Current Pharmaceutical Biotechnology - Volume 4, Issue 6, 2003

The collection of articles published under the motto “The way down from single genes and proteins to single molecules” is covering the results of groundbreaking developments of the recent years. They include single molecule detection yielding ultimate sensitivity and closely linked to the analysis of molecular noise by correlation spectroscopy. Several important examples from the field of nucleic acid as well as protein analysis as diagnostic tools are presented as well as the impact on the studies of single cells and biomembranes. The development of high sensitivity analysis at the single molecule level has in turn prompted the development of high-throughput protocols nowadays used in drug screening. In the attempt to make the vast and still growing knowledge of the human genome available for understanding the ultimate cause of disease and their therapy, these four special issues will fulfill a very important goal.

The identification of genes predisposing to human diseases is of paramount importance for understanding the molecular basis of the disease and individually different drug response, and will establish new routes to diagnosis and therapeutic advances of immense medical benefit. A key step common to all strategies for disease gene identification is the systematic analysis of candidate gene sequences to identify specific sequence variations associated with disease or any other phenotype of pharmaceutical relevance. In this article, current concepts and approaches to haplotype-based candidate gene analysis are reviewed. Moreover, a comprehensive summary of recent studies and data on the amount, nature, pattern and structure of genetic variation in candidate genes is given. These data demonstrate altogether remarkable gene sequence and haplotype diversity. Numerous individually different forms of a gene may exist. This presents challenges to the traditional views of the concept of ‘a’ gene with far-reaching implications on the functional analysis of candidate gene variation, on the establishment of ‘sequence’-‘structure’-‘function’ and complex haplotype / genotype-phenotype relationships, on the identification, evaluation and prioritization of drug targets and the concept of a ‘personalized medicine’ in general. Moreover, present and future approaches to the identification of candidate and disease genes will be addressed. These include whole genome-based approaches such as integrative genomics as well as functional genomics-based approaches to analyze and model complex biological and medical processes. The analysis of whole complex systems in particular will provide the basis to make ‘maximally informed’ guesses on candidate genes and address complex variability patterns in genes as well as complex genotype-phenotype relationships comprehensively at an advanced level.

The interactions of nucleic acids technology and the technology of arrayed nucleic acids are described, showing the interdependence of nucleic acids chemistry, surface chemistry, (micro-) technology and the requirements of biomedical applications. The methods and problems of the production of large numbers of oligonucleotides as well as the methods of arraying oligonucleotides are highlighted. The basic approaches, in-situ synthesis and postsynthetic immobilization, are described with a special emphasis on the postsynthetic immobilization of ready-made oligonucleotides on support materials. Techniques for the detection of nucleic acids interactions on arrays are outlined in brief.

The data generated by DNA arrays are often described as the molecular “portrait” of a particular physiological / pathological sample. Although the emotional reactions could range from revulsion to adoration when viewing those portraits, as it might be when viewing some contemporary art, array technology has fundamentally changed the way researchers approach many biomedical questions. With its ability to monitor the expression level of tens of thousands genes simultaneously, microarray technology has been able to identify “markers” for complex diseases such as cancer. While massive amounts of work lie ahead to validate those marker genes, many researchers are turning their attentions to the low-density, focused arrays. When incorporated with current knowledge on specific biological pathways, these specially tailored macroarrays may be better fitted for purposes such as diagnosis, drug discovery and validation, and prognostic assessment of clinical treatments.

Whereas the majority of microarray applications still deal with expression analysis for gathering information about levels of gene products at certain cell states, other approaches simply ask the question whether particular genes, which are usually indicative for particular microorganisms and pathogens, are present in a sample or not. Investigations that are more detailed try to evaluate the presence of particular subtypes of a given pathogen. The combination of microarray technology and virus diagnostics promises to generate an ideal platform for fast, sensitive, specific, and parallelized virus diagnostics. Performing virus diagnostics on microarrays, however, requires other basic techniques to be optimized. This is necessary in order to obtain unambiguous and reproducible results, which are compatible with the needs for clinical routine. Parameters that have to be considered include supports, coupling chemistry, chemical oligonucleotide synthesis, signal enhancement strategies, and optimal coordination of PCR reactions, hybridizations, and signal detection, as well as interpretation strategies. Finally, considerations should be given to economic aspects, one chipone patient strategies and low integrated arrays as a custom-tailored way to fast and accurate diagnostic tools.

Microarray technology enables researchers to investigate the expression of several thousand genes simultaneously. The whole transcriptional response of these genes in normal cells or tissue, in disease condition, as an response to biological, genetical or chemical stimuli or during normal biological processes such as cell cycle or embryonic development can be investigated. This leads to a huge amount of data, from which the relevant information has to be extracted by statistical and computational methods. Several software packages for the analysis of gene expression data are available, both commercially and freely. They differ particularly with regard to the implemented analytical methods, the graphical display and the manageability. In this paper the commercial software packages arraySCOUT, GeneSpring and Spotfire DecisionSite for Functional Genomics are compared and their applicability for analysis of gene expression data is studied. Small artificial and application test datasets are used to compare the computational results of the software packages. As far as possible results are verified with standard statistical software package SAS.

The creation of enormous libraries of chemicals and their subsequent screening for bioactivity has been accelerated through recent developments in encoding solid supports. The ability to accurately identify the structure of a biomolecule that has exhibited activity is invaluable and is closer to realisation in the advent of smart nanoscience. In this review the evolution of encoding solid supports as platforms for combinatorial synthesis is traced. Current approaches to encoding solid supports are reviewed and their potential for use as supports for the high-throughput screening of split and mix libraries explored. Finally, a brief consideration of the status of the application of encoded libraries is provided including creative chemical and colloidal encoding.

Fluorescence methods are commonly used in pharmaceutical drug discovery to assay the binding of drug-like compounds to signaling proteins and other bio-particles. For binding studies of non-fluorescent compounds, a competitive format may be used in which the binding of the compound results in displacement of another fluorescently labeled ligand. Highly-sensitive measurements within nano-liter sized open probe volumes can be accomplished using a confocal epiillumination geometry and thus key tools for such drug-binding studies include fluorescence correlation spectroscopy (FCS) and its related techniques. This paper reviews the general protocol for application of FCS to biomolecular compound-binding assays and it focuses on methods for the reduction of experimental photon count data to obtain the normalized autocorrelation function (ACF), on theoretical models of the ACF, and on statistical and systematic errors in the experimental ACF. Results from a detailed Monte Carlo simulation of FCS, which are useful for testing theoretical models and validating short-duration assay capabilities, are discussed. An illustrative example is presented on the use of FCS to assay binding of Alexa-488-labeled Bak peptide with Bcl-xL, which is an intracellular protein that acts to protect against programmed cell death.

The rapid increase of compound libraries as well as new targets emerging from the Human Genome Project require constant progress in pharmaceutical research. An important tool is High-Throughput Screening (HTS), which has evolved as an indispensable instrument in the pre-clinical target-to-IND (Investigational New Drug) discovery process. HTS requires machinery, which is able to test more than 100,000 potential drug candidates per day with respect to a specific biological activity. This calls for certain experimental demands especially with respect to sensitivity, speed, and statistical accuracy, which are fulfilled by using fluorescence technology instrumentation. In particular the recently developed family of fluorescence techniques, FIDA (Fluorescence Intensity Distribution Analysis), which is based on confocal single-molecule detection, has opened up a new field of HTS applications. This report describes the application of these new techniques as well as of common fluorescence techniques - such as confocal fluorescence lifetime and anisotropy - to HTS. It gives experimental examples and presents advantages and disadvantages of each method. In addition the most common artifacts (auto-fluorescence or quenching by the drug candidates) emerging from the fluorescence detection techniques are highlighted and correction methods for confocal fluorescence read-outs are presented, which are able to circumvent this deficiency.

Reliable, efficient and cost-effective modalities are urgently needed for mass screening of gene mutations. Previous reports have shown that SSCP or genechip methods require substantial time and monetary costs, thus limiting their appeal. Sequence Specific Primer Polymerase Chain Reaction (SSP-PCR) is a reliable and cost-effective method that utilizes the 3'-end discrimination properties of polymerase. However, the applicability of conventional SSP-PCR is limited due to the difficulties associated with determining optimal conditions and because mis-matched primers are amplified, resulting in signal noise during end-point assay. To overcome this problem, we eliminated the reverse primers from SSP-PCR, thus preventing amplification of mis-matched primers. We designated this method Sequence-Specific Primer Cycle Elongation (SSPCE). However, the detection of elongated sequence specific primers was difficult using conventional electrophoresis due to the small amounts of amplification product present. We therefore combined SSPCE and Fluorescence Correlation Spectroscopy, which is a novel technique used to determine the number and size of fluorophores at nano-molar concentrations, and designated the method SSPCE-FCS. We compared conventional SSP-PCR and SSPCE-FCS with regard to determining optimal conditions using two Mitochondrial SNPs (G → A at position 1598, G → A at position 12192). We were able to determine the optimal conditions for the SNP at position 1598 using either method. However, optimal conditions could only be determined for SSPCE-FCS with the 12192 mutation because non-specific amplification was observed at a wide range of annealing temperatures in SSP-PCR. We then applied this method to three other SNPs and the results were consistent with the results of sequencing data.