Cancer cells display aberrant receptors on their surface that can serve as targets for the development of directed drug therapies. As such, our group has utilized two parallel approaches to redirect the cytotoxic properties of a ribosome-inactivating protein (RIP), Shiga-Like Toxin 1 (SLT 1), by altering its receptor specificity to target and kill cancer cells. The first combinatorial protein library was constructed such that a randomized 7 AA long peptide was inserted within the cytotoxic domain (A chain) of SLT-1. A high-throughput protein-based screening campaign identified a novel A chain toxin variant (named SLT 1AIYSNKLM) capable of targeting and killing human melanoma cells. This variant harbours a peptide insert (IYSNKLM) that directs the A chain to kill human melanoma cell lines. Equilibrium binding studies using 125I-radiolabeled SLT-1AIYSNKLM were conducted to determine the equilibrium binding constant and receptor density on 518-A2 human melanoma cells. When injected into SCID mice bearing a human melanoma xenograft, nanoSPECT/CT imaging as well as the biodistribution profile showed marked tumour uptake and retention of the radiolabeled toxin variant. Furthermore, preliminary experiments have shown that the SLT-1AIYSNKLM receptor is a protein, highlighting the potential for this method to be used in the discovery of novel biomarkers. A second approach was employed to demonstrate that our toxin-based combinatorial library system can be adapted to target known cancer biomarkers. Specifically, SLT-1 A chain variants harbouring 12-residue inserts were expressed in a phage display library. The library was screened against cell lines expressing the human colon cancer marker carcinoembryonic antigen (CEA; CD66e; CEACAM-5) to identify candidates that not only targeted, but internalized into cancer cells within a 1 h period. Variant, CSTA-10, was found to kill CEA-expressing BxPC-3 cells. Overall, the directed evolution of an RIP template such as SLT-1 represents a novel and powerful strategy for the identification of tumour-targeted toxin variants.

In April 2010, approximately 4.9 million barrels of oil were accidentally released into the Gulf of Mexico during the Deepwater Horizon Macondo Mc252 Oil Spill. Some of the surface oil was carried by prevailing winds and currents and reached the coast of Louisiana impacting marsh and marine ecosystems. One and a half years after this incident, a set of oiled marsh samples (2 grab samples) coupled with nearby subtidal and intertidal cores (12 cores) were collected from Barataria Bay, Louisiana to determine the probable source of petroleum residues present and to characterize the chemical composition of the oil. Plus, pre-spill core which was collected from Barataria Bay in 2007 was analyzed to identify the background hydrocarbon composition of the area. Polycyclic aromatic hydrocarbons (PAH), total petroleum hydrocarbons (TPH), biomarker, and stable carbon isotope compositions of selected samples were detected using a GC-MS and an elemental analyzer Conflo system coupled to a DeltaPlusXP isotope ratio mass spectrometer. The comprehensive chemical data allowed us to classify the pre and post-spill samples into 4 Groups. According to this classification, Group 1 and Group 2 samples had the highest concentrations of petroleum-derived hydrocarbons. Group 3 and background samples, on the other hand, was dominated by biogenic signatures. Although a direct connection between the detected and spilled Macondo oils results are complicated due to confounding factors (e.g., already present hydrocarbons and weathering processes), our biomarker data indicates that both oils have similar signatures. This close genetic relationship was also identified by stable carbon isotope analysis. The impact of the Macondo Mc252 Oil Spill in Barataria Bay appears to be limited to areas closer to the source. The oil has undergone moderate weathering and has penetrated into, the at least, the top 9 cm sediments. Additionally, to examine the decadal-scale history of sedimentation in these marshes, a sediment core was analyzed for the radioisotope 137Cs. The observed sedimentation rate of 0.39 cm/yr shows that oil pollutant input into Barataria Bay has been ongoing for at least 50-60 years.

Intakes of the highly unsaturated fatty acids (HUFA, ≥20 Carbons, ≥3 double bonds) eicosapentaenoic acid (20:5n-3; EPA) and docosahexaenoic acid (22:6n-3, DHA) greater than 0.25 g/d are currently recommended for health benefits. Targets for omega-3 blood biomarkers have also been proposed based on associations with protection against coronary heart disease mortality. The relationship between diet intakes and blood biomarkers is not well defined, particularly differences between men and women. North American intakes and blood biomarkers of EPA and DHA are typically below recommendations and targets. To address this disparity, adherence to dietary advice strategies to increase EPA + DHA intake was investigated over one year. Adherence was sustained up to 12 weeks and long-term adherence was well characterized by the % of DHA in erythrocytes. For women, n-3 HUFA blood biomarkers increased following nutraceutical or combined strategy dietary advice but not seafood or functional food advice. To assist in the assessment of EPA + DHA intakes, food sources of EPA and DHA in Canada were incorporated into a semi-quantitative, nutrient-specific food frequency questionnaire (FFQ) and validated. The FFQ is an adequate tool for estimating habitual EPA and DHA intake and ranking Canadian adults by their intakes. The blood biomarker response to recommended intakes of 0.25, 0.5 and 1 g/d EPA + DHA was also characterized in adult men and women. Blood n-3 HUFA biomarkers increased in a dose-dependent manner and aligned with blood targets associated with primary cardiac arrest risk reduction. Sex differences in the DHA:EPA ratio in blood observed with low intakes at baseline disappeared following 0.25 g/d EPA + DHA. These findings are applicable towards informing achievable dietary guidelines for EPA + DHA intake and improving measurement of EPA + DHA intake in relation to blood n-3 HUFA biomarkers.

Polycyclic aromatic hydrocarbons (PAHs) are a group of toxic air pollutants formed during incomplete combustion and are ubiquitously distributed in the environment. To determine particle-bound PAHs in archived PM2.5 samples taken with low flow rate in Atlanta, a sensitive and robust method was developed for measuring 28 PAHs and methyl PAHs in PM samples using isotope dilution gas chromatography/high resolution mass spectrometry (GC/HRMS). The method was then used to analyze PM2.5 samples collected at three sites (rural, urban, suburban-highway) from the Assessment of Spatial Aerosol Composition in Atlanta (ASACA) network. Distinct seasonal and spatial variations were observed in PAH concentration. Particle-bound PAH levels were significantly higher in winter than in summer. The suburban-highway site had higher PM2.5-bound PAH concentration than did the urban site, and the rural site had the lowest PAH levels. Retene, a proposed biomass burning tracer, captured both the high leaves-grasses-bushes-branches burning season and the high wood burning months, suggesting that it might be a better marker for all biomass burning, while potassium ion might be a more specific tracer for wood burning. Human exposure to PAHs can be assessed by characterizing their hydroxy PAH (OH-PAH) metabolites in urine samples. A method was developed to measure 24 urinary OH-PAHs, metabolites of 8 parent PAHs, using enzymatic de-conjugation, automatic liquid-liquid extraction, and GC/HRMS. A study was then carried out to evaluating the variability of the urinary biomarker levels in a non-occupationally exposed non-smoking reference group. Levels of urinary PAH metabolites varied widely both within-subject and between-subjects and the within-day variance far exceeded the between-day variance. There were also considerable temporal correlations for these biomarkers. Sample size calculations were conducted and taking 24-hour voids would require the least number of subjects, which should be considered during epidemiological study design. Finally, a study was conducted to evaluate exposure to ambient PAHs in an urban setting among 8 non-occupationally exposed non-smoking volunteers employing both personal air sampling and urine biomonitoring. PAH levels varied largely in air samples taken at home, at work, and while driving or jogging. Monitoring urinary OH-PAH levels can capture both inhalation and dietary exposures. Total inhaled PAH was correlated with total excreted OH-PAHs, suggesting that by combining personal air sampling and biomonitoring, exposure to environmental PAHs can be well characterized even for low-level exposure.

This thesis, based on five research papers, presents results from development and evaluation ofmethods for identifying the interaction site of antibodies on their antigens and the functional investigation of these in different assays. As antibodies have proven to be invaluable tools in diagnostics, therapy and basic research, the demand of characterizing these binding molecules has increased. Techniques for epitope mapping in a streamlined manner are therefore needed, particularly in high throughput projects as the Human Protein Atlas that aims to systematically generate two antibodies with separate epitopes towards all human proteins.  In paper I we describe an approach to map the epitopes of polyclonal and monoclonal antibodies for the first time using staphylococcal display. This method was combined with peptide scanning and alanine scanning using suspension bead arrays, to create a streamlined approach of highresolution characterization of epitopes recognized by antibodies as demonstrated in paper II. Single epitopes were identified for the monoclonal antibodies and several (one to five) separate epitopes scattered throughout the antigen sequence were determined for each polyclonal antibody. Further, antibodies of different species origin showed overlapping binding epitopes. In paper III we studied the epitope patterns of polyclonal antibodies generated with the same antigen in different animals. Although common epitope regions could be identified the exact epitope pattern was not repeated, as some epitopes did not reoccur in the repeated immunizations. In paper IV, a potential biomarker for colon cancer, RBM3, was investigated using validated antibodies by epitope mapping and siRNA analysis. Finally, in paper V, a method for generating epitope-specific antibodies based on affinity purification of a polyclonal antibody is described. The generated antibodies were used in several immunoassays and showed a great difference in functionality. Paired antibodies with separate epitopes were successfully generated and could be used in a sandwich assay or to validate each other in immunohistochemistry. Taken together, in these studies we have demonstrated valuable concepts for the characterization of antibody epitopes. / QC 20120111

Breast cancer is the main cause of cancer death in women. Improved treatments, prevention programs and earlier detection are reducing the rate of death; however, there is still a high percentage of mortality by this cancer. Identification of novel targets to predict response to specific treatments is a key goal for personalizing breast cancer therapy and to improve survival. Few years ago, PARP inhibitors appeared as a promising therapy, particularly in BRCA-mutated cancers. However, there was a clear need to conduct further preclinical and translational work to improve the rational development of PARP inhibition in breast cancer. In this work we described PARP1 expression in breast tumour samples and characterized the effects of its inhibition in preclinical models. We found that nuclear PARP1 protein overexpression was associated with malignant transformation and poor prognosis in breast cancer. PARP1 overexpression was more common in triple negative subtype, but was also detectable in small subsets of estrogen receptor positive and HER2 positive breast cancers. In preclinical models, PARP1 played distinct roles in different molecular subtypes of breast cancer. Moreover, we described that olaparib (novel PARP inhibitor) had antitumour effects in different breast cancer subtypes, and its combination with trastuzumab (anti-HER2 antibody) enhanced the antitumour effects of this therapy. / El càncer de mama és la principal causa de mort per càncer en dones. La millora dels tractaments i la detecció precoç estan reduint la taxa de mort, però segueix sent elevada. Identificar noves dianes per predir la resposta a tractaments és clau per millorar les teràpies contra aquest càncer i la supervivència. Els inhibidors de PARP van aparèixer com una teràpia prometedora, particularment en càncers BRCA-mutants, però, cal dur a terme més estudis preclínics i translacionals per fomentar un desenvolupament racional d’aquesta teràpia en càncer de mama. Aquest treball descriu l’expressió de PARP1 en mostres de tumors mamaris i caracteritza els efectes de la seva inhibició a models preclínics. Vam observar que la sobreexpressió nuclear de la proteïna PARP1 fou associada amb: la transformació maligna; mal pronòstic en càncer de mama; i fou més freqüent al subtipus triple-negatiu, però també es va detectar en un subgrup de càncers de mama receptors d’estrogen positius i HER2 positius. En models preclínics, PARP1 va exercir rols diferents als diferents subtipus de càncer de mama. Per altra banda, vam descriure que olaparib (inhibidor de PARP) té efectes antitumorals en els diversos subtipus, i combinat amb trastuzumab (anticòs anti-HER2) potencia els efectes antitumorals d’aquesta teràpia.

The human genome contains tens of thousands of gene loci which code for an even greater number of protein and RNA products. The highly complex temporal and spatial expression of these genes makes possible all the biological processes of life. Altered gene expression by mutation or deregulation is fundamental for the development of many human diseases. The ultimate aim of this thesis was to identify gene expression changes relevant to cancer. The advent of genome-wide expression profiling techniques, such as microarrays, has provided powerful new tools to identify such changes and researchers are now faced with an explosion of gene expression data. Processing, comparing and integrating these data present major challenges. I approached these challenges by developing and assessing novel methods for cross-platform analysis of expression data, scalable subspace clustering, and curation of experimental gene regulation data from the published literature. I found that combining results from different expression platforms increases reliability of coexpression predictions. However, I also observed that global correlation between platforms was generally low, and few gene pairs reached reasonable thresholds for high-confidence coexpression. Therefore, I developed a novel subspace clustering algorithm, able to identify coexpressed genes in experimental subsets of very large gene expression datasets. Biological assessment against several metrics indicates that this algorithm performs well. I also developed a novel meta-analysis method to identify consistently reported genes from differential expression studies when raw data are unavailable. This method was applied to thyroid cancer, producing a ranked list of significantly over-represented genes. Tissue microarray analysis of some of these candidates and others identified a number of promising biomarkers for diagnostic and prognostic classification of thyroid cancer. Finally, I present ORegAnno (www.oreganno.org), a resource for the community-driven curation of experimentally verified regulatory sequences. This resource has proven a great success with ~30,000 sequences entered from over 900 publications by ~50 contributing users. These data, methods and resources contribute to our overall understanding of gene regulation, gene expression, and the changes that occur in cancer. Such an understanding should help identify new cancer mechanisms, potential treatment targets, and have significant diagnostic and prognostic implications.

Zugl.: Göttingen, Universiẗat, Diss., 2007.

Humboldt-Universiẗat, Diss.--Berlin, 2007.

Ulm, Univ., Diss., 2009.