The interaction of proteins with surfaces is a major process involved in protein microarrays. Understanding protein-surface interactions is key to improving the performance of protein microarrays, but current understanding of the behavior of proteins on surfaces is lacking. Prevailing theories on the subject, which suggest that proteins should be stabilized when tethered to surfaces, do not explain the experimentally observed fact that proteins are often denatured on surfaces. This document outlines several studies done to develop a model which is capable of predicting the stabilization and destabilization of proteins tethered to surfaces. As the start point of the research, part of this research showed that the stability of five mainly-alpha, orthogonal-bundle proteins tethered to surfaces can be correlated to the shape of the loop region where the tether is placed and the free rotation ability of the part of proteins near surfaces. To test the expandability of the protein stability prediction pattern derived for mainly-alpha, orthogonal-bundle proteins, same analysis is performed for proteins from other structure motifs. Besides the study in these small two-state proteins, a further analysis of surface-induced change of folding mechanism is also studied with a multi-state lysozyme protein 7LZM. The result showed that by tethering a protein on a surface, the melting temperature of a part of the protein changed, which leads to an avoidance of the meta-stable state. Besides the change of folding mechanism, by tethering the lysozyme protein to a certain site, the protein could both keep a stable structure and a good orientation, allowing active sites to be available to other proteins in bulk solution. All the work described above are done with a purely repulsive surface model which was widely used to roughly simulate solid surfaces in protein microarrays. For a next-level understanding of protein-surface interactions, a novel coarse-grain surface model was developed, parameterized, and validated according to experimental results from different groups. A case study of interaction between lysozyme protein 7LZM and three types of surfaces with the novel model has been performed. The results showed that protein stabilities and structures are dependent on the types of surfaces and their different hydrophobicities. This result is consistent with previously published experimental work.

DNA microarrays are chip-based, analysis tools which can perform hundreds of thousands of parallel assays to determine the identity of genes or gene expression levels present in a sample. They have been identified as a key technology in genomic sciences and emergent medical techniques; however, despite their abundant use in research laboratories, microarrays have not been used in the clinical setting to the fullest potential due to the difficulty of obtaining reproducible results. Microarrays work on the principle of DNA hybridization, and can only be as accurate as this process is robust. Fundamental, molecular-level understanding of hybridization on surfaces is needed to further refine these devices.This work shows how orientation of DNA probes with respect to the surface affects the thermodynamics and stability of hybridization. Ideal surface hybridization (a DNA duplex bound to the surface on one end) is compared to more realistic conditions such as interaction between DNA and the surface in multiple locations. This research also describes the effect of mismatch location and number of mismatches on a single target strand. The results clarify key details of the biophysics involved in microarray performance and this knowledge can be used to improve next-generation devices. The disparity between surface and bulk hybridization behavior is examined here in molecular level detail that is not currently possible with experimental techniques.

Ischemic injury is a common and debilitating outcome of natural illness and as a complication of commonly performed medical procedures. Whereas naturally occurring ischemic insults are often the result of unpredictable events, such as in the case of stroke or heart attack, the risk of operative and perioperative ischemia is somewhat better characterized in the clinical setting. Given the prevalence and severity of outcomes in ischemic injury, there is significant interest in developing better pharmacological and procedural approaches to improve patient outcomes. One approach that has shown significant promise in the laboratory setting, particularly in the context of planned medical procedures, is the use of delayed anesthetic preconditioning. Delayed anesthetic preconditioning is a phenomenon whereby a prior exposure to clinical concentrations of commonly used inhaled anesthetics, including isoflurane, induces the production of endogenous protective proteins that are able to provide robust protection against subsequent, potentially toxic, ischemic insults. Although many aspects of delayed anesthetic preconditioning have been previously described, a complete understanding of preconditioning mechanism has yet to emerge. The studies described in this dissertation aim to further our understanding of molecular mechanisms involved in delayed anesthetic preconditioning. In the first project, I used DNA microarray to identify genes that were differentially expressed in adult rat liver, kidney and heart following a clinically relevant exposure to the inhaled anesthetic isoflurane. By selecting those genes that were differentially expressed in multiple tissues, I was able to identify a small group of interesting genes for further study. In my second study, I chose from our list two related genes, metallothioneins I + II, to analyze for a role in anesthetic-mediated protection. Using a combination of approaches, I was able to establish that metallotioneins I + II play an essential role in delayed anesthetic preconditioning. In the final study of this dissertation I explore a possible role for metallothioneins I + II as sensor molecules, involved in detecting cellular oxidative stress. Taken together, these three studies represent an important contribution to our understanding of the mechanisms of delayed anesthetic preconditioning and how they might contribute to protecting against ischemic stroke.

Immune-mediated polyneuropathies like chronic inflammatory demyelinating polyradiculoneuropathy or Guillain-Barré syndrome are rare diseases of the peripheral nervous system. A subgroup of patients harbors autoantibodies against nodal or paranodal antigens, associated with a distinct phenotype and treatment response. In a part of patients with pathologic paranodal or nodal immunoreactivity the autoantigens remain difficult or impossible to determine owing to limitations of the used detection approach - usually ELISAs (enzyme-linked-immunosorbent-assays) - and incomplete knowledge of the possible autoantigens. Due to their high-throughput, low sample consumption and high sensitivity as well as the possibility to display many putative nodal and paranodal autoantigens simultaneously, peptide microarray-based approaches are prime candidates for the discovery of novel autoantigens, point-of-care diagnostics and, in addition, monitoring of pathologic autoimmune response. Current applications of peptide microarrays are however limited by high false-positive rates and the associated need for detailed follow-up studies and validation. Here, robust peptide microarray-based detection of antibodies and the efficient validation of binding signals by on-chip neutralization is demonstrated. First, autoantigens were displayed as overlapping peptide libraries in microarray format. Copies of the biochips were used for the fine mapping of antibody epitopes. Next, binding signals were validated by antibody neutralization in solution. Since neutralizing peptides are obtained in the process of microarray fabrications, neither throughput nor costs are significantly altered. Similar in-situ validation approaches could contribute to future autoantibody characterization and detection methods as well as to therapeutic research. Areas of application could be expanded to any autoimmune-mediated neurological disease as a long-term vision. / Immunvermittelte Polyneuropathien wie die chronisch-inflammatorische demyelinisierende Polyradikuloneuropathie oder das Guillain-Barré-Syndrom sind seltene Erkrankungen des peripheren Nervensystems. Bei einem Teil dieser Patienten lassen sich Autoantikörper gegen nodale oder paranodale Antigene nachweisen, was mit einem bestimmten Phänotyp und Therapienansprechen assoziiert ist. Aufgrund der Einschränkungen verwendeter Detektionsansätze – üblicherweise ELISAs (Enzyme-linked Immunosorbent Assays) – sowie der unvollständigen Kenntnis potenzieller Autoantigene bleibt es bisher zum Teil schwierig bis unmöglich bei nachgewiesener pathologischer paranodaler bzw. nodaler Immunreaktivität die entsprechenden Autoantigene zu identifizieren. Die hohe Durchsatzleistung, der geringe Verbrauch an Probenmaterial, die hohe Sensitivität sowie die Möglichkeit zahlreiche mutmaßliche nodale und paranodale Autoantigene zeitgleich darzustellen machen Peptid-Microarray-basierte Ansätze zu wesentlichen Kandidaten für die Entdeckung neuer Autoantigene, für Point-of-Care-Diagnostik und darüber hinaus für das Monitoring pathologischer Autoimmunantworten. Durch die hohe Rate falsch positiver Ergebnisse sowie die damit verbundene Notwendigkeit detaillierter Folgestudien und Validierungen sind die gegenwärtigen Anwendungen von Peptid-Microarrays jedoch limitiert. In dieser Arbeit wird eine robuste, Peptid-Microarray-basierte Detektion von Antikörpern sowie eine effiziente Validierung der Bindungssignale mittels On-chip Neutralisation demonstriert. Zuerst wurden die Autoantigene als überlappende Peptidbüchereien im Microarray-Format dargestellt. Kopien der Biochips wurden für die Feinkartierung der Antikörper-Epitope verwendet. Mittels Antikörperneutralisation in Lösung wurden die Bindungssignale anschließend validiert. Da die neutralisierenden Peptide im Microarray- Herstellungsprozess gewonnen werden, ergeben sich weder beim Durchsatz noch bei den Kosten signifikante Änderungen. Vergleichbare In-situ-Validierungsansätze könnten zu künftigen Autoantikörper Charakterisierungen, Detektionsmethoden sowie zu therapeutischen Forschungsansätzen beitragen. Als langfristige Vision könnten die Anwendungsgebiete auf jede beliebige autoimmun-vermittelte neurologische Krankheit ausgeweitet werden.

Genome-wide microarray technology has facilitated the systematic discovery of diagnostic biomarkers of cancers and other pathologies. However, meta-analyses of published arrays using melanoma as a test cancer has uncovered significant inconsistences that hinder advances in clinical practice. In this study a computational model for the integrated analysis of microarray datasets is proposed in order to provide a robust ranking of genes in terms of their relative significance; both genome-wide relative significance (GWRS) and genome-wide global significance (GWGS). When applied to five melanoma microarray datasets published between 2000 and 2011, a new 12-gene diagnostic biomarker signature for melanoma was defined (i.e., EGFR, FGFR2, FGFR3, IL8, PTPRF, TNC, CXCL13, COL11A1, CHP2, SHC4, PPP2R2C, and WNT4). Of these, CXCL13, COL11A1, PTPRF and SHC4 are components of the MAPK pathway and were validated by immunocyto- and immunohisto-chemistry. These proteins were found to be overexpressed in metastatic and primary melanoma cells in vitro and in melanoma tissue in situ compared to melanocytes cultured from healthy skin epidermis and normal healthy human skin. One challenge for the integrated analysis of microarray data is that the microarray data are produced using different platforms and bio-samples, e.g. including both cell line- and biopsy-based microarray datasets. In order to address these challenges, the computational model was further enhanced the stratification of datasets into either biopsy or cell line derived datasets, and via the weighting of microarray data based on quality criteria of data. The methods enhancement was applied to 14 microarray datasets of three cancers (breast, prostate, and melanoma) based on classification accuracy and on the capability to identify predictive biomarkers. Four novel measures for evaluating the capability to identify predictive biomarkers are proposed: (1) classifying independent testing data using wrapper feature selection with machine leaning, (2) assessing the number of common genes with the genes retrieved in independent testing data, (3) assessing the number of common genes with the genes retrieved in across multiple training datasets, (4) assessing the number of common genes with the genes validated in the literature. This enhancement of computational approach (i) achieved reliable classification performance across multiple datasets, (ii) recognized more significant genes into the top-ranked genes as compared to the genes detected by the independent test data, and (iii) detected more meaningful genes than were validated in previous melanoma studies in the literature.

MicroRNAs are small RNA molecules that could possibly play a major role in fertility. In the experiment, spermatozoa were extracted from bovine followed by an extraction of total RNA. Bovine spermatozoa were extracted from two bulls of different fertility, high and low fertility. An expression array was done to compare the expression levels of the microRNAs. It was shown that thousands of microRNAs are present in bovine spermatozoa but only a small amount was significantly expressed. The microRNAs from low fertility bulls were more highly expressed than those in high fertility bulls. A Bioanalyzer gel was used to confirm the results of the microarray data. The microRNAs were present in the bull’s spermatozoa at 25 nucleotides. The functions of the significantly expressed microRNAs are not known but there is a great possibility that their functions affect fertility.

Chicken is an important non-mammalian vertebrate model organism for biomedical research, especially for vaccine production and the study of embryology and development. Chicken is also an important agricultural species and major food source for high-quality protein worldwide. In addition, chicken is an important model organism for comparative and evolution genomics. Exploitation of this genome as a biomedical model is hindered by its incomplete structural and functional annotation. This incomplete annotation makes it difficult for researchers to model their functional genomics datasets. Improving structural and functional annotation of the chicken genome will allow researchers to derive biological meaning from their functional genomics datasets. The objectives of this study were to identify proteins expressed in multiple chicken tissues, to functionally annotate experimentally confirmed proteins expressed in different chicken tissues, to quantify and assess the Gene Ontology (GO) annotation quality, and to facilitate functional annotation of microarray data. The results of this research have proven to be fundamental resource for improving the structural and functional annotation of chicken genome. Specifically, we have improved the structural annotation of the chicken genome by adding support to predicted proteins. In addition, we have improved the functional annotation of the chicken genome by assigning useful biological information to proteomics datasets and the whole genome chicken array. The Gene Ontology Annotation Quality (GAQ) and Array GO Mapper (AGOM) tools developed in this study will sustainably continue to facilitate functional modeling of chicken arrays and high-throughput experimental datasets from microarray and proteomics studies. The ultimate positive impact of these results is to facilitate the field of biomedical research with useful information for comparative biology, better understanding of chicken biological systems, diseases, drug discovery and eventually development of therapies.

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