Pharmacological Improvement of Cystic Fibrosis Transmembrane Conductance Regulator Function Rescues Airway Epithelial Homeostasis and Host Defense in Children with Cystic Fibrosis.

Rationale: Pharmacological improvement of cystic fibrosis transmembrane conductance regulator (CFTR) function with elexacaftor/tezacaftor/ivacaftor (ETI) provides unprecedented improvements in lung function and other clinical outcomes in patients with cystic fibrosis (CF). However, ETI effects on impaired mucosal homeostasis and host defense at the molecular and cellular levels in the airways of patients with CF remain unknown. Objectives: To investigate effects of ETI on the transcriptome of nasal epithelial and immune cells from children with CF at the single-cell level. Methods: Nasal swabs from 13 children with CF and at least one F508del allele aged 6 to 11 years were collected at baseline and 3 months after initiation of ETI, subjected to single-cell RNA sequencing, and compared with swabs from 12 age-matched healthy children. Measurements and Main Results: Proportions of CFTR-positive cells were decreased in epithelial basal, club, and goblet cells, but not in ionocytes, from children with CF at baseline and were restored by ETI therapy to nearly healthy levels. Single-cell transcriptomics revealed an impaired IFN signaling and reduced expression of major histocompatibility complex classes I and II encoding genes in epithelial cells of children with CF at baseline, which was partially restored by ETI. In addition, ETI therapy markedly reduced the inflammatory phenotype of immune cells, particularly of neutrophils and macrophages. Conclusions: Pharmacological improvement of CFTR function improves innate mucosal immunity and reduces immune cell inflammatory responses in the upper airways of children with CF at the single-cell level, highlighting the potential to restore epithelial homeostasis and host defense in CF airways by early initiation of ETI therapy.

Directed acyclic graph for epidemiological studies in childhood food allergy: Construction, user's guide, and application.

Understanding modifiable prenatal and early life causal determinants of food allergy is important for the prevention of the disease. Randomized clinical trials studying environmental and dietary determinants of food allergy may not always be feasible. Identifying risk/protective factors for early-life food allergy often relies on observational studies, which may be affected by confounding bias. The directed acyclic graph (DAG) is a causal diagram useful to guide causal inference from observational epidemiological research. To date, research on food allergy has made little use of this promising method. We performed a literature review of existing evidence with a systematic search, synthesized 32 known risk/protective factors, and constructed a comprehensive DAG for early-life food allergy development. We present an easy-to-use online tool for researchers to re-construct, amend, and modify the DAG along with a user's guide to minimize confounding bias. We estimated that adjustment strategies in 57% of previous observational studies on modifiable factors of childhood food allergy could be improved if the researchers determined their adjustment sets by DAG. Future researchers who are interested in the causal inference of food allergy development in early life can apply the DAG to identify covariates that should and should not be controlled in observational studies.

[The Medical Informatics Initiative at a glance-establishing a health research data infrastructure in Germany]. / Die Medizininformatik-Initiative im Überblick ­ Aufbau einer Gesundheitsforschungsdateninfrastruktur in Deutschland.

The Medical Informatics Initiative (MII) funded by the Federal Ministry of Education and Research (BMBF) 2016-2027 is successfully laying the foundations for data-based medicine in Germany. As part of this funding, 51 new professorships, 21 junior research groups, and various new degree programs have been established to strengthen teaching, training, and continuing education in the field of medical informatics and to improve expertise in medical data sciences. A joint decentralized federated research data infrastructure encompassing the entire university medical center and its partners was created in the form of data integration centers (DIC) at all locations and the German Portal for Medical Research Data (FDPG) as a central access point. A modular core dataset (KDS) was defined and implemented for the secondary use of patient treatment data with consistent use of international standards (e.g., FHIR, SNOMED CT, and LOINC). An officially approved nationwide broad consent was introduced as the legal basis. The first data exports and data use projects have been carried out, embedded in an overarching usage policy and standardized contractual regulations. The further development of the MII health research data infrastructures within the cooperative framework of the Network of University Medicine (NUM) offers an excellent starting point for a German contribution to the upcoming European Health Data Space (EHDS), which opens opportunities for Germany as a medical research location.

Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats.

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

Proteomic and transcriptomic profiling of brainstem, cerebellum and olfactory tissues in early- and late-phase COVID-19.

Neurological symptoms, including cognitive impairment and fatigue, can occur in both the acute infection phase of coronavirus disease 2019 (COVID-19) and at later stages, yet the mechanisms that contribute to this remain unclear. Here we profiled single-nucleus transcriptomes and proteomes of brainstem tissue from deceased individuals at various stages of COVID-19. We detected an inflammatory type I interferon response in acute COVID-19 cases, which resolves in the late disease phase. Integrating single-nucleus RNA sequencing and spatial transcriptomics, we could localize two patterns of reaction to severe systemic inflammation, one neuronal with a direct focus on cranial nerve nuclei and a separate diffuse pattern affecting the whole brainstem. The latter reflects a bystander effect of the respiratory infection that spreads throughout the vascular unit and alters the transcriptional state of mainly oligodendrocytes, microglia and astrocytes, while alterations of the brainstem nuclei could reflect the connection of the immune system and the central nervous system via, for example, the vagus nerve. Our results indicate that even without persistence of severe acute respiratory syndrome coronavirus 2 in the central nervous system, local immune reactions are prevailing, potentially causing functional disturbances that contribute to neurological complications of COVID-19.