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BackgroundHomeostatic turnover of the extracellular matrix conditions the structure and function of the healthy lung. In lung transplantation, long-term management remains limited by chronic lung allograft dysfunction, an umbrella term used for a heterogeneous entity ultimately associated with pathological airway and/or parenchyma remodeling.ObjectiveThis study assessed whether the local cross-talk between the pulmonary microbiota and host cells is a key determinant in the control of lower airway remodeling posttransplantation.MethodsMicrobiota DNA and host total RNA were isolated from 189 bronchoalveolar lavages obtained from 116 patients post lung transplantation. Expression of a set of 11 genes encoding either matrix components or factors involved in matrix synthesis or degradation (anabolic and catabolic remodeling, respectively) was quantified by real-time quantitative PCR. Microbiota composition was characterized using 16S ribosomal RNA gene sequencing and culture.ResultsWe identified 4 host gene expression profiles, among which catabolic remodeling, associated with high expression of metallopeptidase-7, -9, and -12, diverged from anabolic remodeling linked to maximal thrombospondin and platelet-derived growth factor D expression. While catabolic remodeling aligned with a microbiota dominated by proinflammatory bacteria (eg, Staphylococcus, Pseudomonas, and Corynebacterium), anabolic remodeling was linked to typical members of the healthy steady state (eg, Prevotella, Streptococcus, and Veillonella). Mechanistic assays provided direct evidence that these bacteria can impact host macrophage-fibroblast activation and matrix deposition.ConclusionsHost-microbes interplay potentially determines remodeling activities in the transplanted lung, highlighting new therapeutic opportunities to ultimately improve long-term lung transplant outcome.

Background: Double-blind placebo-controlled food challenges (DBPCFCs) remain the gold standard for the diagnosis of food allergy; however, challenges require significant time and resources and place the patient at an increased risk for severe allergic adverse events. There have been continued efforts to identify alternative diagnostic methods to replace or minimize the need for oral food challenges (OFCs) in the diagnosis of food allergy.Methods: Data was extracted for all IRB-approved, Stanford-initiated clinical protocols involving standardized screening OFCs to a cumulative dose of 500 mg protein to any of 11 food allergens in participants with elevated skin prick test (SPT) and/or specific IgE (sIgE) values to the challenged food across 7 sites. Baseline population characteristics, biomarkers, and challenge outcomes were analyzed to develop diagnostic criteria predictive of positive OFCs across multiple allergens in our multi-allergic cohorts.Results: A total of 1247 OFCs completed by 427 participants were analyzed in this cohort. Eighty-five percent of all OFCs had positive challenges. A history of atopic dermatitis and multiple food allergies were significantly associated with a higher risk of positive OFCs. The majority of food-specific SPT, sIgE, and sIgE/total IgE (tIgE) thresholds calculated from cumulative tolerated dose (CTD)-dependent receiver operator curves (ROC) had high discrimination of OFC outcome (area under the curves > 0.75). Participants with values above the thresholds were more likely to have positive challenges.Conclusions: This is the first study, to our knowledge, to not only adjust for tolerated allergen dose in predicting OFC outcome, but to also use this method to establish biomarker thresholds. The presented findings suggest that readily obtainable biomarker values and patient demographics may be of use in the prediction of OFC outcome and food allergy. In the subset of patients with SPT or sIgE values above the thresholds, values appear highly predictive of a positive OFC and true food allergy. While these values are relatively high, they may serve as an appropriate substitute for food challenges in clinical and research settings.

Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm–5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm–3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes.