Browsing by Subject "Microarray"

Vocal learning is the ability modify vocal output based on auditory input and is the basis of human speech acquisition. It is shared by few distantly related bird and mammal orders, and is thus very likely to be an example of convergent evolution, having evolved independently in multiple lineages. This complex behavior is presumed to require networks of regulated genes to develop the necessary neural circuits for learning and maintaining vocalizations. Deciphering these networks has been limited by the lack of high throughput genomic tools in vocal learning avian species and the lack of a solid computational framework to understand the relationship between gene expression and behavior. This dissertation provides new insights into the evolution and mechanisms of vocal learning by taking a top-down, systems biology approach to understanding gene expression regulation across avian and mammalian species. First, I worked with colleagues to develop a zebra finch Agilent oligonucleotide microarray, including developing programs for more accurate annotation of oligonucleotides and genes. I then used these arrays and tools in multiple collaborative, but related projects, to measure transcriptome expression data in vocal learning and non-learning avian species, under a number of behavioral paradigms, with a focus on song production. To make sense of the avian microarray data, I compiled microarray data from other sources, including expression analyses across over 900 human brain regions generated by Allen Brain Institute. To compare these data sets, I developed and performed a variety of computational analyses including clustering, linear models, gene set enrichment analysis, motif discovery, and phylogenetic inference, providing a novel framework to study the gene regulatory networks associated with a complex behavior. Using the developed framework, we are able to better understand vocal learning at different levels: how the brain regions for vocal learning evolved and how those brain regions function during the production of learned vocalizations. At the evolutionary level, we identified genes with unique expression patterns in the brains of vocal learning birds and humans. Interesting candidates include genes related to formation of neural connections, in particular the SLIT/ROBO axon guidance pathway. This algorithm also allowed us to identify the analogous regions that are a part of vocal learning circuit across species, providing the first quantitative evidence relating the human vocal learning circuit to the avian vocal learning circuit. With the avian song system verified as a model for human speech at the molecular level, we conducted an experiment to better understand what is happening in those brain regions during singing by profiling gene expression in a time course as birds are producing song. Surprisingly, an overwhelming majority of the gene expression identified was strongly enriched in a particular region. We also found a tight coupling between the behavioral function of a particular region and the gene expression pattern. To gain insight into the mechanisms of this gene regulation, we conducted a genomic scan of transcription factor binding sites in zebra finch. Many transcription factor binding sites were enriched in the promoters of genes with a particular temporal patterns, several of which had already been hypothesized to play a role in the neural system. Using this data set of gene expression profiles and transcription factor binding sites along with separate experiments conducted in mouse, we were able uncover evidence that the transcription factor CARF plays a role in neuron homeostasis. These results have broadened our understanding of the molecular basis of vocal learning at multiple levels. Overall, this dissertation outlines a novel way of approaching the study of the relationship between genes and behavior.

Alternative mRNA processing can result in the generation of multiple, qualitatively different RNA transcripts from the same gene and is a powerful engine of complexity in higher organisms. Recent deep sequencing studies have indicated that essentially all human genes containing more than a single exon generate multiple RNA transcripts. Functional roles of alternative processing have been established in virtually all areas of biological regulation, particularly in development and cancer. Changes in alternative mRNA processing can now be detected from over a billion dollars' worth of conventional gene expression microarray data archived over the past 20 years using a program we created called SplicerAV. Application of SplicerAV to publicly available microarray data has granted new insights into previously existing studies of oncogene over-expression and clinical cancer prognosis.

Adaptation of SplicerAV to the new Affymetrix Human Exon arrays has resulted in the creation of SplicerEX, the first program that can automatically categorize microarray detected changes in alternative processing into biologically pertinent categories. We use SplicerEX's automatic event categorization to identify changes in global mRNA processing during B cell transformation and show that the conventional U133 platform is able to detect 3' located changes in mRNA processing five times more frequently than the Human Exon array.

The translational potential of diagnostic and prognostic platforms developed using expression microarray technology is evident. However, the majority of array-based diagnostics have yet to make their way into the clinical laboratory. Current approaches tend to focus on development of multi-gene classifiers of disease subtypes, but very few studies evaluate the translational potential of these assays. Likewise, only a handful of studies focus on development of approaches to optimize array-based tests for the ultimate goal of clinical utility. Prior to translation into the clinical setting, molecular diagnostic platforms should demonstrate a number of characteristics to ensure optimal and efficient testing and patient care. Assays should be accurate and precise, technically and biologically robust, and should take into account normal sources of biological variance that could ultimately affect test results. The overarching goal of the research presented in this dissertation is to develop methods for evaluating and optimizing the translational potential of molecular diagnostics developed using expression microarray technology.

The first research section of this dissertation is focused on our evaluation of the impact of intratumor heterogeneity on precision in microarray-based assays in breast cancer. We conducted genome-wide expression profiling on 50 needle core biopsies from 18 breast cancer patients. Global profiles of expression were characterized using unsupervised clustering methods and variance components models. Array-based measures of estrogen (ER) and progesterone receptor (PR) status were compared to immunohistochemistry. The precision of genomic predictors of ER pathway status, recurrence risk, and sensitivity to chemotherapeutics were evaluated by interclass correlation. Results demonstrated that intratumor variation was substantially less than the total variation observed across the patient population. Nevertheless, a fraction of genes exhibited significant intratumor heterogeneity in expression. A high degree of reproducibility was observed in single gene predictors of ER (intraclass correlation coefficient (ICC)=0.94) and PR expression (ICC=0.90), and in a multi-gene predictor of ER pathway activation (ICC=0.98) with high concordance with immunohistochemistry. Substantial agreement was also observed for multi-gene signatures of cancer recurrence (ICC=0.71), and chemotherapeutic sensitivity (ICC=0.72 and 0.64). Together, these results demonstrated that intratumor heterogeneity, although present at the level of individual gene expression, does not preclude precise micro-array based predictions of tumor behavior or clinical outcome in breast cancer patients.

Leading into the second research section, we observed that in some cancer types, certain genes behave as molecular switches and have either an "on" or "off" expression state. Specifically, we observed these molecular switch genes exist in breast cancer as robust diagnostic and prognostic markers, including ER, PR, and HER2, and define tumor subtypes associated with different treatment and patient survival. We hypothesized that clinically relevant molecular switch (bimodal) genes exist in epithelial ovarian cancer, a type of cancer with no established molecular subgroups. To test this hypothesis, we applied a bimodal discovery algorithm to a publically available ovarian cancer expression microarray dataset (GSE9891:285 tumors; 246 malignant serous (MS), 20 endometrioid (EM), 18 low malignant potential (LMP) ovarian carcinomas). Genes with robust bimodal expression were identified across all ovarian tumor types and within selected subtypes. Of these bimodal genes, 73 demonstrated differential expression between LMP vs. MS and EM, and 22 genes distinguished MS from EM. Fourteen bimodal genes had significant association with survival among MS tumors. When these genes were combined into a single survival score, the median survival for patients with a favorable versus unfavorable score was 65 versus 29 months (p<0.0001, HR=0.4221). Two independent datasets (n=53 high grade, advanced stage serous and n=119 advanced stage ovarian tumors) validated the survival score performance. Taken together, the results of this study revealed that genes with bimodal expression patterns not only define clinically relevant molecular subtypes of ovarian carcinoma, but also provide ideal targets for translation into the clinical laboratory.

Finally, the third research section of this dissertation focuses on development of robust blood-based molecular markers of decompression stress (DS). DS is defined as the pathophysiological response to inert gas coming out of solution in the blood and tissues when a body experiences a reduction in ambient pressure. To date, there are no established molecular markers of DS. We hypothesized that comparing gene expression before and after human decompression exposures by genome-wide expression profiling would identify candidate molecular markers of DS. Peripheral blood was collected 1hr before and 2hr after 93 hyperoxic, heliox experimental dives (n=54). Control arms included samples collected 1 hour before and 2 hours after high pressure oxygen breathing (n= 9) and surface exercise (n=9), and samples collected at 7am and 5pm for time of day (n=11). Pre and post-dive expression data collected from normoxic nitrox experimental dives were utilized for independent validation. Blood samples were collected into PaxGene RNA tubes. RNA was extracted and processed for globin reduction prior to cDNA synthesis and Affymetrix U133A GeneChip hybridization. 746 genes were differentially expressed following hyperoxic, heliox decompression exposures (permutation adjusted p-value cutoff 1.0E-4). After filtering control significant genes, 726 genes remained. Pathway analysis demonstrated a significant portion of genes were associated with innate immune response (p<0.0001). A 362 multi-gene signature of significant, covariant genes was then applied to the independent dataset and demonstrated differentiation between pre and post-dive samples (p=0.0058). There was no significant correlation between signature and venous bubble grade or bottom time in the validation study. Our results showed that expression profiling of peripheral blood following decompression exposures, while controlling for experimental and normal sources of biological variance, identifies a reproducible multi-gene signature of differentially expressed genes, primarily comprising genes associated with innate immune response and independent of venous bubble grade or dive profile.

Taken together, the research and results presented in this dissertation represent considerable advances in the development of approaches to guide microarray-based diagnostics towards the ultimate goal of clinical translation.

hCB-ECs show varying morphology under co-culture conditions. They are known to form networks when co-cultured on matrices like Matrigel, collagen gels and SMCs. Optimizing the co-culture model for the formation of networks can enable better understanding of angiogenesis and can aid in the area of tissue engineered organs while creating a model with no networks can help in producing a smooth layer of cells for tissue engineered blood vessels. Additionally, the study of networks on smooth muscle cell surfaces gives a better approach to understanding the in vivo phenomena.

The main goal of this study was to identify conditions that would support the formation of networks and to study the gene level alterations in the hCB-EC cells between co-culture and monoculture during the formation and absence of networks. To study the morphological changes co-cultures were setup by varying the hCB-EC densities at 26,316 cells/cm2, 52,632 cells/cm2, 80,000 cells/cm2 and 105,263 cells/cm2 on SMCs. Lower seeding densities of hCB-ECs led to network formations while a confluent layer was observed at the highest density. Medium components were altered to identify factors which contributed to network formation and it was found that absence of VEGF led to delayed cell migration and network formation while absence of heparin produced sparser networks in co-culture. Microarray analysis using four different hCB-EC sources plated at the highest and lowest densities resulted in higher expression of ECM remodeling and endothelial cell migration genes consistent to the low density conditions were networks were abundant while high density conditions expressed downregulation of cell cycle associated genes.

My thesis research has centered on the concept of the peripheral blood cell (PBC) as an indicator of disease and environmental exposure. The PBC is not only easily accessible and constantly replenished, but it provides a snapshot of an individual's health. Doctors have long utilized PBCs as indicators of health based on count, morphology or the expression of particular cell surface markers. Using these methods, PBCs can serve as indicators of infection, inflammation or certain types of hematological malignancies. Now PBCs can be characterized as a function of their gene expression profiles in response to disease and toxicant exposure. Advances in cDNA microarray technology have made it possible to analyze global gene expression in small volumes of whole blood, or even in a sorted population of blood cells. The resulting gene expression data can serve as a molecular phenotype, or signature, of disease or toxicant exposure. These signatures serve a twofold purpose. First, they act as biological markers (biomarkers) that can indicate the presence of disease or aid monitoring the response to treatment. Second, they provide insight into the underlying biological mechanisms that are at work, by revealing genes, networks and pathways that are affected by the disease or toxin. This paradigm has been applied in a number of contexts, including infection, inflammation, leukemia, lymphoma, neurological disorders, cardiovascular disease, environmental exposures and solid tumors.

In the work presented here, we describe signatures of lead (Pb) exposure and breast cancer based on peripheral blood gene expression. Our objective in generating a blood-based signature of lead exposure was to develop a potential predictor of past and present exposure. This is particularly relevant because of continued widespread lead exposure through both environmental and occupational sources. Pb causes significant toxicities in a number of different organ systems including the hematological, endocrine, neurological and renal systems. Pb is considered a potential carcinogen due to evidence that it causes cancer in animal models and contributes to an elevated cancer risk in humans. Pb is thought to contribute to cancer risk indirectly through a variety of mechanisms, such as inhibition of DNA synthesis and repair, oxidative damage, interaction with DNA-binding proteins and tumor suppressor proteins, causing chromosomal aberrations and alterations to gene transcription. In addition, it has been shown to exacerbate the effects of other mutagens. Recent work also indicates that even low-level Pb exposure (defined here as levels below the threshold of detection of many common tests or below the level set by the CDC as an "elevated blood lead level" in children, or 10µg/dL) can impact health, especially in children, who are more susceptible to these negative health consequences.

We hypothesized that we could detect subtle and lasting changes in the PBC transcriptome that correlated to Pb exposure. We used a mouse model of per os Pb exposure to generate signatures corresponding to two different doses of Pb. One dose reflected a high-level exposure and the other a low-level exposure. We also analyzed the gene expression changes following removal of the Pb source. We were able to generate robust, dose-specific signatures of Pb exposure. This supports the growing body of evidence that even low levels of Pb exposure can have biological effects, and that there is likely no safe level of exposure. We also utilized a collection of pathway signatures to identify those pathways that were activated or repressed in response to Pb exposure compared to controls. We observed an increase in interferon-gamma pathway activity in response to low-level Pb exposure and an increase in E2F1 pathway activity in response to high-level Pb exposure. These results support previous findings that low-level Pb exposure can increase interferon-gamma production, whereas high-level Pb has been shown to increase DNA synthesis. The Pb signatures we report here were not predictive of a past lead exposure. These results suggest that the effect of Pb exposure on PBC gene expression is transient, perhaps due to the rapid turnover of blood cells and the absorption of Pb by the bones. We have proposed further studies to identify cells in the bone marrow that may serve as indicators of past Pb exposure based previous reports on the lasting effects of genotoxic stress on this tissue.

We also describe a predictor of human breast cancer based on peripheral blood gene expression. The objective of this study was to identify and characterize PBC gene expression patterns associated with the presence of a breast tumor. This work has the potential to make a significant impact on breast cancer screening and diagnosis. Despite the success of mammography in reducing mortality from breast cancer, many cancers go undetected due to factors such as breast density, age of the woman, or type of cancer. A blood-based breast tumor predictor would potentially offer an easy and noninvasive means of detecting primary breast cancer as well as monitoring patients for recurrences or metastases. In addition, the concept of using a blood-based biomarker for cancer detection would have positive implications for other types of cancer. For instance, patients with ovarian cancer are typically diagnosed at a late stage because of the absence of definitive symptoms and the lack of effective screenings methods.

We were able to successfully identify robust predictors of both mouse mammary tumors and human breast tumors based on PBC gene expression. The human breast tumor predictor exhibits a high level of sensitivity and specificity in distinguishing breast cancer patients and controls in an independent validation cohort. However, the true novelty in this study is that it integrates a factor modeling approach and a transgenic mouse model of breast cancer to identify biologically meaningful gene expression changes in the mouse PBC transcriptome. These genes were then used as the starting point for developing a human breast cancer predictor. This establishes an experimental system in which we can address questions that are inherently difficult to answer in human studies, such as whether this predictor is useful in detecting breast tumors early or in monitoring patients for recurrence or metastasis. In fact, our work suggests that tumor-associated gene expression changes in the PBCs can be detected in asymptomatic mice. Our results support those of previous studies, which identified blood gene expression profiles that are associated with a variety of solid tumors, including breast cancer. However, the sensitivity and specificity of our predictor are higher than that of the previously reported breast cancer signature. This may suggest that our strategy of using a mouse model to first identify informative genes allowed us to focus on those genes most relevant to the presence of a breast tumor and overcome the influence of the high degree of variation in blood gene expression in our human population. In order to be clinically useful, the predictor we report here would need to be tested in additional, large validation sets to establish its utility in an early detection setting and its specificity in distinguishing breast cancer from other cancer types as well as other potentially confounding conditions such as infection and inflammation. We describe some preliminary experiments in the mouse model intended to address these important questions.

The adaptive response of vascular endothelial cells to shear stress alteration induced by global hemodynamic changes is an essential component of normal endothelial physiology in vivo; and an understanding of the transient regulation of endothelial phenotype during adaptation will advance our understanding of endothelial biology and yield new insights into the mechanism of atherogenesis. The objective of this study was to characterize the adaptive response of arterial endothelial cells to acute increases in shear stress magnitude and frequency in well-defined in vitro settings. Porcine endothelial cells were preconditioned by a basal level shear stress of ±15dynes/cm^2 at 1 Hz for 24 hours, and an acute increase in shear stress magnitude (30 ±15 dynes/cm^2) or frequency (2 Hz) was then applied. Endothelial permeability to bovine serum albumin was measured and gene expression profiling was performed using microarrays at multiple time points during a period of 6 hours after the shear stress alteration. The instantaneous endothelial permeability was found to increase rapidly in response to the acute increase in shear stress magnitude. Endothelial permeability nearly doubled after 40 minutes exposure to the elevated shear magnitude, and then decreased gradually. However, less dependency of endothelial permeability on shear stress frequency was observed. Endothelial permeability increased slowly from 120 minutes to 6 hours after exposure to the elevated shear frequency, but the increase was not statistically significant and was relatively small (1.2 fold increase at 6 hours). The transcriptomics studies identified 86 genes that were sensitive to the elevated shear magnitude and 37 genes sensitive to the elevated frequency. A significant number of the identified genes are previously unknown as sensitive to shear stress. The acute increase in shear magnitude promoted the expression of a group of anti-inflammatory and anti-oxidative genes; while the acute increase in shear frequency upregulated a set of cell-cycle regulating genes and angiogenesis genes. The adaptive response of global gene expression profile to the elevated shear magnitude is found to be triphasic, consisting of an induction period, an early adaptive response (ca. 45 minutes) and a late remodeling response. However, no apparent temporal regulation pattern of global gene expression was found during the adaptation to the elevated shear frequency. The results from this dissertation suggest that endothelial cells exhibit a specific phenotype during the adaptive response to changes in shear stress; and the transient phenotype is different than that of fully-adapted endothelial cells and may alter arterial atherosusceptibility.