Forensisch relevante SNP-Genotypisierung mittels elektronischer Microarray-Technologie

Die vorliegende Dissertation entstand im Rahmen eines multizentrischen EU-geförderten Projektes, das die Anwendungsmöglichkeiten von Einzelnukleotid-Polymorphismen (SNPs) zur Individualisierung von Personen im Kontext der Zuordnung von biologischen Tatortspuren oder auch bei der Identifizierung unbekannter Toter behandelt. Die übergeordnete Zielsetzung des Projektes bestand darin, hochauflösende Genotypisierungsmethoden zu etablieren und zu validieren, die mit hoher Genauigkeit aber geringen Aufwand SNPs im Multiplexformat simultan analysieren können. Zunächst wurden 29 Y-chromosomale und 52 autosomale SNPs unter der Anforderung ausgewählt, dass sie als Multiplex eine möglichst hohe Individualisierungschance aufweisen. Anschließend folgten die Validierungen beider Multiplex-Systeme und der SNaPshot™-Minisequenzierungsmethode in systematischen Studien unter Beteiligung aller Arbeitsgruppen des Projektes. Die validierte Referenzmethode auf der Basis einer Minisequenzierung diente einerseits für die kontrollierte Zusammenarbeit unterschiedlicher Laboratorien und andererseits als Grundlage für die Entwicklung eines Assays zur SNP-Genotypisierung mittels der elektronischen Microarray-Technologie in dieser Arbeit. Der eigenständige Hauptteil dieser Dissertation beschreibt unter Verwendung der zuvor validierten autosomalen SNPs die Neuentwicklung und Validierung eines Hybridisierungsassays für die elektronische Microarray-Plattform der Firma Nanogen Dazu wurden im Vorfeld drei verschiedene Assays etabliert, die sich im Funktionsprinzip auf dem Microarray unterscheiden. Davon wurde leistungsorientiert das Capture down-Assay zur Weiterentwicklung ausgewählt. Nach zahlreichen Optimierungsmaßnahmen hinsichtlich PCR-Produktbehandlung, gerätespezifischer Abläufe und analysespezifischer Oligonukleotiddesigns stand das Capture down-Assay zur simultanen Typisierung von drei Individuen mit je 32 SNPs auf einem Microarray bereit. Anschließend wurde dieses Verfahren anhand von 40 DNA-Proben mit bekannten Genotypen für die 32 SNPs validiert und durch parallele SNaPshot™-Typisierung die Genauigkeit bestimmt. Das Ergebnis beweist nicht nur die Eignung des validierten Analyseassays und der elektronischen Microarray-Technologie für bestimmte Fragestellungen, sondern zeigt auch deren Vorteile in Bezug auf Schnelligkeit, Flexibilität und Effizienz. Die Automatisierung, welche die räumliche Anordnung der zu untersuchenden Fragmente unmittelbar vor der Analyse ermöglicht, reduziert unnötige Arbeitsschritte und damit die Fehlerhäufigkeit und Kontaminationsgefahr bei verbesserter Zeiteffizienz. Mit einer maximal erreichten Genauigkeit von 94% kann die Zuverlässigkeit der in der forensischen Genetik aktuell eingesetzten STR-Systeme jedoch noch nicht erreicht werden. Die Rolle des neuen Verfahrens wird damit nicht in einer Ablösung der etablierten Methoden, sondern in einer Ergänzung zur Lösung spezieller Probleme wie z.B. der Untersuchung stark degradierter DNA-Spuren zu finden sein.

Validation of a new method to assess the long-term Hypothalamic-Pituitary-Adrenal Activity using hair glucocorticoids as biomarkers. - Studies on pigs and laboratory Sprague-Dawley Rats

The evaluation of chronic activity of the hypothalamic-pituitary-adrenal (HPA) axis is critical for determining the impact of chronic stressful situations. The potential use of hair glucocorticoids as a non-invasive, retrospective, biomarker of long term HPA activity is of great interest, and it is gaining acceptance in humans and animals. However, there are still no studies in literature examining hair cortisol concentration in pigs and corticosterone concentration in laboratory rodents. Therefore, we developed and validated, for the first time, a method for measuring hair glucocorticoids concentration in commercial sows and in Sprague-Dawley rats. Our preliminary data demonstrated: 1) a validated and specific washing protocol and extraction assay method with a good sensitivity in both species; 2) the effect of the reproductive phase, housing conditions and seasonality on hair cortisol concentration in sows; 3) similar hair corticosterone concentration in male and female rats; 4) elevated hair corticosterone concentration in response to chronic stress manipulations and chronic ACTH administration, demonstrating that hair provides a good direct index of HPA activity over long periods than other indirect parameters, such adrenal or thymus weight. From these results we believe that this new non-invasive tool needs to be applied to better characterize the overall impact in livestock animals and in laboratory rodents of chronic stressful situations that negatively affect animals welfare. Nevertheless, further studies are needed to improve this methodology and maybe to develop animal models for chronic stress of high interest and translational value in human medicine.

Proposal for a 10-high-power-fields scoring method for the assessment of tumor budding in colorectal cancer

Although tumor budding is linked to adverse prognosis in colorectal cancer, it remains largely unreported in daily diagnostic work due to the absence of a standardized scoring method. Our aim was to assess the inter-observer agreement of a novel 10-high-power-fields method for assessment of tumor budding at the invasive front and to confirm the prognostic value of tumor budding in our setting of colorectal cancers. Whole tissue sections of 215 colorectal cancers with full clinico-pathological and follow-up information were stained with cytokeratin AE1/AE3 antibody. Presence of buds was scored across 10-high-power fields at the invasive front by two pathologists and two additional observers were asked to score 50 cases of tumor budding randomly selected from the larger cohort. The measurements were correlated to the patient and tumor characteristics. Inter-observer agreement and correlation between observers' scores were excellent (P<0.0001; intraclass correlation coefficient=0.96). A test subgroup of 65 patients (30%) was used to define a valid cutoff score for high-grade tumor budding and the remaining 70% of the patients were entered into the analysis. High-grade budding was defined as an average of ≥10 buds across 10-high-power fields. High-grade budding was associated with a higher tumor grade (P<0.0001), higher TNM stage (P=0.0003), vascular invasion (P<0.0001), infiltrating tumor border configuration (P<0.0001) and reduced survival (P<0.0001). Multivariate analysis confirmed its independent prognostic effect (P=0.007) when adjusting for TNM stage and adjuvant therapy. Using 10-high-power fields for evaluating tumor budding has independent prognostic value and shows excellent inter-observer agreement. Like the BRE and Gleason scores in breast and prostate cancers, respectively, tumor budding could be a basis for a prognostic score in colorectal cancer.

Measuring substrate binding and affinity of purified membrane transport proteins using the scintillation proximity assay

The scintillation proximity assay (SPA) is a rapid radioligand binding assay. Upon binding of radioactively labeled ligands (here L-[(3)H]arginine or D-[(3)H]glucose) to acceptor proteins immobilized on fluoromicrospheres (containing the scintillant), a light signal is stimulated and measured. The application of SPA to purified, detergent-solubilized membrane transport proteins allows substrate-binding properties to be assessed (e.g., substrate specificity and affinity), usually within 1 d. Notably, the SPA makes it possible to study specific transporters without interference from other cellular components, such as endogenous transporters. Reconstitution of the target transporter into proteoliposomes is not required. The SPA procedure allows high sample throughput and simple sample handling without the need for washing or separation steps: components are mixed in one well and the signal is measured directly after incubation. Therefore, the SPA is an excellent tool for high-throughput screening experiments, e.g., to search for substrates and inhibitors, and it has also recently become an attractive tool for drug discovery.

Rapid and reliable genotyping of polymorphic loci modifying correct splicing of CFTR pre-mRNA using mass spectrometry

We describe a fast and unambiguous method for haplotyping the (TG)mTn repeat in IVS8 and determining three other single nucleotide polymorphisms (SNPs) in exons 10, 14a and 24 in the cystic fibrosis transmembrane conductance regulator (CFTR) gene affecting correct splicing of the CFTR pre-mRNA using primer extension and mass spectrometry. The diagnostic products are generated by primer extension (PEX) reactions, which require a single detection primer complementary to a region downstream of a target strand's variable site. On addition of a polymerase and an appropriate mixture of dNTP's and 2', 3'-dideoxynucleotide triphosphates (ddNTP's), the primer is extended through the mutation region until the first ddNTP is incorporated and the mass of the extension products determines the composition of the variable site. Analysis of patient DNA assigned the correct and unambiguous haplotype for the (TG)mTn repeat in intron 8 of the CFTR gene. Additional crucial SNPs influencing correct splicing in exon 10, 14 and 24 can easily be detected by biplexing the assay to genotype allelic variants important for correct splicing of the CFTR pre-mRNA. Different PEX reactions with subsequent mass spectrometry generate sufficient data, to enable unambiguous and easy haplotyping of the (TG)mTn repeat in the CFTR gene. The method can be easily extended to the inclusion of additional SNPs of interest by biplexing some of the PEX reactions. All experimental steps required for PEX are amenable to the high degree of automation desirable for a high-throughput diagnostic setting, facilitating the work of clinicians involved in the diagnosis of non-classic cystic fibrosis.

HIGH PERFORMANCE, LOW COST SUBSPACE DECOMPOSITION AND POLYNOMIAL ROOTING FOR REAL TIME DIRECTION OF ARRIVAL ESTIMATION: ANALYSIS AND IMPLEMENTATION

This thesis develops high performance real-time signal processing modules for direction of arrival (DOA) estimation for localization systems. It proposes highly parallel algorithms for performing subspace decomposition and polynomial rooting, which are otherwise traditionally implemented using sequential algorithms. The proposed algorithms address the emerging need for real-time localization for a wide range of applications. As the antenna array size increases, the complexity of signal processing algorithms increases, making it increasingly difficult to satisfy the real-time constraints. This thesis addresses real-time implementation by proposing parallel algorithms, that maintain considerable improvement over traditional algorithms, especially for systems with larger number of antenna array elements. Singular value decomposition (SVD) and polynomial rooting are two computationally complex steps and act as the bottleneck to achieving real-time performance. The proposed algorithms are suitable for implementation on field programmable gated arrays (FPGAs), single instruction multiple data (SIMD) hardware or application specific integrated chips (ASICs), which offer large number of processing elements that can be exploited for parallel processing. The designs proposed in this thesis are modular, easily expandable and easy to implement. Firstly, this thesis proposes a fast converging SVD algorithm. The proposed method reduces the number of iterations it takes to converge to correct singular values, thus achieving closer to real-time performance. A general algorithm and a modular system design are provided making it easy for designers to replicate and extend the design to larger matrix sizes. Moreover, the method is highly parallel, which can be exploited in various hardware platforms mentioned earlier. A fixed point implementation of proposed SVD algorithm is presented. The FPGA design is pipelined to the maximum extent to increase the maximum achievable frequency of operation. The system was developed with the objective of achieving high throughput. Various modern cores available in FPGAs were used to maximize the performance and details of these modules are presented in detail. Finally, a parallel polynomial rooting technique based on Newton’s method applicable exclusively to root-MUSIC polynomials is proposed. Unique characteristics of root-MUSIC polynomial’s complex dynamics were exploited to derive this polynomial rooting method. The technique exhibits parallelism and converges to the desired root within fixed number of iterations, making this suitable for polynomial rooting of large degree polynomials. We believe this is the first time that complex dynamics of root-MUSIC polynomial were analyzed to propose an algorithm. In all, the thesis addresses two major bottlenecks in a direction of arrival estimation system, by providing simple, high throughput, parallel algorithms.

Absolute quantitative real-time polymerase chain reaction for the measurement of human papillomavirus E7 mRNA in cervical cytobrush specimens.

BACKGROUND: Few reports of the utilization of an accurate, cost-effective means for measuring HPV oncogene transcripts have been published. Several papers have reported the use of relative quantitation or more expensive Taqman methods. Here, we report a method of absolute quantitative real-time PCR utilizing SYBR-green fluorescence for the measurement of HPV E7 expression in cervical cytobrush specimens. RESULTS: The construction of a standard curve based on the serial dilution of an E7-containing plasmid was the key for being able to accurately compare measurements between cervical samples. The assay was highly reproducible with an overall coefficient of variation of 10.4%. CONCLUSION: The use of highly reproducible and accurate SYBR-based real-time polymerase chain reaction (PCR) assays instead of performing Taqman-type assays allows low-cost, high-throughput analysis of viral mRNA expression. The development of such assays will help in refining the current screening programs for HPV-related carcinomas.

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Targeting TRAF6 for Cancer Therapeutical Development

Tumor necrosis factor (TNF)-Receptor Associated Factors (TRAFs) are a family of signal transducer proteins. TRAF6 is a unique member of this family in that it is involved in not only the TNF superfamily, but the toll-like receptor (TLR)/IL-1R (TIR) superfamily. The formation of the complex consisting of Receptor Activator of Nuclear Factor κ B (RANK), with its ligand (RANKL) results in the recruitment of TRAF6, which activates NF-κB, JNK and MAP kinase pathways. TRAF6 is critical in signaling with leading to release of various growth factors in bone, and promotes osteoclastogenesis. TRAF6 has also been implicated as an oncogene in lung cancer and as a target in multiple myeloma. In the hopes of developing small molecule inhibitors of the TRAF6-RANK interaction, multiple steps were carried out. Computational prediction of hot spot residues on the protein-protein interaction of TRAF6 and RANK were examined. Three methods were used: Robetta, KFC2, and HotPoint, each of which uses a different methodology to determine if a residue is a hot spot. These hot spot predictions were considered the basis for resolving the binding site for in silico high-throughput screening using GOLD and the MyriaScreen database of drug/lead-like compounds. Computationally intensive molecular dynamics simulations highlighted the binding mechanism and TRAF6 structural changes upon hit binding. Compounds identified as hits were verified using a GST-pull down assay, comparing inhibition to a RANK decoy peptide. Since many drugs fail due to lack of efficacy and toxicity, predictive models for the evaluation of the LD50 and bioavailability of our TRAF6 hits, and these models can be used towards other drugs and small molecule therapeutics as well. Datasets of compounds and their corresponding bioavailability and LD50 values were curated based, and QSAR models were built using molecular descriptors of these compounds using the k-nearest neighbor (k-NN) method, and quality of these models were cross-validated.

Advanced Protein Modeling Method: Benchmarking and Applications in Computer-Aided Drug Discovery

Development of homology modeling methods will remain an area of active research. These methods aim to develop and model increasingly accurate three-dimensional structures of yet uncrystallized therapeutically relevant proteins e.g. Class A G-Protein Coupled Receptors. Incorporating protein flexibility is one way to achieve this goal. Here, I will discuss the enhancement and validation of the ligand-steered modeling, originally developed by Dr. Claudio Cavasotto, via cross modeling of the newly crystallized GPCR structures. This method uses known ligands and known experimental information to optimize relevant protein binding sites by incorporating protein flexibility. The ligand-steered models were able to model, reasonably reproduce binding sites and the co-crystallized native ligand poses of the β2 adrenergic and Adenosine 2A receptors using a single template structure. They also performed better than the choice of template, and crude models in a small scale high-throughput docking experiments and compound selectivity studies. Next, the application of this method to develop high-quality homology models of Cannabinoid Receptor 2, an emerging non-psychotic pain management target, is discussed. These models were validated by their ability to rationalize structure activity relationship data of two, inverse agonist and agonist, series of compounds. The method was also applied to improve the virtual screening performance of the β2 adrenergic crystal structure by optimizing the binding site using β2 specific compounds. These results show the feasibility of optimizing only the pharmacologically relevant protein binding sites and applicability to structure-based drug design projects.

Quantitative high performance liquid chromatography/tandem mass spectrometric analysis of the four classes of F2-isoprostanes in human urine

Isoprostanes (iPs) are free radical catalyzed prostaglandin isomers. Analysis of individual isomers of PGF2α—F2-iPs—in urine has reflected lipid peroxidation in humans. However, up to 64 F2-iPs may be formed, and it is unknown whether coordinate generation, disposition, and excretion of F2-iPs occurs in humans. To address this issue, we developed methods to measure individual members of the four structural classes of F2-iPs, using liquid chromatography/tandem mass spectrometry (LC/MS/MS), in which sample preparation is minimized. Authentic standards of F2-iPs of classes III, IV, V, and VI were used to identify class-specific ions for multiple reaction monitoring. Using iPF2α-VI as a model compound, we demonstrated the reproducibility of the assay in human urine. Urinary levels of all F2-iPs measured were elevated in patients with familial hypercholesterolemia. However, only three of eight F2-iPs were elevated in patients with congestive heart failure, compared with controls. Paired analyses by GC/MS and LC/MS/MS of iPF2α-VI in hypercholesterolemia and of 8,12-iso-iPF2α-VI in congestive heart failure were highly correlated. This approach will permit high throughput analysis of multiple iPs in human disease.

Direct genetic analysis by matrix-assisted laser desorption/ionization mass spectrometry

An approach to analyzing single-nucleotide polymorphisms (SNPs) found in the human genome has been developed that couples a recently developed invasive cleavage assay for nucleic acids with detection by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The invasive cleavage assay is a signal amplification method that enables the analysis of SNPs by MALDI-TOF MS directly from human genomic DNA without the need for initial target amplification by PCR. The results presented here show the successful genotyping by this approach of twelve SNPs located randomly throughout the human genome. Conventional Sanger sequencing of these SNP positions confirmed the accuracy of the MALDI-TOF MS analysis results. The ability to unambiguously detect both homozygous and heterozygous genotypes is clearly demonstrated. The elimination of the need for target amplification by PCR, combined with the inherently rapid and accurate nature of detection by MALDI-TOF MS, gives this approach unique and significant advantages in the high-throughput genotyping of large numbers of SNPs, useful for locating, identifying, and characterizing the function of specific genes.

Development of a sensitive multi-well colorimetric assay for active NFκB

The transcription factor nuclear factor κB (NFκB) is a key factor in the immune response triggered by a wide variety of molecules such as inflammatory cytokines, or some bacterial and viral products. This transcription factor represents a new target for the development of anti-inflammatory molecules, but this type of research is currently hampered by the lack of a convenient and rapid screening assay for NFκB activation. Indeed, NFκB DNA-binding capacity is traditionally estimated by radioactive gel shift assay. Here we propose a new DNA-binding assay based on the use of multi-well plates coated with a cold oligonucleotide containing the consensus binding site for NFκB. The presence of the DNA-bound transcription factor is then detected by anti-NFκB antibodies and revealed by colorimetry. This assay is easy to use, non-radioactive, highly reproducible, specific for NFκB, more sensitive than regular radioactive gel shift and very convenient for high throughput screening.

Alkaline-mediated differential interaction (AMDI): A simple automatable single-nucleotide polymorphism assay

The key requirements for high-throughput single-nucleotide polymorphism (SNP) typing of DNA samples in large-scale disease case-control studies are automatability, simplicity, and robustness, coupled with minimal cost. In this paper we describe a fluorescence technique for the detection of SNPs that have been amplified by using the amplification refractory mutation system (ARMS)-PCR procedure. Its performance was evaluated using 32 sequence-specific primer mixes to assign the HLA-DRB alleles to 80 lymphoblastoid cell line DNAs chosen from our database for their diversity. All had been typed previously by alternative methods, either direct sequencing or gel electrophoresis. We believe the detection system that we call AMDI (alkaline-mediated differential interaction) satisfies the above criteria and is suitable for general high-throughput SNP typing.

Matrix effects: The Achilles heel of quantitative high-performance liquid chromatography-electrospray-tandem mass spectrometry

High-performance liquid chromatography coupled by an electrospray ion source to a tandem mass spectrometer (HPLC-EST-MS/ MS) is the current analytical method of choice for quantitation of analytes in biological matrices. With HPLC-ESI-MS/MS having the characteristics of high selectivity, sensitivity, and throughput, this technology is being increasingly used in the clinical laboratory. An important issue to be addressed in method development, validation, and routine use of HPLC-ESI-MS/MS is matrix effects. Matrix effects are the alteration of ionization efficiency by the presence of coeluting substances. These effects are unseen in the chromatograrn but have deleterious impact on methods accuracy and sensitivity. The two common ways to assess matrix effects are either by the postextraction addition method or the postcolumn infusion method. To remove or minimize matrix effects, modification to the sample extraction methodology and improved chromatographic separation must be performed. These two parameters are linked together and form the basis of developing a successful and robust quantitative HPLC-EST-MS/MS method. Due to the heterogenous nature of the population being studied, the variability of a method must be assessed in samples taken from a variety of subjects. In this paper, the major aspects of matrix effects are discussed with an approach to address matrix effects during method validation proposed. (c) 2004 The Canadian Society of Clinical Chemists. All rights reserved.