Lung Allograft Microbiome Association with Gastroesophageal Reflux, Inflammation, and Allograft Dysfunction.

Rationale: It remains unclear how gastroesophageal reflux disease (GERD) affects allograft microbial community composition in lung transplant recipients and its impact on lung allograft inflammation and function. Objectives: Our objective was to compare the allograft microbiota in lung transplant recipients with or without clinically diagnosed GERD in the first year after transplant and assess associations between GERD, allograft microbiota, inflammation, and acute and chronic lung allograft dysfunction (ALAD and CLAD). Methods: A total of 268 BAL samples were collected from 75 lung transplant recipients at a single transplant center every 3 months after transplant for 1 year. Ten transplant recipients from a separate transplant center provided samples before and after antireflux Nissen fundoplication surgery. Microbial community composition and density were measured using 16S ribosomal RNA gene sequencing and quantitative polymerase chain reaction, respectively, and inflammatory markers and bile acids were quantified. Measurements and Main Results: We observed a range of allograft community composition with three discernible types (labeled community state types [CSTs] 1-3). Transplant recipients with GERD were more likely to have CST1, characterized by high bacterial density and relative abundance of the oropharyngeal colonizing genera Prevotella and Veillonella. GERD was associated with more frequent transitions to CST1. CST1 was associated with lower inflammatory cytokine concentrations than pathogen-dominated CST3 across the range of microbial densities observed. Cox proportional hazard models revealed associations between CST3 and the development of ALAD/CLAD. Nissen fundoplication decreased bacterial load and proinflammatory cytokines. Conclusions: GERD was associated with a high bacterial density, Prevotella- and Veillonella-dominated CST1. CST3, but not CST1 or GERD, was associated with inflammation and early development of ALAD and CLAD. Nissen fundoplication was associated with a reduction in microbial density in BAL fluid samples, especially the CST1-specific genus, Prevotella.

Autoimmune PaneLs as PrEdictors of Toxicity in Patients TReated with Immune Checkpoint InhibiTors (ALERT).

BACKGROUND: Immune-checkpoint inhibitors (ICI) can lead to immune-related adverse events (irAEs) in a significant proportion of patients. The mechanisms underlying irAEs development are mostly unknown and might involve multiple immune effectors, such as T cells, B cells and autoantibodies (AutoAb). METHODS: We used custom autoantigen (AutoAg) microarrays to profile AutoAb related to irAEs in patients receiving ICI. Plasma was collected before and after ICI from cancer patients participating in two clinical trials (NCT03686202, NCT02644369). A one-time collection was obtained from healthy controls for comparison. Custom arrays with 162 autoAg were used to detect IgG and IgM reactivities. Differences of median fluorescent intensity (MFI) were analyzed with Wilcoxon sign rank test and Kruskal-Wallis test. MFI 500 was used as threshold to define autoAb reactivity. RESULTS: A total of 114 patients and 14 healthy controls were included in this study. irAEs of grade (G) ≥ 2 occurred in 37/114 patients (32%). We observed a greater number of IgG and IgM reactivities in pre-ICI collections from patients versus healthy controls (62 vs 32 p < 0.001). Patients experiencing irAEs G ≥ 2 demonstrated pre-ICI IgG reactivity to a greater number of AutoAg than patients who did not develop irAEs (39 vs 33 p = 0.040). We observed post-treatment increase of IgM reactivities in subjects experiencing irAEs G ≥ 2 (29 vs 35, p = 0.021) and a decrease of IgG levels after steroids (38 vs 28, p = 0.009). CONCLUSIONS: Overall, these results support the potential role of autoAb in irAEs etiology and evolution. A prospective study is ongoing to validate our findings (NCT04107311).

Performance Characteristics of Next-Generation Sequencing for the Detection of Antimicrobial Resistance Determinants in Escherichia coli Genomes and Metagenomes.

Short-read sequencing can provide detection of multiple genomic determinants of antimicrobial resistance from single bacterial genomes and metagenomic samples. Despite its increasing application in human, animal, and environmental microbiology, including human clinical trials, the performance of short-read Illumina sequencing for antimicrobial resistance gene (ARG) detection, including resistance-conferring single nucleotide polymorphisms (SNPs), has not been systematically characterized. Using paired-end 2 × 150 bp (base pair) Illumina sequencing and an assembly-based method for ARG prediction, we determined sensitivity, positive predictive value (PPV), and sequencing depths required for ARG detection in an Escherichia coli isolate of sequence type (ST) 38 spiked into a synthetic microbial community at varying abundances. Approximately 300,000 reads or 15× genome coverage was sufficient to detect ARGs in E. coli ST38, with comparable sensitivity and PPV to ~100× genome coverage. Using metagenome assembly of mixed microbial communities, ARG detection at E. coli relative abundances of 1% would require assembly of approximately 30 million reads to achieve 15× target coverage. The minimum sequencing depths were validated using public data sets of 948 E. coli genomes and 10 metagenomic rectal swab samples. A read-based approach using k-mer alignment (KMA) for ARG prediction did not substantially improve minimum sequencing depths for ARG detection compared to assembly of the E. coli ST38 genome or the combined metagenomic samples. Analysis of sequencing depths from recent studies assessing ARG content in metagenomic samples demonstrated that sequencing depths had a median estimated detection frequency of 84% (interquartile range: 30%-92%) for a relative abundance of 1%. IMPORTANCE Systematically determining Illumina sequencing performance characteristics for detection of ARGs in metagenomic samples is essential to inform study design and appraisal of human, animal, and environmental metagenomic antimicrobial resistance studies. In this study, we quantified the performance characteristics of ARG detection in E. coli genomes and metagenomes and established a benchmark of ~15× coverage for ARG detection for E. coli in metagenomes. We demonstrate that for low relative abundances, sequencing depths of ~30 million reads or more may be required for adequate sensitivity for many applications.