among these 10 patients. Fifty-six species were found in endometrial biopsies and 90 were found in vaginal swabs. On average, 28 % of species were found in both the endometrial biopsy and vaginal swab of a given patient. Of the 56 species found in the endometrial biopsies, 13 were not found in the vaginal swabs. Of the 90 species found in vaginal swabs, 47 were not found in the endometrium. Our culturomics-based approach sheds a different light on the current understanding of the endometrial microbiome. The data suggest the potential existence of a unique endometrial microbiome that is not merely a presentation of cross-contamination derived from sampling. However, we cannot exclude cross-contamination completely. In addition, we observe that the microbiome of the vagina is richer in species than that of the endometrium, which contradicts the current sequence-based literature.
BackgroundWorldwide, healthcare-associated SARS-CoV-2 infections are a major problem: they are associated with increased morbidity, mortality, and hospitalization costs. In-depth studies across the pandemic are crucial to understand and prevent transmission in hospital settings. The principal aims of this study were to characterise patients and validate ECDC definitions of healthcare-associated COVID-19 infections.MethodsWe set up a retrospective observational study spanning the first three waves of the COVID-19 pandemic in a Belgian university hospital: it describes the characteristics of COVID-19 patients admitted, with either healthcare- or community-associated infections. We performed a cluster analysis through epidemiological and viral genome analyses of the healthcare-associated infections, in order to validate the ECDC definitions of healthcare-associated COVID-19 infections.ResultsBetween week 10 of 2020 and week 22 of 2021, 168 patients were hospitalized with healthcare-associated COVID-19. The following factors were found more often in symptomatic healthcare- than in community-associated hospitalized patients: older age, increased frailty, smoking habits, and comorbidities. The genome-based cluster analyses showed that different viral lineages predominated in different timeframes. We observed a good correlation of epidemiological data with genome sequencing results in at least 12 different outbreaks in our hospital, thus validating the ECDC definitions. ConclusionsThis in-depth characterization sheds new light on the problem of healthcare-associated COVID-19 infections, in particular on patients’ characteristics, epidemiology, and cluster dynamics. Even though epidemiological evaluation of nosocomial infections is vital, management of nosocomial outbreaks can undoubtedly benefit from genome sequencing analyses to reinforce their strategy.
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