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GO terms associated with upregulated genes bearing new ∙NO-induced H3K9ac peaks around promoter regions

Supplementary Materials and Methods. Figure S1. Nitric oxide induces significant changes in histone posttranslational modifications. Figure S2. NO-mediated gene expression changes are consistent with the induction of an oncogenic phenotype. Figure S3. Nitric oxide alters the distribution patterns of H3K9ac and H3K9me2 across the genome. Figure S4. NO-upregulated genes with a new promoter-associated H3K9ac peak are involved in critical tumorigenic pathways. Table S1. NO-mediated changes in global levels of histone acetylation. Table S2. List of NO-regulated miRNAs (1.5 Fold Change cut-off). Table S3. Control ChIP-PCR primer sequences. Table S4. List of transcription factors with binding motifs found exclusively around promoters of upregulated genes with a new NO-induced H3K9ac peak.

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over 50 000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for severe acute respiratory syndrome coronavirus 2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of 9 academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

Sickle cell disease (SCD) complications are associated with increased morbidity and risk of mortality. We sought to identify a circulating transcriptomic profile predictive of these poor outcomes in SCD. Training and testing cohorts consisting of adult patients with SCD were recruited and prospectively followed. A pathway-based signature derived from grouping peripheral blood mononuclear cell transcriptomes distinguished 2 patient clusters with differences in survival in the training cohort. These findings were validated in a testing cohort in which the association between cluster 1 molecular profiling and mortality remained significant in a fully adjusted model. In a third cohort of West African children with SCD, cluster 1 differentiated SCD severity using a published scoring index. Finally, a risk score composed of assigning weights to cluster 1 profiling, along with established clinical risk factors using tricuspid regurgitation velocity, white blood cell count, history of acute chest syndrome, and hemoglobin levels, demonstrated a higher hazard ratio for mortality in both the training and testing cohorts compared with clinical risk factors or cluster 1 data alone. Circulating transcriptomic profiles are a powerful method to risk-stratify severity of disease and poor outcomes in both children and adults, respectively, with SCD and highlight potential associated molecular pathways.

B lymphopoiesis requires that immunoglobulin genes be accessible to RAG1-RAG2 recombinase. However, the RAG proteins bind widely to open chromatin, which suggests that additional mechanisms must restrict RAG-mediated DNA cleavage. Here we show that developmental downregulation of interleukin 7 (IL-7)-receptor signaling in small pre-B cells induced expression of the bromodomain-family member BRWD1, which was recruited to a specific epigenetic landscape at Igk dictated by pre-B cell receptor (pre-BCR)-dependent Erk activation. BRWD1 enhanced RAG recruitment, increased gene accessibility and positioned nucleosomes 5' to each Jκ recombination signal sequence. BRWD1 thus targets recombination to Igk and places recombination within the context of signaling cascades that control B cell development. Our findings represent a paradigm in which, at any particular antigen-receptor locus, specialized mechanisms enforce lineage- and stage-specific recombination.

Background The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method ‘N-of-1-pathways’ is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. Results Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). Conclusions The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies. Software http://lussierlab.org/publications/N-of-1-pathways

The Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) represents an unprecedented collaboration across diverse healthcare institutions including private, county, and state hospitals and health systems, a consortium of Federally Qualified Health Centers, and two Department of Veterans Affairs hospitals. CAPriCORN builds on the strengths of our institutions to develop a cross-cutting infrastructure for sustainable and patient-centered comparative effectiveness research in Chicago. Unique aspects include collaboration with the University HealthSystem Consortium to aggregate data across sites, a centralized communication center to integrate patient recruitment with the data infrastructure, and a centralized institutional review board to ensure a strong and efficient human subject protection program. With coordination by the Chicago Community Trust and the Illinois Medical District Commission, CAPriCORN will model how healthcare institutions can overcome barriers of data integration, marketplace competition, and care fragmentation to develop, test, and implement strategies to improve care for diverse populations and reduce health disparities.

Gata5 is a transcription factor expressed in the lung, but its physiological role is unknown. To test whether and how Gata5 regulates airway constrictor responsiveness, we studied Gata5(-/-), Gata5(+/-), and wild-type mice on the C57BL/6J background. Cholinergic airway constrictor responsiveness was assessed invasively in mice without and with induction of allergic airway inflammation through ovalbumin sensitization and aerosol exposure. Gata5-deficient mice displayed native airway constrictor hyperresponsiveness (AHR) in the absence of allergen-induced inflammation. Gata5-deficient mice retained their relatively greater constrictor responsiveness even in ovalbumin-induced experimental asthma. Gata5 deficiency did not alter the distribution of cell types in bronchoalveolar lavage fluid, but bronchial epithelial mucus metaplasia was more prominent in Gata5(-/-) mice after allergen challenge. Gene expression profiles revealed that apolipoprotein E (apoE) was the fifth most down-regulated transcript in Gata5-deficient lungs, and quantitative RT-PCR and immunostaining confirmed reduced apoE expression in Gata5(-/-) mice. Quantitative RT-PCR also revealed increased IL-13 mRNA in the lungs of Gata5-deficient mice. These findings for the first time show that Gata5 regulates apoE and IL-13 expression in vivo and that its deletion causes AHR. Gata5-deficient mice exhibit an airway phenotype that closely resembles that previously reported for apoE(-/-) mice: both exhibit cholinergic AHR in native and experimental asthma states, and there is excessive goblet cell metaplasia after allergen sensitization and challenge. The Gata5-deficient phenotype also shares features that were previously reported for IL-13-treated mice. Together, these results indicate that Gata5 deficiency induces AHR, at least in part, by blunting apoE and increasing IL-13 expression.

Objective Applying the science of networks to quantify the discriminatory impact of the ICD-9-CM to ICD-10-CM transition between clinical specialties. Materials and Methods Datasets were the Center for Medicaid and Medicare Services ICD-9-CM to ICD-10-CM mapping files, general equivalence mappings, and statewide Medicaid emergency department billing. Diagnoses were represented as nodes and their mappings as directional relationships. The complex network was synthesized as an aggregate of simpler motifs and tabulation per clinical specialty. Results We identified five mapping motif categories: identity, class-to-subclass, subclass-to-class, convoluted, and no mapping. Convoluted mappings indicate that multiple ICD-9-CM and ICD-10-CM codes share complex, entangled, and non-reciprocal mappings. The proportions of convoluted diagnoses mappings (36% overall) range from 5% (hematology) to 60% (obstetrics and injuries). In a case study of 24 008 patient visits in 217 emergency departments, 27% of the costs are associated with convoluted diagnoses, with ‘abdominal pain’ and ‘gastroenteritis’ accounting for approximately 3.5%. Discussion Previous qualitative studies report that administrators and clinicians are likely to be challenged in understanding and managing their practice because of the ICD-10-CM transition. We substantiate the complexity of this transition with a thorough quantitative summary per clinical specialty, a case study, and the tools to apply this methodology easily to any clinical practice in the form of a web portal and analytic tables. Conclusions Post-transition, successful management of frequent diseases with convoluted mapping network patterns is critical. The http://lussierlab.org/transition-to-ICD10CM web portal provides insight in linking onerous diseases to the ICD-10 transition.

OBJECTIVES/SPECIFIC AIMS: This research project envisions the integration of Homeless Management Information System (HMIS) and UI Health Cerner electronic medical record (EMR) system with the following goals: (1) enable sharing of data about the status of the housing insecure and homeless. (2) Identify and match patient record accurately. (3) Record housing insecurity or homelessness information with structured data elements in the EMR. METHODS/STUDY POPULATION: We created a Master Person Index (MPI) of the homeless individuals from HMSI using OpenEMPI software package, which is an open source implementation of an Enterprise Master Patient Index (EMPI). An entity model was generated based on the selective data elements from HMIS database, which were relevant for the patient identity management and healthcare service management. An automated script was implemented to extract data from HMIS and load it into OpenEMPI to build the MPI. Once the MPI is setup, the Emergency Department users were able to perform patient identity matching and confirm housing insecure or homeless status of their patients by querying the index using the web-based tool. We developed structured data elements to record homelessness information, which will allow us to measure the prevalence of this risk among patients. We are also exploring the possibility to integrate the systems the using the IHE PIX/PDQ profile, which provides ways for healthcare applications to query a patient information server for a patient based on user-defined search criteria, and retrieve a patient’s information directly into the application. RESULTS/ANTICIPATED RESULTS: We implemented a MPI of homeless individuals, which would allow the emergency department users to perform patient identity matching of housing insecure or homeless patients, without undue privacy intrusions. We are confident that IHE PIX/PDQ profile is able to support the integration of healthcare and housing and homeless services systems and enable the data sharing in an efficient way. DISCUSSION/SIGNIFICANCE OF IMPACT: The project addressed the gap in the sharing of data about housing insecure or homeless persons between healthcare and housing and social services that will result in improvements in coordination of care, reduce the cycle time from recognition of risk to the referral to housing and services and improve health outcomes and residential stability. Successful completion of this integration project will give us a model that we can scale to many other communities.

Transcriptional regulation during CD4+ T cell fate decisions enables their differentiation into distinct states, guiding immune responses towards antibody production via Tfh cells or inflammation by Teff cells. Tfh–Teff fate commitment is regulated by mutual antagonism between the transcription factors Bcl6 and Blimp-1. Here we examined how T cell receptor (TCR) signals establish and arbitrate Bcl6–Blimp-1 counter-antagonism. We found that the TCR-signal induced transcription factor IRF4 is essential for the differentiation of Bcl6-expressing Tfh and Blimp-1-expressing Teff cells. Increased TCR signaling raised IRF4 amounts and promoted Teff fates at the expense of Tfh ones. Importantly, orthogonal induction of IRF4 expression redirected Tfh fate trajectories towards those of Teff and this occurred independently of IL-2 signals. Mechanistically, we linked greater IRF4 abundance with its recruitment towards low affinity binding sites within Teff cis-regulatory elements, including those of Prdm1. We propose that the Irf4 locus functions as the “reader” of TCR signal strength, in turn, concentration-dependent activity of IRF4 “writes” T helper fate choice.

Altered nitric oxide (•NO) metabolism underlies cancer pathology, but mechanisms explaining many •NO-associated phenotypes remain unclear. We have found that cellular exposure to •NO changes histone posttranslational modifications (PTM) by directly inhibiting the catalytic activity of JmjC-domain containing histone demethylases. Herein, we describe how •NO exposure links modulation of histone PTMs to gene expression changes that promote oncogenesis. Through high-resolution mass spectrometry, we generated an extensive map of •NO-mediated histone PTM changes at 15 critical lysine residues on the core histones H3 and H4. Concomitant microarray analysis demonstrated that exposure to physiologic •NO resulted in the differential expression of over 6,500 genes in breast cancer cells. Measurements of the association of H3K9me2 and H3K9ac across genomic loci revealed that differential distribution of these particular PTMs correlated with changes in the level of expression of numerous oncogenes, consistent with epigenetic code. Our results establish that •NO functions as an epigenetic regulator of gene expression mediated by changes in histone PTMs. Cancer Res; 75(24); 5299–308. ©2015 AACR.

The objective of this study is to determine the degree of similarities between the clinical terms used by physicians and nurses in their documentation.

The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machine-readable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues.

Transcriptional regulation during CD4+ T cell fate decisions enables their differentiation into distinct states, guiding immune responses toward antibody production via Tfh cells or inflammation by Teff cells. Tfh-Teff cell fate commitment is regulated by mutual antagonism between the transcription factors Bcl6 and Blimp-1. Here we examined how T cell receptor (TCR) signals establish and arbitrate Bcl6-Blimp-1 counter-antagonism. We found that the TCR-signal-induced transcription factor Irf4 is essential for the differentiation of Bcl6-expressing Tfh and Blimp-1-expressing Teff cells. Increased TCR signaling raised Irf4 amounts and promoted Teff cell fates at the expense of Tfh ones. Importantly, orthogonal induction of Irf4 expression redirected Tfh cell fate trajectories toward those of Teff. Mechanistically, we linked greater Irf4 abundance with its recruitment toward low-affinity binding sites within Teff cell cis-regulatory elements, including those of Prdm1. We propose that the Irf4 locus functions as the "reader" of TCR signal strength, and in turn, concentration-dependent activity of Irf4 "writes" T helper fate choice.

Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms. We term this method empirical JTK_CYCLE with asymmetry search, and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction, as well as to five other methods: cyclohedron test, address reduction, stable persistence, ANOVA, and F24. We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate. Our analysis also provides insight into experimental design and we find that, for a fixed number of samples, better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density. Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. These include a wide range of oxidation reduction and metabolic genes, as well as genes with transcripts that have multiple splice forms., Author Summary Much biomedical research focuses on how the expression of genes changes over time. Many genes’ activities vary periodically. For example, circadian rhythms repeat daily with the light-dark cycle. Understanding how such rhythms couple to biological processes requires statistical methods that can identify cycling time series in typical genome-wide data. In this paper, we improve on a method used to identify cycling time series by better estimating the statistical significance of periodic patterns and, in turn, by searching for a wider range of patterns than traditionally investigated. We apply these methods to a compilation of data on gene expression in fruit flies, an important model organism. We find that our method allows us to discover rhythmic biological activities that the other methods tested are unable to reveal.

Summary: Collecting data from large studies on high-throughput platforms, such as microarray or next-generation sequencing, typically requires processing samples in batches. There are often systematic but unpredictable biases from batch-to-batch, so proper randomization of biologically relevant traits across batches is crucial for distinguishing true biological differences from experimental artifacts. When a large number of traits are biologically relevant, as is common for clinical studies of patients with varying sex, age, genotype and medical background, proper randomization can be extremely difficult to prepare by hand, especially because traits may affect biological inferences, such as differential expression, in a combinatorial manner. Here we present ARTS (automated randomization of multiple traits for study design), which aids researchers in study design by automatically optimizing batch assignment for any number of samples, any number of traits and any batch size. Availability and implementation: ARTS is implemented in Perl and is available at github.com/mmaiensc/ARTS. ARTS is also available in the Galaxy Tool Shed, and can be used at the Galaxy installation hosted by the UIC Center for Research Informatics (CRI) at galaxy.cri.uic.edu. Contact: mmaiensc@uic.edu Supplementary information: Supplementary data are available at Bioinformatics online.

In the United States, International Classification of Disease Clinical Modification (ICD-9-CM, the ninth revision) diagnosis codes are commonly used to identify patient cohorts and to conduct financial analyses related to disease. In October 2015, the healthcare system of the United States will transition to ICD-10-CM (the tenth revision) diagnosis codes. One challenge posed to clinical researchers and other analysts is conducting diagnosis-related queries across datasets containing both coding schemes. Further, healthcare administrators will manage growth, trends, and strategic planning with these dually-coded datasets. The majority of the ICD-9-CM to ICD-10-CM translations are complex and nonreciprocal, creating convoluted representations and meanings. Similarly, mapping back from ICD-10-CM to ICD-9-CM is equally complex, yet different from mapping forward, as relationships are likewise nonreciprocal. Indeed, 10 of the 21 top clinical categories are complex as 78% of their diagnosis codes are labeled as “convoluted” by our analyses. Analysis and research related to external causes of morbidity, injury, and poisoning will face the greatest challenges due to 41 745 (90%) convolutions and a decrease in the number of codes. We created a web portal tool and translation tables to list all ICD-9-CM diagnosis codes related to the specific input of ICD-10-CM diagnosis codes and their level of complexity: “identity” (reciprocal), “class-to-subclass,” “subclass-to-class,” “convoluted,” or “no mapping.” These tools provide guidance on ambiguous and complex translations to reveal where reports or analyses may be challenging to impossible.Web portal: http://www.lussierlab.org/transition-to-ICD9CM/Tables annotated with levels of translation complexity: http://www.lussierlab.org/publications/ICD10to9

Macrolides are clinically important antibiotics thought to inhibit bacterial growth by impeding the passage of newly synthesized polypeptides through the nascent peptide exit tunnel of the bacterial ribosome. Recent data challenged this view by showing that macrolide antibiotics can differentially affect synthesis of individual proteins. To understand the general mechanism of macrolide action, we used genome-wide ribosome profiling and analyzed the redistribution of ribosomes translating highly expressed genes in bacterial cells treated with high concentrations of macrolide antibiotics. The metagene analysis indicated that inhibition of early rounds of translation, which would be characteristic of the conventional view of macrolide action, occurs only at a limited number of genes. Translation of most genes proceeds past the 5'-proximal codons and can be arrested at more distal codons when the ribosome encounters specific short sequence motifs. The problematic sequence motifs are confined to the nascent peptide residues in the peptidyl transferase center but not to the peptide segment that contacts the antibiotic molecule in the exit tunnel. Therefore, it appears that the general mode of macrolide action involves selective inhibition of peptide bond formation between specific combinations of donor and acceptor substrates. Additional factors operating in the living cell but not functioning during in vitro protein synthesis may modulate site-specific action of macrolide antibiotics.

In this manuscript, we present our experiences using the Apache Hadoop framework for high data volume and computationally intensive applications, and discuss some best practice guidelines in a clinical informatics setting. There are three main aspects in our approach: (a) process and integrate diverse, heterogeneous data sources using standard Hadoop programming tools and customized MapReduce programs; (b) after fine-grained aggregate results are obtained, perform data analysis using the Mahout data mining library; (c) leverage the column oriented features in HBase for patient centric modeling and complex temporal reasoning. This framework provides a scalable solution to meet the rapidly increasing, imperative "Big Data" needs of clinical and translational research. The intrinsic advantage of fault tolerance, high availability and scalability of Hadoop platform makes these applications readily deployable at the enterprise level cluster environment.

Latest genotyping solutions allow for rapid testing of more than two million markers in one experiment. Fully automated instruments such as Affymetrix GeneTitan enable processing of large numbers of samples in a truly high-throughput manner. In concert with solutions like Axiom, fully customizable array plates can now utilize automated workflows that can leverage multi-channel instrumentation like the GeneTitan. With the growing size of raw data output, the serial computational architecture of the software, typically distributed by the vendors on turnkey desktop solutions for quality control and genotype calling, becomes legacy rather than an advantage. Advanced software techniques provide power, flexibility, and can be deployed in an HPC environment, but become technically inconvenient for biologists to use. Here we present a pipeline that uses Galaxy as a mechanism to lower the barrier for complex analysis, and increase efficiency by leveraging high-throughput computing.

Objective An area of need in cancer informatics is the ability to store images in a comprehensive database as part of translational cancer research. To meet this need, we have implemented a novel tandem database infrastructure that facilitates image storage and utilisation. Background We had previously implemented the Thoracic Oncology Program Database Project (TOPDP) database for our translational cancer research needs. While useful for many research endeavours, it is unable to store images, hence our need to implement an imaging database which could communicate easily with the TOPDP database. Methods The Thoracic Oncology Research Program (TORP) imaging database was designed using the Research Electronic Data Capture (REDCap) platform, which was developed by Vanderbilt University. To demonstrate proof of principle and evaluate utility, we performed a retrospective investigation into tumour response for malignant pleural mesothelioma (MPM) patients treated at the University of Chicago Medical Center with either of two analogous chemotherapy regimens and consented to at least one of two UCMC IRB protocols, 9571 and 13473A. Results A cohort of 22 MPM patients was identified using clinical data in the TOPDP database. After measurements were acquired, two representative CT images and 0–35 histological images per patient were successfully stored in the TORP database, along with clinical and demographic data. Discussion We implemented the TORP imaging database to be used in conjunction with our comprehensive TOPDP database. While it requires an additional effort to use two databases, our database infrastructure facilitates more comprehensive translational research. Conclusions The investigation described herein demonstrates the successful implementation of this novel tandem imaging database infrastructure, as well as the potential utility of investigations enabled by it. The data model presented here can be utilised as the basis for further development of other larger, more streamlined databases in the future.

BackgroundPatient adherence to medication regimens is critical in most chronic disease treatment plans. This study uses a patient-centered tablet app, “My Interventional Drug-Eluting Stent Educational App (MyIDEA).” This is an educational program designed to improve patient medication adherence. ObjectiveOur goal is to describe the design, methodology, limitations, and results of the MyIDEA tablet app. We created a mobile technology-based patient education app to improve dual antiplatelet therapy adherence in patients who underwent a percutaneous coronary intervention and received a drug-eluting stent. MethodsPatient advisers were involved in the development process of MyIDEA from the initial wireframe to the final launch of the product. The program was restructured and redesigned based on the patient advisers’ suggestions as well as those from multidisciplinary team members. To accommodate those with low health literacy, we modified the language and employed attractive color schemes to improve ease of use. We assumed that the target patient population may have little to no experience with electronic tablets, and therefore, we designed the interface to be as intuitive as possible. ResultsThe MyIDEA app has been successfully deployed to a low-health-literate elderly patient population in the hospital setting. A total of 6 patients have interacted with MyIDEA for an average of 17.6 minutes/session. ConclusionsIncluding patient advisers in the early phases of a mobile patient education development process is critical. A number of changes in text order, language, and color schemes occurred to improve ease of use. The MyIDEA program has been successfully deployed to a low-health-literate elderly patient population. Leveraging patient advisers throughout the development process helps to ensure implementation success.