EFFECT OF PAXLOVID TREATMENT DURING ACUTE COVID-19 ON LONG COVID ONSET: AN EHR-BASED TARGET TRIAL EMULATION FROM THE N3C AND RECOVER CONSORTIA

Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. In this study, we examined whether treatment with Paxlovid in the acute phase of COVID-19 helps prevent the onset of PASC. We used electronic health records from the National Covid Cohort Collaborative (N3C) to define a cohort of 426,352 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation (TTE) framework to estimate the effect of Paxlovid treatment on PASC incidence. We estimated overall PASC incidence using a computable phenotype. We also measured the onset of novel cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment did not have a significant effect on overall PASC incidence (relative risk [RR] = 0.98, 95% confidence interval [CI] 0.95-1.01). However, it had a protective effect on cognitive (RR = 0.90, 95% CI 0.84-0.96) and fatigue (RR = 0.95, 95% CI 0.91-0.98) symptom clusters, which suggests that the etiology of these symptoms may be more closely related to viral load than that of respiratory symptoms.

Genetic and Survey Data Improves Performance of Machine Learning Model for Long COVID

Over 200 million SARS-CoV-2 patients have or will develop persistent symptoms (long COVID). Given this pressing research priority, the National COVID Cohort Collaborative (N3C) developed a machine learning model using only electronic health record data to identify potential patients with long COVID. We hypothesized that additional data from health surveys, mobile devices, and genotypes could improve prediction ability. In a cohort of SARS-CoV-2 infected individuals (n=17,755) in the All of Us program, we applied and expanded upon the N3C long COVID prediction model, testing machine learning infrastructures, assessing model performance, and identifying factors that contributed most to the prediction models. For the survey/mobile device information and genetic data, extreme gradient boosting and a convolutional neural network delivered the best performance for predicting long COVID, respectively. Combined survey, genetic, and mobile data increased specificity and the Area Under Curve the Receiver Operating Characteristic score versus the original N3C model.

An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C)

Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts.

The N3C governance ecosystem: A model socio-technical partnership for the future of collaborative analytics at scale

The National COVID Cohort Collaborative (N3C) is a public-private-government partnership established during the Coronavirus pandemic to create a centralized data resource called the "N3C data enclave." This resource contains individual-level health data from participating healthcare sites nationwide to support rapid collaborative analytics. N3C has enabled analytics within a cloud-based enclave of data from electronic health records from over 17 million people (with and without COVID-19) in the USA. To achieve this goal of a shared data resource, N3C implemented a shared governance strategy involving stakeholders in decision-making. The approach leveraged best practices in data stewardship and team science to rapidly enable COVID-19-related research at scale while respecting the privacy of data subjects and participating institutions. N3C balanced equitable access to data, team-based scientific productivity, and individual professional recognition - a key incentive for academic researchers. This governance approach makes N3C research sustainable and effective beyond the initial days of the pandemic. N3C demonstrated that shared governance can overcome traditional barriers to data sharing without compromising data security and trust. The governance innovations described herein are a helpful framework for other privacy-preserving data infrastructure programs and provide a working model for effective team science beyond COVID-19.

Risk of post-acute sequelae of SARS-CoV-2 infection associated with pre-coronavirus disease obstructive sleep apnea diagnoses: an electronic health record-based analysis from the RECOVER initiative

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC).

METHODS

We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities.

RESULTS

Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis.

CONCLUSIONS

Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae.

Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities

OBJECTIVE

To summarize the recent methods and applications that leverage real-world data such as electronic health records (EHRs) with social determinants of health (SDoH) for public and population health and health equity and identify successes, challenges, and possible solutions.

METHODS

In this opinion review, grounded on a social-ecological-model-based conceptual framework, we surveyed data sources and recent informatics approaches that enable leveraging SDoH along with real-world data to support public health and clinical health applications including helping design public health intervention, enhancing risk stratification, and enabling the prediction of unmet social needs.

RESULTS

Besides summarizing data sources, we identified gaps in capturing SDoH data in existing EHR systems and opportunities to leverage informatics approaches to collect SDoH information either from structured and unstructured EHR data or through linking with public surveys and environmental data. We also surveyed recently developed ontologies for standardizing SDoH information and approaches that incorporate SDoH for disease risk stratification, public health crisis prediction, and development of tailored interventions.

CONCLUSIONS

To enable effective public health and clinical applications using real-world data with SDoH, it is necessary to develop both non-technical solutions involving incentives, policies, and training as well as technical solutions such as novel social risk management tools that are integrated into clinical workflow. Ultimately, SDoH-powered social risk management, disease risk prediction, and development of SDoH tailored interventions for disease prevention and management have the potential to improve population health, reduce disparities, and improve health equity.

Clinical encounter heterogeneity and methods for resolving in networked EHR data: a study from N3C and RECOVER programs

OBJECTIVE

Clinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multisite electronic health record (EHR) data are networked together. This article presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite "macrovisits."

MATERIALS AND METHODS

Encounters were composed of data from 75 partner sites harmonized to a common data model as part of the NIH Researching COVID to Enhance Recovery Initiative, a project of the National Covid Cohort Collaborative. Summary statistics were computed for overall and site-level data to assess issues and identify modifications. Two algorithms were developed to refine atomic encounters into cleaner, analyzable longitudinal clinical visits.

RESULTS

Atomic inpatient encounters data were found to be widely disparate between sites in terms of length-of-stay (LOS) and numbers of OMOP CDM measurements per encounter. After aggregating encounters to macrovisits, LOS and measurement variance decreased. A subsequent algorithm to identify hospitalized macrovisits further reduced data variability.

DISCUSSION

Encounters are a complex and heterogeneous component of EHR data and native data issues are not addressed by existing methods. These types of complex and poorly studied issues contribute to the difficulty of deriving value from EHR data, and these types of foundational, large-scale explorations, and developments are necessary to realize the full potential of modern real-world data.

CONCLUSION

This article presents method developments to manipulate and resolve EHR encounter data issues in a generalizable way as a foundation for future research and analysis.

FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model

BACKGROUND

Knowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by the research and healthcare AI communities. From the standardization perspective, community-based standards such as the Fast Healthcare Interoperability Resources (FHIR) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) are increasingly used to represent and standardize EHR data for clinical data analytics, however, the potential of such a standard on building CKG has not been well investigated.

OBJECTIVE

To develop and evaluate methods and tools that expose the OMOP CDM-based clinical data repositories into virtual clinical KGs that are compliant with FHIR Resource Description Framework (RDF) specification.

METHODS

We developed a system called FHIR-Ontop-OMOP to generate virtual clinical KGs from the OMOP relational databases. We leveraged an OMOP CDM-based Medical Information Mart for Intensive Care (MIMIC-III) data repository to evaluate the FHIR-Ontop-OMOP system in terms of the faithfulness of data transformation and the conformance of the generated CKGs to the FHIR RDF specification.

RESULTS

A beta version of the system has been released. A total of more than 100 data element mappings from 11 OMOP CDM clinical data, health system and vocabulary tables were implemented in the system, covering 11 FHIR resources. The generated virtual CKG from MIMIC-III contains 46,520 instances of FHIR Patient, 716,595 instances of Condition, 1,063,525 instances of Procedure, 24,934,751 instances of MedicationStatement, 365,181,104 instances of Observations, and 4,779,672 instances of CodeableConcept. Patient counts identified by five pairs of SQL (over the MIMIC database) and SPARQL (over the virtual CKG) queries were identical, ensuring the faithfulness of the data transformation. Generated CKG in RDF triples for 100 patients were fully conformant with the FHIR RDF specification.

CONCLUSION

The FHIR-Ontop-OMOP system can expose OMOP database as a FHIR-compliant RDF graph. It provides a meaningful use case demonstrating the potentials that can be enabled by the interoperability between FHIR and OMOP CDM. Generated clinical KGs in FHIR RDF provide a semantic foundation to enable explainable AI applications in healthcare.

Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease

Research on rare diseases has received increasing attention, in part due to the realized profitability of orphan drugs. Biomedical informatics holds promise in accelerating translational research on rare disease, yet challenges remain, including the lack of diagnostic codes for rare diseases and privacy concerns that prevent research access to electronic health records when few patients exist. The Integrated Clinical and Environmental Exposures Service (ICEES) provides regulatory-compliant open access to electronic health record data that have been integrated with environmental exposures data, as well as analytic tools to explore the integrated data. We describe a proof-of-concept application of ICEES to examine demographics, clinical characteristics, environmental exposures, and health outcomes among a cohort of patients enriched for phenotypes associated with cystic fibrosis (CF), idiopathic bronchiectasis (IB), and primary ciliary dyskinesia (PCD). We then focus on a subset of patients with CF, leveraging the availability of a diagnostic code for CF and serving as a benchmark for our development work. We use ICEES to examine select demographics, co-diagnoses, and environmental exposures that may contribute to poor health outcomes among patients with CF, defined as emergency department or inpatient visits for respiratory issues. We replicate current understanding of the pathogenesis and clinical manifestations of CF by identifying co-diagnoses of asthma, chronic nasal congestion, cough, middle ear disease, and pneumonia as factors that differentiate patients with poor health outcomes from those with better health outcomes. We conclude by discussing our preliminary findings in relation to other published work, the strengths and limitations of our approach, and our future directions.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study

BACKGROUND

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use.

METHODS

A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis.

RESULTS

Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations.

CONCLUSIONS

Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

Development and Application of an Open Tool for Sharing and Analyzing Integrated Clinical and Environmental Exposures Data: Asthma Use Case

Background

The Integrated Clinical and Environmental Exposures Service (ICEES) serves as an open-source, disease-agnostic, regulatory-compliant framework and approach for openly exposing and exploring clinical data that have been integrated at the patient level with a variety of environmental exposures data. ICEES is equipped with tools to support basic statistical exploration of the integrated data in a completely open manner.

Objective

This study aims to further develop and apply ICEES as a novel tool for openly exposing and exploring integrated clinical and environmental data. We focus on an asthma use case.

Methods

We queried the ICEES open application programming interface (OpenAPI) using a functionality that supports chi-square tests between feature variables and a primary outcome measure, with a Bonferroni correction for multiple comparisons (α=.001). We focused on 2 primary outcomes that are indicative of asthma exacerbations: annual emergency department (ED) or inpatient visits for respiratory issues; and annual prescriptions for prednisone.

Results

Of the 157,410 patients within the asthma cohort, 26,332 (16.73%) had 1 or more annual ED or inpatient visits for respiratory issues, and 17,056 (10.84%) had 1 or more annual prescriptions for prednisone. We found that close proximity to a major roadway or highway, exposure to high levels of particulate matter ≤2.5 μm (PM_2.5) or ozone, female sex, Caucasian race, low residential density, lack of health insurance, and low household income were significantly associated with asthma exacerbations (P<.001). Asthma exacerbations did not vary by rural versus urban residence. Moreover, the results were largely consistent across outcome measures.

Conclusions

Our results demonstrate that the open-source ICEES can be used to replicate and extend published findings on factors that influence asthma exacerbations. As a disease-agnostic, open-source approach for integrating, exposing, and exploring patient-level clinical and environmental exposures data, we believe that ICEES will have broad adoption by other institutions and application in environmental health and other biomedical fields.

Developing an ETL tool for converting the PCORnet CDM into the OMOP CDM to facilitate the COVID-19 data integration

OBJECTIVE

The large-scale collection of observational data and digital technologies could help curb the COVID-19 pandemic. However, the coexistence of multiple Common Data Models (CDMs) and the lack of data extract, transform, and load (ETL) tool between different CDMs causes potential interoperability issue between different data systems. The objective of this study is to design, develop, and evaluate an ETL tool that transforms the PCORnet CDM format data into the OMOP CDM.

METHODS

We developed an open-source ETL tool to facilitate the data conversion from the PCORnet CDM and the OMOP CDM. The ETL tool was evaluated using a dataset with 1000 patients randomly selected from the PCORnet CDM at Mayo Clinic. Information loss, data mapping accuracy, and gap analysis approaches were conducted to assess the performance of the ETL tool. We designed an experiment to conduct a real-world COVID-19 surveillance task to assess the feasibility of the ETL tool. We also assessed the capacity of the ETL tool for the COVID-19 data surveillance using data collection criteria of the MN EHR Consortium COVID-19 project.

RESULTS

After the ETL process, all the records of 1000 patients from 18 PCORnet CDM tables were successfully transformed into 12 OMOP CDM tables. The information loss for all the concept mapping was less than 0.61%. The string mapping process for the unit concepts lost 2.84% records. Almost all the fields in the manual mapping process achieved 0% information loss, except the specialty concept mapping. Moreover, the mapping accuracy for all the fields were 100%. The COVID-19 surveillance task collected almost the same set of cases (99.3% overlaps) from the original PCORnet CDM and target OMOP CDM separately. Finally, all the data elements for MN EHR Consortium COVID-19 project could be captured from both the PCORnet CDM and the OMOP CDM.

CONCLUSION

We demonstrated that our ETL tool could satisfy the data conversion requirements between the PCORnet CDM and the OMOP CDM. The outcome of the work would facilitate the data retrieval, communication, sharing, and analysis between different institutions for not only COVID-19 related project, but also other real-world evidence-based observational studies.

NSAID use and clinical outcomes in COVID-19 patients: A 38-center retrospective cohort study

BACKGROUND

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use.

METHODS

A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of COVID-19 inpatients was constructed by matching cases (treated with NSAIDs) and controls (not treated) from 857,061 patients with COVID-19. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis.

RESULTS

Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations.

CONCLUSIONS

Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our findings are the largest EHR-based analysis of the effect of NSAIDs on outcome in COVID-19 patients to date. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

Characterizing Long COVID: Deep Phenotype of a Complex Condition

BACKGROUND

Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies.

METHODS

The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19.

FUNDING

We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies.

INTERPRETATION

Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID.

FUNDING

U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411.

Electronic Medical Record Search Engine (EMERSE): An Information Retrieval Tool for Supporting Cancer Research

PURPOSE

The Electronic Medical Record Search Engine (EMERSE) is a software tool built to aid research spanning cohort discovery, population health, and data abstraction for clinical trials. EMERSE is now live at three academic medical centers, with additional sites currently working on implementation. In this report, we describe how EMERSE has been used to support cancer research based on a variety of metrics.

METHODS

We identified peer-reviewed publications that used EMERSE through online searches as well as through direct e-mails to users based on audit logs. These logs were also used to summarize use at each of the three sites. Search terms for two of the sites were characterized using the natural language processing tool MetaMap to determine to which semantic types the terms could be mapped.

RESULTS

We identified a total of 326 peer-reviewed publications that used EMERSE through August 2019, although this is likely an underestimation of the true total based on the use log analysis. Oncology-related research comprised nearly one third (n = 105; 32.2%) of all research output. The use logs showed that EMERSE had been used by multiple people at each site (nearly 3,500 across all three) who had collectively logged into the system > 100,000 times. Many user-entered search queries could not be mapped to a semantic type, but the most common semantic type for terms that did match was “disease or syndrome,” followed by “pharmacologic substance.”

CONCLUSION

EMERSE has been shown to be a valuable tool for supporting cancer research. It has been successfully deployed at other sites, despite some implementation challenges unique to each deployment environment.

Enabling Longitudinal Exploratory Analysis of Clinical COVID Data

As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from longitudinal clinical data, a team of collaborators worked to apply existing event sequence visual analytics technologies to a longitudinal clinical data from a cohort of 998 patients with high rates of COVID-19 infection. This paper describes the initial steps toward this goal, including: (1) the data transformation and processing work required to prepare the data for visual analysis, (2) initial findings and observations, and (3) qualitative feedback and lessons learned which highlight key features as well as limitations to address in future work.

Research informatics and the COVID-19 pandemic: Challenges, innovations, lessons learned, and recommendations

The recipients of NIH's Clinical and Translational Science Awards (CTSA) have worked for over a decade to build informatics infrastructure in support of clinical and translational research. This infrastructure has proved invaluable for supporting responses to the current COVID-19 pandemic through direct patient care, clinical decision support, training researchers and practitioners, as well as public health surveillance and clinical research to levels that could not have been accomplished without the years of ground-laying work by the CTSAs. In this paper, we provide a perspective on our COVID-19 work and present relevant results of a survey of CTSA sites to broaden our understanding of the key features of their informatics programs, the informatics-related challenges they have experienced under COVID-19, and some of the innovations and solutions they developed in response to the pandemic. Responses demonstrated increased reliance by healthcare providers and researchers on access to electronic health record (EHR) data, both for local needs and for sharing with other institutions and national consortia. The initial work of the CTSAs on data capture, standards, interchange, and sharing policies all contributed to solutions, best illustrated by the creation, in record time, of a national clinical data repository in the National COVID-19 Cohort Collaborative (N3C). The survey data support seven recommendations for areas of informatics and public health investment and further study to support clinical and translational research in the post-COVID-19 era.

A novel approach for exposing and sharing clinical data: the Translator Integrated Clinical and Environmental Exposures Service

OBJECTIVE

This study aimed to develop a novel, regulatory-compliant approach for openly exposing integrated clinical and environmental exposures data: the Integrated Clinical and Environmental Exposures Service (ICEES).

MATERIALS AND METHODS

The driving clinical use case for research and development of ICEES was asthma, which is a common disease influenced by hundreds of genes and a plethora of environmental exposures, including exposures to airborne pollutants. We developed a pipeline for integrating clinical data on patients with asthma-like conditions with data on environmental exposures derived from multiple public data sources. The data were integrated at the patient and visit level and used to create de-identified, binned, "integrated feature tables," which were then placed behind an OpenAPI.

RESULTS

Our preliminary evaluation results demonstrate a relationship between exposure to high levels of particulate matter ≤2.5 µm in diameter (PM2.5) and the frequency of emergency department or inpatient visits for respiratory issues. For example, 16.73% of patients with average daily exposure to PM2.5 >9.62 µg/m3 experienced 2 or more emergency department or inpatient visits for respiratory issues in year 2010 compared with 7.93% of patients with lower exposures (n = 23 093).

DISCUSSION

The results validated our overall approach for openly exposing and sharing integrated clinical and environmental exposures data. We plan to iteratively refine and expand ICEES by including additional years of data, feature variables, and disease cohorts.

CONCLUSIONS

We believe that ICEES will serve as a regulatory-compliant model and approach for promoting open access to and sharing of integrated clinical and environmental exposures data.

FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data

BACKGROUND

Informatics tools to support the integration and subsequent interrogation of spatiotemporal data such as clinical data and environmental exposures data are lacking. Such tools are needed to support research in environmental health and any biomedical field that is challenged by the need for integrated spatiotemporal data to examine individual-level determinants of health and disease.

RESULTS

We have developed an open-source software application-FHIR PIT (Health Level 7 Fast Healthcare Interoperability Resources Patient data Integration Tool)-to enable studies on the impact of individual-level environmental exposures on health and disease. FHIR PIT was motivated by the need to integrate patient data derived from our institution's clinical warehouse with a variety of public data sources on environmental exposures and then openly expose the data via ICEES (Integrated Clinical and Environmental Exposures Service). FHIR PIT consists of transformation steps or building blocks that can be chained together to form a transformation and integration workflow. Several transformation steps are generic and thus can be reused. As such, new types of data can be incorporated into the modular FHIR PIT pipeline by simply reusing generic steps or adding new ones. We validated FHIR PIT in the context of a driving use case designed to investigate the impact of airborne pollutant exposures on asthma. Specifically, we replicated published findings demonstrating racial disparities in the impact of airborne pollutants on asthma exacerbations.

CONCLUSIONS

While FHIR PIT was developed to support our driving use case on asthma, the software can be used to integrate any type and number of spatiotemporal data sources at a level of granularity that enables individual-level study. We expect FHIR PIT to facilitate research in environmental health and numerous other biomedical disciplines.

Recruiting for a pragmatic trial using the electronic health record and patient portal: successes and lessons learned

Objective

Querying electronic health records (EHRs) to find patients meeting study criteria is an efficient method of identifying potential study participants. We aimed to measure the effectiveness of EHR-driven recruitment in the context of ADAPTABLE (Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-Term Effectiveness)-a pragmatic trial aiming to recruit 15 000 patients.

Materials and Methods

We compared the participant yield of 4 recruitment methods: in-clinic recruitment by a research coordinator, letters, direct email, and patient portal messages. Taken together, the latter 2 methods comprised our EHR-driven electronic recruitment workflow.

Results

The electronic recruitment workflow sent electronic messages to 12 254 recipients; 13.5% of these recipients visited the study website, and 4.2% enrolled in the study. Letters were sent to 427 recipients; 5.6% visited the study website, and 3.3% enrolled in the study. Coordinators recruited 339 participants in clinic; 23.6% visited the study website, and 16.8% enrolled in the study. Five-hundred-nine of the 580 UNC enrollees (87.8%) were recruited using an electronic method.

Discussion

Electronic recruitment reached a wide net of patients, recruited many participants to the study, and resulted in a workflow that can be reused for future studies. In-clinic recruitment saw the highest yield, suggesting that a combination of recruitment methods may be the best approach. Future work should account for demographic skew that may result by recruiting from a pool of patient portal users.

Conclusion

The success of electronic recruitment for ADAPTABLE makes this workflow well worth incorporating into an overall recruitment strategy, particularly for a pragmatic trial.

Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon

This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program ('Translator'). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.

FDTool: a Python application to mine for functional dependencies and candidate keys in tabular data

Functional dependencies (FDs) and candidate keys are essential for table decomposition, database normalization, and data cleansing. In this paper, we present FDTool, a command line Python application to discover minimal FDs in tabular datasets and infer equivalent attribute sets and candidate keys from them. The runtime and memory costs associated with seven published FD discovery algorithms are given with an overview of their theoretical foundations. Previous research establishes that FD_Mine is the most efficient FD discovery algorithm when applied to datasets with many rows (> 100,000 rows) and few columns (< 14 columns). This puts it in a special position to rule mine clinical and demographic datasets, which often consist of long and narrow sets of participant records. The structure of FD_Mine is described and supplemented with a formal proof of the equivalence pruning method used. FDTool is a re-implementation of FD_Mine with additional features added to improve performance and automate typical processes in database architecture. The experimental results of applying FDTool to 13 datasets of different dimensions are summarized in terms of the number of FDs checked, the number of FDs found, and the time it takes for the code to terminate. We find that the number of attributes in a dataset has a much greater effect on the runtime and memory costs of FDTool than does row count. The last section explains in detail how the FDTool application can be accessed, executed, and further developed.

Optimizing research in symptomatic uterine fibroids with development of a computable phenotype for use with electronic health records

BACKGROUND

Women with symptomatic uterine fibroids can report a myriad of symptoms, including pain, bleeding, infertility, and psychosocial sequelae. Optimizing fibroid research requires the ability to enroll populations of women with image-confirmed symptomatic uterine fibroids.

OBJECTIVE

Our objective was to develop an electronic health record-based algorithm to identify women with symptomatic uterine fibroids for a comparative effectiveness study of medical or surgical treatments on quality-of-life measures. Using an iterative process and text-mining techniques, an effective computable phenotype algorithm, composed of demographics, and clinical and laboratory characteristics, was developed with reasonable performance. Such algorithms provide a feasible, efficient way to identify populations of women with symptomatic uterine fibroids for the conduct of large traditional or pragmatic trials and observational comparative effectiveness studies. Symptomatic uterine fibroids, due to menorrhagia, pelvic pain, bulk symptoms, or infertility, are a source of substantial morbidity for reproductive-age women. Comparing Treatment Options for Uterine Fibroids is a multisite registry study to compare the effectiveness of hormonal or surgical fibroid treatments on women's perceptions of their quality of life. Electronic health record-based algorithms are able to identify large numbers of women with fibroids, but additional work is needed to develop electronic health record algorithms that can identify women with symptomatic fibroids to optimize fibroid research. We sought to develop an efficient electronic health record-based algorithm that can identify women with symptomatic uterine fibroids in a large health care system for recruitment into large-scale observational and interventional research in fibroid management.

STUDY DESIGN

We developed and assessed the accuracy of 3 algorithms to identify patients with symptomatic fibroids using an iterative approach. The data source was the Carolina Data Warehouse for Health, a repository for the health system's electronic health record data. In addition to International Classification of Diseases, Ninth Revision diagnosis and procedure codes and clinical characteristics, text data-mining software was used to derive information from imaging reports to confirm the presence of uterine fibroids. Results of each algorithm were compared with expert manual review to calculate the positive predictive values for each algorithm.

RESULTS

Algorithm 1 was composed of the following criteria: (1) age 18-54 years; (2) either ≥1 International Classification of Diseases, Ninth Revision diagnosis codes for uterine fibroids or mention of fibroids using text-mined key words in imaging records or documents; and (3) no International Classification of Diseases, Ninth Revision or Current Procedural Terminology codes for hysterectomy and no reported history of hysterectomy. The positive predictive value was 47% (95% confidence interval 39-56%). Algorithm 2 required ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids and positive text-mined key words and had a positive predictive value of 65% (95% confidence interval 50-79%). In algorithm 3, further refinements included ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids on separate outpatient visit dates, the exclusion of women who had a positive pregnancy test within 3 months of their fibroid-related visit, and exclusion of incidentally detected fibroids during prenatal or emergency department visits. Algorithm 3 achieved a positive predictive value of 76% (95% confidence interval 71-81%).

CONCLUSION

An electronic health record-based algorithm is capable of identifying cases of symptomatic uterine fibroids with moderate positive predictive value and may be an efficient approach for large-scale study recruitment.

Development of a heterologous, multigenotype vaccine against hepatitis C virus infection

BACKGROUND

Unquestionably viral diversity and genetic heterogeneity in hepatitis C virus (HCV) infection and other viral diseases play an essential role in viral immune escape and the development of chronicity. Despite this knowledge most vaccine approaches against HCV have excluded this important issue. Moreover the feasibility of developing an effective HCV vaccine has been questioned, mainly because prophylactic immunity against HCV cannot be achieved in chimpanzees by either vaccination or previous HCV infection, and reinfection in men has been reported, most likely due to genetic shift and immune escape. To analyse and characterize a new technique of a 'multigenotype'- and/or 'library'-vaccine, we established an envelope 1 (E1) plasmid vaccine against HCV and characterized humoral and cellular immune responses after vaccination in a mouse model.

MATERIAL AND METHODS

Normally genetic information of one or two target proteins is cloned into a DNA-vaccine. In our approach we cloned a defined number of different genotypes and subtypes (defined vaccine, DV) or the genetic information from 20 patients (undefined) into a plasmid (library vaccine, LV).

RESULTS

As expected, immunized animals showed both stronger humoral (ELISA) and cellular (T-cell proliferation, ELISPOT) immune responses against genotype 1, since the stimulating antigen was genotype 1 derived. However, not all genotype 1 immunized animals recognized this viral antigen leading to the assumption that some epitopes lost their immunogenicity through a change in the amino acid sequence. Interestingly, some of the genotype 4 and 5 immunized mice sera were able to react against E1 protein.

CONCLUSION

Most of the assays showed immune reactivity against the DV or LV vaccine demonstrating the cross-reactive potential of such a vaccination approach. This cloning and immunization strategy based on the viral heterogeneity of the virus has in our view major implications for HCV, a virus with a broad viral genetic diversity, and may become in the future in the context of DNA- or viral-based vaccination strategies a possibility to overcome viral immune escape both in the prophylactic or therapeutic setting.

Prion protein expression in bovine podocytes and extraglomerular mesangial cells

The cellular form of the prion protein (PrP(c)) is thought to be a substrate for an abnormal isoform of the prion protein (PrP(sc)). One emerging hypothesis is that the proposed conversion phenomenon takes place at the site at which the infectious agent meets PrP(c). PrP(c) is abundant in the central nervous system, but little is known about the cell-type-specific distribution of PrP(c) in non-neuronal tissues of cattle. We have studied whether PrP(c), a protein found predominantly in neurons, also exists in bovine podocytes, since neurons and podocytes share a large number of similarities. We have therefore examined the expression of PrP(c) by immunohistochemistry, reverse transcription/polymerase chain reaction and enzyme-linked immunosorbent analysis. Immunostained serial sections and specific antibodies against PrP(c) have revealed that PrP(c) is selectively localized in podocytes and is particularly strongly expressed in extraglomerular mesangial cells but not in endothelial or intraglomerular mesangial cells. The selective expression of PrP(c) in podocytes is of special importance, as it suggests that these cells represent possible targets for peripheral infection with prions and demonstrates that PrP(c) can be added to the list of neuronal factors expressed in mammalian podocytes.

Prophylactic and therapeutic vaccination with dendritic cells against hepatitis C virus infection

Antigen uptake and presentation capacities enable DC to prime and activate T cells. Recently, several studies demonstrated a diminished DC function in hepatitis C virus (HCV) infected patients showing impaired abilities to stimulate allogenic T cells and to produce IFN-gamma in HCV infected patients. Moreover, DC of patients who have resolved HCV infection behave like DC from healthy donors responding to maturation stimuli, decrease antigen uptake, up-regulate expression of appropriate surface marker, and are potent stimulators of allogenic T cells. A number of studies have demonstrated in tumour models and models of infectious diseases strong induction of immune responses after DC vaccination. Because DC are essential for T-cell activation and since viral clearance in HCV infected patients is associated with a vigorous T-cell response, we propose a new type of HCV vaccine based on ex vivo stimulated and matured DC loaded with HCV specific antigens. This vaccine circumvents the impaired DC maturation and the down regulated DC function of HCV infected patients in vivo by giving the necessary maturation stimuli and the HCV antigens in a different setting and location ex vivo. Strong humoral and cellular immune responses were detected after HCV core DC vaccination. Furthermore, DC vaccination shows partial protection in a therapeutic and prophylactic model of HCV infection. In conclusion, mice immunized with HCV core pulsed DC generated a specific antiviral response in a mouse HCV challenge model. Our results indicate that HCV core pulsed DC may serve as a new modality for immunotherapy of HCV especially in chronically infected patients.

The normal cellular prion protein (PrPc) is strongly expressed in bovine endocrine pancreas

Expression of the cellular prion protein (PrP(c)) has been shown to be crucial for the development of transmissible spongiform encephalopathies and for the accumulation of the disease-associated conformer (PrP(sc)) in the brain and other tissues. One of the emerging hypotheses is that the conversion phenomenon could take place at the site where the infectious agent meets PrP(c). In this work we have studied whether PrP(c), a protein found predominantly in neurons, could also exist in pancreatic endocrine cells since neuroectoderm-derived cells and pancreatic islet cells share a large number of similarities. For this purpose we have examined the expression of PrP(c) in a series of fetal and postnatal bovine pancreatic tissue by immunohistochemistry and RT-PCR. Using immunostained serial sections and specific antibodies against bovine PrP(c), insulin, glucagon, somatostatin, chromogranin A and chromogranin B we found that PrP(c) is highly expressed in all endocrine cells of fetal and adult pancreatic islets with a particular strong expression in A-cells. Moreover it became evident that the PrP(c) gene-neighbour chromogranin B as well as chromogranin A are coexpressed together with PrP(c). The selective expression of PrP(c) in the bovine endocrine pancreas is of particular importance regarding possible iatrogenic transmission routes and demonstrates also that bovine pancreatic islet cells could represent an interesting model to study the control of PrP-gene expression.

Some physico-chemical parameters that influence proteinase K resistance and the infectivity of PrP Sc after high pressure treatment

Crude brain homogenates of terminally diseased hamsters infected with the 263 K strain of scrapie (PrP Sc) were heated and/or pressurized at 800 MPa at 60 degrees C for different times (a few seconds or 5, 30, 120 min) in phosphate-buffered saline (PBS) of different pH and concentration. Prion proteins were analyzed on immunoblots for their proteinase K (PK) resistance, and in hamster bioassays for their infectivity. Samples pressurized under initially neutral conditions and containing native PrP Sc were negative on immunoblots after PK treatment, and a 6-7 log reduction of infectious units per gram was found when the samples were pressurized in PBS of pH 7.4 for 2 h. A pressure-induced change in the protein conformation of native PrP Sc may lead to less PK resistant and less infectious prions. However, opposite results were obtained after pressurizing native infectious prions at slightly acidic pH and in PBS of higher concentration. In this case an extensive fraction of native PrP Sc remained PK resistant after pressure treatment, indicating a protective effect possibly due to induced aggregation of prion proteins in such buffers.

Detection of cattle-derived BSE prions using transgenic mice overexpressing bovine PrP(C)

In interspecies transmissions of transmissible spongiform encephalopathies, the agent has to overcome a species barrier that is largely influenced by the rate of homology between the prion proteins (PrP(C)) of the two involved species. Generating transgenic mice expressing PrP(C) of a foreign species is an approach to develop TSE models that are at least partly devoid of a species barrier. The availability of such animals would enable the detection of low doses of infectivity in tissues or bodily fluids derived from other species. We generated transgenic mice that overexpress bovine PrP(C) (Tgbov XV mice) for the development of an improved detection assay for cattle derived BSE prions. These mice succumbed to the disease 250 days after inoculation with a brain homogenate from BSE diseased cattle. Diagnosis of BSE in transgenic mice was confirmed using Western blot, as well as histological and immunohistochemical methods. In contrast, transgenic mice overexpressing murine PrP(C) (tga20 mice) did not display shorter incubation times than nontransgenic RIII mice infected with the same inoculum. The expression of a chimaeric PrP of murine and bovine sequences rendered such mice partly, if not entirely resistant to an infection with the BSE agent.

The use of monoclonal antibody epitopes for tagging PrP in conversion experiments

The key event in the pathogenesis of spongiform encephalopathies is a conformational transition of a normal cellular protein, PrPsen, to its pathological isoform, PrPres. The mechanism of PrPres formation is unknown but is likely to involve a direct interaction between PrPsen and PrPres. The molecular basis of PrPres formation has been studied extensively using transgenic mice and scrapie-infected tissue cultures that express heterologous PrP molecules. However, these experiments are dependant on the discrimination of endogenous host PrP and exogenous PrP molecules. Here we give a short review on the PrP-specific epitopes that have been used for tagging exogenous PrP molecules and present a novel PrP-specific epitope that is well suitable for in vivo and in vitro conversion experiments.

Type-independent detection of foot-and-mouth disease virus by monoclonal antibodies that bind to amino-terminal residues of capsid protein VP2

The characterization of monoclonal antibodies raised against the foot-and-mouth disease virus isolates A22 Iraq/1964, Asia1 Shamir-Israel/1989, and SAT1 Zimbabwe/1989 with regard to neutralizing activity and sensitivity of their epitopes for treatment with trypsin, resulted in the identification of one non-neutralizing antibody in each panel that binds to a trypsin-sensitive epitope. Furthermore, each of these antibodies recognized 27 isolates of different provenance, representative of six serotypes. These antibodies are recommended for type-independent antigen detection by ELISA. The epitopes for these antibodies reside at the intertypically conserved N-terminus of capsid protein VP2. The two are specified by the lysines at positions two and three, but differ from each other as indicated by the variable heavy chain sequences of their antibodies.

Generation of recombinant parapoxviruses: non-essential genes suitable for insertion and expression of foreign genes

Orf virus (OV) is an epitheliotropic poxvirus and belongs to the genus Parapoxvirus (PPV). PPV, especially OV, is regarded as a promising candidate for an expression vector. Among available live vaccines only strain D1701 represents a highly attenuated OV strain with clearly reduced pathogenicity. Therefore, we started to identify potentially non-essential genes or regions of D1701, which might be suitable for insertion and expression of foreign genes. The present contribution reviews some of the progress using the vegf-e (homologue of the mammalian vascular endothelial growth factor) gene locus for the generation of recombinant D1701. The vegf-e gene of D1701 is dispensable for virus growth in vitro and in vivo, and represents a major virulence determinant of OV. It is shown that foreign genes can be inserted and functionally expressed in the vegf-e locus, also leading to the induction of a specific immune response in the non-permissive host. Furthermore, it is reported that adaptation to VERO cells led to the deletion of three further regions of the OV D1701 genome, which seems to be combined with additional virus attenuation in sheep. Molecular analysis of this OV D1701 variant allows the identification of new, potentially non-essential sites in the viral genome.

Polyhistidine-tagged hepatitis B core particles as carriers of HIV-1/gp120 epitopes of different HIV-1 subtypes

The hepatitis B core antigen is a widely accepted carrier particle to enhance the immunogenicity of foreign epitopes. From electron cryomicroscopy, the immunodominant region between amino acid positions 79 to 81 is known to protrude from the surface of the shells. It can be replaced by heterologous sequences without interfering with the particle-forming capacity in many cases. Here we have introduced various V3 sequences of the envelope protein of different subtypes (A, B, O) of HIV-1/gp120 in order to enhance their immunogenicity and broaden the immune response against the virus. To improve purification efficiency and solubility of the E. coli-expressed hybrids, six histidine residues were fused to amino acid 156. An adjustable purification scheme was utilised including denaturation, Ni(2+)-NTA affinity chromatography and particle renaturation under high salt conditions, resulting in highly pure antigen preparations. The hybrids reacted specifically with sera of HIV-1-infected patients. They further induced an autologous, subtype-specific anti-HIV-1 antibody response superior to that of Keyhole limpet-haemocyanine-coupled peptides.

The porcine humoral immune response against pseudorabies virus specifically targets attachment sites on glycoprotein gC

High titers of virus-neutralizing antibodies directed against glycoprotein gC of Pseudorabies virus (PRV) (Suid herpesvirus 1) are generally observed in the serum of immunized pigs. A known function of the glycoprotein gC is to mediate attachment of PRV to target cells through distinct viral heparin-binding domains (HBDs). Therefore, it was suggested that the virus-neutralizing activity of anti-PRV sera is directed against HBDs on gC. To address this issue, sera with high virus-neutralizing activity against gC were used to characterize the anti-gC response. Epitope mapping demonstrated that amino acids of HBDs are part of an antigenic antibody binding domain which is located in the N-terminal part of gC. Binding of antibodies to this antigenic domain of gC was further shown to interfere with the viral attachment. Therefore, these results show that the viral HBDs are accessible targets for the humoral anti-PRV response even after tolerance induction against self-proteins, which utilize similar HBDs to promote host protein-protein interactions. The findings indicate that the host's immune system can specifically block the attachment function of PRV gC. Since HBDs promote the attachment of a number of herpesviruses, the design of future antiherpesvirus vaccines should aim to induce a humoral immune response that prevents HBD-mediated viral attachment.

Characterization of porcine T lymphocytes and their immune response against viral antigens

T lymphocytes play a central role in the antigen-specific immune response against various pathogens. To detect and to characterize porcine T lymphocytes, monoclonal antibodies (mAb) against leukocyte differentiation antigens had been raised and classified for their specificity. Analyses of porcine T lymphocytes with specific mAb against CD4 and CD8 differentiation antigens revealed differences in the composition of the porcine T-lymphocyte population compared to other species. In addition to the known subpopulations, CD4+CD8- T helper cells and CD4-CD8+ cytolytic T lymphocytes, extra-thymic CD4+CD8+ T lymphocytes and a substantial proportion of CD2-CD4-CD8- T cell receptor (TcR)-gamma delta+ T cells could be detected in swine. Functional analyses of porcine T-lymphocyte subpopulations revealed the existence of two T-helper cell fractions with the phenotype CD4+CD8- and CD4+CD8+. Both were reactive in primary immune responses in vitro, whereas only cells derived from the CD4+CD8+ T-helper-cell subpopulation were able to respond to recall antigen in a secondary immune response. With regard to T lymphocytes with cytolytic activities, two subsets within the CD4-CD8+ T-cell subpopulation could be defined by the expression of CD6 differentiation antigens: CD6- cells which showed spontaneous cytolytic activity and CD6+ MHC I-restricted cytolytic T lymphocytes including virus-specific cytolytic T lymphocytes. These results enable now a detailed view into the porcine T-cell population and the reactivity of specific T cells involved in the porcine immune response against pathogens. Furthermore this knowledge offers the possibility to investigate specific interactions of porcine T lymphocytes with virus-specific epitopes during vaccination and viral infections.

A novel epitope for the specific detection of exogenous prion proteins in transgenic mice and transfected murine cell lines

Prion diseases are closely linked to the conversion of host-encoded cellular prion protein (PrPC) into its pathological isoform (PrPSc). PrP conversion experiments in scrapie infected tissue culture cells, transgenic mice, and cell-free systems usually require unique epitopes and corresponding monoclonal antibodies (MAbs) for the immunological discrimination of exogenously introduced and endogenous PrP compounds (e.g., MAb 3F4, which is directed to an epitope on hamster and human but not on murine PrP). In the current work, we characterize a novel MAb designated L42 that reacts to PrP of a variety of species, including cattle, sheep, goat, dog, human, cat, mink, rabbit, and guinea pig, but does not bind to mouse, hamster, and rat PrP. Therefore, MAb L42 may allow future in vitro conversion and transgenic studies on PrPs of the former species. The MAb L42 epitope on PrPC includes a tyrosine residue at position 144, whereas mouse, rat, and hamster PrPs incorporate tryptophane at this site. To verify this observation, we generated PrP expression vectors coding for authentic or mutated murine PrPCs (i.e., codon 144 encoding tyrosine instead of tryptophan). After transfection into neuroblastoma cells, MAb L42 did not react with immunoblotted wild-type murine PrPC, whereas L42 epitope-tagged murine PrPC was strongly recognized. Immunoblot and fluorescence-activated cell sorting data revealed that tagged PrPC was correctly posttranslationally processed and translocated to the cell surface.

[Phenotypic and functional characterization of porcine T-lymphocytes]

Monoclonal antibodies (mAb) against leucocyte differentiation antigens have altered the way in which immunologists examine the immune system. These mAb allow to identify distinct surface molecules on leukocyte populations, by which these cells can be classified, isolated and studied for their functional properties. This review summarises the knowledge about differentiation antigens useful in the characterisation of porcine T-lymphocytes. Furthermore it focuses on several properties of porcine T-lymphocytes and T-lymphocyte subpopulations.

Vaccine-induced, pseudorabies virus-specific, extrathymic CD4+CD8+ memory T-helper cells in swine

Pseudorabies virus (PRV; suid herpesvirus 1) infection causes heavy economic losses in the pig industry. Therefore, vaccination with live attenuated viruses is practiced in many countries. This vaccination was demonstrated to induce extrathymic virus-specific memory CD4+CD8+ T lymphocytes. Due to their major histocompatibility complex (MHC) class II-restricted proliferation, it is generally believed that these T lymphocytes function as memory T-helper cells. To directly prove this hypothesis, 15-amino-acid, overlapping peptides of the viral glycoprotein gC were used for screening in proliferation assays with peripheral blood mononuclear cells of vaccinated d/d haplotype inbred pigs. In these experiments, two naturally processed T-cell epitopes (T1 and T2) which are MHC class II restricted were identified. It was shown that extrathymic CD4+CD8+ T cells are the T-lymphocyte subpopulation that responds to epitope T2. In addition, we were able to show that cytokine secretion can be induced in these T cells through recall with inactivated PRV and demonstrated that activated PRV-primed CD4+CD8+ T cells are able to induce PRV-specific immunoglobulin synthesis by PRV-primed, resting B cells. Taken together, these results demonstrate that the glycoprotein gC takes part in the priming of humoral anti-PRV memory responses. The experiments identified the first T-cell epitopes so far known to induce the generation of virus-specific CD4+CD8+ memory T lymphocytes and showed that CD4+CD8+ T cells are memory T-helper cells. Therefore, this study describes the generation of virus-specific CD4+CD8+ T cells, which is observed during vaccination, as a part of the potent humoral anti-PRV memory response induced by the vaccine.

Synthetic peptide vaccines yield monoclonal antibodies to cellular and pathological prion proteins of ruminants

Transmissible spongiform encephalopathies are closely linked to the accumulation of a pathological isoform of a host-encoded prion protein (PrP(C)), designated PrP(Sc). In an attempt to generate mono- and polyclonal antibodies to ruminant PrP, 32 mice were vaccinated with peptide vaccines which were synthesized according to the amino acid sequence of ovine PrP. By this approach five PrP-reactive polyclonal antisera directed against four different domains of the protein were stimulated. Splenocytes of mice which had developed PrP-reactive antibodies were used for the generation of monoclonal antibodies (MAbs). Obtained PrP-specific MAbs were directed to three different domains of ruminant PrP which differed from the three previously described major MAb binding sites in rodent PrP. MAbs exhibited reactivity with non-denatured ruminant PrP(C) in ELISA and immunoprecipitation and with denatured ovine and bovine PrP(Sc) in immunoblot. Cross-reactivity was observed with PrP(C) of nine other mammalian species and with pathological PrP preferably of ruminants and weakly with that of hamster and mouse. The generated MAbs will be useful tools for the development of diagnostic tests for BSE and scrapie as well as for pathogenesis studies of these diseases.

Detection of virus or virus specific nucleic acid in foodstuff or bioproducts--hazards and risk assessment

There are two possibilities for virus contamination of foodstuff and bioproducts of animal origin: i) the presence of endogenous virus as a result of an acute or subclinical infection of animal raw material used for food processing or ii) contamination of food in the course of processing or thereafter. The latter must be considered as the highest risk for human consumers since the viral contamination mostly is caused by virus shedding people and the transmitted viruses are obligate human pathogens. Food from animals consumed as raw material (e.g. oysters) is listed in a high risk category concerning viral contamination (e.g. hepatovirus). Virus contamination of bioproducts such as vaccines, blood products or biological material used in surgery and for transplantations also is more hazardous because the application of contaminating virus usually occurs by circumvention of the natural barrier systems of the body. Moreover, in many cases immunosuppressed people are treated with bioproducts. Due to an enclosing shield of high protein and lipid content in food and bioproducts viruses are well protected against physical and chemical influences, however most preparation procedures for food are destructive for viruses. The detection of pseudorabies virus and pestivirus in biological fluids was tested using polymerase chain reaction (PCR), reverse transcriptase (RT)-PCR and cell culture propagation. PCR is a powerful method to detect viral nucleic acid whereas the detection of infectious virus in cell cultures is more limited, e.g. due to protein and lipid destroying conditions. Virus contamination of bioproducts should be considered as a hazard no matter which method has been used for its detection. Examples are given about the contamination of cell lines and vaccines.

Establishment of persistent hepatitis C virus infection and replication in vitro

Hepatitis C virus (HCV) is a major cause of chronic viral hepatitis. Development of anti-viral strategies has been hampered by the lack of efficient cell systems to propagate HCV in vitro. To establish a long-term culture system, we tested human hepatoma (HuH7, HepG2) and porcine non-hepatoma (PK15, STE) cell lines, as well as several culture and infection conditions. As a marker for virus replication, minus-strand HCV RNA in infected cells was detected by an enhanced detection system using nested RT-PCR followed by hybridization analysis. Short-term efficiency of HCV infection (10 days) was slightly increased by addition of polyethylene glycol (PEG) and/or dimethyl sulfoxide (DMSO) to culture media during inoculation of HuH7, PK15 and STE cells, but no augmentation in long-term culture was achieved, suggesting enhanced attachment of HCV to cells rather than more efficient infection. A stabilizing effect on HCV propagation was observed for 50 days in a serum-free medium with stimulation of the low-density lipoprotein (LDL) receptor expression by lovastatin. Using partially serum-free culture conditions, long-term persistence of HCV in cells and release of virions into supernatant was achieved for up to 130 days. Infectivity of released virions in supernatants after long-term culturing (day 30-80) was shown by successful infection of fresh cells. In conclusion, supplementation with PEG, DMSO and lovastatin during inoculation did not enhance virus replication substantially, but continued stimulation of LDL-receptor expression resulted in infections which persisted for over 4 months. These data support the hypothesis of an LDL-receptor mediated uptake of HCV into cells in vitro.