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Introduction: Veterinary clinical microbiology laboratories play a key role in antimicrobial stewardship, surveillance of antimicrobial resistance and prevention of healthcare associated-infections. However, there is a shortage of international harmonized guidelines covering all steps of veterinary bacterial culture from sample receipt to reporting. Methods: In order to gain insights, the European Network for Optimization of Veterinary Antimicrobial Treatment (ENOVAT) designed an online survey focused on the practices and interpretive criteria used for bacterial culture and identification (C&ID), and antimicrobial susceptibility testing (AST) of animal bacterial pathogens. Results: A total of 241 microbiology laboratories in 34 European countries completed the survey, representing a mixture of academic (37.6%), governmental (27.4%), and private (26.5%) laboratories. The C&ID turnaround varied from 1 to 2 days (77.8%) to 3-5 days (20%), and 6-8 days (1.6%), with similar timeframes for AST. Individual biochemical tests and analytical profile index (API) biochemical test kits or similar were the most frequent tools used for bacterial identification (77% and 56.2%, respectively), followed by PCR (46.6%) and MALDI-TOF MS (43.3%). For AST, Kirby-Bauer disk diffusion (DD) and minimum inhibitory concentration (MIC) determination were conducted by 43.8% and 32.6% of laboratories, respectively, with a combination of EUCAST and CLSI clinical breakpoints (CBPs) preferred for interpretation of the DD (41.2%) and MIC (47.6%) results. In the absence of specific CBPs, laboratories used human CBPs (53.3%) or veterinary CBPs representing another body site, organism or animal species (51.5%). Importantly, most laboratories (47.9%) only report the qualitative interpretation of the result (S, R, and I). As regards testing for AMR mechanisms, 48.5% and 46.7% of laboratories routinely screened isolates for methicillin resistance and ESBL production, respectively. Notably, selective reporting of AST results (i.e. excluding highest priority critically important antimicrobials from AST reports) was adopted by 39.5% of laboratories despite a similar proportion not taking any approach (37.6%) to guide clinicians towards narrower-spectrum or first-line antibiotics. Discussion: In conclusion, we identified a broad variety of methodologies and interpretative criteria used for C&ID and AST in European veterinary microbiological diagnostic laboratories. The observed gaps in veterinary microbiology practices emphasize a need to improve and harmonize professional training, innovation, bacterial culture methods and interpretation, AMR surveillance and reporting strategies. [ABSTRACT FROM AUTHOR]

IntroductionVeterinary clinical microbiology laboratories play a key role in antimicrobial stewardship, surveillance of antimicrobial resistance and prevention of healthcare associated-infections. However, there is a shortage of international harmonized guidelines covering all steps of veterinary bacterial culture from sample receipt to reporting.MethodsIn order to gain insights, the European Network for Optimization of Veterinary Antimicrobial Treatment (ENOVAT) designed an online survey focused on the practices and interpretive criteria used for bacterial culture and identification (C&ID), and antimicrobial susceptibility testing (AST) of animal bacterial pathogens.ResultsA total of 241 microbiology laboratories in 34 European countries completed the survey, representing a mixture of academic (37.6%), governmental (27.4%), and private (26.5%) laboratories. The C&ID turnaround varied from 1 to 2 days (77.8%) to 3–5 days (20%), and 6– 8 days (1.6%), with similar timeframes for AST. Individual biochemical tests and analytical profile index (API) biochemical test kits or similar were the most frequent tools used for bacterial identification (77% and 56.2%, respectively), followed by PCR (46.6%) and MALDI-TOF MS (43.3%). For AST, Kirby-Bauer disk diffusion (DD) and minimum inhibitory concentration (MIC) determination were conducted by 43.8% and 32.6% of laboratories, respectively, with a combination of EUCAST and CLSI clinical breakpoints (CBPs) preferred for interpretation of the DD (41.2%) and MIC (47.6%) results. In the absence of specific CBPs, laboratories used human CBPs (53.3%) or veterinary CBPs representing another body site, organism or animal species (51.5%). Importantly, most laboratories (47.9%) only report the qualitative interpretation of the result (S, R, and I). As regards testing for AMR mechanisms, 48.5% and 46.7% of laboratories routinely screened isolates for methicillin resistance and ESBL production, respectively. Notably, selective reporting of AST results (i.e. excluding highest priority critically important antimicrobials from AST reports) was adopted by 39.5% of laboratories despite a similar proportion not taking any approach (37.6%) to guide clinicians towards narrower-spectrum or first-line antibiotics.DiscussionIn conclusion, we identified a broad variety of methodologies and interpretative criteria used for C&ID and AST in European veterinary microbiological diagnostic laboratories. The observed gaps in veterinary microbiology practices emphasize a need to improve and harmonize professional training, innovation, bacterial culture methods and interpretation, AMR surveillance and reporting strategies.

Reproductive technologies are increasingly being adopted to improve the conservation management of threatened species. The storage and transport of sperm is a vital aspect of the practical implementation of reproductive technologies, however, bacterial contamination during the storage and transport of sperm samples presents a biosecurity risk and can contribute to a reduction in sperm longevity during storage. The present study: 1) characterised the urinary microbiome (bacterial species composition and abundance) using culture-independent 16S rRNA sequencing; 2) quantified the effect of various doses of gentamicin and streptomycin-penicillin on bacterial abundance (colony-forming units; CFUs) and; 3) quantified the effect of antibiotic supplementation on the sperm viability (proportion live/dead) of spermic urine during a 12-day cold-storage period, using the common eastern froglet, Crinia signifera. Overall, urine samples were found to host a diverse array of bacteria, dominated by the phyla Proteobacteria, Firmicutes, and Bacteroidetes. Bacterial abundance was significantly reduced in all antibiotic treatment groups compared to the control group. Antibiotic supplementation had no effect on sperm viability between day 0 and day 6 of storage, however both antibiotic treatments significantly improved sperm viability from days 9 to 12 of storage compared to the control group. Overall, the results of this study provide novel insight into the urinary microbiome, being the first to use a metagenomics approach to characterise the bacterial community present in the urine of an amphibian. Importantly, this study provides evidence that antibiotic supplementation with either gentamicin or streptomycin-penicillin, minimises bacterial proliferation and improves sperm viability during cold storage. These findings will contribute to the development of biosecurity protocols aimed at reducing the risk of disease transmission and cross-infection from unwanted bacteria and infectious agents in amphibian captive breeding programs.

In this study, we collected voluntary recall records of tattoo and permanent makeup ink from the U.S. Food and Drug Administration (US FDA) Enforcement Report Database. The recall records contain information, such as recall date, manufacturer, ink color, reason for recall, and the microorganisms detected from the ink samples. Between 2003 and 2021, a total of 15 voluntary tattoo ink recalls occurred in the U.S. market, involving over 200 tattoo inks marketed by 13 manufacturers and one distributor. Fourteen recalls were due to microbial contamination, and one recall was due to allergic reaction. As follow-up, a microbiological survey of 28 tattoo inks of new batches from seven manufacturers having products that were previously recalled was conducted. Aerobic plate count (APC) and enrichment culture methods based on the FDA’s Bacteriological Analytical Manual (BAM) were used to detect microbial contamination. The results revealed that six out of 28 tattoo inks were contaminated with bacteria and were produced by two manufacturers. The level of microbial contamination was less than 250 CFU/g in three of the tattoo inks and between 1 × 103 and 1 × 105 CFU/g in the other three inks. Eleven bacterial isolates were identified, including spore-forming Bacillus-related species and potentially pathogenic species. Overall, this study shows that some tattoo ink products produced by manufacturers with a recall history continue to be contaminated with microorganisms. This highlights the need for ongoing monitoring and quality control of such products.

Introduction: Microbial isolates from culture can be identified using 16S or whole-genome sequencing which generates substantial costs and requires time and expertise. Protein fingerprinting via Matrix-assisted Laser Desorption Ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid bacterial identification in routine diagnostics but shows a poor performance and resolution on commensal bacteria due to currently limited database entries. The aim of this study was to develop a MALDI-TOF MS plugin database (CLOSTRITOF) allowing for rapid identification of non-pathogenic human commensal gastrointestinal bacteria. Methods: We constructed a database containing mass spectral profiles (MSP) from 142 bacterial strains representing 47 species and 21 genera within the class Clostridia. Each strain-specific MSP was constructed using >20 raw spectra measured on a microflex Biotyper system (Bruker-Daltonics) from two independent cultures. Results: For validation, we used 58 sequence-confirmed strains and the CLOSTRITOF database successfully identified 98 and 93% of the strains, respectively, in two independent laboratories. Next, we applied the database to 326 isolates from stool of healthy Swiss volunteers and identified 264 (82%) of all isolates (compared to 170 (52.1%) with the Bruker-Daltonics library alone), thus classifying 60% of the formerly unknown isolates. Discussion: We describe a new open-source MSP database for fast and accurate identification of the Clostridia class from the human gut microbiota. CLOSTRI-TOF expands the number of species which can be rapidly identified by MALDI-TOF MS. [ABSTRACT FROM AUTHOR]

Microbial induced carbonate precipitation (MICP) through the ureolysis metabolic pathway is one of the most studied topics in biocementation due to its high efficiency. Although excellent outcomes have proved the potential of this technique, microorganisms face some obstacles when considering complicated situations in the real field, such as bacterial adaptability and survivability issues. This study made the first attempt to seek solutions to this issue from the air, exploring ureolytic airborne bacteria with resilient features to find a solution to survivability issues. Samples were collected using an air sampler in Sapporo, Hokkaido, a cold region where sampling sites were mostly covered with dense vegetation. After two rounds of screening, 12 out of 57 urease-positive isolates were identified through 16S rRNA gene analysis. Four potentially selected strains were then evaluated in terms of growth pattern and activity changes within a range of temperatures (15°C–35°C). The results from sand solidification tests using two Lederbergia strains with the best performance among the isolates showed an improvement in unconfined compressive strength up to 4–8 MPa after treatment, indicating a high MICP efficiency. Overall, this baseline study demonstrated that the air could be an ideal isolation source for ureolytic bacteria and laid a new pathway for MICP applications. More investigations on the performance of airborne bacteria under changeable environments may be required to further examine their survivability and adaptability.

Introduction: We compared the performance of real-time PCR with culture-based methods for identifying bacteria in sputum samples from patients with chronic obstructive pulmonary disease (COPD) in three studies. Methods: This was an exploratory analysis of sputum samples collected during an observational study of 127 patients (AERIS; NCT01360398), phase 2 study of 145 patients (NTHI-004; NCT02075541), and phase 2b study of 606 patients (NTHI-MCAT-002; NCT03281876). Bacteria were identified by culture-based microbiological methods in local laboratories using fresh samples or by real-time PCR in a central laboratory using frozen samples. Haemophilus influenzae positivity with culture was differentiated from H. haemolyticus positivity by microarray analysis or PCR. The feasibility of bacterial detection by culture-based methods on previously frozen samples was also examined in the NTHI-004 study. Results: Bacterial detection results from both culture-based and PCR assays were available from 2,293 samples from AERIS, 974 from the NTHI-004 study, and 1736 from the NTHI-MCAT-002 study. Quantitative real-time PCR (qPCR) showed higher positivity rates than culture for H. influenzae (percentages for each study: 43.4% versus 26.2%, 47.1% versus 23.6%, 32.7% versus 10.4%) and Moraxella catarrhalis (12.9% versus 6.3%, 19.0% versus 6.0%, 15.5% versus 4.1%). In the NTHI-004 and NTHI-MCAT-002 studies, positivity rates were higher with qPCR for Streptococcus pneumoniae (15.6% versus 6.1%, 15.5% versus 3.8%); in AERIS, a lower rate with qPCR than with culture (11.0% versus 17.4%) was explained by misidentification of S. pseudopneumoniae/mitis isolates via conventional microbiological methods. Concordance analysis showed lowest overall agreement for H. influenzae (82.0%, 75.6%, 77.6%), due mainly to culture-negative/qPCR-positive samples, indicating lower sensitivity of the culture-based methods. The lowest positive agreement (culture-positive/ qPCR-positive samples) was observed for S. pneumoniae (35.1%, 71.2%, 71.2%). Bacterial load values for each species showed a proportion of culture negative samples with a load detected by qPCR; for some samples, the loads were in line with those observed in culture-positive samples. In the NTHI-004 study, of fresh samples that tested culture-positive, less than 50% remained culture-positive when tested from freeze/thawed samples. In the NTHI-004 study, of fresh samples that tested culture-positive, less than 50% remained culture-positive when tested from freeze/thawed samples. Discussion: Real-time PCR on frozen sputum samples has enhanced sensitivity and specificity over culture-based methods, supporting its use for the identification of common respiratory bacterial species in patients with COPD. [ABSTRACT FROM AUTHOR]

IntroductionWe compared the performance of real-time PCR with culture-based methods for identifying bacteria in sputum samples from patients with chronic obstructive pulmonary disease (COPD) in three studies.MethodsThis was an exploratory analysis of sputum samples collected during an observational study of 127 patients (AERIS; NCT01360398), phase 2 study of 145 patients (NTHI-004; NCT02075541), and phase 2b study of 606 patients (NTHI-MCAT-002; NCT03281876). Bacteria were identified by culture-based microbiological methods in local laboratories using fresh samples or by real-time PCR in a central laboratory using frozen samples. Haemophilus influenzae positivity with culture was differentiated from H. haemolyticus positivity by microarray analysis or PCR. The feasibility of bacterial detection by culture-based methods on previously frozen samples was also examined in the NTHI-004 study.ResultsBacterial detection results from both culture-based and PCR assays were available from 2,293 samples from AERIS, 974 from the NTHI-004 study, and 1736 from the NTHI-MCAT-002 study. Quantitative real-time PCR (qPCR) showed higher positivity rates than culture for H. influenzae (percentages for each study: 43.4% versus 26.2%, 47.1% versus 23.6%, 32.7% versus 10.4%) and Moraxella catarrhalis (12.9% versus 6.3%, 19.0% versus 6.0%, 15.5% versus 4.1%). In the NTHI-004 and NTHI-MCAT-002 studies, positivity rates were higher with qPCR for Streptococcus pneumoniae (15.6% versus 6.1%, 15.5% versus 3.8%); in AERIS, a lower rate with qPCR than with culture (11.0% versus 17.4%) was explained by misidentification of S. pseudopneumoniae/mitis isolates via conventional microbiological methods. Concordance analysis showed lowest overall agreement for H. influenzae (82.0%, 75.6%, 77.6%), due mainly to culture-negative/qPCR-positive samples, indicating lower sensitivity of the culture-based methods. The lowest positive agreement (culture-positive/qPCR-positive samples) was observed for S. pneumoniae (35.1%, 71.2%, 71.2%). Bacterial load values for each species showed a proportion of culture-negative samples with a load detected by qPCR; for some samples, the loads were in line with those observed in culture-positive samples. In the NTHI-004 study, of fresh samples that tested culture-positive, less than 50% remained culture-positive when tested from freeze/thawed samples. In the NTHI-004 study, of fresh samples that tested culture-positive, less than 50% remained culture-positive when tested from freeze/thawed samples.DiscussionReal-time PCR on frozen sputum samples has enhanced sensitivity and specificity over culture-based methods, supporting its use for the identification of common respiratory bacterial species in patients with COPD.

IntroductionMicrobial isolates from culture can be identified using 16S or whole-genome sequencing which generates substantial costs and requires time and expertise. Protein fingerprinting via Matrix-assisted Laser Desorption Ionization–time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid bacterial identification in routine diagnostics but shows a poor performance and resolution on commensal bacteria due to currently limited database entries. The aim of this study was to develop a MALDI-TOF MS plugin database (CLOSTRI-TOF) allowing for rapid identification of non-pathogenic human commensal gastrointestinal bacteria.MethodsWe constructed a database containing mass spectral profiles (MSP) from 142 bacterial strains representing 47 species and 21 genera within the class Clostridia. Each strain-specific MSP was constructed using >20 raw spectra measured on a microflex Biotyper system (Bruker-Daltonics) from two independent cultures.ResultsFor validation, we used 58 sequence-confirmed strains and the CLOSTRI-TOF database successfully identified 98 and 93% of the strains, respectively, in two independent laboratories. Next, we applied the database to 326 isolates from stool of healthy Swiss volunteers and identified 264 (82%) of all isolates (compared to 170 (52.1%) with the Bruker-Daltonics library alone), thus classifying 60% of the formerly unknown isolates.DiscussionWe describe a new open-source MSP database for fast and accurate identification of the Clostridia class from the human gut microbiota. CLOSTRI-TOF expands the number of species which can be rapidly identified by MALDI-TOF MS.

Rapid identification and antimicrobial susceptibility testing (AST) of bacteria are key interventions to curb the spread and emergence of antimicrobial resistance. The current gold standard identification and AST methods provide comprehensive diagnostic information but often take 3 to 5 days. Here, a compound Raman microscopy (CRM), which integrates Raman spectroscopy and stimulated Raman scattering microscopy in one system, is presented and demonstrated for rapid identification and AST of pathogens in urine. We generated an extensive bacterial Raman spectral dataset and applied deep learning to identify common clinical bacterial pathogens. In addition, we employed stimulated Raman scattering microscopy to quantify bacterial metabolic activity to determine their antimicrobial susceptibility. For proofof-concept, we demonstrated an integrated assay to diagnose urinary tract infection pathogens, S. aureus and E. coli. Notably, the CRM system has the unique ability to provide Gram-staining classification and AST results within ~3 h directly from urine samples and shows great potential for clinical applications. [ABSTRACT FROM AUTHOR]

Foodborne pathogenic microorganisms have become major threats that endanger human life and health. The current technology cannot perform rapid screening of foodborne pathogenic bacteria, and fail to timely control food safety risks. Here, we develop a novel microfluidic sensor for real-time and label-free bacteria classification at the single-cell level. Concretely, a low-aspect-ratio SiN micropore with PDMS coating was fabricated, which could significantly reduce the noise of the sensing system, and makes the microfluidic pore sensor sensitive to bacteria discrimination. The prepared SiN micropore equipped with the high temporal-spatial resolution was applied to observe bacterial translocation “events” and the current pulse signals could be obtained, which depend on the size, charge, and morphology of the target bacteria. According to the variation of the current pulse signals produced by different bacteria across the micropore, three common foodborne pathogens such as Salmonella enteric, Listeria monocytogenes, and Escherichia coli were identified. Due to convenience, rapidity, and accuracy, the label-free method we report here has great potential for the identification of diverse foodborne microorganisms at single-cell sensitivity.

We previously reported on FRAGTE (hereafter termed FRAGTE1), a promising algorithm for sieving (pre-selecting genome pairs for whole-genome species demarcation). However, the overall amount of pairs sieved by FRAGTE1 is still large, requiring seriously unaffordable computing cost, especially for large datasets. Here, we present FRAGTE2. Tests on simulated genomes, real genomes, and metagenome-assembled genomes revealed that (i) FRAGTE2 outstandingly reduces ~50–60.10% of the overall amount of pairs sieved by FRAGTE1, dramatically decreasing the computing cost required for whole-genome species demarcation afterward; (ii) FRAGTE2 shows superior sensitivity than FRAGTE1; (iii) FRAGTE2 shows higher specificity than FRAGTE1; and (iv) FRAGTE2 is faster than or comparable with FRAGTE1. Besides, FRAGTE2 is independent of genome completeness, the same as FRAGTE1. We therefore recommend FRAGTE2 tailored for sieving to facilitate species demarcation in prokaryotes. [ABSTRACT FROM AUTHOR]

We previously reported on FRAGTE (hereafter termed FRAGTE1), a promising algorithm for sieving (pre-selecting genome pairs for whole-genome species demarcation). However, the overall amount of pairs sieved by FRAGTE1 is still large, requiring seriously unaffordable computing cost, especially for large datasets. Here, we present FRAGTE2. Tests on simulated genomes, real genomes, and metagenome-assembled genomes revealed that (i) FRAGTE2 outstandingly reduces ~50–60.10% of the overall amount of pairs sieved by FRAGTE1, dramatically decreasing the computing cost required for whole-genome species demarcation afterward; (ii) FRAGTE2 shows superior sensitivity than FRAGTE1; (iii) FRAGTE2 shows higher specificity than FRAGTE1; and (iv) FRAGTE2 is faster than or comparable with FRAGTE1. Besides, FRAGTE2 is independent of genome completeness, the same as FRAGTE1. We therefore recommend FRAGTE2 tailored for sieving to facilitate species demarcation in prokaryotes.

Human skin microbiome dysbiosis can have clinical consequences. Characterizing taxonomic composition of bacterial communities associated with skin disorders is important for dermatological advancement in both diagnosis and novel treatments. This study aims to analyze and improve the accuracy of taxonomic classification of skin bacteria with MinION™ nanopore sequencing using a defined skin mock community and a skin microbiome sample. We compared the Oxford Nanopore Technologies recommended procedures and concluded that their protocols highly bias the relative abundance of certain skin microbiome genera, most notably a large overrepresentation of Staphylococcus and underrepresentation of Cutibacterium and Corynebacterium. We demonstrated that changes in the amplification protocols improved the accuracy of the taxonomic classification for these three main skin bacterial genera. This study shows that MinION™ nanopore could be an efficient technology for full-length 16S rRNA sequencing; however, the analytical advantage is strongly influenced by the methodologies. The suggested alternatives in the sample processing improved characterization of a complex skin microbiome community using MinION™ nanopore sequencing. [ABSTRACT FROM AUTHOR]

Human skin microbiome dysbiosis can have clinical consequences. Characterizing taxonomic composition of bacterial communities associated with skin disorders is important for dermatological advancement in both diagnosis and novel treatments. This study aims to analyze and improve the accuracy of taxonomic classification of skin bacteria with MinION™ nanopore sequencing using a defined skin mock community and a skin microbiome sample. We compared the Oxford Nanopore Technologies recommended procedures and concluded that their protocols highly bias the relative abundance of certain skin microbiome genera, most notably a large overrepresentation of Staphylococcus and underrepresentation of Cutibacterium and Corynebacterium. We demonstrated that changes in the amplification protocols improved the accuracy of the taxonomic classification for these three main skin bacterial genera. This study shows that MinION™ nanopore could be an efficient technology for full-length 16S rRNA sequencing; however, the analytical advantage is strongly influenced by the methodologies. The suggested alternatives in the sample processing improved characterization of a complex skin microbiome community using MinION™ nanopore sequencing.

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. The framework of fIDBAC. fIDBAC platform contains a bacterial type-strain genome database, genome analysis pipeline, and separated analysis tools. Genome assemblies from the public database were quality checked and filtered. And the identification strategy was evaluated by three high-quality datasets—GEBA, FDA-ARGROS, and NCTC-3000 datasets. [ABSTRACT FROM AUTHOR]

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.

We evaluated the performance of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) associated with the Bruker BioTyperTM V7.0.0 database for the identification of 713 bacterial strains isolated from seafood products and sea water samples (ANSES B3PA collection) under culture conditions that may have been significantly different from those used to create the reference spectrum vs. the 16S rDNA sequencing. We identified 78.8% of seafood isolates with 46.7% at the species level (Bruker score above 2) and 21.2% (Bruker score between 1.7 and 2) at the genus level by the two identification methods, except for 3.8% of isolates with a difference of identification between the two methods (Bruker score between 1.7 and 2). There were 41.9% isolates (Bruker score below 1.7) with the identification at the genus level. We identified 94.4% of seafood isolates with 16S rDNA sequencing. The MALDI-TOF allowed a better strain identification to the species level contrary to the 16s rDNA sequencing, which allowed an identification mainly to the genus level. MALDI-TOF MS in association with the Bruker database and 16S rDNA sequencing are powerful tools to identify a wide variety of bacteria from seafood but require further identification by biochemical, molecular technique or other conventional tests.

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis is a rapid and reliable method for bacterial identification. Classification algorithms, as a critical part of the MALDI-TOF MS analysis approach, have been developed using both traditional algorithms and machine learning algorithms. In this study, a method that combined helix matrix transformation with a convolutional neural network (CNN) algorithm was presented for bacterial identification. A total of 14 bacterial species including 58 strains were selected to create an in-house MALDI-TOF MS spectrum dataset. The 1D array-type MALDI-TOF MS spectrum data were transformed through a helix matrix transformation into matrix-type data, which was fitted during the CNN training. Through the parameter optimization, the threshold for binarization was set as 16 and the final size of a matrix-type data was set as 25 × 25 to obtain a clean dataset with a small size. A CNN model with three convolutional layers was well trained using the dataset to predict bacterial species. The filter sizes for the three convolutional layers were 4, 8, and 16. The kernel size was three and the activation function was the rectified linear unit (ReLU). A back propagation neural network (BPNN) model was created without helix matrix transformation and a convolution layer to demonstrate whether the helix matrix transformation combined with CNN algorithm works better. The areas under the receiver operating characteristic (ROC) curve of the CNN and BPNN models were 0.98 and 0.87, respectively. The accuracies of the CNN and BPNN models were 97.78 ± 0.08 and 86.50 ± 0.01, respectively, with a significant statistical difference (p < 0.001). The results suggested that helix matrix transformation combined with the CNN algorithm enabled the feature extraction of the bacterial MALDI-TOF MS spectrum, which might be a proposed solution to identify bacterial species.

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis is a rapid and reliable method for bacterial identification. Classification algorithms, as a critical part of the MALDI-TOF MS analysis approach, have been developed using both traditional algorithms and machine learning algorithms. In this study, a method that combined helix matrix transformation with a convolutional neural network (CNN) algorithm was presented for bacterial identification. A total of 14 bacterial species including 58 strains were selected to create an in-house MALDI-TOF MS spectrum dataset. The 1D array-type MALDI-TOF MS spectrum data were transformed through a helix matrix transformation into matrix-type data, which was fitted during the CNN training. Through the parameter optimization, the threshold for binarization was set as 16 and the final size of a matrix-type data was set as 25 × 25 to obtain a clean dataset with a small size. A CNN model with three convolutional layers was well trained using the dataset to predict bacterial species. The filter sizes for the three convolutional layers were 4, 8, and 16. The kernel size was three and the activation function was the rectified linear unit (ReLU). A back propagation neural network (BPNN) model was created without helix matrix transformation and a convolution layer to demonstrate whether the helix matrix transformation combined with CNN algorithm works better. The areas under the receiver operating characteristic (ROC) curve of the CNN and BPNN models were 0.98 and 0.87, respectively. The accuracies of the CNN and BPNN models were 97.78 ± 0.08 and 86.50 ± 0.01, respectively, with a significant statistical difference (p < 0.001). The results suggested that helix matrix transformation combined with the CNN algorithm enabled the feature extraction of the bacterial MALDI-TOF MS spectrum, which might be a proposed solution to identify bacterial species. [ABSTRACT FROM AUTHOR]

Usually, 18–48 h are needed for the identification of microbial pathogens causing urinary tract infections (UTIs) by urine culture. Moreover, antimicrobial susceptibility testing (AST) takes an additional 18–24 h. Rapid identification and AST of the pathogens allow fast and precise treatment. The objective of this study was to shorten the time of diagnosis of UTIs by combining pathogen screening through flow cytometry, microbial identification by matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS), and AST using the VITEK 2 system for the direct analysis of urine samples. We analyzed 1,638 urine samples from patients with suspected UTIs submitted to the microbiology laboratory for culture. Each urine sample had an approximate volume of 30 mL and was divided into three aliquots. Urine processing included differential centrifugation and two washes to enrich the bacterial fraction for direct MALDI-TOF MS and direct AST. From a total of 1,638 urine samples, 307 were found to be positive through UF-1000i screening. Among them, 265 had significant growth of a single-microorganism. Direct identification was obtained in 229 (86.42%) out of these 265 samples, and no pathogens were misidentified. Moreover, species-level identification was obtained in 163 (88.59%) out of the 184 samples with Gram-negative bacteria, and 27 (38.03%) out of the 71 samples with Gram-positive bacteria. VITEK 2 AST was performed for 117 samples with a single-microorganism. Enterobacteriaceae data showed an agreement rate of antimicrobial categories of 94.83% (1,229/1,296), with minor, major, and very major error rates of 4.17% (54/1,296), 0.92% (12/1,296), and 0.08% (1/1,296), respectively. For Enterococcus spp., the overall categorical agreement was 92.94% (158/170), with a minor error rate of 2.94% (5/170) and major error rate of 4.12% (7/170). The turnaround time of this combined protocol to diagnose UTIs was 1 h for pathogen identification and 6–24 h for AST; noteworthily, only 6–8 h are needed for AST of Enterobacteriaceae using the VITEK 2 system. Overall, our findings show that the combination of flow cytometry, MALDI-TOF MS, and VITEK 2 provided a direct, rapid, and reliable identification and AST method for assessing urine samples, especially for Gram-negative bacterial infections.

Usually, 18–48 h are needed for the identification of microbial pathogens causing urinary tract infections (UTIs) by urine culture. Moreover, antimicrobial susceptibility testing (AST) takes an additional 18–24 h. Rapid identification and AST of the pathogens allow fast and precise treatment. The objective of this study was to shorten the time of diagnosis of UTIs by combining pathogen screening through flow cytometry, microbial identification by matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS), and AST using the VITEK 2 system for the direct analysis of urine samples. We analyzed 1,638 urine samples from patients with suspected UTIs submitted to the microbiology laboratory for culture. Each urine sample had an approximate volume of 30 mL and was divided into three aliquots. Urine processing included differential centrifugation and two washes to enrich the bacterial fraction for direct MALDI-TOF MS and direct AST. From a total of 1,638 urine samples, 307 were found to be positive through UF-1000i screening. Among them, 265 had significant growth of a single-microorganism. Direct identification was obtained in 229 (86.42%) out of these 265 samples, and no pathogens were misidentified. Moreover, species-level identification was obtained in 163 (88.59%) out of the 184 samples with Gram-negative bacteria, and 27 (38.03%) out of the 71 samples with Gram-positive bacteria. VITEK 2 AST was performed for 117 samples with a single-microorganism. Enterobacteriaceae data showed an agreement rate of antimicrobial categories of 94.83% (1,229/1,296), with minor, major, and very major error rates of 4.17% (54/1,296), 0.92% (12/1,296), and 0.08% (1/1,296), respectively. For Enterococcus spp., the overall categorical agreement was 92.94% (158/170), with a minor error rate of 2.94% (5/170) and major error rate of 4.12% (7/170). The turnaround time of this combined protocol to diagnose UTIs was 1 h for pathogen identification and 6–24 h for AST; noteworthily, only 6–8 h are needed for AST of Enterobacteriaceae using the VITEK 2 system. Overall, our findings show that the combination of flow cytometry, MALDI-TOF MS, and VITEK 2 provided a direct, rapid, and reliable identification and AST method for assessing urine samples, especially for Gram-negative bacterial infections. [ABSTRACT FROM AUTHOR]

Many ecological experiments are based on the extraction and downstream analyses of microorganisms from different environmental samples. Due to its high throughput, cost-effectiveness and rapid performance, Matrix Assisted Laser Desorption/Ionization Mass Spectrometry with Time-of-Flight detector (MALDI-TOF MS), which has been proposed as a promising tool for bacterial identification and classification, could be advantageously used for dereplication of recurrent bacterial isolates. In this study, we compared whole-cell MALDI-TOF MS-based analyses of 49 bacterial cultures to two well-established bacterial identification and classification methods based on nearly complete 16S rRNA gene sequence analyses: a phylotype-based approach, using a closest type strain assignment, and a sequence similarity-based approach involving a 98.65% sequence similarity threshold, which has been found to best delineate bacterial species. Culture classification using reference-based MALDI-TOF MS was comparable to that yielded by phylotype assignment up to the genus level. At the species level, agreement between 16S rRNA gene analysis and MALDI-TOF MS was found to be limited, potentially indicating that spectral reference databases need to be improved. We also evaluated the mass spectral similarity technique for species-level delineation which can be used independently of reference databases. We established optimal mass spectral similarity thresholds which group MALDI-TOF mass spectra of common environmental isolates analogically to phylotype- and sequence similarity-based approaches. When using a mass spectrum similarity approach, we recommend a mass range of 4–10 kDa for analysis, which is populated with stable mass signals and contains the majority of phylotype-determining peaks. We show that a cosine similarity (CS) threshold of 0.79 differentiate mass spectra analogously to 98.65% species-level delineation sequence similarity threshold, with corresponding precision and recall values of 0.70 and 0.73, respectively. When matched to species-level phylotype assignment, an optimal CS threshold of 0.92 was calculated, with associated precision and recall values of 0.83 and 0.64, respectively. Overall, our research indicates that a similarity-based MALDI-TOF MS approach can be routinely used for efficient dereplication of isolates for downstream analyses, with minimal loss of unique organisms. In addition, MALDI-TOF MS analysis has further improvement potential unlike 16S rRNA gene analysis, whose methodological limits have reached a plateau.

Timeliness and accuracy in the diagnosis of microbial infections are associated with decreased mortality and reduced length of hospitalization, especially for severe, life-threatening infections. A rapid diagnosis also allows for early streamlining of empirical antimicrobial therapies, thus contributing to limit the emergence and spread of antimicrobial resistance. The introduction of matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry (MS) for routine identification of microbial pathogens has profoundly influenced microbiological diagnostics, and is progressively replacing biochemical identification methods. Compared to currently used identification methods, MALDI-TOF MS has the advantage of identifying bacteria and yeasts directly from colonies grown on culture plates for primary isolation in a few minutes and with considerable material and labor savings. The reliability and accuracy of MALDI-TOF MS in identification of clinically relevant bacteria and yeasts has been demonstrated by several studies showing that the performance of MALDI-TOF MS is comparable or superior to phenotypic methods currently in use in clinical microbiology laboratories, and can be further improved by database updates and analysis software upgrades. Besides microbial identification from isolated colonies, new perspectives are being explored for MALDI-TOF MS, such as identification of pathogens directly from positive blood cultures, sub-species typing, and detection of drug resistance determinants. In this review, we summarize the state of the art in routine identification of microbial pathogens by MALDI-TOF MS, and highlight recent advancements of this technology in special applications, such as strain typing, assessment of drug susceptibility, and detection of virulence factors.

Many ecological experiments are based on the extraction and downstream analyses of microorganisms from different environmental samples. Due to its high throughput, cost-effectiveness and rapid performance, Matrix Assisted Laser Desorption/Ionization Mass Spectrometry with Time-of-Flight detector (MALDI-TOF MS), which has been proposed as a promising tool for bacterial identification and classification, could be advantageously used for dereplication of recurrent bacterial isolates. In this study, we compared whole-cell MALDI-TOF MS-based analyses of 49 bacterial cultures to two well-established bacterial identification and classification methods based on nearly complete 16S rRNA gene sequence analyses: a phylotype-based approach, using a closest type strain assignment, and a sequence similarity-based approach involving a 98.65% sequence similarity threshold, which has been found to best delineate bacterial species. Culture classification using reference-based MALDI-TOF MS was comparable to that yielded by phylotype assignment up to the genus level. At the species level, agreement between 16S rRNA gene analysis and MALDI-TOF MS was found to be limited, potentially indicating that spectral reference databases need to be improved. We also evaluated the mass spectral similarity technique for species-level delineation which can be used independently of reference databases. We established optimal mass spectral similarity thresholds which group MALDI-TOF mass spectra of common environmental isolates analogically to phylotype- and sequence similarity-based approaches. When using a mass spectrum similarity approach, we recommend a mass range of 4–10 kDa for analysis, which is populated with stable mass signals and contains the majority of phylotype-determining peaks. We show that a cosine similarity (CS) threshold of 0.79 differentiate mass spectra analogously to 98.65% species-level delineation sequence similarity threshold, with corresponding precision and recall values of 0.70 and 0.73, respectively. When matched to species-level phylotype assignment, an optimal CS threshold of 0.92 was calculated, with associated precision and recall values of 0.83 and 0.64, respectively. Overall, our research indicates that a similarity-based MALDI-TOF MS approach can be routinely used for efficient dereplication of isolates for downstream analyses, with minimal loss of unique organisms. In addition, MALDI-TOF MS analysis has further improvement potential unlike 16S rRNA gene analysis, whose methodological limits have reached a plateau. [ABSTRACT FROM AUTHOR]

Timeliness and accuracy in the diagnosis of microbial infections are associated with decreased mortality and reduced length of hospitalization, especially for severe, lifethreatening infections. A rapid diagnosis also allows for early streamlining of empirical antimicrobial therapies, thus contributing to limit the emergence and spread of antimicrobial resistance. The introduction of matrix-assisted laser desorption/ionizationtime of flight (MALDI-TOF) mass spectrometry (MS) for routine identification of microbial pathogens has profoundly influenced microbiological diagnostics, and is progressively replacing biochemical identification methods. Compared to currently used identification methods, MALDI-TOF MS has the advantage of identifying bacteria and yeasts directly from colonies grown on culture plates for primary isolation in a few minutes and with considerable material and labor savings. The reliability and accuracy of MALDI-TOF MS in identification of clinically relevant bacteria and yeasts has been demonstrated by several studies showing that the performance of MALDI-TOF MS is comparable or superior to phenotypic methods currently in use in clinical microbiology laboratories, and can be further improved by database updates and analysis software upgrades. Besides microbial identification from isolated colonies, new perspectives are being explored for MALDI-TOF MS, such as identification of pathogens directly from positive blood cultures, sub-species typing, and detection of drug resistance determinants. In this review, we summarize the state of the art in routine identification of microbial pathogens by MALDI-TOF MS, and highlight recent advancements of this technology in special applications, such as strain typing, assessment of drug susceptibility, and detection of virulence factors. [ABSTRACT FROM AUTHOR]

With the decline in the cost of whole-genome sequencing because of the introduction of next-generation sequencing (NGS) techniques, many public health and clinical laboratories have started to use bacterial whole genomes for epidemiological surveillance and clinical investigation. For epidemiological and clinical purposes in this “NGS era,” whole-genome-scale single nucleotide polymorphism (wgSNP) analysis for genotyping is considered suitable. In this paper, we present an online service, PathoBacTyper (http://halst.nhri.org.tw/PathoBacTyper/), for pathogenic bacteria identification and genotyping based on wgSNP analysis. More than 400 pathogenic bacteria can be identified and genotyped through this service. Four data sets containing 59 Salmonella Heidelberg isolates from three outbreaks with the same pulsed-field gel electrophoresis pattern, 34 Salmonella Typhimurium isolates from six outbreaks, 103 isolates of hospital-associated vancomycin-resistant Enterococcus faecium and 15 Legionella pneumophila isolates from clinical and environmental samples in Israel were used for demonstrating the operation and testing the performance of the PathoBacTyper service. The test results reveal the applicability of this service for epidemiological typing and clinical investigation.

Currently microorganisms are best identified using 16S rRNA and 18S rRNA gene sequencing. However, in recent years matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has emerged as a potential tool for microbial identification and diagnosis. During the MALDI-TOF MS process, microbes are identified using either intact cells or cell extracts. The process is rapid, sensitive, and economical in terms of both labor and costs involved. The technology has been readily imbibed by microbiologists who have reported usage of MALDI-TOF MS for a number of purposes like, microbial identification and strain typing, epidemiological studies, detection of biological warfare agents, detection of water- and food-borne pathogens, detection of antibiotic resistance and detection of blood and urinary tract pathogens etc. The limitation of the technology is that identification of new isolates is possible only if the spectral database contains peptide mass fingerprints of the type strains of specific genera/species/subspecies/strains. This review provides an overview of the status and recent applications of mass spectrometry for microbial identification. It also explores the usefulness of this exciting new technology for diagnosis of diseases caused by bacteria, viruses, and fungi. [ABSTRACT FROM AUTHOR]