Involvement of heparin-like glycosaminoglycans and expression of a novel heparan sulfate binding protein (p24) during human placentation and in a model for human implantation

Previous studies in our laboratory have indicated that heparan sulfate proteoglycans (HSPGs) play an important role in murine embryo implantation. To investigate the potential function of HSPGs in human implantation, two human cell lines (RL95 and JAR) were selected to model uterine epithelium and embryonal trophectoderm, respectively. A heterologous cell-cell adhesion assay showed that initial binding between JAR and RL95 cells is mediated by cell surface glycosaminoglycans (GAG) with heparin-like properties, i.e., heparan sulfate and dermatan sulfate. Furthermore, a single class of highly specific, protease-sensitive heparin/heparan sulfate binding sites exist on the surface of RL95 cells. Three heparin binding, tryptic peptide fragments were isolated from RL95 cell surfaces and their amino termini partially sequenced. Reverse transcription-polymerase chain reaction (RT-PCR) generated 1 to 4 PCR products per tryptic peptide. Northern blot analysis of RNA from RL95 cells using one of these RT-PCR products identified a 1.2 Kb mRNA species (p24). The amino acid sequence predicted from the cDNA sequence contains a putative heparin-binding domain. A synthetic peptide representing this putative heparin binding domain was used to generate a rabbit polyclonal antibody (anti-p24). Indirect immunofluorescence studies on RL95 and JAR cells as well as binding studies of anti-p24 to intact RL95 cells demonstrate that p24 is distributed on the cell surface. Western blots of RL95 membrane preparations identify a 24 kDa protein (p24) highly enriched in the 100,000 g pellet plasma membrane-enriched fraction. p24 eluted from membranes with 0.8 M NaCl, but not 0.6 M NaCl, suggesting that it is a peripheral membrane component. Solubilized p24 binds heparin by heparin affinity chromatography and $\sp{125}$I-heparin binding assays. Furthermore, indirect immunofluorescence studies indicate that cytotrophoblast of floating and attached villi of the human fetal-maternal interface are recognized by anti-p24. The study also indicates that the HSPG, perlecan, accumulates where chorionic villi are attached to uterine stroma and where p24-expressing cytotrophoblast penetrate the stroma. Collectively, these data indicate that p24 is a cell surface membrane-associated heparin/heparan sulfate binding protein found in cytotrophoblast, but not many other cell types of the fetal-maternal interface. Furthermore, p24 colocalizes with HSPGs in regions of cytotrophoblast invasion. These observations are consistent with a role for HSPGs and HSPG binding proteins in human trophoblast-uterine cell interactions. ^

The role of nitric oxide in small intestinal motility in a model of acute inflammation

Motility responses of the small intestine of iNOS deficient mice (iNOS −/−) and their wildtype littermates (iNOS+/+) to the inflammatory challenge of lipopolysaccharide (LPS) were investigated. LPS administration failed to attenuate intestinal transit in iNOS−/− mice but depressed transit in their iNOS+/+ littermates. Supporting an inhibitory role for sustained nitric oxide (NO) synthesis in the regulation of intestinal motility during inflammation, iNOS immunoreactivity was upregulated in all regions of the small intestine of iNOS+/+ mice. In contrast, neuronal NOS was barely affected. Cyclooxygenase activation was determined by prostaglandin E2 (PGE2) concentration. Following LPS challenge, PGE2 levels were elevated in all intestinal segments in both animal groups. Moreover, COX-1 and COX-2 protein levels were elevated in iNOS+/+ mice in response to LPS, while COX-2 levels were similarly increased in iNOS −/− intestine. However, no apparent relationship was observed between increased prostaglandin concentrations and attenuated intestinal transit. The presence of heme oxygenase 1 (HO-1) in the murine small intestine was also investigated. In both animal groups HO-1 immunoreactivity in the proximal intestine increased in response to treatment, while the constitutive protein levels detected in the middle and distal intestine were unresponsive to LPS administration. No apparent correlation of HO-1 to the suppression of small intestinal motility induced by LPS administration was detected. The presence of S-nitrosylated contractile proteins in the small intestine was determined. γ-smooth muscle actin was basally nitrosylated as well as in response to LPS, but myosin light chain kinase and myosin regulatory chain (MLC20) were not. In conclusion, in a model of acute intestinal inflammation, iNOS-produced NO plays a significant role in suppressing small intestinal motility while nNOS, COX-1, COX-2 and HO-1 do not participate in this event. S-nitrosylation of γ-smooth muscle actin is associated with elevated levels of nitric oxide in the smooth muscle of murine small intestine. ^

ROLE OF MIR-19A RELEASED BY INFLAMMATORY BREAST CANCER CELLS IN THE REGULATION OF DENDRITIC CELL FUNCTIONS: IN VITRO MODEL OF CROSSTALK IN THE TUMOR MICROENVIRONMENT OF INFLAMMATORY BREAST CANCER

Inflammatory breast cancer (IBC) is a rare but very aggressive form of locally advanced breast cancer (1-6% of total breast cancer patients in United States), with a 5-year overall survival rate of only 40.5%, compared with 85% of the non-IBC patients. So far, a unique molecular signature for IBC able to explain the dramatic differences in the tumor biology between IBC and non-IBC has not been identified. As immune cells in the tumor microenvironment plays an important role in regulating tumor progression, we hypothesized that tumor-associated dendritic cells (TADC) may be responsible for regulating the development of the aggressive characteristics of IBC. MiRNAs can be released into the extracellular space and mediate the intercellular communication by regulating target gene expression beyond their cells of origin. We hypothesized that miRNAs released by IBC cells can induce an increased activation status, secretion of pro-inflammatory cytokines and migration ability of TADC. In an in vitro model of IBC tumor microenvironment, we found that the co-cultured of the IBC cell line SUM-149 with immature dendritic cells (iDCSUM-149) induced a higher degree of activation and maturation of iDCSUM-149 upon stimulation with lipopolysaccharide (LPS) compared with iDCs co-cultured with the non-IBC cell line SUM-159 (iDCSUM-159), resulting in: increased expression of the costimulatory and activation markers; higher production of pro-inflammatory cytokines (TNF-a, IL-6); and 3) higher migratory ability. These differences were due to the exosome-mediated transfer of miR-19a and miR-146a from SUM-149 and SUM-159, respectively, to iDCs, causing the downregulation of the miR-19a target genes PTEN, SOCS-1 and the miR-146a target genes IRAK1, TRAF6. PTEN, SOCS-1 and IRAK1, TRAF6 are important negative and positive regulator of cytokine- and TLR-mediated activation/maturation signaling pathway in DCs. Increased levels of IL-6 induced the upregulation of miR-19a synthesis in SUM-149 cells that was associated with the induction of CD44+CD24-ALDH1+ cancer stem cells (CSCs) with epithelial-to-mesenchymal transition (EMT) characteristics. In conclusion, in IBC tumor microenvironment IL-6/miR-19a axis can represent a self-sustaining loop able to maintain a pro-inflammatory status of DCs, leading to the development of tumor cells with high metastatic potential (EMT CSCs) responsible of the poor prognosis in IBC patients.

STUDYING AGGREGATE FORMATION BY AMYOTROPHIC LATERAL SCLEROSIS-ASSOCIATED MUTANT SOD1 PROTEIN IN DROSOPHILA MODEL

A common pathological hallmark of most neurodegenerative disorders is the presence of protein aggregates in the brain. Understanding the regulation of aggregate formation is thus important for elucidating disease pathogenic mechanisms and finding effective preventive avenues and cures. Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s disease, is a selective neurodegenerative disorder predominantly affecting motor neurons. The majority of ALS cases are sporadic, however, mutations in superoxide dismutase 1 (SOD1) are responsible for about 20% of familial ALS (fALS). Mutated SOD1 proteins are prone to misfold and form protein aggregates, thus representing a good candidate for studying aggregate formation. The long-term goal of this project is to identify regulators of aggregate formation by mutant SOD1 and other ALS-associated disease proteins. The specific aim of this thesis project is to assess the possibility of using the well-established Drosophila model system to study aggregation by human SOD1 (hSOD1) mutants. To this end, using wild type and the three mutant hSOD1 (A4V, G85R and G93A) most commonly found among fALS, I have generated 16 different SOD1 constructs containing either eGFP or mCherry in-frame fluorescent reporters, established and tested both cell- and animal-based Drosophila hSOD1 models. The experimental strategy allows for clear visualization of ectopic hSOD1 expression as well as versatile co-expression schemes to fully investigate protein aggregation specifically by mutant hSOD1. I have performed pilot cell-transfection experiments and verified induced expression of hSOD1 proteins. Using several tissue- or cell type-specific Gal4 lines, I have confirmed the proper expression of hSOD1 from established transgenic fly lines. Interestingly, in both Drosophila S2 cells and different fly tissues including the eye and motor neurons, robust aggregate formation by either wild type or mutant hSOD1 proteins was not observed. These preliminary observations suggest that Drosophila might not be a good experimental organism to study aggregation and toxicity of mutant hSOD1 protein. Nevertheless this preliminary conclusion implies the potential existence of a potent protective mechanism against mutant hSOD1 aggregation and toxicity in Drosophila. Thus, results from my SOD1-ALS project in Drosophila will help future studies on how to best employ this classic model organism to study ALS and other human brain degenerative diseases.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Metastatic lung cancer in a new mouse model inheriting p53 and latent K-ras mutations

Lung cancer is a devastating disease with very poor prognosis. The design of better treatments for patients would be greatly aided by mouse models that closely resemble the human disease. The most common type of human lung cancer is adenocarcinoma with frequent metastasis. Unfortunately, current models for this tumor are inadequate due to the absence of metastasis. Based on the molecular findings in human lung cancer and metastatic potential of osteosarcomas in mutant p53 mouse models, I hypothesized that mice with both K-ras and p53 missense mutations might develop metastatic lung adenocarcinomas. Therefore, I incorporated both K-rasLA1 and p53RI72HΔg alleles into mouse lung cells to establish a more faithful model for human lung adenocarcinoma and for translational and mechanistic studies. Mice with both mutations ( K-rasLA1/+ p53R172HΔg/+) developed advanced lung adenocarcinomas with similar histopathology to human tumors. These lung adenocarcinomas were highly aggressive and metastasized to multiple intrathoracic and extrathoracic sites in a pattern similar to that seen in lung cancer patients. This mouse model also showed gender differences in cancer related death and developed pleural mesotheliomas in 23.2% of them. In a preclinical study, the new drug Erlotinib (Tarceva) decreased the number and size of lung lesions in this model. These data demonstrate that this mouse model most closely mimics human metastatic lung adenocarcinoma and provides an invaluable system for translational studies. ^ To screen for important genes for metastasis, gene expression profiles of primary lung adenocarcinomas and metastases were analyzed. Microarray data showed that these two groups were segregated in gene expression and had 79 highly differentially expressed genes (more than 2.5 fold changes and p<0.001). Microarray data of Bub1b, Vimentin and CCAM1 were validated in tumors by quantitative real-time PCR (QPCR). Bub1b , a mitotic checkpoint gene, was overexpressed in metastases and this correlated with more chromosomal abnormalities in metastatic cells. Vimentin, a marker of epithelial-mesenchymal transition (EMT), was also highly expressed in metastases. Interestingly, Twist, a key EMT inducer, was also highly upregulated in metastases by QPCR, and this significantly correlated with the overexpression of Vimentin in the same tumors. These data suggest EMT occurs in lung adenocarcinomas and is a key mechanism for the development of metastasis in K-ras LA1/+ p53R172HΔg/+ mice. Thus, this mouse model provides a unique system to further probe the molecular basis of metastatic lung cancer.^

High-resolution microarray analysis of chromosome 20q in human colon cancer metastasis model systems

Amplification of human chromosome 20q DNA is the most frequently occurring chromosomal abnormality detected in sporadic colorectal carcinomas and shows significant correlation with liver metastases. Through comprehensive high-resolution microarray comparative genomic hybridization and microarray gene expression profiling, we have characterized chromosome 20q amplicon genes associated with human colorectal cancer metastasis in two in vitro metastasis model systems. The results revealed increasing complexity of the 20q genomic profile from the primary tumor-derived cell lines to the lymph node and liver metastasis derived cell lines. Expression analysis of chromosome 20q revealed a subset of over expressed genes residing within the regions of genomic copy number gain in all the tumor cell lines, suggesting these are Chromosome 20q copy number responsive genes. Bases on their preferential expression levels in the model system cell lines and known biological function, four of the over expressed genes mapping to the common intervals of genomic copy gain were considered the most promising candidate colorectal metastasis-associated genes. Validation of genomic copy number and expression array data was carried out on these genes, with one gene, DNMT3B, standing out as expressed at a relatively higher levels in the metastasis-derived cell lines compared with their primary-derived counterparts in both the models systems analyzed. The data provide evidence for the role of chromosome 20q genes with low copy gain and elevated expression in the clonal evolution of metastatic cells and suggests that such genes may serve as early biomarkers of metastatic potential. The data also support the utility of the combined microarray comparative genomic hybridization and expression array analysis for identifying copy number responsive genes in areas of low DNA copy gain in cancer cells. ^

Adenosine induced pulmonary fibrosis and the contribution of the adenosine A2B receptor to the induction of osteopontin in a mouse model of pulmonary fibrosis

Pulmonary fibrosis is a devastating and lethal lung disease with no current cure. Research into cellular signaling pathways able to modulate aspects of pulmonary inflammation and fibrosis will aid in the development of effective therapies for its treatment. Our laboratory has generated a transgenic/knockout mouse with systemic elevations in adenosine due to the partial lack of its metabolic enzyme, adenosine deaminase (ADA). These mice spontaneously develop progressive lung inflammation and severe pulmonary fibrosis suggesting that aberrant adenosine signaling is influencing the development and/or progression of the disease in these animals. These mice also show marked increases in the pro-fibrotic mediator, osteopontin (OPN), which are reversed through ADA therapy that serves to lower lung adenosine levels and ameliorate aspects of the disease. OPN is known to be regulated by intracellular signaling pathways that can be accessed through adenosine receptors, particularly the low affinity A2BR receptor, suggesting that adenosine receptor signaling may be responsible for the induction of OPN in our model. In-vitro, adenosine and the broad spectrum adenosine receptor agonist, NECA, were able to induce a 2.5-fold increase in OPN transcripts in primary alveolar macrophages. This induction was blocked through antagonism of the A2BR receptor pharmacologically, and through the deletion of the receptor subtype in these cells genetically, supporting the hypothesis that the A2BR receptor was responsible for the induction of OPN in our model. These findings demonstrate for the first time that adenosine signaling is an important modulator of pulmonary fibrosis in ADA-deficient mice and that this is in part due to signaling through the A2BR receptor which leads to the induction of the pro-fibrotic molecule, otseopontin. ^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^