The metabolic intermediate acetyl-CoA links anabolic and catabolic processes and coordinates metabolism with cellular signaling by influencing protein acetylation. In this study we demonstrate that in Arabidopsis (Arabidopsis thaliana), two distinctly localized acetate-activating enzymes, ACETYL-COA SYNTHETASE (ACS) in plastids and ACETATE NON-UTILIZING1 (ACN1) in peroxisomes, function redundantly to prevent the accumulation of excess acetate. In contrast to the near wild-type morphological and metabolic phenotypes of acs or acn1 mutants, the acs acn1 double mutant is delayed in growth and sterile, which is associated with hyperaccumulation of cellular acetate and decreased accumulation of acetyl-CoA-derived intermediates of central metabolism. Using multiple mutant stocks and stable isotope-assisted metabolic analyses, we demonstrate the twin metabolic origins of acetate from the oxidation of ethanol and the nonoxidative decarboxylation of pyruvate, with acetaldehyde being the common intermediate precursor of acetate. Conversion from pyruvate to acetate is activated under hypoxic conditions, and ACS recovers carbon that would otherwise be lost from the plant as ethanol. Plastid-localized ACS metabolizes cellular acetate and contributes to the de novo biosynthesis of fatty acids and Leu; peroxisome-localized ACN1 enables the incorporation of acetate into organic acids and amino acids. Thus, the activation of acetate in distinct subcellular compartments provides plants with the metabolic flexibility to maintain physiological levels of acetate and a metabolic mechanism for the recovery of carbon that would otherwise be lost as ethanol, for example following hypoxia.

Objective:To describe an investigation into 5 clinical cases of carbapenem-resistant Acinetobacter baumannii (CRAB).Design:Epidemiological investigation supplemented by whole-genome sequencing (WGS) of clinical and environmental isolates.Setting:A tertiary-care academic health center in Boston, Massachusetts.Patients or participants:Individuals identified with CRAB clinical infections.Methods:A detailed review of patient demographic and clinical data was conducted. Clinical isolates underwent phenotypic antimicrobial susceptibility testing and WGS. Infection control practices were evaluated, and CRAB isolates obtained through environmental sampling were assessed by WGS. Genomic relatedness was measured by single-nucleotide polymorphism (SNP) analysis.Results:Four clinical cases spanning 4 months were linked to a single index case; isolates differed by 1–7 SNPs and belonged to a single cluster. The index patient and 3 case patients were admitted to the same room prior to their development of CRAB infection, and 2 case patients were admitted to the same room within 48 hours of admission. A fourth case patient was admitted to a different unit. Environmental sampling identified highly contaminated areas, and WGS of 5 environmental isolates revealed that they were highly related to the clinical cluster.Conclusions:We report a cluster of highly resistant Acinetobacter baumannii that occurred in a burn ICU over 5 months and then spread to a separate ICU. Two case patients developed infections classified as community acquired under standard epidemiological definitions, but WGS revealed clonality, highlighting the risk of burn patients for early-onset nosocomial infections. An extensive investigation identified the role of environmental reservoirs.

BackgroundDetection of nosocomial outbreaks often relies on epidemiological definitions of community and nosocomial acquisition. We report a cluster of three carbapenem-resistant Acinetobacter baumannii (CRAB) infections linked to a single source patient with infections occurring within 2 days of admission to a burn intensive care unit (ICU). The epidemiological investigation was supplemented by whole-genome sequencing (WGS) of clinical and environmental isolates.MethodsStudy participants included burn ICU patients identified with infections caused by CRAB. A detailed review of patient demographic and clinical data was conducted. Clinical A. baumannii isolates were assessed by antimicrobial susceptibility testing and WGS. Review of infection control practices on the affected unit was followed by environmental sampling. A. baumannii isolates obtained through environmental sampling were assessed for carbapenem resistance and then underwent WGS for comparison to the clinical isolates.ResultsThree cases of CRAB infection in the affected unit spanning a period of 3 months were linked to a preceding source patient, with CRAB isolates from the four patients differing by 5–7 single nucleotide variations. All case patients had been admitted to the same room within 2 days before development of CRAB infection. Environmental sampling performed while the third case patient occupied the room identified highly contaminated areas, and environmental CRAB isolates linked the patient isolates. The contaminated areas were subsequently re-sampled after enhanced terminal cleaning of the room. No additional CRAB was isolated, but other pathogenic organisms were recovered.ConclusionWe report a cluster of three infections caused by highly resistant A. baumannii that occurred in a burn intensive care unit over a period of 3 months, linked to a single source patient. Three case patients developed infections classified as community-acquired using standard epidemiological definitions, however, whole-genome sequencing revealed clonality. An extensive investigation identified the role of environmental reservoirs. Burn patients may be particularly vulnerable to early-onset nosocomial infection from environmental contamination. Disclosures All authors: No reported disclosures.

Background Each year in the United States there are over 1.7 million cases of sepsis that account for a third of hospital deaths. A key to reducing morbidity and mortality rates is early, appropriate antibiotic therapy. Most new diagnostic approaches still suffer from insufficient sensitivity to low bacterial loads in blood and limited sets of detection targets for bacterial species identification (ID) and antimicrobial resistance (AMR) determination. As such, blood culture remains the gold standard for diagnosing bacteremia despite limitations such as > 2-day turnaround time (TAT), incompatibility with fastidious organisms, and frequent inability to recover causative pathogens. Methods 31 clinically relevant bacterial pathogens, made up of 17 gram-positive and 14 gram-negative bacterial species, were spiked into 2 to 4 healthy donor blood samples at 1 to 5 CFU/mL. The samples were run through our proprietary Blood2Bac™ pipeline, sequenced on a nanopore platform, and data were passed through Keynome®, our proprietary machine learning algorithm to determine species ID and AMR. Results By assessing the efficiency of pathogen DNA enrichment and genome coverage post sequencing, we report high performance of 3 CFU/mL for 3 bacterial species and ≤ 2 CFU/mL for the 28 remaining species, which includes S. aureus, E. coli, and Streptococcus spp., three of the leading causes of sepsis. For all 31 bacterial species tested, Keynome called species ID with 100% accuracy. In addition, Keynome also predicted the AMR profile of pathogens with 100% accuracy for 19 drug/species AMR combinations, including ciprofloxacin for E. coli, clindamycin for S. aureus, and aztreonam for K. pneumoniae. Conclusion Blood2Bac is able to enrich a wide range of bacterial pathogens directly from blood and enable bacterial whole genome sequencing with an estimated TAT of 12 hours. When coupled with Keynome, our process provides accurate species ID and AMR calls for key BSI pathogens even at single-digit CFU/mL concentrations. Our species-agnostic and culture-free process enables detection of a diverse range of bacterial species with high sensitivity, providing a robust and comprehensive diagnostic. Disclosures Chiahao Tsui, n/a, Day Zero Diagnostics (Employee, Shareholder) Lisa S. Cunden, PhD, Day Zero Diagnostics (Shareholder) Nicole Billings, PhD, Day Zero Diagnostics (Employee) Imaly A. Nanayakkara, PhD, Day Zero Diagnostics (Employee, Shareholder) Ian Herriott, BS, Day Zero Diagnostics (Employee, Shareholder) Rachel R. Martin, n/a, Day Zero DIagnostics (Employee) Michelle Chen, MS, Day Zero Diagnostics (Employee, Shareholder) Febriana Pangestu, n/a, Day Zero Diagnostics (Employee, Shareholder) Paul Knysh, PhD, Day Zero Diagnostics (Employee) Cabell Maddux, n/a, Day Zero Diagnostics (Employee, Shareholder) Zachary Munro, n/a, Day Zero Diagnostics Inc. (Employee, Shareholder) Miriam Huntley, PhD, Day Zero Diagnostics (Employee, Shareholder)

Background: Whole-genome sequencing (WGS) is well established as a high-resolution method for measuring bacterial relatedness to better understand infection transmission in cases of healthcare-associated infections (HAIs). However, sequencing is still rarely used in HAI investigations due to a lack of access to computational analysis platforms with actionable turnaround times. Single-nucleotide polymorphism (SNP) analysis is typically used to determine bacterial relatedness. However, SNP-based methods often require a suite of bioinformatics tools that can be difficult to use and interpret without the expertise of a trained computational biologist. These obstacles become more significant in the case of prospective, real-time surveillance of HAIs, which can require the analysis of a large number of isolates. To enable the use of WGS for proactive determination of infection outbreaks, a rapid, automated method that can scale to large data sets is needed. Methods: Here, we demonstrate the capabilities of ksim, a novel automated algorithm to determine the clonality of bacterial samples using WGS. ksim measures the number of shared kmers (genomic subsequences of length k) between bacterial samples to determine their relatedness. ksim also filters out accessory genomic regions, such as plasmids, that can confound genetic relatedness estimates. We benchmarked the accuracy and speed of ksim relative to an SNP-based pipeline on simulated data sets (with sequencing reads generated in silico) and on 9 clinical-cluster data sets (6 publicly available and 3 real-time data sets from Massachusetts General Hospital [MGH]). We also used ksim to determine the relatedness of >5,000 historical clinical bacterial isolates from MGH, collected between 2015 and 2019. Results: ksim first preprocesses raw sequencing data to generate a common data structure, after which it computes the genomic distance between bacterial samples in ∼0.2 seconds in simple cases and in ∼4 seconds in complex cases when accessory genome filtering is required. In simulations across 5 species, ksim determined clonality (defined as <40 SNPs) with high accuracy (sensitivity, 99.7% and specificity, 99.6%). ksim performance on 9 clinical HAI data sets demonstrated its sensitivity (99.4%) and specificity (90.8%) compared to an SNP-based pipeline. ksim efficiently analyzed >5,000 clinical samples from MGH and found previously unidentified transmission clusters. Conclusions:ksim shows promise for rapid clonality determination in HAI outbreaks and has the potential to scale to tens of thousands of samples. This method could enable infection control teams to use WGS for prospective outbreak detection via an automated computational pipeline without the need for specialized computational biology training.Funding: Day Zero Diagnostics and the NIH provided Funding: for this study.Disclosures: Mohamad Sater reports salary from Day Zero Diagnostics.