IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression

Immune checkpoint blockade therapy induces the AHR-activating enzyme IL4I1, which promotes tumor progression, through its effects on tumor cell motility and adaptive immunity

Identification of Streptococcus pneumoniae: Development of a Standardized Protocol for Optochin Susceptibility Testing Using Total Lab Automation

Purpose. Optochin susceptibility is one parameter used in the laboratory to identify Streptococcus pneumoniae. However, a single standardized procedure does not exist. Optochin is included neither in the current EUCAST breakpoint tables nor in the CLSI performance standards for antimicrobial susceptibility testing. We wanted to establish an evidence-based protocol for optochin testing for our Total Lab Automation. Methods. We tested seven different agars and four different reading time points (7 h, 12 h, 18 h, and 24 h). To accommodate for serotype diversity, all tests were done with 99 different strains covering 34 different serotypes of S. pneumoniae. We calculated a multivariable linear regression using data from 5544 inhibition zones. Results. Reading was possible for all strains at 12 h. Agar type and manufacturer influenced the size of the inhibition zones by up to 2 mm and they varied considerably depending on serotype (up to 3 mm for serotype 3). Depending on agar and reading time point, up to 38% of inhibition zones were smaller than the cut-off of 14 mm; that is, the result of the test was false-negative. Conclusions. Shortening incubation time from 24 h to 12 h for optochin susceptibility testing is feasible. Agar and incubation time have to be chosen carefully to avoid false-negative results

Comparison of Microbiomes from Different Niches of Upper and Lower Airways in Children and Adolescents with Cystic Fibrosis

Changes in the airway microbiome may be important in the pathophysiology of chronic lung disease in patients with cystic fibrosis. However, little is known about the microbiome in early cystic fibrosis lung disease and the relationship between the microbiomes from different niches in the upper and lower airways. Therefore, in this cross-sectional study, we examined the relationship between the microbiome in the upper (nose and throat) and lower (sputum) airways from children with cystic fibrosis using next generation sequencing. Our results demonstrate a significant difference in both α and β-diversity between the nose and the two other sampling sites. The nasal microbiome was characterized by a polymicrobial community while the throat and sputum communities were less diverse and dominated by a few operational taxonomic units. Moreover, sputum and throat microbiomes were closely related especially in patients with clinically stable lung disease. There was a high inter-individual variability in sputum samples primarily due to a decrease in evenness linked to increased abundance of potential respiratory pathogens such as Pseudomonas aeruginosa. Patients with chronic Pseudomonas aeruginosa infection exhibited a less diverse sputum microbiome. A high concordance was found between pediatric and adult sputum microbiomes except that Burkholderia was only observed in the adult cohort. These results indicate that an adult-like lower airways microbiome is established early in life and that throat swabs may be a good surrogate in clinically stable children with cystic fibrosis without chronic Pseudomonas aeruginosa infection in whom sputum sampling is often not feasible

Patients’ characteristics.

<p>BMI, body mass index; FEV1% pred, forced expiratory volume in 1 second % predicted; LCI, lung clearance index, SDS standard deviation score</p><p>Patients’ characteristics.</p

Spatial analysis of the airways microbiome.

<p>Nasal swabs are represented by black dots, throat swabs by red dots, sputum samples by green dots. (A) Principal Component Analysis of samples obtained from clinically stable children with CF. In panel (B) samples from patients during exacerbation were added. Pearson correlations were performed to highlight which OTUs were responsible for the divergence among the samples. Correlation was considered significant when the coefficient of correlation was higher than 0.6 and p-value < 0.01. (C) Principal Coordinates Analysis was performed on samples obtained from clinically stable CF patients or (D) on all available samples from CF patients irrespective of the clinical status.</p

Descriptive analysis of the sequencing reads.

<p>(A) Distribution of the number of reads over the whole dataset. (B) Number of samples with detection of the indicated percentage of the microbiome at the genus level by culture. Strains isolated by culture were classified at the genus level and correspondence with the NGS dataset was analyzed.</p

Relative abundance of the bacterial genera depending on the sampling site.

<p>The colored segments of each bar represent the proportion of reads mapping to different bacterial genera.</p

Alpha-diversity of the upper and lower airways microbiomes from clinically stable children with CF.

<p>Alpha-diversity was calculated with the non parametric Shannon index (A), richness was estimated with the Chao1 estimate (B) and evenness was calculated based on the Shannon index (C). Alpha-diversity variation among nose, throat and sputum microbiome was analyzed with a linear mixed model with random effects for CF patients and paired comparisons were done with a Tukey post-hoc test for pairwise comparison.</p

Correlation between alpha-diversity and bacterial load in CF airways.

<p>The alpha-diversity is represented by the non parametric Shannon index (A) and the evenness index based on the Shannon index (B). The microbial load was measured via the proxy of the number of 16S genes. Samples from the three sampling sites are represented.</p