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.

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

RATIONALE: Pharmacological improvement of CFTR function with elexacaftor/tezacaftor/ivacaftor (ETI) provides unprecedented improvements in lung function and other clinical outcomes to patients with cystic fibrosis (CF). However, ETI effects on impaired mucosal homeostasis and host defense at the molecular and cellular level in the airways of CF patients remain unknown. OBJECTIVES: To investigate effects of ETI on the transcriptiome 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 three months after initiation of ETI, subjected to scRNA-seq and compared to 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 on ETI therapy to near healthy levels. Single cell transcriptomics revealed an impaired interferon signalling and reduced expression of MHC I and II encoding genes in epithelial cells of CF children at baseline, which was partially restored by ETI. Additionally, 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. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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.

Immune-epithelial cell cross-talk enhances antiviral responsiveness to SARS-CoV-2 in children.

The risk of developing severe COVID-19 rises dramatically with age. Schoolchildren are significantly less likely than older people to die from SARS-CoV-2 infection, but the molecular mechanisms underlying this age-dependence are unknown. In primary infections, innate immunity is critical due to the lack of immune memory. Children, in particular, have a significantly stronger interferon response due to a primed state of their airway epithelium. In single-cell transcriptomes of nasal turbinates, we find increased frequencies of immune cells and stronger cytokine-mediated interactions with epithelial cells, resulting in increased epithelial expression of viral sensors (RIG-I, MDA5) via IRF1. In vitro, adolescent peripheral blood mononuclear cells produce more cytokines, priming A549 cells for stronger interferon responses to SARS-CoV-2. Taken together, our findings suggest that increased numbers of immune cells in the airways of children and enhanced cytokine-based interactions with epithelial cells tune the setpoint of the epithelial antiviral system. Our findings shed light on the molecular basis of children's remarkable resistance to COVID-19 and may suggest a novel concept for immunoprophylactic treatments.

The promise of digital healthcare technologies.

Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.

Transcriptomics-inferred dynamics of SARS-CoV-2 interactions with host epithelial cells.

Virus-host interactions can reveal potentially effective and selective therapeutic targets for treating infection. Here, we performed an integrated analysis of the dynamics of virus replication and the host cell transcriptional response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using human Caco-2 colon cancer cells as a model. Time-resolved RNA sequencing revealed that, upon infection, cells immediately transcriptionally activated genes associated with inflammatory pathways that mediate the antiviral response, which was followed by an increase in the expression of genes involved in ribosome and mitochondria function, thus suggesting rapid alterations in protein production and cellular energy supply. At later stages, between 24 and 48 hours after infection, the expression of genes involved in metabolic processes-in particular, those related to xenobiotic metabolism-was decreased. Mathematical modeling incorporating SARS-CoV-2 replication suggested that SARS-CoV-2 proteins inhibited the host antiviral response and that virus transcripts exceeded the translation capacity of the host cells. Targeting kinase-dependent pathways that exhibited increases in transcription in host cells was as effective as a virus-targeted inhibitor at repressing viral replication. Our findings in this model system delineate a sequence of SARS-CoV-2 virus-host interactions that may facilitate the identification of druggable host pathways to suppress infection.

Federated electronic health records for the European Health Data Space.

The European Commission's draft for the European Health Data Space (EHDS) aims to empower citizens to access their personal health data and share it with physicians and other health-care providers. It further defines procedures for the secondary use of electronic health data for research and development. Although this planned legislation is undoubtedly a step in the right direction, implementation approaches could potentially result in centralised data silos that pose data privacy and security risks for individuals. To address this concern, we propose federated personal health data spaces, a novel architecture for storing, managing, and sharing personal electronic health records that puts citizens at the centre-both conceptually and technologically. The proposed architecture puts citizens in control by storing personal health data on a combination of personal devices rather than in centralised data silos. We describe how this federated architecture fits within the EHDS and can enable the same features as centralised systems while protecting the privacy of citizens. We further argue that increased privacy and control do not contradict the use of electronic health data for research and development. Instead, data sovereignty and transparency encourage active participation in studies and data sharing. This combination of privacy-by-design and transparent, privacy-preserving data sharing can enable health-care leaders to break the privacy-exploitation barrier, which currently limits the secondary use of health data in many cases.

Eos and OMOCL: Towards a seamless integration of openEHR records into the OMOP Common Data Model.

BACKGROUND: The reuse of data from electronic health records (EHRs) for research purposes promises to improve the data foundation for clinical trials and may even support to enable them. Nevertheless, EHRs are characterized by both, heterogeneous structure and semantics. To standardize this data for research, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standard has recently seen an increase in use. However, the conversion of these EHRs into the OMOP CDM requires complex and resource intensive Extract Transform and Load (ETL) processes. This hampers the reuse of clinical data for research. To solve the issues of heterogeneity of EHRs and the lack of semantic precision on the care site, the openEHR standard has recently seen wider adoption. A standardized process to integrate openEHR records into the CDM potentially lowers the barriers of making EHRs accessible for research. Yet, a comprehensive approach about the integration of openEHR records into the OMOP CDM has not yet been made. METHODS: We analyzed both standards and compared their models to identify possible mappings. Based on this, we defined the necessary processes to transform openEHR records into CDM tables. We also discuss the limitation of openEHR with its unspecific demographics model and propose two possible solutions. RESULTS: We developed the OMOP Conversion Language (OMOCL) which enabled us to define a declarative openEHR archetype-to-CDM mapping language. Using OMOCL, it was possible to define a set of mappings. As a proof-of-concept, we implemented the Eos tool, which uses the OMOCL-files to successfully automatize the ETL from real-world and sample EHRs into the OMOP CDM. DISCUSSION: Both Eos and OMOCL provide a way to define generic mappings for an integration of openEHR records into OMOP. Thus, it represents a significant step towards achieving interoperability between the clinical and the research data domains. However, the transformation of openEHR data into the less expressive OMOP CDM leads to a loss of semantics.

Single-cell biology: what does the future hold?

In this Editorial, our Chief Editor and members of our Advisory Editorial Board discuss recent breakthroughs, current challenges, and emerging opportunities in single-cell biology and share their vision of "where the field is headed."

Genome-wide DNA methylation sequencing identifies epigenetic perturbations in the upper airways under long-term exposure to moderate levels of ambient air pollution.

While the link between exposure to high levels of ambient particulate matter (PM) and increased incidences of respiratory and cardiovascular diseases is widely recognized, recent epidemiological studies have shown that low PM concentrations are equally associated with adverse health effects. As DNA methylation is one of the main mechanisms by which cells regulate and stabilize gene expression, changes in the methylome could constitute early indicators of dysregulated signaling pathways. So far, little is known about PM-associated DNA methylation changes in the upper airways, the first point of contact between airborne pollutants and the human body. Here, we focused on cells of the upper respiratory tract and assessed their genome-wide DNA methylation pattern to explore exposure-associated early regulatory changes. Using a mobile epidemiological laboratory, nasal lavage samples were collected from a cohort of 60 adults that lived in districts with records of low (Simmerath) or moderate (Stuttgart) PM10 levels in Germany. PM10 concentrations were verified by particle measurements on the days of the sample collection and genome-wide DNA methylation was determined by enzymatic methyl sequencing at single-base resolution. We identified 231 differentially methylated regions (DMRs) between moderately and lowly PM10 exposed individuals. A high proportion of DMRs overlapped with regulatory elements, and DMR target genes were involved in pathways regulating cellular redox homeostasis and immune response. In addition, we found distinct changes in DNA methylation of the HOXA gene cluster whose methylation levels have previously been linked to air pollution exposure but also to carcinogenesis in several instances. The findings of this study suggest that regulatory changes in upper airway cells occur at PM10 levels below current European thresholds, some of which may be involved in the development of air pollution-related diseases.

90K/LGALS3BP expression is upregulated in COVID-19 but may not restrict SARS-CoV-2 infection.

Glycoprotein 90K, encoded by the interferon-stimulated gene LGALS3BP, displays broad antiviral activity. It reduces HIV-1 infectivity by interfering with Env maturation and virion incorporation, and increases survival of Influenza A virus-infected mice via antiviral innate immune signaling. Its antiviral potential in SARS-CoV-2 infection remains largely unknown. Here, we analyzed the expression of 90K/LGALS3BP in 44 hospitalized COVID-19 patients at multiple levels. We quantified 90K protein concentrations in serum and PBMCs as well as LGALS3BP mRNA levels. Complementary, we analyzed two single cell RNA-sequencing datasets for expression of LGALS3BP in respiratory specimens and PBMCs from COVID-19 patients. Finally, we analyzed the potential of 90K to interfere with SARS-CoV-2 infection of HEK293T/ACE2, Calu-3 and Caco-2 cells using authentic virus. 90K protein serum concentrations were significantly elevated in COVID-19 patients compared to uninfected sex- and age-matched controls. Furthermore, PBMC-associated concentrations of 90K protein were overall reduced by SARS-CoV-2 infection in vivo, suggesting enhanced secretion into the extracellular space. Mining of published PBMC scRNA-seq datasets uncovered monocyte-specific induction of LGALS3BP mRNA expression in COVID-19 patients. In functional assays, neither 90K overexpression in susceptible cell lines nor exogenous addition of purified 90K consistently inhibited SARS-CoV-2 infection. Our data suggests that 90K/LGALS3BP contributes to the global type I IFN response during SARS-CoV-2 infection in vivo without displaying detectable antiviral properties in vitro.

Software-Tool Support for Collaborative, Virtual, Multi-Site Molecular Tumor Boards.

The availability of high-throughput molecular diagnostics builds the foundation for Molecular Tumor Boards (MTBs). Although more fine-grained data is expected to support decision making of oncologists, assessment of data is complex and time-consuming slowing down the implementation of MTBs, e.g., due to retrieval of the latest medical publications, assessment of clinical evidence, or linkage to the latest clinical guidelines. We share our findings from analysis of existing tumor board processes and defininion of clinical processes for the adoption of MTBs. Building on our findings, we have developed a real-world software prototype together with oncologists and medical professionals, which supports the preparation and conduct of MTBs and enables collaboration between medical experts by sharing medical knowledge even across the hospital locations. We worked in interdisciplinary teams of clinicians, oncologists, medical experts, medical informaticians, and software engineers using design thinking methodology. With their input, we identified challenges and limitations of the current MTB approaches, derived clinical process models using Business Process and Modeling Notation (BMPN), and defined personas, functional and non-functional requirements for software tool support. Based on it, we developed software prototypes and evaluated them with clinical experts from major university hospitals across Germany. We extended the Kanban methodology enabling holistic tracking of patient cases from "backlog" to "follow-up" in our app. The feedback from interviewed medical professionals showed that our clinical process models and software prototype provide suitable process support for the preparation and conduction of molecular tumor boards. The combination of oncology knowledge across hospitals and the documentation of treatment decision can be used to form a unique medical knowledge base by oncologists for oncologists. Due to the high heterogeneity of tumor diseases and the spread of the latest medical knowledge, a cooperative decision-making process including insights from similar patient cases was considered as a very valuable feature. The ability to transform prepared case data into a screen presentation was recognized as an essential feature speeding up the preparation process. Oncologists require special software tool support to incorporate and assess molecular data for the decision-making process. In particular, the need for linkage to the latest medical knowledge, clinical evidence, and collaborative tools to discuss individual cases were named to be of importance. With the experiences from the COVID-19 pandemic, the acceptance of online tools and collaborative working is expected to grow. Our virtual multi-site approach proved to allow a collaborative decision-making process for the first time, which we consider to have a positive impact on the overall treatment quality.

Machine Learning for Medical Data Integration.

Making health data available for secondary use enables innovative data-driven medical research. Since modern machine learning (ML) methods and precision medicine require extensive amounts of data covering most of the standard and edge cases, it is essential to initially acquire large datasets. This can typically only be achieved by integrating different datasets from various sources and sharing data across sites. To obtain a unified dataset from heterogeneous sources, standard representations and Common Data Models (CDM) are needed. The process of mapping data into these standardized representations is usually very tedious and requires many manual configuration and refinement steps. A potential way to reduce these efforts is to use ML methods not only for data analysis, but also for the integration of health data on the syntactic, structural, and semantic level. However, research on ML-based medical data integration is still in its infancy. In this article, we describe the current state of the literature and present selected methods that appear to have a particularly high potential to improve medical data integration. Moreover, we discuss open issues and possible future research directions.

RECAST: Study protocol for an observational study for the understanding of the increased REsilience of Children compared to Adults in SARS-CoV-2 infecTion.

INTRODUCTION: The SARS-CoV-2 pandemic remains a threat to public health. Soon after its outbreak, it became apparent that children are less severely affected. Indeed, opposing clinical manifestations between children and adults are observed for other infections. The SARS-CoV-2 outbreak provides the unique opportunity to study the underlying mechanisms. This protocol describes the methods of an observational study that aims to characterise age dependent differences in immune responses to primary respiratory infections using SARS-CoV-2 as a model virus and to assess age differences in clinical outcomes including lung function. METHODS AND ANALYSIS: The study aims to recruit at least 120 children and 60 adults that are infected with SARS-CoV-2 and collect specimen for a multiomics analysis, including single cell RNA sequencing of nasal epithelial cells and peripheral blood mononuclear cells, mass cytometry of whole blood samples and nasal cells, mass spectrometry-based serum and plasma proteomics, nasal epithelial cultures with functional in vitro analyses, SARS-CoV-2 antibody testing, sequencing of the viral genome and lung function testing. Data obtained from this multiomics approach are correlated with medical history and clinical data. Recruitment started in October 2020 and is ongoing. ETHICS AND DISSEMINATION: The study was reviewed and approved by the Ethics Committee of Charité - Universitätsmedizin Berlin (EA2/066/20). All collected specimens are stored in the central biobank of Charité - Universitätsmedizin Berlin and are made available to all participating researchers and on request. TRIAL REGISTRATION NUMBER: DRKS00025715, pre-results publication.

resVAE ensemble: Unsupervised identification of gene sets in multi-modal single-cell sequencing data using deep ensembles.

Feature identification and manual inspection is currently still an integral part of biological data analysis in single-cell sequencing. Features such as expressed genes and open chromatin status are selectively studied in specific contexts, cell states or experimental conditions. While conventional analysis methods construct a relatively static view on gene candidates, artificial neural networks have been used to model their interactions after hierarchical gene regulatory networks. However, it is challenging to identify consistent features in this modeling process due to the inherently stochastic nature of these methods. Therefore, we propose using ensembles of autoencoders and subsequent rank aggregation to extract consensus features in a less biased manner. Here, we performed sequencing data analyses of different modalities either independently or simultaneously as well as with other analysis tools. Our resVAE ensemble method can successfully complement and find additional unbiased biological insights with minimal data processing or feature selection steps while giving a measurement of confidence, especially for models using stochastic or approximation algorithms. In addition, our method can also work with overlapping clustering identity assignment suitable for transitionary cell types or cell fates in comparison to most conventional tools.

Global hypomethylation in childhood asthma identified by genome-wide DNA-methylation sequencing preferentially affects enhancer regions.

BACKGROUND: Childhood asthma is a result of a complex interaction of genetic and environmental components causing epigenetic and immune dysregulation, airway inflammation and impaired lung function. Although different microarray based EWAS studies have been conducted, the impact of epigenetic regulation in asthma development is still widely unknown. We have therefore applied unbiased whole genome bisulfite sequencing (WGBS) to characterize global DNA-methylation profiles of asthmatic children compared to healthy controls. METHODS: Peripheral blood samples of 40 asthmatic and 42 control children aged 5-15 years from three birth cohorts were sequenced together with paired cord blood samples. Identified differentially methylated regions (DMRs) were categorized in genotype-associated, cell-type-dependent, or prenatally primed. Network analysis and subsequent natural language processing of DMR-associated genes was complemented by targeted analysis of functional translation of epigenetic regulation on the transcriptional and protein level. RESULTS: In total, 158 DMRs were identified in asthmatic children compared to controls of which 37% were related to the eosinophil content. A global hypomethylation was identified affecting predominantly enhancer regions and regulating key immune genes such as IL4, IL5RA, and EPX. These DMRs were confirmed in n = 267 samples and could be linked to aberrant gene expression. Out of the 158 DMRs identified in the established phenotype, 56 were perturbed already at birth and linked, at least in part, to prenatal influences such as tobacco smoke exposure or phthalate exposure. CONCLUSION: This is the first epigenetic study based on whole genome sequencing to identify marked dysregulation of enhancer regions as a hallmark of childhood asthma.

Patient-specific modeling of stroma-mediated chemoresistance of pancreatic cancer using a three-dimensional organoid-fibroblast co-culture system.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are considered to play a fundamental role in pancreatic ductal adenocarcinoma (PDAC) progression and chemoresistance. Patient-derived organoids have demonstrated great potential as tumor avatars for drug response prediction in PDAC, yet they disregard the influence of stromal components on chemosensitivity. METHODS: We established direct three-dimensional (3D) co-cultures of primary PDAC organoids and patient-matched CAFs to investigate the effect of the fibroblastic compartment on sensitivity to gemcitabine, 5-fluorouracil and paclitaxel treatments using an image-based drug assay. Single-cell RNA sequencing was performed for three organoid/CAF pairs in mono- and co-culture to uncover transcriptional changes induced by tumor-stroma interaction. RESULTS: Upon co-culture with CAFs, we observed increased proliferation and reduced chemotherapy-induced cell death of PDAC organoids. Single-cell RNA sequencing data evidenced induction of a pro-inflammatory phenotype in CAFs in co-cultures. Organoids showed increased expression of genes associated with epithelial-to-mesenchymal transition (EMT) in co-cultures and several potential receptor-ligand interactions related to EMT were identified, supporting a key role of CAF-driven induction of EMT in PDAC chemoresistance. CONCLUSIONS: Our results demonstrate the potential of personalized PDAC co-cultures models not only for drug response profiling but also for unraveling the molecular mechanisms involved in the chemoresistance-supporting role of the tumor stroma.