Pseudomonas aeruginosa is one of the most common opportunistic pathogens causing severe nosocomial infections for its patterns of multidrug resistance, particularly for carbapenems. Timely epidemiological surveillance could greatly facilitate infection control of P. aeruginosa and many deadly pathogens alike. IR Biotyper (IRBT), is a novel real-time typing tool, based on a Fourier-transform infrared (FTIR) spectroscopy system. It is critical to comprehensively establish and evaluate the feasibility of IRBT in P. aeruginosa strain typing. In the current study, we first established standards and schemes for its routine laboratory application, and we found that Mueller-Hinton agar plates give better discriminatory power than blood agar plates. Data showed that the cut-off value of 0.15 with an additional 0.025 range was optimal. Secondly, 27 clinically isolated carbapenem-resistant P. aeruginosa (CRPA) strains collected from October 2010 to September 2011 were evaluated for typing effectiveness by comparing IRBT to the other commonly used typing methods, such as multi-locus sequence typing (MLST), pulsed-field gel electrophoresis (PFGE) and whole-genome sequencing (WGS)-based typing. When using WGS-based typing as the reference method, the typing method of FTIR spectroscopy (AR = 0.757, SID = 0.749) could better cluster P. aeruginosa strains than MLST and in silico serotyping (AR = 0.544, SID = 0.470). Though PFGE showed the highest discriminatory power, low concordance was observed between PFGE and the other methods. Above all, this study demonstrates the utility of the IRBT as a quick, low-cost, real-time typing tool for the detection of CRPA strains.

IR Biotyper (IRBT), which is a spectroscopic system for microorganism typing based on Fourier transform infrared (FTIR) technology, has been used to detect the spread of clones in clinical microbiology laboratories. However, the use of IRBT to detect probiotics has rarely been reported. Herein, we evaluated the discriminatory power of IRBT to type Lactiplantibacillus plantarum isolates at the strain level and explored its application potential in probiotic preliminary selection. Twenty Lactiplantibacillus isolates collected from pickled radishes during successive fermentation were used to test the robustness of IRBT at the strain level. IRBT was then compared with genotyping methods such as whole-genome sequencing (WGS), pulsed-field gel electrophoresis (PFGE), and multilocus sequence typing (MLST) to evaluate its discrimination power. IRBT distributed the 20 isolates into five clusters, with L. argentoratensis isolate C7-83 being the most distant from the other isolates, which belonged to L. plantarum. IRBT showed good reproducibility, although deviation in the discriminative power of IRBT was found at the strain level across laboratories, probably due to technical variance. All examined methods allowed bacterial identification at the strain level, but IRBT had higher discriminatory power than MLST and was comparable to the WGS and PFGE. In the phenotypic comparison study, we observed that the clustering results of probiotic physiological attributes (e.g., sensitivity to acid and bile salts, hydrophobicity of the cell surface, and resistance to antibiotics) were consistent with the typing results of IRBT. Our results indicated that IRBT is a robust tool for L. plantarum strain typing that could improve the efficiency of probiotic identification and preliminary screening, and can potentially be applied in probiotic traceability and quality control.

To study the contamination of microorganisms in the food industry, pharmaceutical industry, clinical diagnosis, or bacterial taxonomy, accurate identification of species is a key starting point of further investigation. The conventional method of identification by the 16S rDNA gene or other marker gene comparison is not accurate, because it uses a tiny part of the genomic information. The average nucleotide identity calculated between two whole bacterial genomes was proven to be consistent with DNA-DNA hybridization and adopted as the gold standard of bacterial species delineation. Furthermore, there are more bacterial genomes available in public databases recently. All of those contribute to a genome era of bacterial species identification. However, wrongly labeled and low-quality bacterial genome assemblies, especially from type strains, greatly affect accurate identification. In this study, we employed a multi-step strategy to create a type-strain genome database, by removing the wrongly labeled and low-quality genome assemblies. Based on the curated database, a fast bacterial genome identification platform (fIDBAC) was developed (http://fbac.dmicrobe.cn/). The fIDBAC is aimed to provide a single, coherent, and automated workflow for species identification, strain typing, and downstream analysis, such as CDS prediction, drug resistance genes, virulence gene annotation, and phylogenetic analysis.

In total, 49 clinical samples were analyzed using two typing schemes, Enhanced Centers for Disease Control and Prevention (ECDC) and multilocus sequence typing (MLST), to describe the molecular characteristics of circulating Treponema pallidum isolates in Xiamen between 2016 and 2017. In addition, genetic mutations potentially related to antibiotic resistance of T. pallidum were also analyzed. Forty five samples were fully typed by ECDC, and 14 different subtypes were detected. The most common subtype was 16d/f (24.4%), followed by 14d/f (20.0%). All forty nine samples were successfully typed by MLST, while only four allelic profiles were identified, including three SS14-like profiles and one Nichols-like profile. Among them, the major allelic profile was 1.1.8 (85.7%). Interestingly, the allelic profile 1.3.1 widespread in Europe and North America was not detected in this region. Additionally, A2058G mutation in 23S rRNA was found in all detectable samples (38/38), and no mutation in 16S rRNA was observed (36/36). Four non-synonymous single-nucleotide polymorphisms in penicillin-binding protein genes were found in the 35 samples eligible for Sanger sequencing. Among them, the variant in tp0500 (P564I) can only be found in the SS14-like isolates. Homoplastic changes in tp0760 (I415F/I415M) and tp0705 (A506V/A506T) were found. Moreover, the variant tp0705 A506V and the variant tp0705 A506T separately appeared in the SS14-like isolates and Nichols-like isolates, respectively. This study showed that the genotypes of T. pallidum isolates in Xiamen between 2016 and 2017 were different from those in other geographic areas. The resistance-related variants of T. pallidum isolates identified in this study could provide awareness for clinicians in the treatment of syphilis.

BACKGROUND: Genomic composition has been found to be species specific and is used to differentiate bacterial species. To date, almost no published composition-based approaches are able to distinguish between most closely related organisms, including intra-genus species and intra-species strains. Thus, it is necessary to develop a novel approach to address this problem.
RESULTS: Here, we initially determine that the \"tetranucleotide-derived z-value Pearson correlation coefficient\" (TETRA) approach is representative of other published statistical methods. Then, we devise a novel method called \"Tetranucleotide-derived Z-value Manhattan Distance\" (TZMD) and compare it with the TETRA approach. Our results show that TZMD reflects the maximal genome difference, while TETRA does not in most conditions, demonstrating in theory that TZMD provides improved resolution. Additionally, our analysis of real data shows that TZMD improves species differentiation and clearly differentiates similar organisms, including similar species belonging to the same genospecies, subspecies and intraspecific strains, most of which cannot be distinguished by TETRA. Furthermore, TZMD is able to determine clonal strains with the TZMD = 0 criterion, which intrinsically encompasses identical composition, high average nucleotide identity and high percentage of shared genomes.
CONCLUSIONS: Our extensive assessment demonstrates that TZMD has high resolution. This study is the first to propose a composition-based method for differentiating bacteria at the strain level and to demonstrate that composition is also strain specific. TZMD is a powerful tool and the first easy-to-use approach for differentiating clonal and non-clonal strains. Therefore, as the first composition-based algorithm for strain typing, TZMD will facilitate bacterial studies in the future.

Staphylococcus haemolyticus is one of the most significant coagulase-negative staphylococci, and it often causes severe infections. Rapid strain typing of pathogenic S. haemolyticus is indispensable in modern public health infectious disease control, facilitating the identification of the origin of infections to prevent further infectious outbreak. Rapid identification enables the effective control of pathogenic infections, which is tremendously beneficial to critically ill patients. However, the existing strain typing methods, such as multi-locus sequencing, are of relatively high cost and comparatively time-consuming. A practical method for the rapid strain typing of pathogens, suitable for routine use in clinics and hospitals, is still not available. Matrix-assisted laser desorption ionization-time of flight mass spectrometry combined with machine learning approaches is a promising method to carry out rapid strain typing. In this study, we developed a statistical test-based method to determine the reference spectrum when dealing with alignment of mass spectra datasets, and constructed machine learning-based classifiers for categorizing different strains of S. haemolyticus. The area under the receiver operating characteristic curve and accuracy of multi-class predictions were 0.848 and 0.866, respectively. Additionally, we employed a variety of statistical tests and feature-selection strategies to identify the discriminative peaks that can substantially contribute to strain typing. This study not only incorporates statistical test-based methods to manage the alignment of mass spectra datasets but also provides a practical means to accomplish rapid strain typing of S. haemolyticus.

In the last few years, advances in next-generation sequencing (NGS) technology for whole genome sequencing (WGS) of foodborne pathogens have provided drastic improvements in food pathogen outbreak surveillance. WGS of foodborne pathogen enables identification of pathogens from food or environmental samples, including difficult-to-detect pathogens in culture-negative infections. Compared to traditional low-resolution methods such as the pulsed-field gel electrophoresis (PFGE), WGS provides advantages to differentiate even closely related strains of the same species, thus enables rapid identification of food-source associated with pathogen outbreak events for a fast mitigation plan. In this paper, we present UltraStrain, which is a fast and ultra sensitive pathogen detection and strain typing method for Salmonella enterica (S. enterica) based on WGS data analysis. In the proposed method, a noise filtering step is first performed where the raw sequencing data are mapped to a synthetic species-specific reference genome generated from S. enterica specific marker sequences to avoid potential interference from closely related species for low spike samples. After that, a statistical learning based method is used to identify candidate strains, from a database of known S. enterica strains, that best explain the retained S. enterica specific reads.Finally, a refinement step is further performed by mapping all the reads before filtering onto the identified top candidate strains, and recalculating the probability of presence for each candidate strain. Experiment results using both synthetic and real sequencing data show that the proposed method is able to identify the correct S. enterica strains from low-spike samples, and outperforms several existing strain-typing methods in terms of sensitivity and accuracy.

We report a case of fungemia in an immunocompetent patient after administration of probiotic containing Saccharomyces boulardii. We demonstrated the strain relatedness of the yeast from the probiotic capsule and the yeast causing fungal infection using genomic and proteomic typing methods. Our study questions the safety of this preventative biotherapy.