Identification of a conserved cluster in the RH domain of GRK critical for activation by GPCRs

One of the most critical aspects of G Protein Coupled Receptors (GPCRs) regulation is their rapid and acute desensitization following agonist stimulation. Phosphorylation of these receptors by GPCR kinases (GRK) is a major mechanism of desensitization. Considerable evidence from studies of rhodopsin kinase and GRK2 suggests there is an allosteric docking site for the receptor distinct from the GRK catalytic site. While the agonist-activated GPCR appears crucial for GRK activation, the molecular details of this interaction remain unclear. Recent studies suggested an important role for the N- and C-termini and domains in the small lobe of the kinase domain in allosteric activation; however, neither the mechanism of action of that site nor the RH domain contributions have been elucidated. To search for the allosteric site, we first indentified evolutionarily conserved sites within the RH and kinase domains presumably deterministic of protein function employing evolutionary trace (ET) methodology and crystal structures of GRK6. Focusing on a conserved cluster centered on helices 3, 9, and 10 in the RH domain, key residues of GRK5 and 6 were targeted for mutagenesis and functional assays. We found that a number of double mutations within helices 3, 9, and 10 and the N-terminus markedly reduced (50–90%) the constitutive phosphorylation of the β-2 Adrenergic Receptor (β2AR) in intact cells and phosphorylation of light-activated rhodopsin (Rho*) in vitro as compared to wild type (WT) GRK5 or 6. Based on these results, we designed peptide mimetics of GRK5 helix 9 both computationally and through chemical modifications with the goal of both confirming the importance of helix 9 and developing a useful inhibitor to disrupt the GPCR-GRK interaction. Several peptides were found to block Rho* phosphorylation by GRK5 including the native helix 9 sequence, Peptide Builder designed-peptide preserving only the key ET residues, and chemically locked helices. Most peptidomimetics showed inhibition of GRK5 activity greater than 80 % with an IC50 of ∼ 30 µM. Alanine scanning of helix 9 has further revealed both essential and non-essential residues for inhibition. Importantly, substitution of Arg 169 by an alanine in the native helix 9-based peptide gave an almost complete inhibition at 30 µM with an IC50 of ∼ 10 µM. In summary we report a previously unrecognized crucial role for the RH domain of GRK5 and 6, and the subsequent identification of a lead peptide inhibitor of protein-protein interaction with potential for specific blockade of GPCR desensitization. ^

Systematic interpretation of molecular beacon polymerase chain reaction for identifying rpoB mutations in Mycobacterium tuberculosis isolates with mixed resistant and susceptible bacteria

Detection of multidrug-resistant tuberculosis (MDR-TB), a frequent cause of treatment failure, takes 2 or more weeks to identify by culture. RIF-resistance is a hallmark of MDR-TB, and detection of mutations in the rpoB gene of Mycobacterium tuberculosis using molecular beacon probes with real-time quantitative polymerase chain reaction (qPCR) is a novel approach that takes ≤2 days. However, qPCR identification of resistant isolates, particularly for isolates with mixed RIF-susceptible and RIF-resistant bacteria, is reader dependent and limits its clinical use. The aim of this study was to develop an objective, reader-independent method to define rpoB mutants using beacon qPCR. This would facilitate the transition from a research protocol to the clinical setting, where high-throughput methods with objective interpretation are required. For this, DNAs from 107 M. tuberculosis clinical isolates with known susceptibility to RIF by culture-based methods were obtained from 2 regions where isolates have not previously been subjected to evaluation using molecular beacon qPCR: the Texas–Mexico border and Colombia. Using coded DNA specimens, mutations within an 81-bp hot spot region of rpoB were established by qPCR with 5 beacons spanning this region. Visual and mathematical approaches were used to establish whether the qPCR cycle threshold of the experimental isolate was significantly higher (mutant) compared to a reference wild-type isolate. Visual classification of the beacon qPCR required reader training for strains with a mixture of RIF-susceptible and RIF-resistant bacteria. Only then had the visual interpretation by an experienced reader had 100% sensitivity and 94.6% specificity versus RIF-resistance by culture phenotype and 98.1% sensitivity and 100% specificity versus mutations based on DNA sequence. The mathematical approach was 98% sensitive and 94.5% specific versus culture and 96.2% sensitive and 100% specific versus DNA sequence. Our findings indicate the mathematical approach has advantages over the visual reading, in that it uses a Microsoft Excel template to eliminate reader bias or inexperience, and allows objective interpretation from high-throughput analyses even in the presence of a mixture of RIF-resistant and RIF-susceptible isolates without the need for reader training.^

Identification of micro-RNAs targeting genes of PI3K/AKT pathway in ovarian cancer

Ovarian cancer is the leading cause of cancer-related death for females due to lack of specific early detection method. It is of great interest to find molecular-based biomarkers which are sensitive and specific to ovarian cancer for early diagnosis, prognosis and therapeutics. miRNAs have been proposed to be potential biomarkers that could be used in cancer prevention and therapeutics. The current study analyzed the miRNA and mRNA expression data extracted from the Cancer Genome Atlas (TCGA) database. Using simple linear regression and multiple regression models, we found 71 miRNA-mRNA pairs which were negatively associated between 56 miRNAs and 24 genes of PI3K/AKT pathway. Among these miRNA and mRNA target pairs, 9 of them were in agreement with the predictions from the most commonly used target prediction programs including miRGen, miRDB, miRTarbase and miR2Disease. These shared miRNA-mRNA pairs were considered to be the most potential genes that were involved in ovarian cancer. Furthermore, 4 of the 9 target genes encode cell cycle or apoptosis related proteins including Cyclin D1, p21, FOXO1 and Bcl2, suggesting that their regulator miRNAs including miR-16, miR-96 and miR-21 most likely played important roles in promoting tumor growth through dysregulated cell cycle or apoptosis. miR-96 was also found to directly target IRS-1. In addition, the results showed that miR-17 and miR-9 may be involved in ovarian cancer through targeting JAK1. This study might provide evidence for using miRNA or miRNA profile as biomarker.^

Pathway Semantics: An Algebraic Data Driven Algorithm to Generate Hypotheses about Molecular Patterns Underlying Disease Progression

The overarching goal of the Pathway Semantics Algorithm (PSA) is to improve the in silico identification of clinically useful hypotheses about molecular patterns in disease progression. By framing biomedical questions within a variety of matrix representations, PSA has the flexibility to analyze combined quantitative and qualitative data over a wide range of stratifications. The resulting hypothetical answers can then move to in vitro and in vivo verification, research assay optimization, clinical validation, and commercialization. Herein PSA is shown to generate novel hypotheses about the significant biological pathways in two disease domains: shock / trauma and hemophilia A, and validated experimentally in the latter. The PSA matrix algebra approach identified differential molecular patterns in biological networks over time and outcome that would not be easily found through direct assays, literature or database searches. In this dissertation, Chapter 1 provides a broad overview of the background and motivation for the study, followed by Chapter 2 with a literature review of relevant computational methods. Chapters 3 and 4 describe PSA for node and edge analysis respectively, and apply the method to disease progression in shock / trauma. Chapter 5 demonstrates the application of PSA to hemophilia A and the validation with experimental results. The work is summarized in Chapter 6, followed by extensive references and an Appendix with additional material.

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^

PRKCa: Identification of a Novel Downstream Target of WT1

Wilms tumor is a childhood tumor of the kidney arising from the undifferentiated metanephric mesenchyme. Tumorigenesis is attributed to a number of genetic and epigenetic alterations. In 20% of Wilms tumors, Wilms tumor gene 1 (WT1) undergoes inactivating homozygous mutations causing loss of function of the zinc finger transcription factor it encodes. It is hypothesized that mutations in WT1 result in dysregulation of downstream target genes, leading to aberrant kidney development and/or Wilms tumor. These downstream target genes are largely unknown, and identification is important for further understanding Wilms tumor development. Heatmap data of human Wilms tumor protein expression, generated by reverse phase protein assay analysis (RPPA), show significant correlation between WT1 mutation status and low PRKCα expression (p= 0.00013); additionally, p-PRKCα (S657) also shows decreased expression in these samples (p= 0.00373). These data suggest that the WT1 transcription factor regulates PRKCα expression, and that PRKCα plays a potential role in Wilms tumor tumorigenesis. We hypothesize that the WT1 transcription factor directly/indirectly regulates PRKCα and mutations occurring in WT1 lead to decreased expression of PRKCα. Prkcα and Wt1 have been shown to co-localize in E14.5 mesenchymal cells of the developing kidney. siRNA knockdown, in-vivo ablation, and tet-inducible expression of Wt1 each independently confirm regulation of Prkcα expression by Wt1 at both RNA and protein levels, and investigation into possible WT1 binding sites in PRKCα regulatory regions has identified multiple sites to be confirmed by luciferase reporter constructs. With the goal of identifying WT1 and PRKCα downstream targets, RPPA analysis of protein expression in mesenchymal cell culture, following lentiviral delivered shRNA knockdown of Wt1 and shRNA knockdown of Prkcα, will be carried out. Apart from Wilms tumor, WT1 also plays an important role in Acute Myeloid Leukemia (AML). WT1 mutation status has been implicated, controversially, as an independent poor-prognosis factor in leukemia, leading to decreased probability of overall survival, complete remission, and disease free survival. RPPA analysis of AML patient samples showed significant decreases in PRKCα/p-PRKCα protein expression in a subset of patients (Kornblau, personal communication); therefore, the possible role of WT1 and PRKCα in leukemia disease progression is an additional focus of this study. WT1 mutation analysis of diploid leukemia patient samples revealed two patients with mutations predicted to affect WT1 activity; of these two samples, only one corresponded to the low PRKCα expression cohort. Further characterization of the role of WT1 in AML, and further understanding of WT1 regulated PRKCα expression, will be gained following RPPA analysis of protein expression in HL60 leukemia cell lines with lentiviral delivered shRNA knockdown of WT1 and shRNA knockdown of PRKCα.

RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION

My dissertation focuses on two aspects of RNA sequencing technology. The first is the methodology for modeling the overdispersion inherent in RNA-seq data for differential expression analysis. This aspect is addressed in three sections. The second aspect is the application of RNA-seq data to identify the CpG island methylator phenotype (CIMP) by integrating datasets of mRNA expression level and DNA methylation status. Section 1: The cost of DNA sequencing has reduced dramatically in the past decade. Consequently, genomic research increasingly depends on sequencing technology. However it remains elusive how the sequencing capacity influences the accuracy of mRNA expression measurement. We observe that accuracy improves along with the increasing sequencing depth. To model the overdispersion, we use the beta-binomial distribution with a new parameter indicating the dependency between overdispersion and sequencing depth. Our modified beta-binomial model performs better than the binomial or the pure beta-binomial model with a lower false discovery rate. Section 2: Although a number of methods have been proposed in order to accurately analyze differential RNA expression on the gene level, modeling on the base pair level is required. Here, we find that the overdispersion rate decreases as the sequencing depth increases on the base pair level. Also, we propose four models and compare them with each other. As expected, our beta binomial model with a dynamic overdispersion rate is shown to be superior. Section 3: We investigate biases in RNA-seq by exploring the measurement of the external control, spike-in RNA. This study is based on two datasets with spike-in controls obtained from a recent study. We observe an undiscovered bias in the measurement of the spike-in transcripts that arises from the influence of the sample transcripts in RNA-seq. Also, we find that this influence is related to the local sequence of the random hexamer that is used in priming. We suggest a model of the inequality between samples and to correct this type of bias. Section 4: The expression of a gene can be turned off when its promoter is highly methylated. Several studies have reported that a clear threshold effect exists in gene silencing that is mediated by DNA methylation. It is reasonable to assume the thresholds are specific for each gene. It is also intriguing to investigate genes that are largely controlled by DNA methylation. These genes are called “L-shaped” genes. We develop a method to determine the DNA methylation threshold and identify a new CIMP of BRCA. In conclusion, we provide a detailed understanding of the relationship between the overdispersion rate and sequencing depth. And we reveal a new bias in RNA-seq and provide a detailed understanding of the relationship between this new bias and the local sequence. Also we develop a powerful method to dichotomize methylation status and consequently we identify a new CIMP of breast cancer with a distinct classification of molecular characteristics and clinical features.

Identification, purification, sequencing and characterization of human lung fibroblast motility -stimulating factor for human soft tissue sarcoma cells

Paracrine motogenic factors, including motility cytokines and extracellular matrix molecules secreted by normal cells, can stimulate metastatic cell invasion. For extracellular matrix molecules, both the intact molecules and the degradative products may exhibit these activities, which in some cases are not shared by the intact molecules. We found that human peritumoral and lung fibroblasts secrete motility-stimulating activity for several recently established human sarcoma cell strains. The motility of lung metastasis-derived human SYN-1 sarcoma cells was preferentially stimulated by human lung and peritumoral fibroblast motility-stimulating factors (FMSFs). FMSFs were nondialyzable, susceptible to trypsin, and sensitive to dithiothreitol. Cycloheximide inhibited accumulation of FMSF activity in conditioned medium; however, addition of cycloheximide to the migration assay did not significantly affect motility-stimulating activity. Purified hepatocyte growth factor/scatter factor (HGF/SF), rabbit anti-hHGF, and RT-PCR analysis of peritumoral and lung fibroblast HGF/SF mRNA expression indicated that FMSF activity was unrelated to HGF/SF. Partial purification of FMSF by gel exclusion chromatography revealed several peaks of activity, suggesting multiple FMSF molecules or complexes.^ We purified the fibroblast motility-stimulating factor from human lung fibroblast-conditioned medium to apparent homogeneity by sequential heparin affinity chromatography and DEAE anion exchange chromatography. Lysylendopeptidase C digestion of FMSF and sequencing of peptides purified by reverse phase HPLC after digestion identified it as an N-terminal fragment of human fibronectin. Purified FMSF stimulated predominantly chemotaxis but chemokinesis as well of SYN-1 sarcoma cells and was chemotactic for a variety of human sarcoma cells, including fibrosarcoma, leiomyosarcoma, liposarcoma, synovial sarcoma and neurofibrosarcoma cells. The motility-stimulating activity present in HLF-CM was completely eliminated by either neutralization or immunodepletion with a rabbit anti-human-fibronectin antibody, thus further confirming that the fibronectin fragment was the FMSF responsible for the motility stimulation of human soft tissue sarcoma cells. Since human soft tissue sarcomas have a distinctive hematogenous metastatic pattern (predominantly lung), FMSF may play a role in this process. ^

The identification and regulation of a novel interaction occurring between $\beta$-catenin and the actin -bundling protein fascin

Catenins were first characterized as linking the cytoplasmic domains of cadherin cell-cell adhesion molecules to the cortical actin cytoskeleton. In addition to their essential role in modulating cadherin adhesion, catenins have more recently been indicated to participate in cell and developmental signaling pathways. $\beta$-catenin, for example, associates directly with receptor tyrosine kinases and transcription factors such as LEF-1/TCF, and tranduces developmental signals within the Wnt pathway. $\beta$-catenin also appear to a role in regulating cell proliferation via its interaction with the tumor supressor protein APC. I have employed the yeast two-hybrid method to reveal that fascin, a bundler of actin filaments, binds to $\beta$-catenin's central Armadillo-repeat domain. The $\beta$-catenin-fascin interaction exists in cell lines as well as in animal brain tissues as revealed by immunoprecipitation analysis, and substantiated in vitro with purified proteins. Fascin additionally binds to plakoglobin, which contains a more divergent Armadillo-repeat domain. Fascin and E-cadherin utilize a similar binding-site within $\beta$-catenin, such that they form mutually exclusive complexes with $\beta$-catenin. Fascin and $\beta$-catenin co-localize at cell-cell borders and dynamic cell leading edges of epithelial and endothelial cells. Total immunoprecipitable b-catein has several isoforms, only the hyperphosphorylated isoform 1 associated with fascin. An increased $\beta$-catenin-fascin interaction was observed in HGF stimulated cells, and in Xenopus embryos injected with src kinase RNAs. The increased $\beta$-catenin association with fascin is correlated with increased levels of $\beta$-catenin phosphorylation. $\beta$-catenin, but not fascin, can be readily phosphorylated on tyrosine in vivo following src injection of embryos, or in vitro following v-src addition to purified protein components. These observations suggest a role of $\beta$-catenin phosphorylation in regulating its interaction with fascin, and src kinase may be an important regulator of the $\beta$-catenin-fascin association in vivo. The $\beta$-catenin-fascin interaction represents a novel catenin complex, that may conceivably regulate actin cytoskeletal structures, cell adhesion, and cellular motility, perhaps in a coordinate manner with its functions in cadherin and APC complexes. ^