Recently, medical examiners reported two cases of a 21-year-old male and 24-year-old male with high amounts of morphine in their blood at autopsy. It was suspected that the decedents ingested lethal amounts of morphine from home-brewed poppy seed tea. No studies to date have investigated opium alkaloid content extracted from poppy seeds by home-brewing methods. Various poppy seed products were purchased from online sources and extracted with four home-brewing methods representative of recipes found on drug user forums. Morphine, codeine, and thebaine were quantified in the tea extracts by liquid chromatography-tandem mass spectrometry using a validated analytical method. Morphine, codeine, and thebaine concentrations from seeds were <1-2788 mg/kg, <1-247.6 mg/kg, and <1-124 mg/kg, respectively. Alkaloid yield varied between extractions, but regardless of extraction conditions, lethal amounts of morphine can be rinsed from poppy seed coats by home-brewing methods.

Clostridioides difficile has become the leading single cause of hospital-acquired infections. Numerous studies have demonstrated the importance of specific metabolic pathways in aspects of C. difficile pathophysiology, from initial colonization to regulation of virulence factors.

Current hospital cost containment pressures have prompted a critical evaluation of whether nutritional support teams render more clinically effective and efficient patient care than nonteam management. To address this question with regard to enteral feeding, 102 consecutive hospitalized patients who required enteral nutritional support (ENS) by tube feeding during a 3 1/2-month period were prospectively studied. Fifty patients were managed by a nutritional support team; the other 52 were managed by their primary physicians. Choice of enteral formula, formula modifications, frequency of laboratory tests, and amounts of energy and protein received were recorded daily. In addition, each patient was monitored for pulmonary, mechanical, gastrointestinal, and metabolic abnormalities. Team-managed (T) and nonteam-managed (NT) patients received ENS for 632 and 398 days, respectively. The average time period for ENS was significantly longer in the team-managed patients (12.6 +/- 12.1 days vs 7.7 +/- 6.2 days, p less than 0.01). Significantly more of the team patients attained 1.2 X basal energy expenditure (BEE) (37 vs 26, p less than 0.05). Total number of abnormalities in each group was similar (T = 398, NT = 390); however, the abnormalities per day were significantly lower in the team group (T = 0.63 vs NT = 0.98, p less than 0.01). Mechanical (T = 0.05 vs NT = 0.11, p less than 0.01), gastrointestinal (T = 0.99 vs NT = 0.14, p less than 0.05), and metabolic (T = 0.49 vs NT = 0.72, p less than 0.01) abnormalities per day all were significantly lower in the team-managed patients.(ABSTRACT TRUNCATED AT 250 WORDS)

Clostridioides difficile is a Gram-positive, sporulating anaerobe that has become the leading cause of hospital-acquired infections. Over the previous decade, many studies have demonstrated the importance of metabolism in numerous aspects of C. difficile biology from initial colonization to regulation of virulence factors. Additionally, due to growing threats of antibiotic resistance and recurrent infection, targeting components of metabolism presents a novel possible approach to combat this infection. In the past, genome-scale metabolic network analysis of bacteria has enabled systematic investigation of the genetic and metabolic properties that potentially contribute to downstream phenotypes as well as prediction of outcome from perturbations to these pathways. These predictions ultimately create a platform for high-throughput identification and screening of metabolic targets prior to laboratory testing. To accomplish these goals in C. difficile, we constructed highly-curated genome-scale metabolic network reconstructions (GENREs) for a well-studied laboratory strain of the pathogen (str. 630) as well as a more recently characterized hyper-virulent isolate (str. R20291). These computational modeling platforms account for key components of C. difficile core metabolism and nutrient acquisition systems to recapitulate metabolic behaviors within the complex milieu of the gut. Simulating the impact of single-gene deletions resulted in accuracies of ~89.9% for both GENREs compared with transposon mutant libraries. Further analysis of both strains also revealed significant correlations between in silico and experimentally measured growth in carbon source utilization screens (p-values ≤ 0.002), with positive predictive values of ~95.0%. Subsequently, we generated context-specific models by integrating transcriptomic data from C. difficile grown in vitro or during in vivo infection. Simulations also predicted the consistent inverse patterns of carbohydrate and amino acid catabolism that corresponded with differential virulence factor expression measured experimentally. Collectively, our results indicate that GENRE-based analyses of C. difficile are an effective means for gaining novel insight into metabolism as it relates to pathogenesis and provides a platform for the identification of novel therapeutic targets.

Background: Parenteral nutrition-associated cholestasis (PNAC) in the neonatal intensive care unit (NICU) causes significant morbidity and associated healthcare costs. Laboratory detection of PNAC currently relies on elevated serum conjugated bilirubin levels in the aftermath of impaired bile flow. Here, we sought to identify fecal biomarkers, which when integrated with clinical data, would better predict risk for developing PNAC.Methods: Using untargeted metabolomics in 200 serial stool samples from 60 infants, we applied statistical and machine learning approaches to identify clinical features and metabolic biomarkers with the greatest associative potential for risk of developing PNAC. Stools were collected prospectively from infants receiving PN with soybean oil-based lipid emulsion at a level IV NICU.Results: Low birth weight, extreme prematurity, longer duration of PN, and greater number of antibiotic courses were all risk factors for PNAC (P < 0.05). We identified 78 stool biomarkers with early predictive potential (P < 0.05). From these 78 biomarkers, we further identified 12 sphingomyelin lipids with high association for the development of PNAC in precholestasis stool samples when combined with birth anthropometry. Conclusion:We demonstrate the potential for stool metabolomics to enhance early identification of PNAC risk. Earlier detection of high-risk infants would empower

Clostridioides difficile pathogenesis is mediated through its two toxin proteins, TcdA and TcdB, which induce intestinal epithelial cell death and inflammation. It is possible to alter C. difficile toxin production by changing various metabolite concentrations within the extracellular environment. However, it is unknown which intracellular metabolic pathways are involved and how they regulate toxin production. To investigate the response of intracellular metabolic pathways to diverse nutritional environments and toxin production states, we use previously published genome-scale metabolic models of C. difficile strains CD630 and CDR20291 (iCdG709 and iCdR703). We integrated publicly available transcriptomic data with the models using the RIPTiDe algorithm to create 16 unique contextualized C. difficile models representing a range of nutritional environments and toxin states. We used Random Forest with flux sampling and shadow pricing analyses to identify metabolic patterns correlated with toxin states and environment. Specifically, we found that arginine and ornithine uptake is particularly active in low toxin states. Additionally, uptake of arginine and ornithine is highly dependent on intracellular fatty acid and large polymer metabolite pools. We also applied the metabolic transformation algorithm (MTA) to identify model perturbations that shift metabolism from a high toxin state to a low toxin state. This analysis expands our understanding of toxin production in C. difficile and identifies metabolic dependencies that could be leveraged to mitigate disease severity.

No abstract

Fecal Microbiota Transplant (FMT) is an emerging therapy that has had remarkable success in treatment and prevention of recurrentClostridioides difficileinfection (rCDI). FMT has recently been associated with adverse outcomes such as inadvertent transfer of antimicrobial resistance, necessitating development of more targeted bacteriotherapies. To address this challenge, we developed a novel systems biology pipeline to identify candidate probiotic strains that would be predicted to interruptC. difficilepathogenesis. Utilizing metagenomic characterization of human FMT donor samples, we identified those metabolic pathways most associated with successful FMTs and reconstructed the metabolism of encoding species to simulate interactions withC. difficile. This analysis resulted in predictions of high levels of cross-feeding for amino acids in species most associated with FMT success. Guided by thesein silicomodels, we assembled consortia of bacteria with increased amino acid cross-feeding which were then validatedin vitro. We subsequently tested the consortia in a murine model of CDI, demonstrating total protection from severe CDI through decreased toxin levels, recovered gut microbiota, and increased intestinal eosinophils. These results support the novel framework that amino acid cross-feeding is likely a critical mechanism in the initial resolution of CDI by FMT. Importantly, we conclude that our predictive platform based on predicted and testable metabolic interactions between the microbiota andC. difficileled to a rationally designed biotherapeutic framework that may be extended to other enteric infections.