Seroepidemiology aims to understand population-level exposure and immunity to infectious disease. Serological results are normally presented as binary outcomes describing the presence or absence of antibody, despite the fact that many assays measure continuous quantities. A population's antibody titers may include information on multiple serological states - naiveté, recent infection, non-recent infection, childhood infection - not just seropositivity or seronegativity. In the first part of this thesis, I investigate 20,152 general-population serum samples from southern Vietnam collected between 2009 and 2013 from which I report antibody titers to the influenza virus HA1 protein using a continuous titer measurement from a protein microarray assay. I describe titer distributions to subtypes 2009 H1N1 and H3N2, and using a model selection approach for mixture distributions, I determine that 2009 H1N1 is best described by four titer subgroups while H3N2 is best described by three titer subgroups. For H1N1, my interpretation is that the two highest-titer subgroups correspond to recent and historical infection, which is consistent with pandemic attack rates. For H3N2 however, right-censoring of titers makes interpretations difficult to validate. To move beyond this stationary interpretation of titers, I developed two methods for analyzing this serum collection as a time series. First, I attempted to analyze mixture categories in individual time windows. This approach did not lead to a consistent temporal picture of titer change; results differed by site and were sensitive to assumptions in the mixture fitting. Second, I attempted to fit a hybrid-dynamical model with free incidence parameters. This inference was robust to parameter assumptions, consistent across sites, and in agreement with the incidence reported in Vietnam's influenza surveillance network. In addition, my new approach showed evidence that there was a second silent wave of the 2009 influenza pandemic that was not recorded in national surveillance, which is this thesis' main novel result.

Dysfunctional or over and under expressed enzymes play a crucial role in a variety of diseases. A tool that can identify dis-regulated enzymes in individual patients would be beneficial and would allow personalised treatment. For this purpose, a 10,000 membered ‘spit-and-mix’ PNA-encoded peptide library with a cell penetrating peptide was synthesised and interrogated with K562 cell lysate and intact K562 cells. This allowed the specific enzyme activity pattern for ABL tyrosine kinase from both inside a cell and a lysate to be obtained. Hybridisation of this library with a DNA-microarray resulted in bio-fouling by the cell lysate, thereby preventing analysis of the phosphorylation pattern. To allow extraction and purification of the peptide library from the cell lysates, a His-tag was incorporated into the library, and enabled successful library analysis. In addition to this 10,000 member library, a focused 100 PNA-encoded peptide library was synthesised. The library included peptide sequences known to be phosphorylated by specific tyrosine kinases deregulated in acute lymphoblastic leukaemia (ALL) with a PNA-tag complementary to a DNA microarray. Different ligation methods to conjugate the peptides to PNA-tags were screened – this included amide coupling, copper catalysed azide–alkyne cycloaddition, strain promoted azide–alkyne cycloaddition and Diels–Alder cycloaddition. The inverse electron demand Diels–Alder cycloaddition between a tetrazine and norbornene was chosen as the preferred ligation method, and the reaction conditions optimised. To purify the library from cell lysate, a His-tag was again coupled to each member using the strain promoted azide–alkyne cycloaddition. To test the tetrazine ligation, fluorescence in situ hybridisation (FISH) was used in cells, whereby a fluorophore was ligated onto a tetrazine–conjugated PNA probe. This was hybridised onto an mRNA in fixed cells. Results indicated that the ligation needed further optimisation.

abstract: A synbody is a newly developed protein binding peptide which can be rapidly produced by chemical methods. The advantages of the synbody producing process make it a potential human proteome binding reagent. Most of the synbodies are designed to bind to specific proteins. The peptides incorporated in a synbody are discovered with peptide microarray technology. Nevertheless, the targets for unknown synbodies can also be discovered by searching through a protein mixture. The first part of this thesis mainly focuses on the process of target searching, which was performed with immunoprecipitation assays and mass spectrometry analysis. Proteins are pulled down from the cell lysate by certain synbodies, and then these proteins are identified using mass spectrometry. After excluding non-specific bindings, the interaction between a synbody and its real target(s) can be verified with affinity measurements. As a specific example, the binding between 1-4-KCap synbody and actin was discovered. This result proved the feasibility of the mass spectrometry based method and also suggested that a high throughput synbody discovery platform for the human proteome could be developed. Besides the application of synbody development, the peptide microarray technology can also be used for immunosignatures. The composition of all types of antibodies existing in one's blood is related to an individual's health condition. A method, called immunosignaturing, has been developed for early disease diagnosis based on this principle. CIM10K microarray slides work as a platform for blood antibody detection in immunosignaturing. During the analysis of an immunosignature, the data from these slides needs to be validated by using landing light peptides. The second part of this thesis focuses on the validation of the data. A biotinylated peptide was used as a landing light on the new CIM10K slides. The data was collected in several rounds of tests and indicated that the variation among landing lights was significantly reduced by using the newly prepared biotinylated peptide compared with old peptide mixture. Several suggestions for further landing light improvement are proposed based on the results. / Dissertation/Thesis / M.S. Biological Design 2011

abstract: Immunosignaturing is a new immunodiagnostic technology that uses random-sequence peptide microarrays to profile the humoral immune response. Though the peptides have little sequence homology to any known protein, binding of serum antibodies may be detected, and the pattern correlated to disease states. The aim of my dissertation is to analyze the factors affecting the binding patterns using monoclonal antibodies and determine how much information may be extracted from the sequences. Specifically, I examined the effects of antibody concentration, competition, peptide density, and antibody valence. Peptide binding could be detected at the low concentrations relevant to immunosignaturing, and a monoclonal's signature could even be detected in the presences of 100 fold excess naive IgG. I also found that peptide density was important, but this effect was not due to bivalent binding. Next, I examined in more detail how a polyreactive antibody binds to the random sequence peptides compared to protein sequence derived peptides, and found that it bound to many peptides from both sets, but with low apparent affinity. An in depth look at how the peptide physicochemical properties and sequence complexity revealed that there were some correlations with properties, but they were generally small and varied greatly between antibodies. However, on a limited diversity but larger peptide library, I found that sequence complexity was important for antibody binding. The redundancy on that library did enable the identification of specific sub-sequences recognized by an antibody. The current immunosignaturing platform has little repetition of sub-sequences, so I evaluated several methods to infer antibody epitopes. I found two methods that had modest prediction accuracy, and I developed a software application called GuiTope to facilitate the epitope prediction analysis. None of the methods had sufficient accuracy to identify an unknown antigen from a database. In conclusion, the characteristics of the immunosignaturing platform observed through monoclonal antibody experiments demonstrate its promise as a new diagnostic technology. However, a major limitation is the difficulty in connecting the signature back to the original antigen, though larger peptide libraries could facilitate these predictions. / Dissertation/Thesis / Ph.D. Molecular and Cellular Biology 2011

Les résultats d’expériences de microarray furent décriés par le manque de concordance inter-expériences. Les listes immenses de gènes résultant de filtrages statistiques sont difficiles à exploiter. La Food and Drug Administration a montré que le choix d’indicateurs de filtrage de gènes était la source d’une grande disparité entre expériences de microarray issus de laboratoires indépendants. Dans ce contexte, nous avons développé une méthode de sélection basée sur la modélisation de l’allure de la courbe d’expression avec le Log2 du « fold-change » entre les points de cinétique. En effet, des gènes co-régulés au cours d’une cinétique temporelle présentent des courbes d’expression d’allure similaire alors que leur niveau d’expression peut être différent. Nous avons validé la méthode grâce à 2 expériences indépendantes de microar-ray étudiant la différenciation des ovaires d’embryons de souris. Ainsi, nous avons obtenu une liste réduite et pertinente de gènes exprimés. Puis, une analyse de ces résultats dans le cas de la différenciation ovarienne nous a permis d’identifier 9 nouveaux gènes candidats validés in silico et restant à être testés biologiquement. / Microarray results were blamed because of their lack of concordance. Moreover, the huge candidate gene lists from statistical filterings are not useful for biologists. FDA proved that the lack of reliability between microarray experiments came from the choice of gene filtering indicators. In this context, a filtering method was developed based on expression curve shape modelling with the use of Log 2 of fold-change between kinetic points. Actually, the co-regulated genes display similar expression shape but with heterogeneous expression level.Our method was developed and validated thanks to two independent microarray experiments (Affymetric®) from mouse embryonic ovaries. Therefore, a short and relevant list of genes was obtained. Thus, a study of results linked to ovarian differentiation permitted to identify nine new candidate genes that were in silico validated. These genes might be biologically tested (i.e. RT PCR) by the scientific community.

This thesis includes studies on N-, mucin type O-, and glycosaminoglycan (GAG)-linked glycosylation in human biospecimens. Glycosylation plays a central role in biological processes, including protein folding, immune surveillance, and regulation of cell growth. The structures of GAG are regulated in a tissue-specific manner. Heparan sulfate (HS) and chondroitin sulfate (CS) are the two types of GAGs targeted in this thesis. Human leukocytes express both CS and HS GAGs with CS being the more abundant type; however, little is known regarding the properties and structures of GAG chains, their ranges of variability among normal subjects, and changes in structure associated with disease conditions. We measured the relative and absolute disaccharides abundances of HS and CS for purified B, T, NK cells, monocytes, and polymorphonuclear leukocytes (PMNs) using size exclusion chromatography-mass spectrometry (SEC-MS). We found that all leukocytes express HS chains with levels of sulfation more similar to heparin than to organ-derived HS. In addition, CS abundances varied considerably in a leukocyte cell type specific manner. Therefore, our results established the ranges of GAG structures expressed on normal leukocytes as well as necessary for subsequent inquiry into disease conditions. Glioblastoma (GBM) accounts for 30% of human primary brain tumors. It is deadly and highly invasive. In past decades, most GBM research focused on pathophysiological changes in genome. There remains a dearth of knowledge regarding alterations in glycomics, glycoproteomics, and proteomics during GBM tumorigenesis. Therefore, we developed a comprehensive platform for high-throughput sample preparation with surface digestion for tissue microarrays, LC-MS/MS data dependent acquisition, and semi-automated data analysis to integrate glycomics, glycoproteomics, and proteomics for different grade of tumor and different subtypes of GBM. By analyzing GBM tissue microarrays, we found tumor grade and subtype specific changes to the expression of biomolecules. We also identified approximately 100 site-specific N- and mucin type O-glycosylations, the majority of which were previously unreported. Overall, our results improved the fundamental understandings about GBM pathogenesis. / 2018-11-02T00:00:00Z

Publicly-available microarray data constitutes a huge resource for researchers in biological science. A wealth of microarray data is available for the model organism – the mouse. Pluripotent embryonic stem (ES) cells are able to give rise to all of the adult tissues of the organism and, as such, are much-studied for their myriad applications in regenerative medicine. Fully differentiated, somatic cells can also be reprogrammed to pluripotency to give induced pluripotent stem cells (iPSCs). ES cells progress through a range of cellular states between ground state pluripotent stem cells, through the primed state ready for differentiation, to actual differentiation. Microarray data available in public, online repositories is annotated with several important fields, although this accompanying annotation often contains issues which can impact its usefulness to human and / or programmatic interpretation for downstream analysis. This thesis assembles and makes available to the research community the largest-to-date pluripotent mouse ES cell (mESC) microarray dataset and details the manual annotation of those samples for several key fields to allow further investigation of the pluripotent state in mESCs. Microarray samples from a given laboratory or experiment are known to be similar to each other due to batch effects. The same has been postulated about samples which use the same cell line. This work therefore precedes the investigation of transcriptional events in mESCs with an investigation into whether a sample's cell line or source laboratory is a greater contributor to the similarity between samples in this collected pluripotent mESC dataset using a method employing Random Submatrix Total Variability, and so named RaSToVa. Further, an extension of the same permutation and analysis method is developed to enable Discovery of Annotation-Linked Gene Expression Signatures (DALGES), and this is applied to the gathered data to provide the first large-scale analysis of transcriptional profiles and biological pathway activity of three commonly-used mESC cell lines and a selection of iPSC samples, seeking insight into potential biological differences that may result from these. This work then goes on to re-order the pluripotent mESC data by markers of known pluripotency states, from ground state pluripotency through primed pluripotency to earliest differentiation and analyses changes in gene expression and biological pathway activity across this spectrum, using differential expression and a window-scanning approach, seeking to recapitulate transcriptional patterns known to occur in mESCs, revealing the existence of putative “early” and “late” naïve pluripotent states and thereby identifying several lines of enquiry for in-laboratory investigation.

abstract: The goal of this thesis is to test whether Alzheimer's disease (AD) is associated with distinctive humoral immune changes that can be detected in plasma and tracked across time. This is relevant because AD is the principal cause of dementia, and yet, no specific diagnostic tests are universally employed in clinical practice to predict, diagnose or monitor disease progression. In particular, I describe herein a proteomic platform developed at the Center for Innovations in Medicine (CIM) consisting of a slide with 10.000 random-sequence peptides printed on its surface, which is used as the solid phase of an immunoassay where antibodies of interest are allowed to react and subsequently detected with a labeled secondary antibody. The pattern of antibody binding to the microarray is unique for each individual animal or person. This thesis will evaluate the versatility of the microarray platform and how it can be used to detect and characterize the binding patterns of antibodies relevant to the pathophysiology of AD as well as the plasma samples of animal models of AD and elderly humans with or without dementia. My specific aims were to evaluate the emergence and stability of immunosignature in mice with cerebral amyloidosis, and characterize the immunosignature of humans with AD. Plasma samples from APPswe/PSEN1-dE9 transgenic mice were evaluated longitudinally from 2 to 15 months of age to compare the evolving immunosignature with non-transgenic control mice. Immunological variation across different time-points was assessed, with particular emphasis on time of emergence of a characteristic pattern. In addition, plasma samples from AD patients and age-matched individuals without dementia were assayed on the peptide microarray and binding patterns were compared. It is hoped that these experiments will be the basis for a larger study of the diagnostic merits of the microarray-based immunoassay in dementia clinics. / Dissertation/Thesis / Ph.D. Molecular and Cellular Biology 2011

Epithelial ovarian cancer is the most lethal gynecologic malignancy, with a 5-year survival rate of less than 40%. This is due in part to a lack of early detection markers and lack of specific symptoms during early disease. The laying hen is the only accessible animal model which develops epithelial ovarian cancer spontaneously, with features closely resembling the human disease. It has been estimated that approximately 30% of all cancers can be prevented with diet, exercise, and maintenance of an optimal weight, and the chronic low-grade inflammation that accompanies obesity is implicated as a causal factor in the development of cancer. Flaxseed, a rich plant source of anti-inflammatory omega-3 fatty acids and lignans which act as phytoestrogens and antioxidants, exhibits chemosuppressive effects against the development and progression of ovarian cancer. We have shown that a diet of 10% flaxseed reduces the incidence and severity of ovarian cancer when fed to laying hens over 4 years, due in part to the ability of flaxseed to suppress the production of proinflammatory PGE2 in the ovary by decreasing expression of COX enzymes. To investigate other potential specific mechanisms by which flaxseed acts to suppress ovarian cancer, we examined expression and activity of pathways known to be involved in the etiology and progression of human epithelial ovarian cancer in ovarian cancer in the laying hen, and determined whether flaxseed affected these pathways during cancer development. We investigated the effect of flaxseed and its individual components upon oxidative stress in the normal ovary and in ovarian cancer by analyzing expression of target genes of the NRF2 transcription factor. The NRF2 pathway is a "master switch" that regulates expression of ROS-responsive detoxification genes. Results revealed that expression of four genes was significantly downregulated in then ovaries of hens on the defatted flaxmeal (DFM) and whole flaxseed (WF) diets compared to hens on diets that are high in pro-inflammatory omega-6 fatty acids, suggesting that flaxseed decreases oxidative stress in the ovary. Conversely, one target gene was upregulated in ovarian cancer compared to normal ovaries, and this observation was not affected by flaxseed. Additionally, nuclear accumulation Nrf2 protein was not observed in tumor cells, suggesting that flaxseed does not exert chemosuppressive effects by modulating NRF2 signaling in ovarian cancer. To further investigate pathways potentially regulated by flaxseed, we performed a microarray with 44k features and found that a set of genes involved in branching morphogenesis was upregulated in ovarian cancer and significantly decreased by flaxseed, including E-cadherin and miR-200, suggesting that flaxseed impedes the activity of an aberrantly activated developmental program that controls gland formation during ovarian cancer progression. Lack of nuclear accumulation of ZEB1 protein in tumor cells suggests that this decrease in expression is likely not due to EMT. Finally, due to its known roles in controlling developmental programs such as EMT as well as regulating cell growth and proliferation, we performed a set of experiments to examine activity of the TGF-beta pathway. PCR array analysis revealed that SMAD target genes, ligands, receptors, and co-regulatory proteins were upregulated in ovarian tumors from hens on both diet groups, suggesting TGF-beta signaling is enhanced in ovarian cancer. However, expression of SMAD6 and SMAD7 was upregulated in tumors from hens on the flaxseed diet but not control diet, with SMAD7 protein being expressed in both epithelial tumor cells and intratumoral stromal cells. Additionally, immunohistochemical staining for pSMAD2/3 was decreased in epithelial tumor cells and absent from intratumoral stromal cells in tumors from hens on the flaxseed diet compared to tumors from hens on the control diet, and these data together suggest that flaxseed may inhibit pro-oncogenic TGF-beta signaling in ovarian cancer. Finally, flaxseed prevents the downregulation of expression of p15 and the upregulation of CCNA and CCNE in ovarian tumors, suggesting that flaxseed may slow cell cycle progression. Data from these studies provides preliminary evidence that flaxseed exerts pleiotropic effects upon gene expression to negatively regulate pathways driving the progression of ovarian cancer, including aberrant TGF-beta signaling and glandular development. These studies provide groundwork for in vitro studies to test the specific effects of flaxseed upon proteins involved in TGF-beta signaling and upon the expansion of tumor epithelia.

The biological datasets produced as a result of high-throughput genomic research such as specifically microarrays, contain vast amounts of knowledge for entire genome and their expression affiliations. Gene clustering from such data is a challenging task due to the huge data size and high complexity of the algorithms as well as the visualization needs. Most of the existing analysis methods for genome-wide gene expression profiles are sequential programs using greedy algorithms and require subjective human decision. Recently, Zhu et al. proposed a parallel Random matrix theory (RMT) based approach for generating transcriptional networks, which is much more resistant to high level of noise in the data [9] without human intervention. Nowadays GPUs are designed to be used more efficiently for general purpose computing [1] and are vastly superior to CPUs [6] in terms of threading performance. Our kernel functions running on GPU utilizes the functions from both the libraries of Compute Unified Basic Linear Algebra Subroutines (CUBLAS) and Compute Unified Linear Algebra (CULA) which implements the Linear Algebra Package (LAPACK). Our experiment results show that GPU program can achieve an average speed-up of 2~3 times for some simulated datasets.