Highlights d Selection favors P. aeruginosa mutations that promote aggregation in the CF sinuses d Aggregation and bottlenecks fragment populations, amplifying effects of genetic drift d Mutators persist in small populations and drive early genome degradation d Population size and infection-site biogeography impact evolutionary trajectories

Together with prior sinus microbiota studies of adults with CF chronic rhinosinusitis, our study underscores similarities between sinus and lower respiratory tract microbial community structures in CF. We show how community structure tracks with inflammation and several disease measures.

BackgroundThe Ki‐67 index is important for grading neuroendocrine tumors (NETs) in cytology. However, different counting methods exist. Recently, augmented reality microscopy (ARM) has enabled real‐time image analysis using glass slides. The objective of the current study was to compare different traditional Ki‐67 scoring methods in cell block material with newer methods such as ARM.MethodsKi‐67 immunostained slides from 50 NETs of varying grades were retrieved (39 from the pancreas and 11 metastases). Methods with which to quantify the Ki‐67 index in up to 3 hot spots included: 1) “eyeball” estimation (EE); 2) printed image manual counting (PIMC); 3) ARM with live image analysis; and 4) image analysis using whole‐slide images (WSI) (field of view [FOV] and the entire slide).ResultsThe Ki‐67 index obtained using the different methods varied. The pairwise kappa results varied from no agreement for image analysis using digital image analysis WSI (FOV) and histology to near‐perfect agreement for ARM and PIMC. Using surgical pathology as the gold standard, the EE method was found to have the highest concordance rate (84.2%), followed by WSI analysis of the entire slide (73.7%) and then both the ARM and PIMC methods (63.2% for both). The PIMC method was the most time‐consuming whereas image analysis using WSI (FOV) was the fastest method followed by ARM.ConclusionsThe Ki‐67 index for NETs in cell block material varied by the method used for scoring, which may affect grade. PIMC was the most time‐consuming method, and EE had the highest concordance rate. Although real‐time automated counting using image analysis demonstrated inaccuracies, ARM streamlined and hastened the task of Ki‐67 quantification in NETs.

People with the genetic disorder cystic fibrosis (CF) harbor lifelong respiratory infections, with morbidity and mortality frequently linked to chronic lung infections dominated by the opportunistically pathogenic bacterium Pseudomonas aeruginosa. During chronic CF lung infections, a single clone of P. aeruginosa can persist for decades and dominate end-stage CF lung disease due to its propensity to adaptively evolve to the respiratory environment, a process termed pathoadaptation. Chronic rhinosinusitis (CRS), chronic inflammation and infection of the sinonasal space, is highly prevalent in CF and the sinuses may serve as the first site in the respiratory tract to become colonized by bacteria that then proceed to seed lung infections. We identified three evolutionary genetic routes by which P. aeruginosa evolves in the sinuses of people with CF, including through the evolution of mutator lineages and proliferative insertion sequences and culminating in early genomic signatures of host-restriction. Our findings raise the question of whether a significant portion of the pathoadaptive phenotypes previously thought to have evolved in response to selective pressures in the CF lungs may have first arisen in the sinuses and underscore the link between sinonasal and lung disease in CF.

Background: Chronic rhinosinusitis (CRS) is a common, yet underreported and understudied manifestation of upper respiratory disease in people with cystic fibrosis (CF). There are currently no standard of care guidelines for the management of CF CRS, but treatment of upper airway disease may ameliorate lower airway disease. We sought to inform future treatment guidelines by determining whether changes to sinus microbial community diversity and specific taxa known to cause CF lung disease are associated with increased respiratory disease and inflammation. Methods: We performed 16S rRNA gene sequencing, supplemented with cytokine analyses, microscopy, and bacterial culturing, on samples from the sinuses of 27 adults with CF CRS at the University of Pittsburgh CF Sinus Clinic. At each study visit, participants underwent endoscopic paranasal sinus sampling and clinical evaluation. We identified key drivers of microbial community composition and evaluated relationships between diversity and taxa with disease outcomes and inflammation. Findings: Sinus community diversity was low and the composition was unstable, with many participants exhibiting alternating dominance between Pseudomonas aeruginosa and Staphylococci over time. Despite a tendency for dominance by these two taxa, communities were highly individualized and shifted composition during exacerbation of sinus disease symptoms. Exacerbations were also associated with communities dominated by Staphylococcus spp. Reduced microbial community diversity was linked to worse sinus disease and the inflammatory status of the sinuses (including increased IL-1β). Increased IL-1β was also linked to worse sinus endoscopic appearance, and other cytokines were linked to microbial community dynamics. Interpretation: To our knowledge, this is the largest longitudinal study of microbial communities and cytokine secretion in CF CRS. Our work revealed previously unknown instability of sinus microbial communities and a link between inflammation, lack of microbial community diversity, and worse sinus disease.