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PCR-ribotyping has been adopted in many laboratories as the method of choice for C. difficile typing and surveillance. However, issues with the conventional agarose gel-based technique, including inter-laboratory variation and interpretation of banding patterns have impeded progress. The method has recently been adapted to incorporate high-resolution capillary gel-based electrophoresis (CE-ribotyping), so improving discrimination, accuracy and reproducibility. However, reports to date have all represented single-centre studies and inter-laboratory variability has not been formally measured or assessed. Here, we achieved in a multi-centre setting a high level of reproducibility, accuracy and portability associated with a consensus CE-ribotyping protocol. Local databases were built at four participating laboratories using a distributed set of 70 known PCR-ribotypes. A panel of 50 isolates and 60 electronic profiles (blinded and randomized) were distributed to each testing centre for PCR-ribotype identification based on local databases generated using the standard set of 70 PCR-ribotypes, and the performance of the consensus protocol assessed. A maximum standard deviation of only ±3.8bp was recorded in individual fragment sizes, and PCR-ribotypes from 98.2% of anonymised strains were successfully discriminated across four ribotyping centres spanning Europe and North America (98.8% after analysing discrepancies). Consensus CE-ribotyping increases comparability of typing data between centres and thereby facilitates the rapid and accurate transfer of standardized typing data to support future national and international C. difficile surveillance programs.

The human skin microbiome has important roles in skin health and disease. However, bacterial population structure and diversity at the strain level is poorly understood. We compared the skin microbiome at the strain level and genome level of Propionibacterium acnes, a dominant skin commensal, between 49 acne patients and 52 healthy individuals by sampling the pilosebaceous units on their noses. Metagenomic analysis demonstrated that although the relative abundances of P. acnes were similar, the strain population structures were significantly different in the two cohorts. Certain strains were highly associated with acne, and other strains were enriched in healthy skin. By sequencing 66 previously unreported P. acnes strains and comparing 71 P. acnes genomes, we identified potential genetic determinants of various P. acnes strains in association with acne or health. Our analysis suggests that acquired DNA sequences and bacterial immune elements may have roles in determining virulence properties of P. acnes strains, and some could be future targets for therapeutic interventions. This study demonstrates a previously unreported paradigm of commensal strain populations that could explain the pathogenesis of human diseases. It underscores the importance of strain-level analysis of the human microbiome to define the role of commensals in health and disease.

Background Despite scientific advances in typing of C. difficile strains very little is known about how hospital staff use typing results during periods of increased incidence (PIIs). This qualitative study, undertaken alongside a randomised controlled trial (RCT), explored this issue. The trial compared ribotyping versus more rapid genotyping (MLVA or multilocus variable repeat analysis) and found no significant difference in post 48 hour cases ( C difficile transmissions). Methods In-depth qualitative interviews with senior staff in 11/16 hospital trusts in the trial (5 MLVA and 6 Ribotyping). Semi-structured interviews were conducted at end of the trial period. Transcripts were content analysed using framework analysis supported by NVivo-8 software. Common sub-themes were extracted by two researchers independently. These were compared and organised into over-arching categories or ‘super-ordinate themes’. Results The trial recorded that 45% of typing tests had some impact on infection control (IC) activities. Interviews indicated that tests had little impact on initial IC decisions. These were driven by hospital protocols and automatically triggered when a PII was identified. To influence decision-making, a laboratory turnaround time < 3 days (ideally 24 hours) was suggested; MLVA turnaround time was 5.3 days. Typing results were predominantly used to modify initiated IC activities such as ward cleaning, audits of practice or staff training; major decisions (e.g. ward closure) were unaffected. Organisational factors could limit utilisation of MLVA results. Results were twice as likely to be reported as ‘aiding management’ (indirect benefit) than impacting on IC activities (direct effect). Some interviewees considered test results provided reassurance about earlier IC decisions; others identified secondary benefits on organisational culture. An underlying benefit of improved discrimination provided by MLVA typing was the ability to explore epidemiology associated with CDI cases in a hospital more thoroughly. Conclusions Ribotyping and MLVA are both valued by users. MLVA had little additional direct impact on initial infection control decisions. This would require reduced turnaround time. The major impact is adjustments to earlier IC measures and retrospective reassurance. For this, turnaround time is less important than discriminatory power. The potential remains for wider use of genotyping to examine transmission routes.

Background Clostridioides difficile is a major cause of hospital-acquired diarrhea and a driver of nosocomial outbreaks, yet rapid, accurate ribotype identification remains challenging. We sought to develop a MALDI-TOF MS–based workflow coupled with machine learning to distinguish epidemic toxigenic ribotypes (RT027 and RT181) from other strains in real time. Results We analyzed MALDI-TOF spectra from 379 clinical isolates collected across ten Spanish hospitals and identified seven discriminant biomarker peaks. Two peaks (2463 and 4993 m/z) were uniquely associated with RT027, while combinations of five additional peaks reliably identified RT181. Our classifiers–implemented both in the commercial Clover MSDAS platform and the open-access AutoCdiff web tool–achieved up to 100% balanced accuracy in ribotype assignment and proved robust in real-time outbreak simulations. Conclusions This study demonstrates that MALDI-TOF MS combined with tailored machine learning can deliver rapid, high-precision ribotype identification for C. difficile . The freely available AutoCdiff models ( https://bacteria.id ) offer an immediately deployable solution for clinical laboratories, with the potential to enhance outbreak surveillance and control.

Background Recent advances have increased the importance of the human microbiome, including the skin microbiome. Despite the hand microbiome research, the factors affecting the composition of the hand microbiome and their personal characteristics are incompletely known. Objectives Despite changing environmental factors and personal variation, we aimed to indicate the interpersonal distinction between skin microbiota using simple and rapid molecular methods. Methods Over a non‐consecutive 10‐day period, samples were taken from 10 adult individuals, and ribotyping analysis of the 16S and 23S genes of S. epidermidis was performed on each skin sample. Additionally, EcoRI and HindIII enzyme reactions and variable number tandem repeat (VNTR) reactions of S. epidermidis obtained from DNA samples were performed. The skin microbiomes of individuals were evaluated along with the microbiome profiles left on the surfaces they touched. Results In the environmental samples taken, it has been observed that people preserve their core skin microbiota characters and carry them to their environment. It was determined that the highest similarity rate was 77.14%, and the lowest similarity rate was 31.74%. Conclusion Our study showed that the core skin microbiota retains its characteristics and leaves traces in environments. The fact that the personal microbiome remains unchanged despite environmental differences and has characteristic features has shown that it can be used in forensic sciences to distinguish individuals from each other. These results with simple and rapid methods further increased the importance and significance of the study. The findings indicate that personal skin microbiota can provide a significant contribution to criminal investigations by increasing accuracy and reliability, especially in forensic analyses.

Taxonomic classification of the thousands–millions of 16S rRNA gene sequences generated in microbiome studies is often achieved using a naïve Bayesian classifier (for example, the Ribosomal Database Project II (RDP) classifier), due to favorable trade-offs among automation, speed and accuracy. The resulting classification depends on the reference sequences and taxonomic hierarchy used to train the model; although the influence of primer sets and classification algorithms have been explored in detail, the influence of training set has not been characterized. We compared classification results obtained using three different publicly available databases as training sets, applied to five different bacterial 16S rRNA gene pyrosequencing data sets generated (from human body, mouse gut, python gut, soil and anaerobic digester samples). We observed numerous advantages to using the largest, most diverse training set available, that we constructed from the Greengenes (GG) bacterial/archaeal 16S rRNA gene sequence database and the latest GG taxonomy. Phylogenetic clusters of previously unclassified experimental sequences were identified with notable improvements (for example, 50% reduction in reads unclassified at the phylum level in mouse gut, soil and anaerobic digester samples), especially for phylotypes belonging to specific phyla (Tenericutes, Chloroflexi, Synergistetes and Candidate phyla TM6, TM7). Trimming the reference sequences to the primer region resulted in systematic improvements in classification depth, and greatest gains at higher confidence thresholds. Phylotypes unclassified at the genus level represented a greater proportion of the total community variation than classified operational taxonomic units in mouse gut and anaerobic digester samples, underscoring the need for greater diversity in existing reference databases.

Purpose Clostridioides difficile is the main cause of antibiotic related diarrhea and some ribotypes (RT), such as RT027, RT181 or RT078, are considered high risk clones. A fast and reliable approach for C. difficile ribotyping is needed for a correct clinical approach. This study analyses high-molecular-weight proteins for C. difficile ribotyping with MALDI-TOF MS. Methods Sixty-nine isolates representative of the most common ribotypes in Europe were analyzed in the 17,000–65,000 m/z region and classified into 4 categories (RT027, RT181, RT078 and ‘Other RTs’). Five supervised Machine Learning algorithms were tested for this purpose: K-Nearest Neighbors, Support Vector Machine, Partial Least Squares-Discriminant Analysis, Random Forest (RF) and Light-Gradient Boosting Machine (GBM). Results All algorithms yielded cross-validation results > 70%, being RF and Light-GBM the best performing, with 88% of agreement. Area under the ROC curve of these two algorithms was > 0.9. RT078 was correctly classified with 100% accuracy and isolates from the RT181 category could not be differentiated from RT027. Conclusions This study shows the possibility of rapid discrimination of relevant C. difficile ribotypes by using MALDI-TOF MS. This methodology reduces the time, costs and laboriousness of current reference methods.

Clostridium difficile strains were typed by a newly developed MALDI-TOF method, high molecular weight typing, and compared to PCR ribotyping. Among 500 isolates representing 59 PCR ribotypes a total of 35 high molecular weight types could be resolved. Although less discriminatory than PCR ribotyping, the method is extremely fast and simple, and supports for cost-effective screening of isolates during outbreak situations.

Key Points Specialized microorganisms catalyse central steps of global element cycling, such as nitrogen fixation or the mineralization of organic matter. There is an urgent need for the development of new methods for in situ microbial analysis, which originates from the restricted morphological diversity of prokaryotes and the limited usefulness of cultivation-based methods for quantifying species and genera at a spatial resolution that is relevant for microorganisms. Fluorescence in situ hybridization (FISH) enables reliable quantification of microbial populations in complex environmental samples. FISH probes that target large taxonomic groups, such as the Bacteria, Archaea and Eukarya domains or the Alpha-, Beta- and Gammaproteobacteria classes are popular. Owing to their broad specificity, these probes can be used to analyse samples from many different environments that range from marine and freshwater environments to sediments and soils. They also facilitate an initial, rapid assessment of the dominance of certain taxa in particular environments. Most of these group-specific probes were published more than 10 years ago, when the ribosomal RNA (rRNA) database was less than 10% of its current size. We address the question: which of these old probes are still valid? We checked the probes thoroughly against the comprehensive rRNA datasets of the SILVA project. The good news is that most probes can still be used for initial identification and quantification of microbial populations. Failure to detect cells — that is, a false-negative FISH result — can be due to lack of cell permeabilization, low cellular ribosome content or low efficiency of probe binding based on the higher-order structure of the rRNA. The new, more sensitive FISH assays have the greatest impact in oligotrophic environments, where the indigenous microbiota has low ribosome content, and in samples in which the background fluorescence hampers reliable quantification of less-frequent populations. With good microscopes, even populations of a relative abundance of 1 in 1,000 cells can be accurately quantified. FISH enables studies of microorganisms in their natural contexts. Metagenomics cannot substitute for the information that can be gained by visualizing the identity and activity of single microbial cells in situ . Rather, it will make available huge sequence datasets that will help in improving existing probe sets and facilitate the development of new probes. Amann and Fuchs provide an update on recent methodological improvements to fluorescence in situ hybridization protocols, with a particular focus on whether the original group-specific probes, which were mostly developed more than 10 years ago, are still valid. The ribosomal-RNA (rRNA) approach to microbial evolution and ecology has become an integral part of environmental microbiology. Based on the patchy conservation of rRNA, oligonucleotide probes can be designed with specificities that range from the species level to the level of phyla or even domains. When these probes are labelled with fluorescent dyes or the enzyme horseradish peroxidase, they can be used to identify single microbial cells directly by fluorescence in situ hybridization. In this Review, we provide an update on the recent methodological improvements that have allowed more reliable quantification of microbial populations in situ in complex environmental samples, with a particular focus on the usefulness of group-specific probes in this era of ever-growing rRNA databases.

Hypervirulent ribotypes (HVRTs) of Clostridioides difficile such as ribotype (RT) 027 are epidemiologically important. This study evaluated whether MALDI-TOF can distinguish between strains of HVRTs and non-HVRTs commonly found in Europe. Obtained spectra of clinical C. difficile isolates (training set, 157 isolates) covering epidemiologically relevant HVRTs and non-HVRTs found in Europe were used as an input for different machine learning (ML) models. Another 83 isolates were used as a validation set. Direct comparison of MALDI-TOF spectra obtained from HVRTs and non-HVRTs did not allow to discriminate between these two groups, while using these spectra with certain ML models could differentiate HVRTs from non-HVRTs with an accuracy >95% and allowed for a sub-clustering of three HVRT subgroups (RT027/RT176, RT023, RT045/078/126/127). MALDI-TOF combined with ML represents a reliable tool for rapid identification of major European HVRTs.