A kinase interacting protein (AKIP1) is a key regulator of cardiac stress.

cAMP-dependent protein kinase (PKA) regulates a myriad of functions in the heart, including cardiac contractility, myocardial metabolism,and gene expression. However, a molecular integrator of the PKA response in the heart is unknown. Here, we show that the PKA adaptor A-kinase interacting protein 1 (AKIP1) is up-regulated in cardiac myocytes in response to oxidant stress. Mice with cardiac gene transfer of AKIP1 have enhanced protection to ischemic stress. We hypothesized that this adaptation to stress was mitochondrial dependent. AKIP1 interacted with the mitochondrial localized apoptosis inducing factor (AIF) under both normal and oxidant stress. When cardiac myocytes or whole hearts are exposed to oxidant and ischemic stress, levels of both AKIP1 and AIF were enhanced. AKIP1 is preferentially localized to interfibrillary mitochondria and up-regulated in this cardiac mitochondrial subpopulation on ischemic injury. Mitochondria isolated from AKIP1 gene transferred hearts showed increased mitochondrial localization of AKIP1, decreased reactive oxygen species generation, enhanced calcium tolerance, decreased mitochondrial cytochrome C release,and enhance phosphorylation of mitochondrial PKA substrates on ischemic stress. These observations highlight AKIP1 as a critical molecular regulator and a therapeutic control point for stress adaptation in the heart.

Privacy-protecting, reliable response data discovery using COVID-19 patient observations.

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.

Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and diverse concepts, such as data elements (i.e., important concepts related to a certain medical condition), a plausible solution is to combine various NLP tools into an ensemble to improve extraction performance. However, it is unclear to what extent ensembles of popular NLP tools improve the extraction of numerous and diverse concepts. Therefore, we built an NLP ensemble pipeline to synergize the strength of popular NLP tools using seven ensemble methods, and to quantify the improvement in performance achieved by ensembles in the extraction of data elements for three very different cohorts. Evaluation results show that the pipeline can improve the performance of NLP tools, but there is high variability depending on the cohort.

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

BACKGROUND: Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. OBJECTIVE: The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. MATERIALS AND METHODS: Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. RESULTS: The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. DISCUSSION AND CONCLUSION: Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks.

pSCANNER: patient-centered Scalable National Network for Effectiveness Research.

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses.

iDASH: integrating data for analysis, anonymization, and sharing.

iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool development and data-sharing capabilities in a private Health Insurance Portability and Accountability Act (HIPAA)-certified cloud. Driving Biological Projects, which span different biological levels (from molecules to individuals to populations) and focus on various health conditions, help guide research and development within this Center. Furthermore, training and dissemination efforts connect the Center with its stakeholders and educate data owners and data consumers on how to share and use clinical and biological data. Through these various mechanisms, iDASH implements its goal of providing biomedical and behavioral researchers with access to data, software, and a high-performance computing environment, thus enabling them to generate and test new hypotheses.

Isoform-specific targeting of PKA to multivesicular bodies.

Although RII protein kinase A (PKA) regulatory subunits are constitutively localized to discrete cellular compartments through binding to A-kinase-anchoring proteins (AKAPs), RI subunits are primarily diffuse in the cytoplasm. In this paper, we report a novel AKAP-dependent localization of RIα to distinct organelles, specifically, multivesicular bodies (MVBs). This localization depends on binding to AKAP11, which binds tightly to free RIα or RIα in complex with catalytic subunit (holoenzyme). However, recruitment to MVBs occurs only with the release of PKA catalytic subunit (PKAc). This recruitment is reversed by reassociation with PKAc, and it is disrupted by the presence of AKAP peptides, mutations in the RIα AKAP-binding site, or knockdown of AKAP11. Cyclic adenosine monophosphate binding not only unleashes active PKAc but also leads to the targeting of AKAP11:RIα to MVBs. Therefore, we show that the RIα holoenzyme is part of a signaling complex with AKAP11, in which AKAP11 may direct RIα functionality after disassociation from PKAc. This model defines a new paradigm for PKA signaling.