Comparison of the performance of multiple whole-genome sequence-based tools for the identification of Bacillus cereus sensu stricto biovar Thuringiensis.

The Bacillus cereus sensu stricto (s.s.) species comprises strains of biovar Thuringiensis (Bt) known for their bioinsecticidal activity, as well as strains with foodborne pathogenic potential. Bt strains are identified (i) based on the production of insecticidal crystal proteins, also known as Bt toxins, or (ii) based on the presence of cry, cyt, and vip genes, which encode Bt toxins. Multiple bioinformatics tools have been developed for the detection of crystal protein-encoding genes based on whole-genome sequencing (WGS) data. However, the performance of these tools is yet to be evaluated using phenotypic data. Thus, the goal of this study was to assess the performance of four bioinformatics tools for the detection of crystal protein-encoding genes. The accuracy of sequence-based identification of Bt was determined in reference to phenotypic microscope-based screening for the production of crystal proteins. A total of 58 diverse B. cereus sensu lato strains isolated from clinical, food, environmental, and commercial biopesticide products underwent WGS. Isolates were examined for crystal protein production using phase contrast microscopy. Crystal protein-encoding genes were detected using BtToxin_Digger, BTyper3, IDOPS (identification of pesticidal sequences), and Cry_processor. Out of 58 isolates, the phenotypic production of crystal proteins was confirmed for 18 isolates. Specificity and sensitivity of Bt identification based on sequences were 0.85 and 0.94 for BtToxin_Digger, 0.97 and 0.89 for BTyper3, 0.95 and 0.94 for IDOPS, and 0.88 and 1.00 for Cry_processor, respectively. Cry_processor predicted crystal protein production with the highest specificity, and BtToxin_Digger and IDOPS predicted crystal protein production with the highest sensitivity. Three out of four tested bioinformatics tools performed well overall, with IDOPS achieving high sensitivity and specificity (>0.90).IMPORTANCEStrains of Bacillus cereus sensu stricto (s.s.) biovar Thuringiensis (Bt) are used as organic biopesticides. Bt is differentiated from the foodborne pathogen Bacillus cereus s.s. by the production of insecticidal crystal proteins. Thus, reliable genomic identification of biovar Thuringiensis is necessary to ensure food safety and facilitate risk assessment. This study assessed the accuracy of whole-genome sequencing (WGS)-based identification of Bt compared to phenotypic microscopy-based screening for crystal protein production. Multiple bioinformatics tools were compared to assess their performance in predicting crystal protein production. Among them, identification of pesticidal sequences performed best overall at WGS-based Bt identification.

Genomic characterization of Listeria monocytogenes and Listeria innocua isolated from milk and dairy samples in Ethiopia.

Listeriosis caused by Listeria monocytogenes often poses a significant threat to vulnerable populations. Dairy products have been implicated in outbreaks of listeriosis worldwide. In Ethiopia, studies have identified Listeria spp. and L. monocytogenes in various dairy products, but the genetic diversity and phylogenetic relationships of these bacteria remain largely unknown in the low- and middle-income countries. Therefore, we conducted whole-genome sequencing on 15 L. monocytogenes and 55 L. innocua isolates obtained from different levels of the dairy supply chains across three regions in Ethiopia. Genomes were assembled and used for MLST genotyping and single nucleotide polymorphism (SNP) analysis to infer phylogenetic relationships. We identified a total of 3 L. monocytogenes (i.e., 2, 145, and 18) and 12 L. innocua (i.e., 1489, 1619, 603, 537, 1010, 3186, 492, 3007, 1087, 474, 1008, and 637) MLST sequence types among the studied isolates. Some of these sequence types showed region-specific occurrence, while others were broadly distributed across regions. Through high-quality SNP analysis, we found that among 13 L. monocytogenes identified as ST 2, 11 of them were highly similar with low genetic variation, differing by only 1 to 10 SNPs, suggesting potential selection in the dairy food supply chain. The L. innocua isolates also exhibited low intra-ST genetic variation with only 0-10 SNP differences, except for the ST 1619, which displayed a greater diversity.

The composition of environmental microbiota in three tree fruit packing facilities changed over seasons and contained taxa indicative of L. monocytogenes contamination.

BACKGROUND: Listeria monocytogenes can survive in cold and wet environments, such as tree fruit packing facilities and it has been implicated in outbreaks and recalls of tree fruit products. However, little is known about microbiota that co-occurs with L. monocytogenes and its stability over seasons in tree fruit packing environments. In this 2-year longitudinal study, we aimed to characterize spatial and seasonal changes in microbiota composition and identify taxa indicative of L. monocytogenes contamination in wet processing areas of three tree fruit packing facilities (F1, F2, F3). METHODS: A total of 189 samples were collected during two apple packing seasons from floors under the washing, drying, and waxing areas. The presence of L. monocytogenes was determined using a standard culturing method, and environmental microbiota was characterized using amplicon sequencing. PERMANOVA was used to compare microbiota composition among facilities over two seasons, and abundance-occupancy analysis was used to identify shared and temporal core microbiota. Differential abundance analysis and random forest were applied to detect taxa indicative of L. monocytogenes contamination. Lastly, three L. monocytogenes-positive samples were sequenced using shotgun metagenomics with Nanopore MinION, as a proof-of-concept for direct detection of L. monocytogenes' DNA in environmental samples. RESULTS: The occurrence of L. monocytogenes significantly increased from 28% in year 1 to 46% in year 2 in F1, and from 41% in year 1 to 92% in year 2 in F3, while all samples collected from F2 were L. monocytogenes-positive in both years. Samples collected from three facilities had a significantly different microbiota composition in both years, but the composition of each facility changed over years. A subset of bacterial taxa including Pseudomonas, Stenotrophomonas, and Microbacterium, and fungal taxa, including Yarrowia, Kurtzmaniella, Cystobasidium, Paraphoma, and Cutaneotrichosporon, were identified as potential indicators of L. monocytogenes within the monitored environments. Lastly, the DNA of L. monocytogenes was detected through direct Nanopore sequencing of metagenomic DNA extracted from environmental samples. CONCLUSIONS: This study demonstrated that a cross-sectional sampling strategy may not accurately reflect the representative microbiota of food processing facilities. Our findings also suggest that specific microorganisms are indicative of L. monocytogenes, warranting further investigation of their role in the survival and persistence of L. monocytogenes. Video Abstract.

Conditional Forest Models Built Using Metagenomic Data Accurately Predicted Salmonella Contamination in Northeastern Streams.

The use of water contaminated with Salmonella for produce production contributes to foodborne disease burden. To reduce human health risks, there is a need for novel, targeted approaches for assessing the pathogen status of agricultural water. We investigated the utility of water microbiome data for predicting Salmonella contamination of streams used to source water for produce production. Grab samples were collected from 60 New York streams in 2018 and tested for Salmonella. Separately, DNA was extracted from the samples and used for Illumina shotgun metagenomic sequencing. Reads were trimmed and used to assign taxonomy with Kraken2. Conditional forest (CF), regularized random forest (RRF), and support vector machine (SVM) models were implemented to predict Salmonella contamination. Model performance was assessed using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. CF models outperformed the other two algorithms based on AUC (0.86, CF; 0.81, RRF; 0.65, SVM) and Kappa score (0.53, CF; 0.41, RRF; 0.12, SVM). The taxa that were most informative for accurately predicting Salmonella contamination based on CF were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and -negative samples. CF and differential abundance tests both identified Aeromonas salmonicida (variable importance [VI] = 0.012) and Aeromonas sp. strain CA23 (VI = 0.025) as the two most informative taxa for predicting Salmonella contamination. Our findings suggest that microbiome-based models may provide an alternative to or complement existing water monitoring strategies. Similarly, the informative taxa identified in this study warrant further investigation as potential indicators of Salmonella contamination of agricultural water. IMPORTANCE Understanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella, can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams. The research reported here both expanded the knowledge on the microbiome composition of surface waters and identified putative novel indicators (i.e., Aeromonas species) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella contamination across regions, waterways, and years not represented in the data set used in this study. Validated indicators identified using microbiome data may be used as targets in the development of rapid (e.g., PCR-based) detection assays for the assessment of microbial safety of agricultural surface waters.

Dynamic Laser Speckle Imaging Meets Machine Learning to Enable Rapid Antibacterial Susceptibility Testing (DyRAST).

Rapid antibacterial susceptibility testing (RAST) methods are of significant importance in healthcare, as they can assist caregivers in timely administration of the correct treatments. Various RAST techniques have been reported for tracking bacterial phenotypes, including size, shape, motion, and redox state. However, they still require bulky and expensive instruments-which hinder their application in resource-limited environments-and/or utilize labeling reagents which can interfere with antibiotics and add to the total cost. Furthermore, the existing RAST methods do not address the potential gradual adaptation of bacteria to antibiotics, which can lead to a false diagnosis. In this work, we present a RAST approach by leveraging machine learning to analyze time-resolved dynamic laser speckle imaging (DLSI) results. DLSI captures the change in bacterial motion in response to antibiotic treatments. Our method accurately predicts the minimum inhibitory concentration (MIC) of ampicillin and gentamicin for a model strain of Escherichia coli (E. coli K-12) in 60 min, compared to 6 h using the currently FDA-approved phenotype-based RAST technique. In addition to ampicillin (a ß-lactam) and gentamicin (an aminoglycoside), we studied the effect of ceftriaxone (a third-generation cephalosporin) on E. coli K-12. The machine learning algorithm was trained and validated using the overnight results of a gold standard antibacterial susceptibility testing method enabling prediction of MIC with a similarly high accuracy yet substantially faster.

The Composition of Microbial Communities in Six Streams, and Its Association With Environmental Conditions, and Foodborne Pathogen Isolation.

Surface water used for produce production is a potential source of pre-harvest contamination with foodborne pathogens. Decisions on how to mitigate food safety risks associated with pre-harvest water use currently rely on generic Escherichia coli-based water quality tests, although multiple studies have suggested that E. coli levels are not a suitable indicator of the food safety risks under all relevant environmental conditions. Hence, improved understanding of spatiotemporal variability in surface water microbiota composition is needed to facilitate identification of alternative or supplementary indicators that co-occur with pathogens. To this end, we aimed to characterize the composition of bacterial and fungal communities in the sediment and water fractions of 68 agricultural water samples collected from six New York streams. We investigated potential associations between the composition of microbial communities, environmental factors and Salmonella and/or Listeria monocytogenes isolation. We found significantly different composition of fungal and bacterial communities among sampled streams and among water fractions of collected samples. This indicates that geography and the amount of sediment in a collected water sample may affect its microbial composition, which was further supported by identified associations between the flow rate, turbidity, pH and conductivity, and microbial community composition. Lastly, we identified specific microbial families that were weakly associated with the presence of Salmonella or Listeria monocytogenes, however, further studies on samples from additional streams are needed to assess whether identified families may be used as indicators of pathogen presence.

The occurrence of Listeria monocytogenes is associated with built environment microbiota in three tree fruit processing facilities.

BACKGROUND: Multistate foodborne disease outbreaks and recalls of apples and apple products contaminated with Listeria monocytogenes demonstrate the need for improved pathogen control in the apple supply chain. Apple processing facilities have been identified in the past as potential sources of persisting L. monocytogenes contamination. In this study, we sought to understand the composition of microbiota in built apple and other tree fruit processing environments and its association with the occurrence of the foodborne pathogen L. monocytogenes. RESULTS: Analysis of 117 samples collected from three apple and other tree fruit packing facilities (F1, F2, and F3) showed that facility F2 had a significantly higher L. monocytogenes occurrence compared to F1 and F3 (p < 0.01). The microbiota in facility F2 was distinct compared to facilities F1 and F3 as supported by the mean Shannon index for bacterial and fungal alpha diversities that was significantly lower in F2, compared to F1 and F3 (p < 0.01). Microbiota in F2 was uniquely predominated by bacterial family Pseudomonadaceae and fungal family Dipodascaceae. CONCLUSIONS: The composition and diversity of microbiota and mycobiota present in the investigated built food processing environments may be indicative of persistent contamination with L. monocytogenes. These findings support the need for further investigation of the role of the microbial communities in the persistence of L. monocytogenes to support the optimization of L. monocytogenes control strategies in the apple supply chain.

Whole-Genome Sequences of Escherichia coli Isolates from Cocoa Beans Imported from Bolivia.

Four Escherichia coli isolates were obtained from a batch of cocoa beans imported from Bolivia. The cocoa beans were rejected by a U.S. chocolate manufacturer due to poor microbiological quality. The four isolates were whole-genome sequenced and the sequences analyzed to identify genotypes, serotypes, and virulence and antimicrobial resistance genes.

Genomic characterization of malonate positive Cronobacter sakazakii serotype O:2, sequence type 64 strains, isolated from clinical, food, and environment samples.

BACKGROUND: Malonate utilization, an important differential trait, well recognized as being possessed by six of the seven Cronobacter species is thought to be largely absent in Cronobacter sakazakii (Csak). The current study provides experimental evidence that confirms the presence of a malonate utilization operon in 24 strains of sequence type (ST) 64, obtained from Europe, Middle East, China, and USA; it offers explanations regarding the genomic diversity and phylogenetic relatedness among these strains, and that of other C. sakazakii strains. RESULTS: In this study, the presence of a malonate utilization operon in these strains was initially identified by DNA microarray analysis (MA) out of a pool of 347 strains obtained from various surveillance studies involving clinical, spices, milk powder sources and powdered infant formula production facilities in Ireland and Germany, and dried dairy powder manufacturing facilities in the USA. All ST64 C. sakazakii strains tested could utilize malonate. Zebrafish embryo infection studies showed that C. sakazakii ST64 strains are as virulent as other Cronobacter species. Parallel whole genome sequencing (WGS) and MA showed that the strains phylogenetically grouped as a separate clade among the Csak species cluster. Additionally, these strains possessed the Csak O:2 serotype. The nine-gene, ~ 7.7 kbp malonate utilization operon was located in these strains between two conserved flanking genes, gyrB and katG. Plasmidotyping results showed that these strains possessed the virulence plasmid pESA3, but in contrast to the USA ST64 Csak strains, ST64 Csak strains isolated from sources in Europe and the Middle East, did not possess the type six secretion system effector vgrG gene. CONCLUSIONS: Until this investigation, the presence of malonate-positive Csak strains, which are associated with foods and clinical cases, was under appreciated. If this trait was used solely to identify Cronobacter strains, many strains would likely be misidentified. Parallel WGS and MA were useful in characterizing the total genome content of these Csak O:2, ST64, malonate-positive strains and further provides an understanding of their phylogenetic relatedness among other virulent C. sakazakii strains.

Analysis and Characterization of Proteins Associated with Outer Membrane Vesicles Secreted by Cronobacter spp.

Little is known about secretion of outer membrane vesicles (OMVs) by Cronobacter. In this study, OMVs isolated from Cronobacter sakazakii, Cronobacter turicensis, and Cronobacter malonaticus were examined by electron microscopy (EM) and their associated outer membrane proteins (OMP) and genes were analyzed by SDS-PAGE, protein sequencing, BLAST, PCR, and DNA microarray. EM of stained cells revealed that the OMVs are secreted as pleomorphic micro-vesicles which cascade from the cell's surface. SDS-PAGE analysis identified protein bands with molecular weights of 18 kDa to >100 kDa which had homologies to OMPs such as GroEL; OmpA, C, E, F, and X; MipA proteins; conjugative plasmid transfer protein; and an outer membrane auto-transporter protein (OMATP). PCR analyses showed that most of the OMP genes were present in all seven Cronobacter species while a few genes (OMATP gene, groEL, ompC, mipA, ctp, and ompX) were absent in some phylogenetically-related species. Microarray analysis demonstrated sequence divergence among the OMP genes that was not captured by PCR. These results support previous findings that OmpA and OmpX may be involved in virulence of Cronobacter, and are packaged within secreted OMVs. These results also suggest that other OMV-packaged OMPs may be involved in roles such as stress response, cell wall and plasmid maintenance, and extracellular transport.

Development of a Custom-Designed, Pan Genomic DNA Microarray to Characterize Strain-Level Diversity among Cronobacter spp.

Cronobacter species cause infections in all age groups; however neonates are at highest risk and remain the most susceptible age group for life-threatening invasive disease. The genus contains seven species:Cronobacter sakazakii, Cronobacter malonaticus, Cronobacter turicensis, Cronobacter muytjensii, Cronobacter dublinensis, Cronobacter universalis, and Cronobacter condimenti. Despite an abundance of published genomes of these species, genomics-based epidemiology of the genus is not well established. The gene content of a diverse group of 126 unique Cronobacter and taxonomically related isolates was determined using a pan genomic-based DNA microarray as a genotyping tool and as a means to identify outbreak isolates for food safety, environmental, and clinical surveillance purposes. The microarray constitutes 19,287 independent genes representing 15 Cronobacter genomes and 18 plasmids and 2,371 virulence factor genes of phylogenetically related Gram-negative bacteria. The Cronobacter microarray was able to distinguish the seven Cronobacter species from one another and from non-Cronobacter species; and within each species, strains grouped into distinct clusters based on their genomic diversity. These results also support the phylogenic divergence of the genus and clearly highlight the genomic diversity among each member of the genus. The current study establishes a powerful platform for further genomics research of this diverse genus, an important prerequisite toward the development of future countermeasures against this foodborne pathogen in the food safety and clinical arenas.