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)

Exploring the Effects of Inhaled Antibiotics on the Cystic Fibrosis Lung Microbiome and Pseudomonas aeruginosa Population Diversity and their Clinical Implications

The CF lung microbiome is composed of a diverse group of microorganisms. Where the constituents of the microbiome originate from remains poorly understood. The work presented herein shows that the home environment may serve as a reservoir for infection in patients with CF. Researchers have demonstrated that the CF lung microbial communities are dynamic and complex. As patients age and disease progression occurs the diversity of organisms colonizing the lower airways generally decreases and patients become dominated by organisms such as Pseudomonas aeruginosa. Several studies have attempted to increase our understanding of the shifts in the microbial communities prior to pulmonary exacerbations. However, there is a tremendous knowledge gap on how the microbiome changes through chronic suppressive inhaled antibiotics used by the majority of CF patients in Canada. Accordingly, we sought to investigate how inhaled aztreonam and tobramycin affect the CF lung microbiome and whether the microbiome can be used as a tool to predict patient response. We showed that the lung microbiome is relatively fixed in adults with CF despite potent inhaled antibacterial therapy. The relative abundance of Staphylococcus was associated with response in all three studies assessing the effects of inhaled antibiotics on the lung microbiome. Specifically, a higher abundance of Staphylococcus at baseline was associated with non-response to inhaled aztreonam and response to inhaled tobramycin – mirroring expected antibacterial activity and suggesting a potential biomarker for treatment response. Keywords: lung microbiome, cystic fibrosis, inhaled antibiotics, Staphylococcus, P. aeruginosa, biomarke

A scoping review of frameworks that address wellness in medical learners

Scoping revie

untargeted metabolomics analysis of fecal extract after resveratrol treatment

In a previous study, we found that resveratrol could inhibit chylomicron secretion and jejunal SR-B1 expression, and the effects were mediated by gut microbiota and some heat-stable metabolites in the feces of mice which received resveratrol treatment. We then conducted metabolomics profiling of fecal extract (FE) from mice receiving 8-week high-fat diet (HFD) or HFD plus resveratrol (0.5% in diet) treatment. HFR-FE was also heated at 95℃ for 5 min, referring to the HRH-FE group. Through LC-MS/MS analysis, we detected 2053 metabolites. Principal component analysis (PCA) revealed that composition of metabolites in HFD-FE and HFR-FE are different, and after heat treatment, a large portion of metabolites in HFR-FE was transformed. We then conducted differential analysis and identified 238 differential metabolites between HFR and HFD, and 432 differential metabolites between HRH and HFD. The KEGG pathway enrichment analysis suggested that sphingolipid metabolism and bile acid metabolism-related pathways were mostly modulated by resveratrol treatment.</p

Longitudinal sampling of the lung microbiota in individuals with cystic fibrosis

Cystic fibrosis (CF) manifests in the lungs resulting in chronic microbial infection. Most morbidity and mortality in CF is due to cycles of pulmonary exacerbations-episodes of acute inflammation in response to the lung microbiome-which are difficult to prevent and treat because their cause is not well understood. We hypothesized that longitudinal analyses of the bacterial component of the CF lung microbiome may elucidate causative agents within this community for pulmonary exacerbations. In this study, 6 participants were sampled thrice-weekly for up to one year. During sampling, sputum, and data (antibiotic usage, spirometry, and symptom scores) were collected. Time points were categorized based on relation to exacerbation as Stable, Intermediate, and Treatment. Retrospectively, a subset of were interrogated via 16S rRNA gene sequencing. When samples were examined categorically, a significant difference between the lung microbiota in Stable, Intermediate, and Treatment samples was observed in a subset of participants. However, when samples were examined longitudinally, no correlations between microbial composition and collected data (antibiotic usage, spirometry, and symptom scores) were observed upon exacerbation onset. In this study, we identified no universal indicator within the lung microbiome of exacerbation onset but instead showed that changes to the CF lung microbiome occur outside of acute pulmonary episodes and are patient-specific.</p

Longitudinal sampling of the lung microbiota in individuals with cystic fibrosis

<div><p>Cystic fibrosis (CF) manifests in the lungs resulting in chronic microbial infection. Most morbidity and mortality in CF is due to cycles of pulmonary exacerbations—episodes of acute inflammation in response to the lung microbiome—which are difficult to prevent and treat because their cause is not well understood. We hypothesized that longitudinal analyses of the bacterial component of the CF lung microbiome may elucidate causative agents within this community for pulmonary exacerbations. In this study, 6 participants were sampled thrice-weekly for up to one year. During sampling, sputum, and data (antibiotic usage, spirometry, and symptom scores) were collected. Time points were categorized based on relation to exacerbation as Stable, Intermediate, and Treatment. Retrospectively, a subset of were interrogated via 16S rRNA gene sequencing. When samples were examined categorically, a significant difference between the lung microbiota in Stable, Intermediate, and Treatment samples was observed in a subset of participants. However, when samples were examined longitudinally, no correlations between microbial composition and collected data (antibiotic usage, spirometry, and symptom scores) were observed upon exacerbation onset. In this study, we identified no universal indicator within the lung microbiome of exacerbation onset but instead showed that changes to the CF lung microbiome occur outside of acute pulmonary episodes and are patient-specific.</p></div

Longitudinal dynamics of two select participants (C and E).

<p>Two participants who were the outliers in terms of the number of pulmonary exacerbations experienced over the course of the study period were chosen for closer examination. <b>A</b>. Sample collection for participant C is shown in relation to, antibiotic use, FEV1, and symptom scores. <b>B</b>. Correlations between collected data, diversity metrics, and OTU relative abundance were calculated and significant correlations were reported (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172811#pone.0172811.s008" target="_blank">S4 Table</a>); a subset of these significant correlations are plotted. <b>C</b>. Sample collection for participant E in relation to antibiotic use, FEV1, and symptom scores. <b>D</b>. Correlations between these collected data and the OTUs present within the microbiome were calculated and significant correlations were reported (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172811#pone.0172811.s009" target="_blank">S5 Table</a>); a subset of these significant correlations are plotted.</p

The effects of exacerbation on the lung microbiome are not consistently seen at the community level.

<p><b>A</b>. Taxonomic summaries of all samples sequenced. These summaries indicate that changes to the lung microbiome upon exacerbation are not often obvious when examining the community-wide taxa composition. Taxa present at <2% are summarized in the gray bar. Participant E experienced 4 exacerbations during the study period which are indicated with black lines. <b>B</b>. Heatmaps indicate the Bray-Curtis dissimilarity between each sample. Here, we can see that samples taken during some exacerbations are more dissimilar to those collected during stability; however, this is not true for every exacerbation. These observations are qualified by statistical measures (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172811#pone.0172811.s006" target="_blank">S2 Table</a>) and were independent of FEV1 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172811#pone.0172811.s007" target="_blank">S3 Table</a>).</p

Examples of stability and variability in the CF lung microbial communities of two select participants (C and E).

<p><b>A</b>. Visualization of the stability of participant C's lung microbial community over the study period. Each OTU is presented as a terminal node on the phylogeny; its presence in each sample evaluated using 16S rRNA gene sequencing is shown extending outwardly from the inner phylogeny in chronological order. The density of the color indicates the relative abundance of the OTU; when the OTU is not identified, the space is left blank. <b>B</b>. Participant E, who experienced 4 exacerbations over the course of the year, has a much more variable lung microbiota than participant C. Similar to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172811#pone.0172811.g005" target="_blank">Fig 5c</a>, OTUs are represented as nodes in the phylogeny whose relative abundance is indicated with varying color density. Rings in the phylogeny are colored to indicate the sample type (Treatment red, Intermediate blue, Stable green). Density of the color indicates relative abundance of the OTU and time periods are colored according to the health state.</p

The CF lung microbiome is distinguished by individual.

<p><b>A</b>. PCoA plots of all participants illustrate the clustering of participant samples, indicated as significant by PERMANOVA (p = 0.001). Health state within participants, as defined as Stable, Intermediate (<1 month pre- or post-Treatment), and Treatment was significant (PERMANOVA, p = 0.016), but was highly confounded by the participant (p = 0.042 of Participant:Health interaction term). <b>B</b>. UPGMA phylogeny depicting the Bray-Curtis dissimilarity between samples. It is apparent that the principle driver of similarity between samples are inter-individual microbial lung composition due to the almost complete separation of participant samples. PC = Principal Coordinate.</p