Enterococci enhance Clostridioides difficile pathogenesis.

Enteric pathogens are exposed to a dynamic polymicrobial environment in the gastrointestinal tract1. This microbial community has been shown to be important during infection, but there are few examples illustrating how microbial interactions can influence the virulence of invading pathogens2. Here we show that expansion of a group of antibiotic-resistant, opportunistic pathogens in the gut-the enterococci-enhances the fitness and pathogenesis of Clostridioides difficile. Through a parallel process of nutrient restriction and cross-feeding, enterococci shape the metabolic environment in the gut and reprogramme C. difficile metabolism. Enterococci provide fermentable amino acids, including leucine and ornithine, which increase C. difficile fitness in the antibiotic-perturbed gut. Parallel depletion of arginine by enterococci through arginine catabolism provides a metabolic cue for C. difficile that facilitates increased virulence. We find evidence of microbial interaction between these two pathogenic organisms in multiple mouse models of infection and patients infected with C. difficile. These findings provide mechanistic insights into the role of pathogenic microbiota in the susceptibility to and the severity of C. difficile infection.

Escape Velocity-the Launch of Microbiome Therapies.

Food and Drug Administration approval of the first microbiome therapies represents a true expansion the treatment paradigm for Clostridioides difficile but raises new questions about the future role of fecal microbiota transplantation. The authors outline the advances in microbiome therapeutic development that have addressed fecal microbiota transplantation's (FMT's) inherent limitations of safety and scalability. The authors also suggest that as microbiome therapeutic development continues for other indications, FMT will likely remain a necessary model of human microbiota dynamics for translational research.

A putative de novo evolved gene required for spermatid chromatin condensation in Drosophila melanogaster.

Comparative genomics has enabled the identification of genes that potentially evolved de novo from non-coding sequences. Many such genes are expressed in male reproductive tissues, but their functions remain poorly understood. To address this, we conducted a functional genetic screen of over 40 putative de novo genes with testis-enriched expression in Drosophila melanogaster and identified one gene, atlas, required for male fertility. Detailed genetic and cytological analyses showed that atlas is required for proper chromatin condensation during the final stages of spermatogenesis. Atlas protein is expressed in spermatid nuclei and facilitates the transition from histone- to protamine-based chromatin packaging. Complementary evolutionary analyses revealed the complex evolutionary history of atlas. The protein-coding portion of the gene likely arose at the base of the Drosophila genus on the X chromosome but was unlikely to be essential, as it was then lost in several independent lineages. Within the last ~15 million years, however, the gene moved to an autosome, where it fused with a conserved non-coding RNA and evolved a non-redundant role in male fertility. Altogether, this study provides insight into the integration of novel genes into biological processes, the links between genomic innovation and functional evolution, and the genetic control of a fundamental developmental process, gametogenesis.

Priorities and Progress in Gram-positive Bacterial Infection Research by the Antibacterial Resistance Leadership Group: A Narrative Review.

The Antibacterial Resistance Leadership Group (ARLG) has prioritized infections caused by gram-positive bacteria as one of its core areas of emphasis. The ARLG Gram-positive Committee has focused on studies responding to 3 main identified research priorities: (1) investigation of strategies or therapies for infections predominantly caused by gram-positive bacteria, (2) evaluation of the efficacy of novel agents for infections caused by methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci, and (3) optimization of dosing and duration of antimicrobial agents for gram-positive infections. Herein, we summarize ARLG accomplishments in gram-positive bacterial infection research, including studies aiming to (1) inform optimal vancomycin dosing, (2) determine the role of dalbavancin in MRSA bloodstream infection, (3) characterize enterococcal bloodstream infections, (4) demonstrate the benefits of short-course therapy for pediatric community-acquired pneumonia, (5) develop quality of life measures for use in clinical trials, and (6) advance understanding of the microbiome. Future studies will incorporate innovative methodologies with a focus on interventional clinical trials that have the potential to change clinical practice for difficult-to-treat infections, such as MRSA bloodstream infections.

Respiratory Microbiome Disruption and Risk for Ventilator-Associated Lower Respiratory Tract Infection.

BACKGROUND: Ventilator-associated lower respiratory tract infection (VA-LRTI) is common among critically ill patients and has been associated with increased morbidity and mortality. In acute critical illness, respiratory microbiome disruption indices (MDIs) have been shown to predict risk for VA-LRTI, but their utility beyond the first days of critical illness is unknown. We sought to characterize how MDIs previously shown to predict VA-LRTI at initiation of mechanical ventilation change with prolonged mechanical ventilation, and if they remain associated with VA-LRTI risk. METHODS: We developed a cohort of 83 subjects admitted to a long-term acute care hospital due to their prolonged dependence on mechanical ventilation; performed dense, longitudinal sampling of the lower respiratory tract, collecting 1066 specimens; and characterized the lower respiratory microbiome by 16S rRNA sequencing as well as total bacterial abundance by 16S rRNA quantitative polymerase chain reaction. RESULTS: Cross-sectional MDIs, including low Shannon diversity and high total bacterial abundance, were associated with risk for VA-LRTI, but associations had wide posterior credible intervals. Persistent lower respiratory microbiome disruption showed a more robust association with VA-LRTI risk, with each day of (base e) Shannon diversity <2.0 associated with a VA-LRTI odds ratio of 1.36 (95% credible interval, 1.10-1.72). The observed association was consistent across multiple clinical definitions of VA-LRTI. CONCLUSIONS: Cross-sectional MDIs have limited ability to discriminate VA-LRTI risk during prolonged mechanical ventilation, but persistent lower respiratory tract microbiome disruption, best characterized by consecutive days with low Shannon diversity, may identify a population at high risk for infection and may help target infection-prevention interventions.

Stopping Hospital Infections With Environmental Services (SHINE): A Cluster-randomized Trial of Intensive Monitoring Methods for Terminal Room Cleaning on Rates of Multidrug-resistant Organisms in the Intensive Care Unit.

BACKGROUND: Multidrug-resistant organisms (MDROs) frequently contaminate hospital environments. We performed a multicenter, cluster-randomized, crossover trial of 2 methods for monitoring of terminal cleaning effectiveness. METHODS: Six intensive care units (ICUs) at 3 medical centers received both interventions sequentially, in randomized order. Ten surfaces were surveyed each in 5 rooms weekly, after terminal cleaning, with adenosine triphosphate (ATP) monitoring or an ultraviolet fluorescent marker (UV/F). Results were delivered to environmental services staff in real time with failing surfaces recleaned. We measured monthly rates of MDRO infection or colonization, including methicillin-resistant Staphylococcus aureus, Clostridioides difficile, vancomycin-resistant Enterococcus, and MDR gram-negative bacilli (MDR-GNB) during a 12-month baseline period and sequential 6-month intervention periods, separated by a 2-month washout. Primary analysis compared only the randomized intervention periods, whereas secondary analysis included the baseline. RESULTS: The ATP method was associated with a reduction in incidence rate of MDRO infection or colonization compared with the UV/F period (incidence rate ratio [IRR] 0.876; 95% confidence interval [CI], 0.807-0.951; P = .002). Including the baseline period, the ATP method was associated with reduced infection with MDROs (IRR 0.924; 95% CI, 0.855-0.998; P = .04), and MDR-GNB infection or colonization (IRR 0.856; 95% CI, 0.825-0.887; P < .001). The UV/F intervention was not associated with a statistically significant impact on these outcomes. Room turnaround time increased by a median of 1 minute with the ATP intervention and 4.5 minutes with UV/F compared with baseline. CONCLUSIONS: Intensive monitoring of ICU terminal room cleaning with an ATP modality is associated with a reduction of MDRO infection and colonization.

Partial ORF1ab Gene Target Failure with Omicron BA.2.12.1.

Mutations in the genome of SARS-CoV-2 can affect the performance of molecular diagnostic assays. In some cases, such as S-gene target failure, the impact can serve as a unique indicator of a particular SARS-CoV-2 variant and provide a method for rapid detection. Here, we describe partial ORF1ab gene target failure (pOGTF) on the cobas SARS-CoV-2 assays, defined by a ≥2-thermocycle delay in detection of the ORF1ab gene compared to that of the E-gene. We demonstrate that pOGTF is 98.6% sensitive and 99.9% specific for SARS-CoV-2 lineage BA.2.12.1, an emerging variant in the United States with spike L452Q and S704L mutations that may affect transmission, infectivity, and/or immune evasion. Increasing rates of pOGTF closely mirrored rates of BA.2.12.1 sequences uploaded to public databases, and, importantly, increasing local rates of pOGTF also mirrored increasing overall test positivity. Use of pOGTF as a proxy for BA.2.12.1 provides faster tracking of the variant than whole-genome sequencing and can benefit laboratories without sequencing capabilities.

Combined Biomarkers Predict Acute Mortality Among Critically Ill Patients With Suspected Sepsis.

OBJECTIVES: Sepsis is associated with high early and total in-hospital mortality. Despite recent revisions in the diagnostic criteria for sepsis that sought to improve predictive validity for mortality, it remains difficult to identify patients at greatest risk of death. We compared the utility of nine biomarkers to predict mortality in subjects with clinically suspected bacterial sepsis. DESIGN: Cohort study. SETTING: The medical and surgical ICUs at an academic medical center. SUBJECTS: We enrolled 139 subjects who met two or more systemic inflammatory response syndrome (systemic inflammatory response syndrome) criteria and received new broad-spectrum antibacterial therapy. INTERVENTIONS: We assayed nine biomarkers (α-2 macroglobulin, C-reactive protein, ferritin, fibrinogen, haptoglobin, procalcitonin, serum amyloid A, serum amyloid P, and tissue plasminogen activator) at onset of suspected sepsis and 24, 48, and 72 hours thereafter. We compared biomarkers between groups based on both 14-day and total in-hospital mortality and evaluated the predictive validity of single and paired biomarkers via area under the receiver operating characteristic curve. MEASUREMENTS AND MAIN RESULTS: Fourteen-day mortality was 12.9%, and total in-hospital mortality was 29.5%. Serum amyloid P was significantly lower (4/4 timepoints) and tissue plasminogen activator significantly higher (3/4 timepoints) in the 14-day mortality group, and the same pattern held for total in-hospital mortality (Wilcoxon p ≤ 0.046 for all timepoints). Serum amyloid P and tissue plasminogen activator demonstrated the best individual predictive performance for mortality, and combinations of biomarkers including serum amyloid P and tissue plasminogen activator achieved greater predictive performance (area under the receiver operating characteristic curve > 0.76 for 14-d and 0.74 for total mortality). CONCLUSIONS: Combined biomarkers predict risk for 14-day and total mortality among subjects with suspected sepsis. Serum amyloid P and tissue plasminogen activator demonstrated the best discriminatory ability in this cohort.

Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA.

MOTIVATION: The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence-absence or quantitative species abundance data. PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-group distance and exposure/intervention effect size must be accurately modeled to estimate statistical power for a microbiome study that will be analyzed with pairwise distances and PERMANOVA. RESULTS: We present a framework for PERMANOVA power estimation tailored to marker-gene microbiome studies that will be analyzed by pairwise distances, which includes: (i) a novel method for distance matrix simulation that permits modeling of within-group pairwise distances according to pre-specified population parameters; (ii) a method to incorporate effects of different sizes within the simulated distance matrix; (iii) a simulation-based method for estimating PERMANOVA power from simulated distance matrices; and (iv) an R statistical software package that implements the above. Matrices of pairwise distances can be efficiently simulated to satisfy the triangle inequality and incorporate group-level effects, which are quantified by the adjusted coefficient of determination, omega-squared (ω2). From simulated distance matrices, available PERMANOVA power or necessary sample size can be estimated for a planned microbiome study.

SARS-CoV-2 evolution during prolonged infection in immunocompromised patients.

Prolonged infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in immunocompromised patients provides an opportunity for viral evolution, potentially leading to the generation of new pathogenic variants. To investigate the pathways of viral evolution, we carried out a study on five patients experiencing prolonged SARS-CoV-2 infection (quantitative polymerase chain reaction-positive for 79-203 days) who were immunocompromised due to treatment for lymphoma or solid organ transplantation. For each timepoint analyzed, we generated at least two independent viral genome sequences to assess the heterogeneity and control for sequencing error. Four of the five patients likely had prolonged infection; the fifth apparently experienced a reinfection. The rates of accumulation of substitutions in the viral genome per day were higher in hospitalized patients with prolonged infection than those estimated for the community background. The spike coding region accumulated a significantly greater number of unique mutations than other viral coding regions, and the mutation density was higher. Two patients were treated with monoclonal antibodies (bebtelovimab and sotrovimab); by the next sampled timepoint, each virus population showed substitutions associated with monoclonal antibody resistance as the dominant forms (spike K444N and spike E340D). All patients received remdesivir, but remdesivir-resistant substitutions were not detected. These data thus help elucidate the trends of emergence, evolution, and selection of mutational variants within long-term infected immunocompromised individuals. IMPORTANCE: SARS-CoV-2 is responsible for a global pandemic, driven in part by the emergence of new viral variants. Where do these new variants come from? One model is that long-term viral persistence in infected individuals allows for viral evolution in response to host pressures, resulting in viruses more likely to replicate efficiently in humans. In this study, we characterize replication in several hospitalized and long-term infected individuals, documenting efficient pathways of viral evolution.

Comparison of SARS-CoV-2 variants of concern in primary human nasal cultures demonstrates Delta as most cytopathic and Omicron as fastest replicating.

The SARS-CoV-2 pandemic was marked with emerging viral variants, some of which were designated as variants of concern (VOCs) due to selection and rapid circulation in the human population. Here, we elucidate functional features of each VOC linked to variations in replication rate. Patient-derived primary nasal cultures grown at air-liquid interface were used to model upper respiratory infection and compared to cell lines derived from human lung epithelia. All VOCs replicated to higher titers than the ancestral virus, suggesting a selection for replication efficiency. In primary nasal cultures, Omicron replicated to the highest titers at early time points, followed by Delta, paralleling comparative studies of population sampling. All SARS-CoV-2 viruses entered the cell primarily via a transmembrane serine protease 2 (TMPRSS2)-dependent pathway, and Omicron was more likely to use an endosomal route of entry. All VOCs activated and overcame dsRNA-induced cellular responses, including interferon (IFN) signaling, oligoadenylate ribonuclease L degradation, and protein kinase R activation. Among the VOCs, Omicron infection induced expression of the most IFN and IFN-stimulated genes. Infections in nasal cultures resulted in cellular damage, including a compromise of cell barrier integrity and loss of nasal cilia and ciliary beating function, especially during Delta infection. Overall, Omicron was optimized for replication in the upper respiratory tract and least favorable in the lower respiratory cell line, and Delta was the most cytopathic for both upper and lower respiratory cells. Our findings highlight the functional differences among VOCs at the cellular level and imply distinct mechanisms of pathogenesis in infected individuals. IMPORTANCE: Comparative analysis of infections by SARS-CoV-2 ancestral virus and variants of concern, including Alpha, Beta, Delta, and Omicron, indicated that variants were selected for efficiency in replication. In infections of patient-derived primary nasal cultures grown at air-liquid interface to model upper respiratory infection, Omicron reached the highest titers at early time points, a finding that was confirmed by parallel population sampling studies. While all infections overcame dsRNA-mediated host responses, infections with Omicron induced the strongest interferon and interferon-stimulated gene response. In both primary nasal cultures and lower respiratory cell line, infections by Delta were most damaging to the cells as indicated by syncytia formation, loss of cell barrier integrity, and nasal ciliary function.

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies.

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

Infrequent intrahousehold transmission of Clostridioides difficile between pet owners and their pets.

Companion animals have been shown to carry Clostridioides difficile strains that are similar or identical to strains found in people, and a small number of studies have shown that pets carry genetically identical C. difficile isolates as their owners, suggesting inter-species transmission. However, the directionality of transmission is ultimately unknown, and the frequency with which animals acquire C. difficile following their owners' infection is unclear. The goal of this study was to assess how often pets belonging to people with C. difficile infection carry genetically related C. difficile isolates. We enrolled pet owners from two medical institutions (University of Pennsylvania Health System (UPHS) and The Ohio State University Wexner Medical Center (OSUWMC)) who had diarrhoea with or without positive C. difficile assays and tested their faeces and their pets' faeces for C. difficile using both anaerobic culture and PCR assays. When microorganisms were obtained from both the owner and pet and had the same toxin profile or ribotype, isolates underwent genomic sequencing. Faecal samples were obtained from a total of 59 humans, 72 dogs and 9 cats, representing 47 complete households (i.e. where a sample was available from the owner and at least one pet). Of these, C. difficile was detected in 30 humans, 10 dogs and 0 cats. There were only two households where C. difficile was detected in both the owner and pet. In one of these households, the C. difficile isolates were of different toxin profiles/ribotypes (A+/B+ / RT 499 from the owner, A-/B- / RT PR22386 from the dog). In the other household, the isolates were genetically identical (one SNP difference). Interestingly, the dog from this household had recently received a course of antibiotics (cefpodoxime and metronidazole). Our findings suggest that inter-species transmission of C. difficile occurs infrequently in households with human C. difficile infections.

Robust airway microbiome signatures in acute respiratory failure and hospital-acquired pneumonia.

Respiratory microbial dysbiosis is associated with acute respiratory distress syndrome (ARDS) and hospital-acquired pneumonia (HAP) in critically ill patients. However, we lack reproducible respiratory microbiome signatures that can increase our understanding of these conditions and potential treatments. Here, we analyze 16S rRNA sequencing data from 2,177 respiratory samples collected from 1,029 critically ill patients (21.7% with ARDS and 26.3% with HAP) and 327 healthy controls, sourced from 17 published studies. After data harmonization and pooling of individual patient data, we identified microbiota signatures associated with ARDS, HAP and prolonged mechanical ventilation. Microbiota signatures for HAP and prolonged mechanical ventilation were characterized by depletion of a core group of microbes typical of healthy respiratory samples, and the ARDS microbiota signature was distinguished by enrichment of potentially pathogenic respiratory microbes, including Pseudomonas and Staphylococcus. Using machine learning models, we identified clinically informative, three- and four-factor signatures that predicted ARDS, HAP and prolonged mechanical ventilation with relatively high accuracy (area under the curve of 0.751, 0.72 and 0.727, respectively). We validated the signatures in an independent prospective cohort of 136 patients on mechanical ventillation and found that patients with microbiome signatures associated with ARDS, HAP or prolonged mechanical ventilation had longer times to successful extubation than patients lacking these signatures (hazard ratios of 1.56 (95% confidence interval (CI) 1.07-2.27), 1.51 (95% CI 1.02-2.23) and 1.50 (95% CI 1.03-2.18), respectively). Thus, we defined and validated robust respiratory microbiome signatures associated with ARDS and HAP that may help to identify promising targets for microbiome therapeutic modulation in critically ill patients.

Severe acute respiratory coronavirus virus 2 (SARS-CoV-2) surface contamination in staff common areas and impact on healthcare worker infection: Prospective surveillance during the coronavirus disease 2019 (COVID-19) pandemic.

We prospectively surveyed SARS-CoV-2 RNA contamination in staff common areas within an acute-care hospital. An increasing prevalence of surface contamination was detected over time. Adjusting for patient census or community incidence of coronavirus disease 2019 (COVID-19), the proportion of contaminated surfaces did not predict healthcare worker COVID-19 infection on study units.

Comparison of SARS-CoV-2 variants of concern in primary human nasal cultures demonstrates Delta as most cytopathic and Omicron as fastest replicating.

The SARS-CoV-2 pandemic was marked with emerging viral variants, some of which were designated as variants of concern (VOCs) due to selection and rapid circulation in the human population. Here we elucidate functional features of each VOC linked to variations in replication rate. Patient-derived primary nasal cultures grown at air-liquid-interface (ALI) were used to model upper-respiratory infection and human lung epithelial cell lines used to model lower-respiratory infection. All VOCs replicated to higher titers than the ancestral virus, suggesting a selection for replication efficiency. In primary nasal cultures, Omicron replicated to the highest titers at early time points, followed by Delta, paralleling comparative studies of population sampling. All SARS-CoV-2 viruses entered the cell primarily via a transmembrane serine protease 2 (TMPRSS2)-dependent pathway, and Omicron was more likely to use an endosomal route of entry. All VOCs activated and overcame dsRNA-induced cellular responses including interferon (IFN) signaling, oligoadenylate ribonuclease L degradation and protein kinase R activation. Among the VOCs, Omicron infection induced expression of the most IFN and IFN stimulated genes. Infections in nasal cultures resulted in cellular damage, including a compromise of cell-barrier integrity and loss of nasal cilia and ciliary beating function, especially during Delta infection. Overall, Omicron was optimized for replication in the upper-respiratory system and least-favorable in the lower-respiratory cell line; and Delta was the most cytopathic for both upper and lower respiratory cells. Our findings highlight the functional differences among VOCs at the cellular level and imply distinct mechanisms of pathogenesis in infected individuals.

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies.

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities with data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

Healthcare microenvironments define multidrug-resistant organism persistence.

BACKGROUND: Multidrug-resistant organisms (MDROs) colonizing the healthcare environment have been shown to contribute to risk for healthcare-associated infections (HAIs), with adverse effects on patient morbidity and mortality. We sought to determine how bacterial contamination and persistent MDRO colonization of the healthcare environment are related to the position of patients and wastewater sites. METHODS: We performed a prospective cohort study, enrolling 51 hospital rooms at the time of admitting a patient with an eligible MDRO in the prior 30 days. We performed systematic sampling and MDRO culture of rooms, as well as 16S rRNA sequencing to define the environmental microbiome in a subset of samples. RESULTS: The probability of detecting resistant gram-negative organisms, including Enterobacterales, Acinetobacter spp, and Pseudomonas spp, increased with distance from the patient. In contrast, Clostridioides difficile and methicillin-resistant Staphylococcus aureus were more likely to be detected close to the patient. Resistant Pseudomonas spp and S. aureus were enriched in these hot spots despite broad deposition of 16S rRNA gene sequences assigned to the same genera, suggesting modifiable factors that permit the persistence of these MDROs. CONCLUSIONS: MDRO hot spots can be defined by distance from the patient and from wastewater reservoirs. Evaluating how MDROs are enriched relative to bacterial DNA deposition helps to identify healthcare micro-environments and suggests how targeted environmental cleaning or design approaches could prevent MDRO persistence and reduce infection risk.