Nanotechnology and microfluidics:formulation design and on-chip manufacture of nanoparticles

Nanoparticles offer an ideal platform for the delivery of small molecule drugs, subunit vaccines and genetic constructs. Besides the necessity of a homogenous size distribution, defined loading efficiencies and reasonable production and development costs, one of the major bottlenecks in translating nanoparticles into clinical application is the need for rapid, robust and reproducible development techniques. Within this thesis, microfluidic methods were investigated for the manufacturing, drug or protein loading and purification of pharmaceutically relevant nanoparticles. Initially, methods to prepare small liposomes were evaluated and compared to a microfluidics-directed nanoprecipitation method. To support the implementation of statistical process control, design of experiment models aided the process robustness and validation for the methods investigated and gave an initial overview of the size ranges obtainable in each method whilst evaluating advantages and disadvantages of each method. The lab-on-a-chip system resulted in a high-throughput vesicle manufacturing, enabling a rapid process and a high degree of process control. To further investigate this method, cationic low transition temperature lipids, cationic bola-amphiphiles with delocalized charge centers, neutral lipids and polymers were used in the microfluidics-directed nanoprecipitation method to formulate vesicles. Whereas the total flow rate (TFR) and the ratio of solvent to aqueous stream (flow rate ratio, FRR) was shown to be influential for controlling the vesicle size in high transition temperature lipids, the factor FRR was found the most influential factor controlling the size of vesicles consisting of low transition temperature lipids and polymer-based nanoparticles. The biological activity of the resulting constructs was confirmed by an invitro transfection of pDNA constructs using cationic nanoprecipitated vesicles. Design of experiments and multivariate data analysis revealed the mathematical relationship and significance of the factors TFR and FRR in the microfluidics process to the liposome size, polydispersity and transfection efficiency. Multivariate tools were used to cluster and predict specific in-vivo immune responses dependent on key liposome adjuvant characteristics upon delivery a tuberculosis antigen in a vaccine candidate. The addition of a low solubility model drug (propofol) in the nanoprecipitation method resulted in a significantly higher solubilisation of the drug within the liposomal bilayer, compared to the control method. The microfluidics method underwent scale-up work by increasing the channel diameter and parallelisation of the mixers in a planar way, resulting in an overall 40-fold increase in throughput. Furthermore, microfluidic tools were developed based on a microfluidics-directed tangential flow filtration, which allowed for a continuous manufacturing, purification and concentration of liposomal drug products.

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

Bioinformatic analysis for exploring relationships between genes and gene products

To carry out their specific roles in the cell, genes and gene products often work together in groups, forming many relationships among themselves and with other molecules. Such relationships include physical protein-protein interaction relationships, regulatory relationships, metabolic relationships, genetic relationships, and much more. With advances in science and technology, some high throughput technologies have been developed to simultaneously detect tens of thousands of pairwise protein-protein interactions and protein-DNA interactions. However, the data generated by high throughput methods are prone to noise. Furthermore, the technology itself has its limitations, and cannot detect all kinds of relationships between genes and their products. Thus there is a pressing need to investigate all kinds of relationships and their roles in a living system using bioinformatic approaches, and is a central challenge in Computational Biology and Systems Biology. This dissertation focuses on exploring relationships between genes and gene products using bioinformatic approaches. Specifically, we consider problems related to regulatory relationships, protein-protein interactions, and semantic relationships between genes. A regulatory element is an important pattern or "signal", often located in the promoter of a gene, which is used in the process of turning a gene "on" or "off". Predicting regulatory elements is a key step in exploring the regulatory relationships between genes and gene products. In this dissertation, we consider the problem of improving the prediction of regulatory elements by using comparative genomics data. With regard to protein-protein interactions, we have developed bioinformatics techniques to estimate support for the data on these interactions. While protein-protein interactions and regulatory relationships can be detected by high throughput biological techniques, there is another type of relationship called semantic relationship that cannot be detected by a single technique, but can be inferred using multiple sources of biological data. The contributions of this thesis involved the development and application of a set of bioinformatic approaches that address the challenges mentioned above. These included (i) an EM-based algorithm that improves the prediction of regulatory elements using comparative genomics data, (ii) an approach for estimating the support of protein-protein interaction data, with application to functional annotation of genes, (iii) a novel method for inferring functional network of genes, and (iv) techniques for clustering genes using multi-source data.

Identification of Anziaic Acid, a Lichen Depside from Hypotrachyna sp., as a New Topoisomerase Poison Inhibitor

Topoisomerase inhibitors are effective for antibacterial and anticancer therapy because they can lead to the accumulation of the intermediate DNA cleavage complex formed by the topoisomerase enzymes, which trigger cell death. Here we report the application of a novel enzyme-based high-throughput screening assay to identify natural product extracts that can lead to increased accumulation of the DNA cleavage complex formed by recombinant Yersinia pestistopoisomerase I as part of a larger effort to identify new antibacterial compounds. Further characterization and fractionation of the screening positives from the primary assay led to the discovery of a depside, anziaic acid, from the lichen Hypotrachyna sp. as an inhibitor for both Y. pestis and Escherichia colitopoisomerase I. In in vitro assays, anziaic acid exhibits antibacterial activity against Bacillus subtilis and a membrane permeable strain of E. coli. Anziaic acid was also found to act as an inhibitor of human topoisomerase II but had little effect on human topoisomerase I. This is the first report of a depside with activity as a topoisomerase poison inhibitor and demonstrates the potential of this class of natural products as a source for new antibacterial and anticancer compounds.

Development of a Lab-on-a-Chip Device for Rapid Nanotoxicity Assessment In Vitro

Increasing useof nanomaterials in consumer products and biomedical applications creates the possibilities of intentional/unintentional exposure to humans and the environment. Beyond the physiological limit, the nanomaterialexposure to humans can induce toxicity. It is difficult to define toxicity of nanoparticles on humans as it varies by nanomaterialcomposition, size, surface properties and the target organ/cell line. Traditional tests for nanomaterialtoxicity assessment are mostly based on bulk-colorimetric assays. In many studies, nanomaterials have found to interfere with assay-dye to produce false results and usually require several hours or days to collect results. Therefore, there is a clear need for alternative tools that can provide accurate, rapid, and sensitive measure of initial nanomaterialscreening. Recent advancement in single cell studies has suggested discovering cell properties not found earlier in traditional bulk assays. A complex phenomenon, like nanotoxicity, may become clearer when studied at the single cell level, including with small colonies of cells. Advances in lab-on-a-chip techniques have played a significant role in drug discoveries and biosensor applications, however, rarely explored for nanomaterialtoxicity assessment. We presented such cell-integrated chip-based approach that provided quantitative and rapid response of cellhealth, through electrochemical measurements. Moreover, the novel design of the device presented in this study was capable of capturing and analyzing the cells at a single cell and small cell-population level. We examined the change in exocytosis (i.e. neurotransmitterrelease) properties of a single PC12 cell, when exposed to CuOand TiO2 nanoparticles. We found both nanomaterials to interfere with the cell exocytosis function. We also studied the whole-cell response of a single-cell and a small cell-population simultaneously in real-time for the first time. The presented study can be a reference to the future research in the direction of nanotoxicity assessment to develop miniature, simple, and cost-effective tool for fast, quantitative measurements at high throughput level. The designed lab-on-a-chip device and measurement techniques utilized in the present work can be applied for the assessment of othernanoparticles' toxicity, as well.

Dissecting the Functional Impacts of Non-Coding Genetic Variation

A large proportion of the variation in traits between individuals can be attributed to variation in the nucleotide sequence of the genome. The most commonly studied traits in human genetics are related to disease and disease susceptibility. Although scientists have identified genetic causes for over 4,000 monogenic diseases, the underlying mechanisms of many highly prevalent multifactorial inheritance disorders such as diabetes, obesity, and cardiovascular disease remain largely unknown. Identifying genetic mechanisms for complex traits has been challenging because most of the variants are located outside of protein-coding regions, and determining the effects of such non-coding variants remains difficult. In this dissertation, I evaluate the hypothesis that such non-coding variants contribute to human traits and diseases by altering the regulation of genes rather than the sequence of those genes. I will specifically focus on studies to determine the functional impacts of genetic variation associated with two related complex traits: gestational hyperglycemia and fetal adiposity. At the genomic locus associated with maternal hyperglycemia, we found that genetic variation in regulatory elements altered the expression of the HKDC1 gene. Furthermore, we demonstrated that HKDC1 phosphorylates glucose in vitro and in vivo, thus demonstrating that HKDC1 is a fifth human hexokinase gene. At the fetal-adiposity associated locus, we identified variants that likely alter VEPH1 expression in preadipocytes during differentiation. To make such studies of regulatory variation high-throughput and routine, we developed POP-STARR, a novel high throughput reporter assay that can empirically measure the effects of regulatory variants directly from patient DNA. By combining targeted genome capture technologies with STARR-seq, we assayed thousands of haplotypes from 760 individuals in a single experiment. We subsequently used POP-STARR to identify three key features of regulatory variants: that regulatory variants typically have weak effects on gene expression; that the effects of regulatory variants are often coordinated with respect to disease-risk, suggesting a general mechanism by which the weak effects can together have phenotypic impact; and that nucleotide transversions have larger impacts on enhancer activity than transitions. Together, the findings presented here demonstrate successful strategies for determining the regulatory mechanisms underlying genetic associations with human traits and diseases, and value of doing so for driving novel biological discovery.

New Advancements of Scalable Statistical Methods for Learning Latent Structures in Big Data

Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.

Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.

Cell Fate Specification and the Regulation of RNA-dependent DNA Methylation in the Arabidopsis Root Meristem

The Arabidopsis root apical meristem (RAM) is a complex tissue capable of generating all the cell types that ultimately make up the root. The work presented in this thesis takes advantage of the versatility of high-throughput sequencing to address two independent questions about the root meristem. Although a lot of information is known regarding the cell fate decisions that occur at the RAM, cortex specification and differentiation remain poorly understood. In the first part of this thesis, I used an ethylmethanesulfonate (EMS) mutagenized marker line to perform a forward genetics screen. The goal of this screen was to identify novel genes involved in the specification and differentiation of the cortex tissue. Mapping analysis from the results obtained in this screen revealed a new allele of BRASSINOSTEROID4 with abnormal marker expression in the cortex tissue. Although this allele proved to be non-cortex specific, this project highlights new technology that allows mapping of EMS-generated mutations without the need to map-cross or back-cross. In the second part of this thesis, using fluorescence activated cell sorting (FACS) coupled with high throughput sequencing, my collaborators and I generated single-base resolution whole genome DNA methylomes, mRNA transcriptomes, and smallRNA transcriptomes for six different populations of cell types in the Arabidopsis root meristem. We were able to discover that the columella is hypermethylated in the CHH context within transposable elements. This hypermethylation is accompanied by upregulation of the RNA-dependent DNA methylation pathway (RdDM), including higher levels of 24-nt silencing RNAs (siRNAs). In summary, our studies demonstrate the versatility of high-throughput sequencing as a method for identifying single mutations or to perform complex comparative genomic analyses.

Identifying the Structure and Fate of Wastewater Derived Organic Micropollutants by High-resolution Mass Spectrometry

Human activities represent a significant burden on the global water cycle, with large and increasing demands placed on limited water resources by manufacturing, energy production and domestic water use. In addition to changing the quantity of available water resources, human activities lead to changes in water quality by introducing a large and often poorly-characterized array of chemical pollutants, which may negatively impact biodiversity in aquatic ecosystems, leading to impairment of valuable ecosystem functions and services. Domestic and industrial wastewaters represent a significant source of pollution to the aquatic environment due to inadequate or incomplete removal of chemicals introduced into waters by human activities. Currently, incomplete chemical characterization of treated wastewaters limits comprehensive risk assessment of this ubiquitous impact to water. In particular, a significant fraction of the organic chemical composition of treated industrial and domestic wastewaters remains uncharacterized at the molecular level. Efforts aimed at reducing the impacts of water pollution on aquatic ecosystems critically require knowledge of the composition of wastewaters to develop interventions capable of protecting our precious natural water resources.

The goal of this dissertation was to develop a robust, extensible and high-throughput framework for the comprehensive characterization of organic micropollutants in wastewaters by high-resolution accurate-mass mass spectrometry. High-resolution mass spectrometry provides the most powerful analytical technique available for assessing the occurrence and fate of organic pollutants in the water cycle. However, significant limitations in data processing, analysis and interpretation have limited this technique in achieving comprehensive characterization of organic pollutants occurring in natural and built environments. My work aimed to address these challenges by development of automated workflows for the structural characterization of organic pollutants in wastewater and wastewater impacted environments by high-resolution mass spectrometry, and to apply these methods in combination with novel data handling routines to conduct detailed fate studies of wastewater-derived organic micropollutants in the aquatic environment.

In Chapter 2, chemoinformatic tools were implemented along with novel non-targeted mass spectrometric analytical methods to characterize, map, and explore an environmentally-relevant “chemical space” in municipal wastewater. This was accomplished by characterizing the molecular composition of known wastewater-derived organic pollutants and substances that are prioritized as potential wastewater contaminants, using these databases to evaluate the pollutant-likeness of structures postulated for unknown organic compounds that I detected in wastewater extracts using high-resolution mass spectrometry approaches. Results showed that application of multiple computational mass spectrometric tools to structural elucidation of unknown organic pollutants arising in wastewaters improved the efficiency and veracity of screening approaches based on high-resolution mass spectrometry. Furthermore, structural similarity searching was essential for prioritizing substances sharing structural features with known organic pollutants or industrial and consumer chemicals that could enter the environment through use or disposal.

I then applied this comprehensive methodological and computational non-targeted analysis workflow to micropollutant fate analysis in domestic wastewaters (Chapter 3), surface waters impacted by water reuse activities (Chapter 4) and effluents of wastewater treatment facilities receiving wastewater from oil and gas extraction activities (Chapter 5). In Chapter 3, I showed that application of chemometric tools aided in the prioritization of non-targeted compounds arising at various stages of conventional wastewater treatment by partitioning high dimensional data into rational chemical categories based on knowledge of organic chemical fate processes, resulting in the classification of organic micropollutants based on their occurrence and/or removal during treatment. Similarly, in Chapter 4, high-resolution sampling and broad-spectrum targeted and non-targeted chemical analysis were applied to assess the occurrence and fate of organic micropollutants in a water reuse application, wherein reclaimed wastewater was applied for irrigation of turf grass. Results showed that organic micropollutant composition of surface waters receiving runoff from wastewater irrigated areas appeared to be minimally impacted by wastewater-derived organic micropollutants. Finally, Chapter 5 presents results of the comprehensive organic chemical composition of oil and gas wastewaters treated for surface water discharge. Concurrent analysis of effluent samples by complementary, broad-spectrum analytical techniques, revealed that low-levels of hydrophobic organic contaminants, but elevated concentrations of polymeric surfactants, which may effect the fate and analysis of contaminants of concern in oil and gas wastewaters.

Taken together, my work represents significant progress in the characterization of polar organic chemical pollutants associated with wastewater-impacted environments by high-resolution mass spectrometry. Application of these comprehensive methods to examine micropollutant fate processes in wastewater treatment systems, water reuse environments, and water applications in oil/gas exploration yielded new insights into the factors that influence transport, transformation, and persistence of organic micropollutants in these systems across an unprecedented breadth of chemical space.

The application of next generation sequencing to profile microbe related cheese quality defects

High throughput next generation sequencing, together with advanced molecular methods, has considerably enhanced the field of food microbiology. By overcoming biases associated with culture dependant approaches, it has become possible to achieve novel insights into the nature of food-borne microbial communities. In this thesis, several different sequencing-based approaches were applied with a view to better understanding microbe associated quality defects in cheese. Initially, a literature review provides an overview of microbe-associated cheese quality defects as well as molecular methods for profiling complex microbial communities. Following this, 16S rRNA sequencing revealed temporal and spatial differences in microbial composition due to the time during the production day that specific commercial cheeses were manufactured. A novel Ion PGM sequencing approach, focusing on decarboxylase genes rather than 16S rRNA genes, was then successfully employed to profile the biogenic amine producing cohort of a series of artisanal cheeses. Investigations into the phenomenon of cheese pinking formed the basis of a joint 16S rRNA and whole genome shotgun sequencing approach, leading to the identification of Thermus species and, more specifically, the pathway involved in production of lycopene, a red coloured carotenoid. Finally, using a more traditional approach, the effect of addition of a facultatively heterofermentative Lactobacillus (Lactobacillus casei) to a Swiss-type cheese, in which starter activity was compromised, was investigated from the perspective of its ability to promote gas defects and irregular eye formation. X-ray computed tomography was used to visualise, using a non-destructive method, the consequences of the undesirable gas formation that resulted. Ultimately this thesis has demonstrated that the application of molecular techniques, such as next generation sequencing, can provide a detailed insight into defect-causing microbial populations present and thereby may underpin approaches to optimise the quality and consistency of a wide variety of cheeses.

Mapping Protein-DNA Interactions Using ChIP-exo and Illumina-Based Sequencing

Chromatin immunoprecipitation (ChIP) provides a means of enriching DNA associated with transcription factors, histone modifications, and indeed any other proteins for which suitably characterized antibodies are available. Over the years, sequence detection has progressed from quantitative real-time PCR and Southern blotting to microarrays (ChIP-chip) and now high-throughput sequencing (ChIP-seq). This progression has vastly increased the sequence coverage and data volumes generated. This in turn has enabled informaticians to predict the identity of multi-protein complexes on DNA based on the overrepresentation of sequence motifs in DNA enriched by ChIP with a single antibody against a single protein. In the course of the development of high-throughput sequencing, little has changed in the ChIP methodology until recently. In the last three years, a number of modifications have been made to the ChIP protocol with the goal of enhancing the sensitivity of the method and further reducing the levels of nonspecific background sequences in ChIPped samples. In this chapter, we provide a brief commentary on these methodological changes and describe a detailed ChIP-exo method able to generate narrower peaks and greater peak coverage from ChIPped material.

Multi-purpose utility of circulating plasma DNA testing in patients with advanced cancers.

Tumor genomic instability and selective treatment pressures result in clonal disease evolution; molecular stratification for molecularly targeted drug administration requires repeated access to tumor DNA. We hypothesized that circulating plasma DNA (cpDNA) in advanced cancer patients is largely derived from tumor, has prognostic utility, and can be utilized for multiplex tumor mutation sequencing when repeat biopsy is not feasible. We utilized the Sequenom MassArray System and OncoCarta panel for somatic mutation profiling. Matched samples, acquired from the same patient but at different time points were evaluated; these comprised formalin-fixed paraffin-embedded (FFPE) archival tumor tissue (primary and/or metastatic) and cpDNA. The feasibility, sensitivity, and specificity of this high-throughput, multiplex mutation detection approach was tested utilizing specimens acquired from 105 patients with solid tumors referred for participation in Phase I trials of molecularly targeted drugs. The median cpDNA concentration was 17 ng/ml (range: 0.5-1600); this was 3-fold higher than in healthy volunteers. Moreover, higher cpDNA concentrations associated with worse overall survival; there was an overall survival (OS) hazard ratio of 2.4 (95% CI 1.4, 4.2) for each 10-fold increase in cpDNA concentration and in multivariate analyses, cpDNA concentration, albumin, and performance status remained independent predictors of OS. These data suggest that plasma DNA in these cancer patients is largely derived from tumor. We also observed high detection concordance for critical 'hot-spot' mutations (KRAS, BRAF, PIK3CA) in matched cpDNA and archival tumor tissue, and important differences between archival tumor and cpDNA. This multiplex sequencing assay can be utilized to detect somatic mutations from plasma in advanced cancer patients, when safe repeat tumor biopsy is not feasible and genomic analysis of archival tumor is deemed insufficient. Overall, circulating nucleic acid biomarker studies have clinically important multi-purpose utility in advanced cancer patients and further studies to pursue their incorporation into the standard of care are warranted.

Flexible strategies for association analysis with genomic phenotypes

Thesis (Ph.D.)--University of Washington, 2016-08

Genometa - A Fast and Accurate Classifier for Short Metagenomic Shotgun Reads

Metagenomic studies use high-throughput sequence data to investigate microbial communities in situ. However, considerable challenges remain in the analysis of these data, particularly with regard to speed and reliable analysis of microbial species as opposed to higher level taxa such as phyla. We here present Genometa, a computationally undemanding graphical user interface program that enables identification of bacterial species and gene content from datasets generated by inexpensive high-throughput short read sequencing technologies. Our approach was first verified on two simulated metagenomic short read datasets, detecting 100% and 94% of the bacterial species included with few false positives or false negatives. Subsequent comparative benchmarking analysis against three popular metagenomic algorithms on an Illumina human gut dataset revealed Genometa to attribute the most reads to bacteria at species level (i.e. including all strains of that species) and demonstrate similar or better accuracy than the other programs. Lastly, speed was demonstrated to be many times that of BLAST due to the use of modern short read aligners. Our method is highly accurate if bacteria in the sample are represented by genomes in the reference sequence but cannot find species absent from the reference. This method is one of the most user-friendly and resource efficient approaches and is thus feasible for rapidly analysing millions of short reads on a personal computer.

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.