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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.

A major concern for plant nurseries and land stewards is managing pathogens that induce soilborne diseases. Plant diseases cost nurseries millions of dollars in lost revenue. Plant pathogens spread into the environment from nursery soil used for out-plantings. Recent research shows that steaming soil at high temperatures can effectively kill plant pathogens, however, few studies investigate the influence of steaming on the soil microbiome, an essential component of plant health. We conducted a controlled experiment where nursery soil underwent a steaming event within a rectangular mesocosm (2.04 x 3.55 x 0.25 m) located at the National Ornamentals Research Site at Dominican University (NORS-DUC) (San Rafael, CA, US). Soil samples were collected at four time points post-steaming. Thirty-two soil samples were used for DNA extraction and 18 for chemical analysis. The 16S rRNA gene and ITS genetic markers were used for amplicon-based sequencing methods to identify the bacterial and fungal soil communities. Results indicate that microbial diversity and composition shifted overtime, but that soil depth was not an important factor. Important bacterial taxa were the Firmicutes, especially 1 day post-steaming, and common fungal taxa included Aspergillus sp., Cladosporium sp., and Pseudallescheria sp.. Plant nutrients such as B, C, Cl, K, Mg, SO4, and Zn in addition to essential soil properties such as CEC, gypsum, moisture, and SAR changed significantly with soil recovery. These results can help nursery practitioners make informed decisions for biotic and nutrient amendments post-steaming to promote soil and plant health.

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 determined using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. Taxa identified as the most informative for accurately predicting Salmonella contamination based on conditional variable importance were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and - negative samples. CF models outperformed the other two algorithms based on AUC (0.82 - CF, 0.76 - RRF, 0.67 - SVM) and Kappa score (0.41- CF, 0.38 - RRF, 0.19 - SVM). CF and differential abundance tests both identified Aeromonas (VI = 0.32) and Tabrizicola (VI = 0.12) as the two most informative taxa for predicting Salmonella contamination. The taxa identified in this study warrant further investigation as indicators of Salmonella contamination in Northeastern freshwater streams.IMPORTANCEUnderstanding 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 and Tabrizicola) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella for regions, waterways, and years not represented in the dataset used in this study.

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 β-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.

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. Electronic supplementary material The online version of this article (10.1186/s40168-019-0726-2) contains supplementary material, which is available to authorized users.

This white paper presents a series of perspectives on current and future phytobiome management, discussed at the Wild and Tamed Phytobiomes Symposium in University Park, PA, U.S.A., in June 2018. To enhance plant productivity and health, and to translate lab- and greenhouse-based phytobiome research to field applications, the academic community and end-users need to address a variety of scientific, practical, and social challenges. Prior discussion of phytobiomes has focused heavily on plant-associated bacterial and fungal assemblages, but the phytobiomes concept covers all factors that influence plant function. Here we discuss various management considerations, including abiotic conditions (e.g., soil and nutrient applications), microorganisms (e.g., bacterial and fungal assemblages, bacterial and fungal inoculants, and viruses), macroorganisms (e.g., arthropods and plant genetics), and societal factors (e.g., communication approaches and technology diffusion). An important near-term goal for this field should be to estimate the potential relative contribution of different components of the phytobiome to plant health, as well as the potential and risk of modifying each in the near future.

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. Electronic supplementary material The online version of this article (10.1186/s13099-018-0238-9) contains supplementary material, which is available to authorized users.

Four Escherichia coli isolates were obtained from a batch of cocoa beans imported from Bolivia. The cocoa beans were rejected by a U.S., 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.

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.

We introduce draft genome sequences of strains CDC1121-73 (human bronchial wash isolate) and GK1025 (powdered infant formula manufacturing facility isolate), which are both malonate-positive Cronobacter sakazakii serogroup O:2, sequence type 64. Assemblies for these strains have sizes of 4,442,307 and 4,599,266 bp and % G+C contents of 56.9 and 56.7, respectively.

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.

Table S1. Metadata for collected samples. Table S2. Chi-square test of L. monocytogenes occurrence among processing sections. Table S3. Chi-square test of L. monocytogenes occurrence among facilities. Table S4. Results of pairwise PERMANOVA analyses for microbial communities. Table S5. Results of pairwise PERMANOVA analyses for fungal communities. Table S6. Comparison of relative abundances of bacterial families identified using Greengenes and SILVA database. Figure S1. Microbial network indicating co-occurrence of fungal families identified in samples collected from all three facilities. Figure S2. Microbial network indicating co-occurrence of bacterial and fungal families in samples collected from three facilities F1, F2, and F3 combined. List L1. Data analyses workflow. (DOCX 487 kb)

Table S1. Metadata for collected samples. Table S2. Chi-square test of L. monocytogenes occurrence among processing sections. Table S3. Chi-square test of L. monocytogenes occurrence among facilities. Table S4. Results of pairwise PERMANOVA analyses for microbial communities. Table S5. Results of pairwise PERMANOVA analyses for fungal communities. Table S6. Comparison of relative abundances of bacterial families identified using Greengenes and SILVA database. Figure S1. Microbial network indicating co-occurrence of fungal families identified in samples collected from all three facilities. Figure S2. Microbial network indicating co-occurrence of bacterial and fungal families in samples collected from three facilities F1, F2, and F3 combined. List L1. Data analyses workflow. (DOCX 487 kb)