Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age

Determining the discrepancy between chronological and physiological age of patients is central to preventative and personalized care. Electronic medical records (EMR) provide rich information about the patient physiological state, but it is unclear whether such information can be predictive of chronological age. Here we present a deep learning model that uses vital signs and lab tests contained within the EMR of Mount Sinai Health System (MSHS) to predict chronological age. The model is trained on 377,686 EMR from patients of ages 18-85 years old. The discrepancy between the predicted and real chronological age is then used as a proxy to estimate physiological age. Overall, the model can predict the chronological age of patients with a standard deviation error of ∼7 years. The ages of the youngest and oldest patients were more accurately predicted, while patients of ages ranging between 40 and 60 years were the least accurately predicted. Patients with the largest discrepancy between their physiological and chronological age were further inspected. The patients predicted to be significantly older than their chronological age have higher systolic blood pressure, higher cholesterol, damaged liver, and anemia. In contrast, patients predicted to be younger than their chronological age have lower blood pressure and shorter stature among other indicators; both groups display lower weight than the population average. Using information from ∼10,000 patients from the entire cohort who have been also profiled with SNP arrays, genome-wide association study (GWAS) uncovers several novel genetic variants associated with aging. In particular, significant variants were mapped to genes known to be associated with inflammation, hypertension, lipid metabolism, height, and increased lifespan in mice. Several genes with missense mutations were identified as novel candidate aging genes. In conclusion, we demonstrate how EMR data can be used to assess overall health via a scale that is based on deviation from the patient's predicted chronological age.

Regulatory T-cell Genes Drive Altered Immune Microenvironment in Adult Solid Cancers and Allow for Immune Contextual Patient Subtyping

Background: The tumor microenvironment is an important factor in cancer immunotherapy response. To further understand how a tumor affects the local immune system, we analyzed immune gene expression differences between matching normal and tumor tissue.Methods: We analyzed public and new gene expression data from solid cancers and isolated immune cell populations. We also determined the correlation between CD8, FoxP3 IHC, and our gene signatures.Results: We observed that regulatory T cells (Tregs) were one of the main drivers of immune gene expression differences between normal and tumor tissue. A tumor-specific CD8 signature was slightly lower in tumor tissue compared with normal of most (12 of 16) cancers, whereas a Treg signature was higher in tumor tissue of all cancers except liver. Clustering by Treg signature found two groups in colorectal cancer datasets. The high Treg cluster had more samples that were consensus molecular subtype 1/4, right-sided, and microsatellite-instable, compared with the low Treg cluster. Finally, we found that the correlation between signature and IHC was low in our small dataset, but samples in the high Treg cluster had significantly more CD8⁺ and FoxP3⁺ cells compared with the low Treg cluster.Conclusions: Treg gene expression is highly indicative of the overall tumor immune environment.Impact: In comparison with the consensus molecular subtype and microsatellite status, the Treg signature identifies more colorectal tumors with high immune activation that may benefit from cancer immunotherapy. Cancer Epidemiol Biomarkers Prev; 27(1); 103-12. ©2017 AACR.

Deficiency of TYROBP, an adapter protein for TREM2 and CR3 receptors, is neuroprotective in a mouse model of early Alzheimer's pathology

Conventional genetic approaches and computational strategies have converged on immune-inflammatory pathways as key events in the pathogenesis of late onset sporadic Alzheimer’s disease (LOAD). Mutations and/or differential expression of microglial specific receptors such as TREM2, CD33, and CR3 have been associated with strong increased risk for developing Alzheimer’s disease (AD). DAP12 (DNAX-activating protein 12)/TYROBP, a molecule localized to microglia, is a direct partner/adapter for TREM2, CD33, and CR3. We and others have previously shown that TYROBP expression is increased in AD patients and in mouse models. Moreover, missense mutations in the coding region of TYROBP have recently been identified in some AD patients. These lines of evidence, along with computational analysis of LOAD brain gene expression, point to DAP12/TYROBP as a potential hub or driver protein in the pathogenesis of AD. Using a comprehensive panel of biochemical, physiological, behavioral, and transcriptomic assays, we evaluated in a mouse model the role of TYROBP in early stage AD. We crossed an Alzheimer’s model mutant APP^KM670/671NL/PSEN1^Δexon9(APP/PSEN1) mouse model with Tyrobp^−/− mice to generate AD model mice deficient or null for TYROBP (APP/PSEN1; Tyrobp^+/− or APP/PSEN1; Tyrobp^−/−). While we observed relatively minor effects of TYROBP deficiency on steady-state levels of amyloid-β peptides, there was an effect of Tyrobp deficiency on the morphology of amyloid deposits resembling that reported by others for Trem2^−/− mice. We identified modulatory effects of TYROBP deficiency on the level of phosphorylation of TAU that was accompanied by a reduction in the severity of neuritic dystrophy. TYROBP deficiency also altered the expression of several AD related genes, including Cd33. Electrophysiological abnormalities and learning behavior deficits associated with APP/PSEN1 transgenes were greatly attenuated on a Tyrobp-null background. Some modulatory effects of TYROBP on Alzheimer’s-related genes were only apparent on a background of mice with cerebral amyloidosis due to overexpression of mutant APP/PSEN1. These results suggest that reduction of TYROBP gene expression and/or protein levels could represent an immune-inflammatory therapeutic opportunity for modulating early stage LOAD, potentially leading to slowing or arresting the progression to full-blown clinical and pathological LOAD.

Open chromatin profiling of human postmortem brain infers functional roles for non-coding schizophrenia loci

Open chromatin provides access to DNA-binding proteins for the correct spatiotemporal regulation of gene expression. Mapping chromatin accessibility has been widely used to identify the location of cis regulatory elements (CREs) including promoters and enhancers. CREs show tissue- and cell-type specificity and disease-associated variants are often enriched for CREs in the tissues and cells that pertain to a given disease. To better understand the role of CREs in neuropsychiatric disorders we applied the Assay for Transposase Accessible Chromatin followed by sequencing (ATAC-seq) to neuronal and non-neuronal nuclei isolated from frozen postmortem human brain by fluorescence-activated nuclear sorting (FANS). Most of the identified open chromatin regions (OCRs) are differentially accessible between neurons and non-neurons, and show enrichment with known cell type markers, promoters and enhancers. Relative to those of non-neurons, neuronal OCRs are more evolutionarily conserved and are enriched in distal regulatory elements. Transcription factor (TF) footprinting analysis identifies differences in the regulome between neuronal and non-neuronal cells and ascribes putative functional roles to a number of non-coding schizophrenia (SCZ) risk variants. Among the identified variants is a Single Nucleotide Polymorphism (SNP) proximal to the gene encoding SNX19. In vitro experiments reveal that this SNP leads to an increase in transcriptional activity. As elevated expression of SNX19 has been associated with SCZ, our data provide evidence that the identified SNP contributes to disease. These results represent the first analysis of OCRs and TF-binding sites in distinct populations of postmortem human brain cells and further our understanding of the regulome and the impact of neuropsychiatric disease-associated genetic risk variants.

Uncovering exposures responsible for birth season - disease effects: a global study

Objective

Birth month and climate impact lifetime disease risk, while the underlying exposures remain largely elusive. We seek to uncover distal risk factors underlying these relationships by probing the relationship between global exposure variance and disease risk variance by birth season.

Material and Methods

This study utilizes electronic health record data from 6 sites representing 10.5 million individuals in 3 countries (United States, South Korea, and Taiwan). We obtained birth month–disease risk curves from each site in a case-control manner. Next, we correlated each birth month–disease risk curve with each exposure. A meta-analysis was then performed of correlations across sites. This allowed us to identify the most significant birth month–exposure relationships supported by all 6 sites while adjusting for multiplicity. We also successfully distinguish relative age effects (a cultural effect) from environmental exposures.

Results

Attention deficit hyperactivity disorder was the only identified relative age association. Our methods identified several culprit exposures that correspond well with the literature in the field. These include a link between first-trimester exposure to carbon monoxide and increased risk of depressive disorder (R = 0.725, confidence interval [95% CI], 0.529-0.847), first-trimester exposure to fine air particulates and increased risk of atrial fibrillation (R = 0.564, 95% CI, 0.363-0.715), and decreased exposure to sunlight during the third trimester and increased risk of type 2 diabetes mellitus (R = −0.816, 95% CI, −0.5767, −0.929).

Conclusion

A global study of birth month–disease relationships reveals distal risk factors involved in causal biological pathways that underlie them.

Intracoronary Imaging, Cholesterol Efflux, and Transcriptomics after Intensive Statin Treatment in Diabetes

Residual atherothrombotic risk remains higher in patients with versus without diabetes mellitus (DM) despite statin therapy. The underlying mechanisms are unclear. This is a retrospective post-hoc analysis of the YELLOW II trial, comparing patients with and without DM (non-DM) who received rosuvastatin 40 mg for 8–12 weeks and underwent intracoronary multimodality imaging of an obstructive nonculprit lesion, before and after therapy. In addition, blood samples were drawn to assess cholesterol efflux capacity (CEC) and changes in gene expression in peripheral blood mononuclear cells (PBMC). There was a significant reduction in low density lipoprotein-cholesterol (LDL-C), an increase in CEC and beneficial changes in plaque morphology including increase in fibrous cap thickness and decrease in the prevalence of thin cap fibro-atheroma by optical coherence tomography in DM and non-DM patients. While differential gene expression analysis did not demonstrate differences in PBMC transcriptome between the two groups on the single-gene level, weighted gene coexpression network analysis revealed two modules of coexpressed genes associated with DM, Collagen Module and Platelet Module, related to collagen catabolism and platelet function respectively. Bayesian network analysis revealed key driver genes within these modules. These transcriptomic findings might provide potential mechanisms responsible for the higher cardiovascular risk in DM patients.

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks

Motivation: Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL).

Results: We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51 key ‘hub’ diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes.

Contacts: rong.chen@mssm.edu or joel.dudley@mssm.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Abstract 3250: Computational drug repositioning and biochemical validation of piperlongumine as a potent therapeutic agent for neuroendocrine prostate cancer

Introduction and Objectives: Neuroendocrine prostate cancer (NEPC) is a highly lethal and drug-resistant variant of prostate cancer (PCa). We have used a genomics-based drug-repositioning approach and identified compounds with therapeutic activity against NEPC. One of the compounds- piperlongumine (PL) is a natural product constituent of the fruit of the Long pepper (Piper longum). The efficacy of this drug was tested in the NEPC cell line model NCI-H660 and compared to several other PCa cell lines in a modified WST-1 assay. Pre-clinical testing in mouse xenograft models of NEPC was also undertaken. Finally, the ability of piperlongumine to inhibit p-STAT3 signaling and promote apoptosis was measured by western blot analyses.

Methods: PCa cell lines (LNCaP, 22Rv1, Du145, PC3, H660 and RWPE) were grown in complete media (RPM1 10%FBS, Advanced DMEM 5%FBS or Keratinocyte-SFM) and treated with piperlongumine for 3 or 7 days. IC50 values were generated from WST assay data using Prism6. Nude mice (n=5) were injected with 1.5x106 H660 cells on each flank, and intraperitoneally administered with either 2.5mg/kg PL (n=3) or DMSO (n=2) daily for 3 weeks after tumors formed 200mm3 in volume. Tumor volume was measured daily with calipers. Western blot analyses were performed on protein lysates extracted from tumors and PCa cells treated with 0-10 μM of drug.

Results: PL was highly effective in inhibiting the growth of H660 cells (IC50 = 0.4 μM) compared to LNCaP, 22Rv1, Du145, PC3, and RWPE cells. This was consistent with an increased level of cPARP1 in H660 cells when compared to other prostate cancer cell lines. On average, the growth rate of untreated tumors was 2.7 times greater than those treated with PL. Similar to previous reports suggesting PL to inhibit STAT3 activity, treated H660 cells exhibited inhibition of STAT3 phosphorylation.

Conclusions: Using computational drug repositioning approaches we have identified several lead compounds for NEPC. One of the compound piperlongumine, a water-soluble plant product with no known side effects inhibits the growth of the highly drug-resistant NEPC cell line H660.

Source of Funding: Deane Prostate Health, Icahn School of Medicine at Mount Sinai. Both Shalini S Yadav and Kamlesh K Yadav are supported by the Prostate Cancer Foundation Young Investigator Awards. Research in Dudley's lab is supported by grants from National Institutes of Health R01 DK098242 and U54 CA189201.

Note: This abstract was not presented at the meeting.

Citation Format: Kamlesh Kumar Yadav, Khader Shameer, Ben Readhead, Jennifer A. Stockert, Cordelia Elaiho, Shalini S. Yadav, Benjamin S. Glicksberg, Kipp W. Johnson, Christine Becker, Andrew Kasarskis, Ashutosh K. Tewari, Joel T. Dudley. Computational drug repositioning and biochemical validation of piperlongumine as a potent therapeutic agent for neuroendocrine prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3250. doi:10.1158/1538-7445.AM2017-3250

Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography

BACKGROUND

Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation.

OBJECTIVES

This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH).

METHODS

Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system. An ensemble machine-learning model with 3 different machine-learning algorithms (support vector machines, random forests, and artificial neural networks) was developed and a majority voting method was used for conclusive predictions with further K-fold cross-validation.

RESULTS

Feature selection using an information gain (IG) algorithm revealed that volume was the best predictor for differentiating between HCM ands. ATH (IG = 0.24) followed by mid-left ventricular segmental (IG = 0.134) and average longitudinal strain (IG = 0.131). The ensemble machine-learning model showed increased sensitivity and specificity compared with early-to-late diastolic transmitral velocity ratio (p < 0.01), average early diastolic tissue velocity (e') (p < 0.01), and strain (p = 0.04). Because ATH were younger, adjusted analysis was undertaken in younger HCM patients and compared with ATH with left ventricular wall thickness >13 mm. In this subgroup analysis, the automated model continued to show equal sensitivity, but increased specificity relative to early-to-late diastolic transmitral velocity ratio, e', and strain.

CONCLUSIONS

Our results suggested that machine-learning algorithms can assist in the discrimination of physiological versus pathological patterns of hypertrophic remodeling. This effort represents a step toward the development of a real-time, machine-learning-based system for automated interpretation of echocardiographic images, which may help novice readers with limited experience.

PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT

Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients. Most of these models are hypothesis driven and repetitively assess the predictive abilities of the same set of biomarkers as predictive features. In this manuscript, we discuss our attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities. We have assessed a large repertoire of variables from electronic medical records of heart failure patients in a single center. The cohort included 1,068 patients with 178 patients were readmitted within a 30-day interval (16.66% readmission rate). A total of 4,205 variables were extracted from EMR including diagnosis codes (n=1,763), medications (n=1,028), laboratory measurements (n=846), surgical procedures (n=564) and vital signs (n=4). We designed a multistep modeling strategy using the Naïve Bayes algorithm. In the first step, we created individual models to classify the cases (readmitted) and controls (non-readmitted). In the second step, features contributing to predictive risk from independent models were combined into a composite model using a correlation-based feature selection (CFS) method. All models were trained and tested using a 5-fold cross-validation method, with 70% of the cohort used for training and the remaining 30% for testing. Compared to existing predictive models for HF readmission rates (AUCs in the range of 0.6-0.7), results from our EMR-wide predictive model (AUC=0.78; Accuracy=83.19%) and phenome-wide feature selection strategies are encouraging and reveal the utility of such datadriven machine learning. Fine tuning of the model, replication using multi-center cohorts and prospective clinical trial to evaluate the clinical utility would help the adoption of the model as a clinical decision system for evaluating readmission status.

Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine

The traditional paradigm of cardiovascular disease research derives insight from large-scale, broadly inclusive clinical studies of well-characterized pathologies. These insights are then put into practice according to standardized clinical guidelines. However, stagnation in the development of new cardiovascular therapies and variability in therapeutic response implies that this paradigm is insufficient for reducing the cardiovascular disease burden. In this state-of-the-art review, we examine 3 interconnected ideas we put forth as key concepts for enabling a transition to precision cardiology: 1) precision characterization of cardiovascular disease with machine learning methods; 2) the application of network models of disease to embrace disease complexity; and 3) using insights from the previous 2 ideas to enable pharmacology and polypharmacology systems for more precise drug-to-patient matching and patient-disease stratification. We conclude by exploring the challenges of applying a precision approach to cardiology, which arise from a deficit of the required resources and infrastructure, and emerging evidence for the clinical effectiveness of this nascent approach.

Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy

BACKGROUND

Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy.

METHODS AND RESULTS

Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions.

CONCLUSIONS

This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.

Transcriptome-based repurposing of apigenin as a potential anti-fibrotic agent targeting hepatic stellate cells

We have used a computational approach to identify anti-fibrotic therapies by querying a transcriptome. A transcriptome signature of activated hepatic stellate cells (HSCs), the primary collagen-secreting cell in liver, and queried against a transcriptomic database that quantifies changes in gene expression in response to 1,309 FDA-approved drugs and bioactives (CMap). The flavonoid apigenin was among 9 top-ranked compounds predicted to have anti-fibrotic activity; indeed, apigenin dose-dependently reduced collagen I in the human HSC line, TWNT-4. To identify proteins mediating apigenin's effect, we next overlapped a 122-gene signature unique to HSCs with a list of 160 genes encoding proteins that are known to interact with apigenin, which identified C1QTNF2, encoding for Complement C1q tumor necrosis factor-related protein 2, a secreted adipocytokine with metabolic effects in liver. To validate its disease relevance, C1QTNF2 expression is reduced during hepatic stellate cell activation in culture and in a mouse model of alcoholic liver injury in vivo, and its expression correlates with better clinical outcomes in patients with hepatitis C cirrhosis (n = 216), suggesting it may have a protective role in cirrhosis progression.These findings reinforce the value of computational approaches to drug discovery for hepatic fibrosis, and identify C1QTNF2 as a potential mediator of apigenin's anti-fibrotic activity.

Novel Therapeutics Identification for Fibrosis in Renal Allograft Using Integrative Informatics Approach

Chronic allograft damage, defined by interstitial fibrosis and tubular atrophy (IF/TA), is a leading cause of allograft failure. Few effective therapeutic options are available to prevent the progression of IF/TA. We applied a meta-analysis approach on IF/TA molecular datasets in Gene Expression Omnibus to identify a robust 85-gene signature, which was used for computational drug repurposing analysis. Among the top ranked compounds predicted to be therapeutic for IF/TA were azathioprine, a drug to prevent acute rejection in renal transplantation, and kaempferol and esculetin, two drugs not previously described to have efficacy for IF/TA. We experimentally validated the anti-fibrosis effects of kaempferol and esculetin using renal tubular cells in vitro and in vivo in a mouse Unilateral Ureteric Obstruction (UUO) model. Kaempferol significantly attenuated TGF-β1-mediated profibrotic pathways in vitro and in vivo, while esculetin significantly inhibited Wnt/β-catenin pathway in vitro and in vivo. Histology confirmed significantly abrogated fibrosis by kaempferol and esculetin in vivo. We developed an integrative computational framework to identify kaempferol and esculetin as putatively novel therapies for IF/TA and provided experimental evidence for their therapeutic activities in vitro and in vivo using preclinical models. The findings suggest that both drugs might serve as therapeutic options for IF/TA.

Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams

Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting. Wellness monitoring devices and companion software applications capable of generating alerts and sharing data with health care providers or social networks are now available. The accessibility and clinical utility of such data for disease or wellness research are currently limited. Designing methods for streaming data capture, real-time data aggregation, machine learning, predictive analytics and visualization solutions to integrate wellness or health monitoring data elements with the electronic medical records (EMRs) maintained by health care providers permits better utilization. Integration of population-scale biomedical, health care and wellness data would help to stratify patients for active health management and to understand clinically asymptomatic patients and underlying illness trajectories. In this article, we discuss various health-monitoring devices, their ability to capture the unique state of health represented in a patient and their application in individualized diagnostics, prognosis, clinical or wellness intervention. We also discuss examples of translational bioinformatics approaches to integrating patient-generated data with existing EMRs, personal health records, patient portals and clinical data repositories. Briefly, translational bioinformatics methods, tools and resources are at the center of these advances in implementing real-time biomedical and health care analytics in the clinical setting. Furthermore, these advances are poised to play a significant role in clinical decision-making and implementation of data-driven medicine and wellness care.

EXPLORING THE REPRODUCIBILITY OF PROBABILISTIC CAUSAL MOLECULAR NETWORK MODELS

Network reconstruction algorithms are increasingly being employed in biomedical and life sciences research to integrate large-scale, high-dimensional data informing on living systems. One particular class of probabilistic causal networks being applied to model the complexity and causal structure of biological data is Bayesian networks (BNs). BNs provide an elegant mathematical framework for not only inferring causal relationships among many different molecular and higher order phenotypes, but also for incorporating highly diverse priors that provide an efficient path for incorporating existing knowledge. While significant methodological developments have broadly enabled the application of BNs to generate and validate meaningful biological hypotheses, the reproducibility of BNs in this context has not been systematically explored. In this study, we aim to determine the criteria for generating reproducible BNs in the context of transcription-based regulatory networks. We utilize two unique tissues from independent datasets, whole blood from the GTEx Consortium and liver from the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Team (STARNET) study. We evaluated the reproducibility of the BNs by creating networks on data subsampled at different levels from each cohort and comparing these networks to the BNs constructed using the complete data. To help validate our results, we used simulated networks at varying sample sizes. Our study indicates that reproducibility of BNs in biological research is an issue worthy of further consideration, especially in light of the many publications that now employ findings from such constructs without appropriate attention paid to reproducibility. We find that while edge-to-edge reproducibility is strongly dependent on sample size, identification of more highly connected key driver nodes in BNs can be carried out with high confidence across a range of sample sizes.

Gold Nanoparticle Reprograms Pancreatic Tumor Microenvironment and Inhibits Tumor Growth

Altered tumor microenvironment (TME) arising from a bidirectional crosstalk between the pancreatic cancer cells (PCCs) and the pancreatic stellate cells (PSCs) is implicated in the dismal prognosis in pancreatic ductal adenocarcinoma (PDAC), yet effective strategies to disrupt the crosstalk is lacking. Here, we demonstrate that gold nanoparticles (AuNPs) inhibit proliferation and migration of both PCCs and PSCs by disrupting the bidirectional communication via alteration of the cell secretome. Analyzing the key proteins identified from a functional network of AuNP-altered secretome in PCCs and PSCs, we demonstrate that AuNPs impair secretions of major hub node proteins in both cell types and transform activated PSCs toward a lipid-rich quiescent phenotype. By reducing activation of PSCs, AuNPs inhibit matrix deposition, enhance angiogenesis, and inhibit tumor growth in an orthotopic co-implantation model in vivo. Auto- and heteroregulations of secretory growth factors/cytokines are disrupted by AuNPs resulting in reprogramming of the TME. By utilizing a kinase dead mutant of IRE1-α, we demonstrate that AuNPs alter the cellular secretome through the ER-stress-regulated IRE1-dependent decay pathway (RIDD) and identify endostatin and matrix metalloproteinase 9 as putative RIDD targets. Thus, AuNPs could potentially be utilized as a tool to effectively interrogate bidirectional communications in the tumor microenvironment, reprogram it, and inhibit tumor growth by its therapeutic function.

Genomic Analysis of Kidney Allograft Injury Identifies Hematopoietic Cell Kinase as a Key Driver of Renal Fibrosis

Renal fibrosis is the common pathway of progression for patients with CKD and chronic renal allograft injury (CAI), but the underlying mechanisms remain obscure. We performed a meta-analysis in human kidney biopsy specimens with CAI, incorporating data available publicly and from our Genomics of Chronic Renal Allograft Rejection study. We identified an Src family tyrosine kinase, hematopoietic cell kinase (Hck), as upregulated in allografts in CAI. Querying the Kinase Inhibitor Resource database revealed that dasatinib, a Food and Drug Administration-approved drug, potently binds Hck with high selectivity. In vitro, Hck overexpression activated the TGF-β/Smad3 pathway, whereas HCK knockdown inhibited it. Treatment of tubular cells with dasatinib reduced the expression of Col1a1 Dasatinib also reduced proliferation and α-SMA expression in fibroblasts. In a murine model with unilateral ureteric obstruction, pretreatment with dasatinib significantly reduced the upregulation of profibrotic markers, phosphorylation of Smad3, and renal fibrosis observed in kidneys pretreated with vehicle alone. Dasatinib treatment also improved renal function, reduced albuminuria, and inhibited expression of profibrotic markers in animal models with lupus nephritis and folic acid nephropathy. These data suggest that Hck is a key mediator of renal fibrosis and dasatinib could be developed as an antifibrotic drug.

Gene expression elucidates functional impact of polygenic risk for schizophrenia

Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.

Transcriptional Profiling of Egg Allergy and Relationship to Disease Phenotype

Background

Egg allergy is one of the most common food allergies of childhood. There is a lack of information on the immunologic basis of egg allergy beyond the role of IgE.

Objective

To use transcriptional profiling as a novel approach to uncover immunologic processes associated with different phenotypes of egg allergy.

Methods

Peripheral blood mononuclear cells (PBMCs) were obtained from egg-allergic children who were defined as reactive (BER) or tolerant (BET) to baked egg, and from food allergic controls (AC) who were egg non-allergic. PBMCs were stimulated with egg white protein. Gene transcription was measured by microarray after 24 h, and cytokine secretion by multiplex assay after 5 days.

Results

The transcriptional response of PBMCs to egg protein differed between BER and BET versus AC subjects. Compared to the AC group, the BER group displayed increased expression of genes associated with allergic inflammation as well as corresponding increased secretion of IL-5, IL-9 and TNF-α. A similar pattern was observed for the BET group. Further similarities in gene expression patterns between BER and BET groups, as well as some important differences, were revealed using a novel Immune Annotation resource developed for this project. This approach identified several novel processes not previously associated with egg allergy, including positive associations with TLR4-stimulated myeloid cells and activated NK cells, and negative associations with an induced Treg signature. Further pathway analysis of differentially expressed genes comparing BER to BET subjects showed significant enrichment of IFN-α and IFN-γ response genes, as well as genes associated with virally-infected DCs.

Conclusions

Transcriptional profiling identified several novel pathways and processes that differed when comparing the response to egg allergen in BET, BER, and AC groups. We conclude that this approach is a useful hypothesis-generating mechanism to identify novel immune processes associated with allergy and tolerance to forms of egg.

Influence of age on androgen deprivation therapy-associated Alzheimer's disease

We recently found an association between androgen deprivation therapy (ADT) and Alzheimer’s disease. As Alzheimer’s disease is a disease of advanced age, we hypothesize that older individuals on ADT may be at greatest risk. We conducted a retrospective multi-institutional analysis among 16,888 individuals with prostate cancer using an informatics approach. We tested the effect of ADT on Alzheimer’s disease using Kaplan–Meier age stratified analyses in a propensity score matched cohort. We found a lower cumulative probability of remaining Alzheimer’s disease-free between non-ADT users age ≥70 versus those age <70 years (p < 0.001) and between ADT versus non-ADT users ≥70 years (p = 0.034). The 5-year probability of developing Alzheimer’s disease was 2.9%, 1.9% and 0.5% among ADT users ≥70, non-ADT users ≥70 and individuals <70 years, respectively. Compared to younger individuals older men on ADT may have the greatest absolute Alzheimer’s disease risk. Future work should investigate the ADT Alzheimer’s disease association in advanced age populations given the greater potential clinical impact.

Identification of type 2 diabetes subgroups through topological analysis of patient similarity

Type 2 diabetes (T2D) is a heterogeneous complex disease affecting more than 29 million Americans alone with a rising prevalence trending toward steady increases in the coming decades. Thus, there is a pressing clinical need to improve early prevention and clinical management of T2D and its complications. Clinicians have understood that patients who carry the T2D diagnosis have a variety of phenotypes and susceptibilities to diabetes-related complications. We used a precision medicine approach to characterize the complexity of T2D patient populations based on high-dimensional electronic medical records (EMRs) and genotype data from 11,210 individuals. We successfully identified three distinct subgroups of T2D from topology-based patient-patient networks. Subtype 1 was characterized by T2D complications diabetic nephropathy and diabetic retinopathy; subtype 2 was enriched for cancer malignancy and cardiovascular diseases; and subtype 3 was associated most strongly with cardiovascular diseases, neurological diseases, allergies, and HIV infections. We performed a genetic association analysis of the emergent T2D subtypes to identify subtype-specific genetic markers and identified 1279, 1227, and 1338 single-nucleotide polymorphisms (SNPs) that mapped to 425, 322, and 437 unique genes specific to subtypes 1, 2, and 3, respectively. By assessing the human disease-SNP association for each subtype, the enriched phenotypes and biological functions at the gene level for each subtype matched with the disease comorbidities and clinical differences that we identified through EMRs. Our approach demonstrates the utility of applying the precision medicine paradigm in T2D and the promise of extending the approach to the study of other complex, multifactorial diseases.