Genomics accurately predicts antimicrobial resistance in Staphylococcus pseudintermedius collected as part of Vet-LIRN resistance monitoring.

Whole-genome sequencing (WGS) has changed our understanding of bacterial pathogens, aiding outbreak investigations and advancing our knowledge of their genetic features. However, there has been limited use of genomics to understand antimicrobial resistance of veterinary pathogens, which would help identify emerging resistance mechanisms and track their spread. The objectives of this study were to evaluate the correlation between resistance genotypes and phenotypes for Staphylococcus pseudintermedius, a major pathogen of companion animals, by comparing broth microdilution antimicrobial susceptibility testing and WGS. From 2017-2019, we conducted antimicrobial susceptibility testing and WGS on S. pseudintermedius isolates collected from dogs in the United States as a part of the Veterinary Laboratory Investigation and Response Network (Vet-LIRN) antimicrobial resistance monitoring program. Across thirteen antimicrobials in nine classes, resistance genotypes correlated with clinical resistance phenotypes 98.4 % of the time among a collection of 592 isolates. Our findings represent isolates from diverse lineages based on phylogenetic analyses, and these strong correlations are comparable to those from studies of several human pathogens such as Staphylococcus aureus and Salmonella enterica. We uncovered some important findings, including that 32.3 % of isolates had the mecA gene, which correlated with oxacillin resistance 97.0 % of the time. We also identified a novel rpoB mutation likely encoding rifampin resistance. These results show the value in using WGS to assess antimicrobial resistance in veterinary pathogens and to reveal putative new mechanisms of resistance.

Serum Unconjugated Bile Acids and Small Bowel Bacterial Overgrowth in Pediatric Intestinal Failure: A Pilot Study.

BACKGROUND: We determined qualitative and quantitative serum unconjugated bile acid (SUBA) levels among children with history of intestinal failure (IF) and suspected small bowel bacterial overgrowth (SBBO). METHODS: This was a single-center, case-control pilot study conducted at Cincinnati Children's Hospital Medical Center. Children with history of IF and suspected SBBO were enrolled as subjects. Age-matched children without IF or suspected SBBO served as controls. All participants underwent small bowel fluid sampling for microbial culture analysis. Additionally, serum fractionated and total bile acids were measured by liquid chromatography-mass spectrometry at enrollment and following treatment for SBBO. RESULTS: SUBA concentrations were elevated in IF subjects (median 1.16 µM, range 0.43-10.65 µM) compared with controls (median 0.10 µM, range 0.05-0.18 µM, P = 0.001). Among SUBA, chenodeoxycholic acid (CDCA) was significantly elevated in subjects (median 0.8 µM, range 0-7.08 µM) compared with controls (median 0 µM, range 0-0.03 µM, P = 0.012). When controls were excluded from analysis, IF subjects with positive aspirates for SBBO demonstrated higher concentration of CDCA (median 7.36 µM, range 1.1-8.28 µM) compared with IF subjects with negative aspirates (median 0.18 µM, range 0-1.06 µM, P = 0.017). Treatment for SBBO did not alter SUBA concentration. CONCLUSIONS: SUBA concentrations are elevated in children with history of IF and presumed SBBO compared with non-IF controls. CDCA was more prevalent in IF subjects with positive aspirates for SBBO compared with IF subjects with negative aspirates. The determination of SUBA concentration may be a useful surrogate to small bowel fluid aspiration in the diagnosis of SBBO in children with history of IF.

Data analysis of MS-based clinical lipidomics studies with crossover design: A tutorial mini-review of statistical methods.

Clinical lipidomics using mass spectrometry (MS) is important to support discovery of biomarkers for diagnosis and understanding the pathophysiology of diseases. Frequently, lipidomics data from clinical studies have large variations among individuals because the human metabolome/lipidome is strongly influenced by genotype, daily activity, diet and gut flora. This inter-personal variability makes data analysis more complex and normally requires a large cohort for robust statistical analysis. Crossover designed experiments treat each subject as his or her own control, thereby reducing the between-subject variability, such that the effects of exposure/treatment are more likely to be identified when using a relatively small number of subjects. This design repeatedly samples an individual when crossing over from one treatment/exposure to another during the course of the study. The acquired datasets have a distinct data structure resulting from repeated longitudinal measurements. A variety of statistical methods are used in published crossover studies, but many appear to ignore the data structure inherent in the experimental design. An appropriate data analysis approach is critical to discovering robust clinical biomarkers. Hereby, we summarize the statistical methodologies suitable for clinical lipidomics studies using crossover design. To help understand and apply these methods to practical cases, we focused on the general concepts of statistical models in the context of analysis of metabolomics data without spending too much effort on mathematical details. Importantly, we aim to evaluate these methods and provide suggestions for data analysis and biomarker discovery. We applied the discussed methods on a MS-based lipidomics dataset from a double-blind random crossover designed clinical dietary intervention study. The strength and potential pitfalls of each method are briefly discussed and a suggestion for analytic workflow proposed.