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.