Terminal Deoxynucleotidyl Transferase Generation of Sequence Diversity on Plasmids

A thesis presented to the faculty of the College of Science and Technology at Morehead State University in partial fulfillment of the requirements for the Degree of Master of Science by Qingbei Zhang on June 19, 2002

MOLECULAR MECHANISMS THAT MEDIATE METASTASIS SUPPRESSOR ACTIVITY OF NM23-H1

Metastasis is the spread of cancer cells from the primary tumor to distant sites. It is the most dangerous attribute of cancer, and also the principle cause of cancerrelated morbidity and mortality. Metastasis suppressor genes are a group of genes that suppress tumor metastasis without significant effect on tumorigenicity. NM23 was the first identified metastasis suppressor gene, and loss of its expression is a frequent hallmark of metastatic growth in multiple cancers (e.g. melanoma, carcinomas of breast, stomach and liver). NM23-H1 possesses at least three enzymatic activities, including nucleoside diphosphate kinase (NDPK), histidine kinase (hisK), and a more recently described 3f-5f exonuclease (EXO). While the hisK has been shown to be linked to the suppression of cell motility, the NDPK has been reported to be unrelated to the suppression of metastatic potential indirectly. Relevance of EXO has not been addressed. Other known 3f-5f exonuclease are closely associated with DNA repair functions, suggesting NM23-H1 may suppress mutations required for metastasis. As a transcription factor, NM23 has been shown to modestly downregulate the transcription on PDGF-A chain, a growth factor oncogene, either alone or in association with another transcriptional factor, Pur@. At the same time, identification of NM23-H1 as a 3f-5fexonuclease suggests the role of NM23-H1 in DNA repair. Etoposide and cisplatin elicited nuclear translocation of H1 within 4 h in HeLa and HepG2 cells, seen as accumulation of H1 in small intranuclear foci, strongly suggesting the DNA repair function of H1. To investigate the enzymatic function contributing to metastasis suppressor activity of H1, complementation system was used by transfecting NM23-H1 with individually disrupted enzymatic function into 2 melanoma cell lines, 1205LU and WM793. Overexpression of H1 in 1205LU suppressed lung metastasis in vivo without effect on indices of transformation (e.g. proliferation, soft agar colonization). EXO- deficient H1 and NDPK-deficient H1 lost suppression of lung metastasis, while hisK-deficient H1 maintained suppressor activity. Consistent with the results in 1205LU cells, EXO-deficient H1 and NDPKdeficient H1 lost suppression of the progression of WM793 cells in protein-free medium, while WT and hisK-deficient H1 prevented the progression. Taken together, these data suggest that the NDPK and/or 3f-5fEXO activity of H1 inhibits the progression of premetastatic cells to the metastatic phenotype, possibly via a DNA repair function or other structural transactions with DNA

A Localization Method Avoiding Flip Ambiguities for micro-UAVs with Bounded Distance Measurement Errors

Localization is a fundamental function in cooperative control of micro unmanned aerial vehicles (UAVs), but is easily affected by flip ambiguities because of measurement errors and flying motions. This study proposes a localization method that can avoid the occurrence of flip ambiguities in bounded distance measurement errors and constrained flying motions; to demonstrate its efficacy, the method is implemented on bilateration and trilateration. For bilateration, an improved bi-boundary model based on the unit disk graph model is created to compensate for the shortage of distance constraints, and two boundaries are estimated as the communication range constraint. The characteristic of the intersections of the communication range and distance constraints is studied to present a unique localization criterion which can avoid the occurrence of flip ambiguities. Similarly, for trilateration, another unique localization criterion for avoiding flip ambiguities is proposed according to the characteristic of the intersections of three distance constraints. The theoretical proof shows that these proposed criteria are correct. A localization algorithm is constructed based on these two criteria. The algorithm is validated using simulations for different scenarios and parameters, and the proposed method is shown to provide excellent localization performance in terms of average estimated error. Our code can be found at: https://github.com/QingbeiGuo/AFALA.git.Comment: 14 pages, 8 figures, IEEE Transactions on Mobile Computing(Accepted

Human Breast Cancer Cell Lines Co-Express Neuronal, Epithelial, and Melanocytic Differentiation Markers In Vitro and In Vivo

Differentiation programs are aberrant in cancer cells allowing them to express differentiation markers in addition to their tissue of origin. In the present study, we demonstrate the multi-lineage differentiation potential of breast cancer cell lines to express multiple neuronal/glial lineage-specific markers as well as mammary epithelial and melanocytic-specific markers. Multilineage expression was detected in luminal (MCF-7 and SKBR3) and basal (MDA-MB-231) types of human breast cancer cell lines. We also observed comparable co-expression of these three cell lineage markers in MDA-MB-435 cells in vitro, in MDA-MB-435 primary tumors derived from parental and single cell clones and in lung metastases in vivo. Furthermore, ectoderm multi-lineage transdifferentiation was also found in human melanoma (Ul-MeL) and glioblastoma cell lines (U87 and D54). These observations indicate that aberrant multi-lineage transdifferentiation or lineage infidelity may be a wide spread phenomenon in cancer

Single Sample Expression-Anchored Mechanisms Predict Survival in Head and Neck Cancer

Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These “causality challenges” hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of “Oncogenic FAIME Features of HNSCC” (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume)

Single Sample Expression-Anchored Mechanisms Predict Survival in Head and Neck Cancer

Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These “causality challenges” hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of “Oncogenic FAIME Features of HNSCC” (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, pOncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical pp = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).</p

Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis

Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. In this report, we demonstrate for the first time how phenotypic knowledge of inheritable cancer traits and of risk factor loci can be utilized jointly with gene expression analysis to efficiently prioritize deregulated microRNAs for biological characterization. Using this approach we characterize miR-204 as a tumor suppressor microRNA and uncover previously unknown connections between microRNA regulation, network topology, and expression dynamics. Specifically, we validate 18 gene targets of miR-204 that show elevated mRNA expression and are enriched in biological processes associated with tumor progression in squamous cell carcinoma of the head and neck (HNSCC). We further demonstrate the enrichment of bottleneckness, a key molecular network topology, among miR-204 gene targets. Restoration of miR-204 function in HNSCC cell lines inhibits the expression of its functionally related gene targets, leads to the reduced adhesion, migration and invasion in vitro and attenuates experimental lung metastasis in vivo. As importantly, our investigation also provides experimental evidence linking the function of microRNAs that are located in the cancer-associated genomic regions (CAGRs) to the observed predisposition to human cancers. Specifically, we show miR-204 may serve as a tumor suppressor gene at the 9q21.1–22.3 CAGR locus, a well established risk factor locus in head and neck cancers for which tumor suppressor genes have not been identified. This new strategy that integrates expression profiling, genetics and novel computational biology approaches provides for improved efficiency in characterization and modeling of microRNA functions in cancer as compared to the state of art and is applicable to the investigation of microRNA functions in other biological processes and diseases.</p

MicroRNA Expression Characterizes Oligometastasis(es)

Cancer staging and treatment presumes a division into localized or metastatic disease. We proposed an intermediate state defined by ≤ 5 cumulative metastasis(es), termed oligometastases. In contrast to widespread polymetastases, oligometastatic patients may benefit from metastasis-directed local treatments. However, many patients who initially present with oligometastases progress to polymetastases. Predictors of progression could improve patient selection for metastasis-directed therapy.Here, we identified patterns of microRNA expression of tumor samples from oligometastatic patients treated with high-dose radiotherapy.Patients who failed to develop polymetastases are characterized by unique prioritized features of a microRNA classifier that includes the microRNA-200 family. We created an oligometastatic-polymetastatic xenograft model in which the patient-derived microRNAs discriminated between the two metastatic outcomes. MicroRNA-200c enhancement in an oligometastatic cell line resulted in polymetastatic progression.These results demonstrate a biological basis for oligometastases and a potential for using microRNA expression to identify patients most likely to remain oligometastatic after metastasis-directed treatment

Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis

Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. In this report, we demonstrate for the first time how phenotypic knowledge of inheritable cancer traits and of risk factor loci can be utilized jointly with gene expression analysis to efficiently prioritize deregulated microRNAs for biological characterization. Using this approach we characterize miR-204 as a tumor suppressor microRNA and uncover previously unknown connections between microRNA regulation, network topology, and expression dynamics. Specifically, we validate 18 gene targets of miR-204 that show elevated mRNA expression and are enriched in biological processes associated with tumor progression in squamous cell carcinoma of the head and neck (HNSCC). We further demonstrate the enrichment of bottleneckness, a key molecular network topology, among miR-204 gene targets. Restoration of miR-204 function in HNSCC cell lines inhibits the expression of its functionally related gene targets, leads to the reduced adhesion, migration and invasion in vitro and attenuates experimental lung metastasis in vivo. As importantly, our investigation also provides experimental evidence linking the function of microRNAs that are located in the cancer-associated genomic regions (CAGRs) to the observed predisposition to human cancers. Specifically, we show miR-204 may serve as a tumor suppressor gene at the 9q21.1–22.3 CAGR locus, a well established risk factor locus in head and neck cancers for which tumor suppressor genes have not been identified. This new strategy that integrates expression profiling, genetics and novel computational biology approaches provides for improved efficiency in characterization and modeling of microRNA functions in cancer as compared to the state of art and is applicable to the investigation of microRNA functions in other biological processes and diseases

Human Breast Cancer Cell Lines Co-Express Neuronal, Epithelial, and Melanocytic Differentiation Markers <i>In Vitro</i> and <i>In Vivo</i>

Differentiation programs are aberrant in cancer cells allowing them to express differentiation markers in addition to their tissue of origin. In the present study, we demonstrate the multi-lineage differentiation potential of breast cancer cell lines to express multiple neuronal/glial lineage-specific markers as well as mammary epithelial and melanocytic-specific markers. Multilineage expression was detected in luminal (MCF-7 and SKBR3) and basal (MDA-MB-231) types of human breast cancer cell lines. We also observed comparable co-expression of these three cell lineage markers in MDA-MB-435 cells in vitro, in MDA-MB-435 primary tumors derived from parental and single cell clones and in lung metastases in vivo. Furthermore, ectoderm multi-lineage transdifferentiation was also found in human melanoma (Ul-MeL) and glioblastoma cell lines (U87 and D54). These observations indicate that aberrant multi-lineage transdifferentiation or lineage infidelity may be a wide spread phenomenon in cancer.</p