An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics.

BACKGROUND: Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples, (2) they do not distinguish between viable and nonviable organisms, and (3) they do not reveal the microbial load quantitatively. An additional practical challenge towards a robust pipeline is the inability to efficiently allocate sequencing resources a priori. Assessment of sequencing depth is generally practiced post hoc, if at all, for most microbiome studies, regardless of the sample type. This practice is inefficient at best, and at worst, poor sequencing depth jeopardizes the interpretation of study results. To address these challenges, we present a workflow for metagenomics-based environmental surveillance that is appropriate for low-biomass samples, distinguishes viability, is quantitative, and estimates sequencing resources. RESULTS: The workflow was developed using a representative microbiome sample, which was created by aggregating 120 surface swabs collected from a medical intensive care unit. Upon evaluating and optimizing techniques as well as developing new modules, we recommend best practices and introduce a well-structured workflow. We recommend adopting liquid-liquid extraction to improve DNA yield and only incorporating whole-cell filtration when the nonbacterial proportion is large. We suggest including propidium monoazide treatment coupled with internal standards and absolute abundance profiling for viability assessment and involving cultivation when demanding comprehensive profiling. We further recommend integrating internal standards for quantification and additionally qPCR when we expect poor taxonomic classification. We also introduce a machine learning-based model to predict required sequencing effort from accessible sample features. The model helps make full use of sequencing resources and achieve desired outcomes. Video Abstract CONCLUSIONS: This workflow will contribute to more accurate and robust environmental surveillance and infection prevention. Lessons gained from this study will also benefit the continuing development of methods in relevant fields.

Escaping the fate of Sisyphus: assessing resistome hybridization baits for antimicrobial resistance gene capture.

Finding, characterizing and monitoring reservoirs for antimicrobial resistance (AMR) is vital to protecting public health. Hybridization capture baits are an accurate, sensitive and cost-effective technique used to enrich and characterize DNA sequences of interest, including antimicrobial resistance genes (ARGs), in complex environmental samples. We demonstrate the continued utility of a set of 19 933 hybridization capture baits designed from the Comprehensive Antibiotic Resistance Database (CARD)v1.1.2 and Pathogenicity Island Database (PAIDB)v2.0, targeting 3565 unique nucleotide sequences that confer resistance. We demonstrate the efficiency of our bait set on a custom-made resistance mock community and complex environmental samples to increase the proportion of on-target reads as much as >200-fold. However, keeping pace with newly discovered ARGs poses a challenge when studying AMR, because novel ARGs are continually being identified and would not be included in bait sets designed prior to discovery. We provide imperative information on how our bait set performs against CARDv3.3.1, as well as a generalizable approach for deciding when and how to update hybridization capture bait sets. This research encapsulates the full life cycle of baits for hybridization capture of the resistome from design and validation (both in silico and in vitro) to utilization and forecasting updates and retirement.

Infectious Period of Severe Acute Respiratory Syndrome Coronavirus 2 in 17 Nursing Home Residents-Arkansas, June-August 2020.

BACKGROUND: To estimate the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in older adults with underlying conditions, we assessed duration of coronavirus disease 2019 (COVID-19) symptoms, reverse-transcription polymerase chain reaction (RT-PCR) positivity, and culture positivity among nursing home residents. METHODS: We enrolled residents within 15 days of their first positive SARS-CoV-2 test (diagnosis) at an Arkansas facility from July 7 to 15, 2020 and instead them for 42 days. Every 3 days for 21 days and then weekly, we assessed COVID-19 symptoms, collected specimens (oropharyngeal, anterior nares, and saliva), and reviewed medical charts. Blood for serology was collected on days 0, 6, 12, 21, and 42. Infectivity was defined by positive culture. Duration of culture positivity was compared with duration of COVID-19 symptoms and RT-PCR positivity. Data were summarized using measures of central tendency, frequencies, and proportions. RESULTS: We enrolled 17 of 39 (44%) eligible residents. Median participant age was 82 years (range, 58-97 years). All had ≥3 underlying conditions. Median duration of RT-PCR positivity was 22 days (interquartile range [IQR], 8-31 days) from diagnosis; median duration of symptoms was 42 days (IQR, 28-49 days). Of 9 (53%) participants with any culture-positive specimens, 1 (11%) severely immunocompromised participant remained culture-positive 19 days from diagnosis; 8 of 9 (89%) were culture-positive ≤8 days from diagnosis. Seroconversion occurred in 12 of 12 (100%) surviving participants with ≥1 blood specimen; all participants were culture-negative before seroconversion. CONCLUSIONS: Duration of infectivity was considerably shorter than duration of symptoms and RT-PCR positivity. Severe immunocompromise may prolong SARS-CoV-2 infectivity. Seroconversion indicated noninfectivity in this cohort.