Microbiota that co-enrich during efforts to recover pathogens from foodborne outbreaks interfere with efficient detection and recovery. Here, dynamics of co-enriching microbiota during recovery of Listeria monocytogenes from naturally contaminated ice cream samples linked to an outbreak are described for three different initial enrichment formulations used by the Food and Drug Administration (FDA), the International Organization of Standardization (ISO), and the United States Department of Agriculture (USDA). Enrichment cultures were analyzed using DNA extraction and sequencing from samples taken every 4 h throughout 48 h of enrichment. Resphera Insight and CosmosID analysis tools were employed for high-resolution profiling of 16S rRNA amplicons and whole genome shotgun data, respectively. During enrichment, other bacterial taxa were identified, including Anoxybacillus, Geobacillus, Serratia, Pseudomonas, Erwinia, and Streptococcus spp. Surprisingly, incidence of L. monocytogenes was proportionally greater at hour 0 than when tested 4, 8, and 12 h later with all three enrichment schemes. The corresponding increase in Anoxybacillus and Geobacillus spp.indicated these taxa co-enriched in competition with L. monocytogenes during early enrichment hours. L. monocytogenes became dominant after 24 h in all three enrichments. DNA sequences obtained from shotgun metagenomic data of Listeria monocytogenes at 48 h were assembled to produce a consensus draft genome which appeared to have a similar tracking utility to pure culture isolates of L. monocytogenes. All three methods performed equally well for enrichment of Listeria monocytogenes. The observation of potential competitive exclusion of L. mono by Anoxybacillus and Geobacillus in early enrichment hours provided novel information that may be used to further optimize enrichment formulations. Application of Resphera Insight for high-resolution analysis of 16S amplicon sequences accurately identified L. monocytogenes. Both shotgun and 16S rRNA data supported the presence of three slightly variable genomes of L. monocytogenes. Moreover, the draft assembly of a consensus genome of L. monocytogenes from shotgun metagenomic data demonstrated the potential utility of this approach to expedite trace-back of outbreak-associated strains, although further validation will be needed to confirm this utility.

A membrane bioreactor (MBR)-based wastewater treatment plant in Saudi Arabia was assessed over a nine-month period for virus removal efficiency. Viral diversity was detected using omics-based approaches. Log reduction values (LRV) of Adenoviruses (AdV) and Enteroviruses (EV) were enumerated using digital polymerase chain reaction (dPCR) and assessed for infectivity using fluorescence-based infection assays. MBR treatment was successful in reducing viral diversity. Plant viruses remained abundant in the treated effluent. Human enteric viruses were present in lower abundance than plant viruses, and were reduced by MBR at varying LRV. AdV copy numbers were reduced by 3.7-log. Infectious AdV was not detected in the effluent. EV copy numbers were reduced by 1.7-log post MBR and infectious EV decreased by an average of 2.0-log. Infectious EV was detected in the chlorinated effluent, occasionally in concentrations that approximate to its 50% infectious dose. Overall, results indicated that a MBR-based wastewater treatment plant (WWTP) effectively reduces viral diversity, viral load, and infectious capacity by up to 4-logs. These findings suggest potential concerns associated with plant and human enteric viruses for reuse events in this country. Local guidelines for assessment of treated water quality should take into consideration both infectious viral concentration and LRV.

: Semi-quantitative bacterial culture is the standard method to diagnose urinary tract infections (UTI), but bacterial growth rate limits diagnostic speed and it is unreliable when patients have been pre-treated with antibiotics. Metagenomics could increase diagnostic speed and accuracy by sequencing the microbiome and resistome directly from urine samples, bypassing culture. However, a semi-quantitative approach – as needed for diagnosing UTIs – has not been established. Metagenomics was deployed to identify and semi-quantify bacterial presence indicative of UTI, predict antimicrobial susceptibility (AMR), and results were compared to semi-quantitative culture. Whole genome sequencing of the corresponding uropathogens was done for comparison. Analysis time and cost were tracked. Forty-one consecutive urine samples underwent metagenomic analysis. All culture positive samples contained >200ng of DNA, suggestive of a threshold below which UTI could be ruled out solely based on DNA quantity. A semi-quantitative Diagnostic Index (DI) was created by multiplying the total DNA quantity by the relative abundance of uropathogens per urine sample. The DI allowed discrimination of UTI from non-UTI samples in all but 1 case. Metagenomic detection of AMR determinants correctly predicted the phenotype of uropathogens in 20 of 32 cases. The metagenomic work-flow was 31h and cost €116 per sample, but could be reduced to 4.5h and €5 for low-DNA-yield non-UTI samples. The genomic determinants of AMR and their distribution across uropathogens need to be better understood for prediction of AMR phenotypes by metagenomics. The introduction of the DI demonstrates the potential of semi-quantitative metagenomics to replace culture as rapid diagnostic method for UTI.