Genomics and the development of new diagnostics and anti-Candida drugs

Pathogenic Candida species remain a significant medical problem despite the availability of antifungal therapies. Two key issues must be addressed to improve the treatment of life-threatening systemic Candida infections. First, advanced diagnostic tools are required to facilitate the early identification of these infections, when therapeutic intervention is more likely to be effective. Second, improved antifungal therapies are needed. These therapies, which might include combinations of antifungals, need to be less toxic to the patient and more potent in killing a broader range of Candida species. Recent advances in unravelling the genomics of these species should facilitate efforts to achieve these goals. We discuss the contribution of genomics to the development of novel antifungals and new diagnostic tools.     

Qualified predictions for microarray and proteomics pattern diagnostics with confidence machines

We focus on the problem of prediction with confidence and describe a recently developed learning algorithm called transductive confidence machine for making qualified region predictions. Its main advantage, in comparison with other classifiers, is that it is well-calibrated, with number of prediction errors strictly controlled by a given predefined confidence level. We apply the transductive confidence machine to the problems of acute leukaemia and ovarian cancer prediction using microarray and proteomics pattern diagnostics, respectively. We demonstrate that the algorithm performs well, yielding well-calibrated and informative predictions whilst maintaining a high level of accuracy.     

Nanotechnologies for biomolecular detection and medical diagnostics

Nanotechnology-based platforms for the high-throughput, multiplexed detection of proteins and nucleic acids in heretofore unattainable abundance ranges promise to bring substantial advances in molecular medicine. The emerging approaches reviewed in this article, with reference to their diagnostic potential, include nanotextured surfaces for proteomics, a two-particle sandwich assay for the biological amplification of low-concentration biomolecular signals, and silicon-based nanostructures for the transduction of molecular binding into electrical and mechanical signals, respectively.     

Development of DNA microarray for pathogen detection

Pathogens pose a significant threat to humans, animals, and plants. Consequently, a considerable effort has been devoted to developing rapid, convenient, and accurate assays for the detection of these unfavorable organisms. Recently, DNA-microarray based technology is receiving much attention as a powerful tool for pathogen detection. After the target gene is first selected for the unique identification of microorganisms, species-specific probes are designed through bioinformatic analysis of the sequences, which uses the information present in the databases. DNA samples, which were obtained from reference and/or clinical isolates, are properly processed and hybridized with species-specific probes that are immobilized on the surface of the microarray for fluorescent detection. In this study, we review the methods and strategies for the development of DNA microarray for pathogen detection, with the focus on probe design.     

Glycopeptide microarray for autoantibody detection in cancer

Evaluation of: Pedersen JW, Blixt O, Bennett EP et al. Seromic profiling of colorectal cancer patients with novel glycopeptide microarray. Int. J. Cancer 128(8), 1860–1871 (2011).

Autoantibodies to cancer-associated antigens hold promise as sensitive biomarkers for cancer detection. Based on this hypothesis, and knowing that O-glycans on proteins constitute a source of possible epitopes recognized by autoantibodies, Pedersen and colleagues have generated a glycopeptide array displaying a comprehensive library of glycopeptides and glycoproteins derived from human mucins. The profiling of sera immunoreactivity of colon cancer patients allowed the identification of cancer-associated autoantibodies to various mucin (MUC)1 and MUC4 glycopeptides carrying aberrant glycosylation. This article provides evidence for the value of glycopeptides displaying cancer-associated glycans in diagnostic applications, and opens new avenues for the expansion to other protein glycoforms, as well as to further applications of such a microarray strategy for other post-translational modifications of proteins in the search for cancer biomarker.     

Glycopeptide microarray for autoantibody detection in cancer

Evaluation of: Pedersen JW, Blixt O, Bennett EP et al. Seromic profiling of colorectal cancer patients with novel glycopeptide microarray. Int. J. Cancer 128(8), 1860-1871 (2011). Autoantibodies to cancer-associated antigens hold promise as sensitive biomarkers for cancer detection. Based on this hypothesis, and knowing that O-glycans on proteins constitute a source of possible epitopes recognized by autoantibodies, Pedersen and colleagues have generated a glycopeptide array displaying a comprehensive library of glycopeptides and glycoproteins derived from human mucins. The profiling of sera immunoreactivity of colon cancer patients allowed the identification of cancer-associated autoantibodies to various mucin (MUC)1 and MUC4 glycopeptides carrying aberrant glycosylation. This article provides evidence for the value of glycopeptides displaying cancer-associated glycans in diagnostic applications, and opens new avenues for the expansion to other protein glycoforms, as well as to further applications of such a microarray strategy for other post-translational modifications of proteins in the search for cancer biomarker.     

Molecular diagnostics for pathogen detection in seeds and planting materials

Plant Cell, Tissue and Organ Culture (PCTOC) -     

Modeling formamide denaturation of probe-target hybrids for improved microarray probe design in microbial diagnostics

Application of high-density microarrays to the diagnostic analysis of microbial communities is challenged by the optimization of oligonucleotide probe sensitivity and specificity, as it is generally unfeasible to experimentally test thousands of probes. This study investigated the adjustment of hybridization stringency using formamide with the idea that sensitivity and specificity can be optimized during probe design if the hybridization efficiency of oligonucleotides with target and non-target molecules can be predicted as a function of formamide concentration. Sigmoidal denaturation profiles were obtained using fluorescently labeled and fragmented 16S rRNA gene amplicon of Escherichia coli as the target with increasing concentrations of formamide in the hybridization buffer. A linear free energy model (LFEM) was developed and microarray-specific nearest neighbor rules were derived. The model simulated formamide melting with a denaturant m-value that increased hybridization free energy (ΔG°) by 0.173 kcal/mol per percent of formamide added (v/v). Using the LFEM and specific probe sets, free energy rules were systematically established to predict the stability of single and double mismatches, including bulged and tandem mismatches. The absolute error in predicting the position of experimental denaturation profiles was less than 5% formamide for more than 90 percent of probes, enabling a practical level of accuracy in probe design. The potential of the modeling approach for probe design and optimization is demonstrated using a dataset including the 16S rRNA gene of Rhodobacter sphaeroides as an additional target molecule. The LFEM and thermodynamic databases were incorporated into a computational tool (ProbeMelt) that is freely available at http://DECIPHER.cee.wisc.edu.     

SNP microarray analysis for genome-wide detection of crossover regions

There is a great deal of interest in understanding the non-random distribution of recombination events over the human genome, because it has important implications for using linkage disequilibrium (LD) to identify human disease genes. So far, only a few recombination hotspots in the human genome have been characterised and the identification of new crossover hotspots will contribute to a better understanding of the mechanisms that govern their formation and distribution. This study shows that high-density single nucleotide polymorphism (SNP) arrays, together with the presented analysis method, are an appropriate tool for generating a whole-genome recombination pattern and for detecting new crossover regions with enhanced recombination frequency. Based on the genotype data of 16 members of a Caucasian three-generation family, we identified 825 crossover regions. The average recombination frequency of females and males was 0.77 and 0.56 cM/Mb, respectively. We detected 24 crossover regions showing elevated recombination activity, which comprised known hotspots, like the MHC II region, confirming the non-random distribution of recombination events along the genome. Interestingly, 29.2% of the identified crossover hotspot regions overlapped with regions flanked by segmental duplications published by Bailey et al. (Science 297:1003–1007, 2002) suggesting that segmental duplications and crossover hotspot regions are mechanistically linked. By extrapolating the results of the present study, we conclude that it might be feasible, at least in part, to estimate to what extent the block-like pattern of LD exactly relies on the genome-wide crossover pattern using the next generation high-density SNP microarrays.Electronic Supplementary Material Supplementary material is available for this article at     

A highly specific microarray method for point mutation detection

Improvements of microarray techniques for genotyping purposes have focused on increasing the reliability of this method. Here we report the development of a genotyping method where a microarray was spotted with stemloop probes, especially designed to optimize the hybridization specificity of complementary DNA sequences. This accurate method was used to screen for four common disease-causing mutations involved in a neurological disorder called Charcot-Marie-Tooth disease (CMT). Healthy individuals' and patients' DNA were amplified and labeled by PCR and hybridized on microarray. The spot signal intensities were 81 to 408 times greater for perfect compared with mismatched target sequences, differing by only one nucleotide (discrimination ratio) for healthy individual "homozygous" DNA. On the other hand, "heterozygous" mutant DNA samples gave rise to signal intensity ratios close to 1, as expected. The genotypes obtained by this method were perfectly consistent with those determined by direct PCR sequencing. Cross-hybridization rates were very low, resulting in further multiplexing improvements. In this study, we also demonstrated the feasibility of real-time hybridization detection of labeled synthetic oligonucleotides with concentrations as low as 2.5 nM.     

SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer

Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity-based processes. Serum proteomic pattern diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. This approach has recently shown tremendous promise in the detection of early-stage cancers. The biomarkers found by SELDI-TOF-based pattern recognition analysis are mostly low molecular weight fragments produced at the specific tumor microenvironment.     

Hybridization kinetics analysis of an oligonucleotide microarray for microRNA detection

MicroRNA (miRNA) microarrays have been successfully used for profiling miRNA expression in many physiological processes such as development, differentiation, oncogenesis, and other disease processes. Detecting miRNA by miRNA microarray is actually based on nucleic acid hybridization between target molecules and their corresponding complementary probes. Due to the small size and high degree of similarity among miRNA sequences, the hybridization condition must be carefully optimized to get specific and reliable signals. Previously, we reported a microarray platform to detect miRNA expression. In this study, we evaluated the sensitivity and specificity of our microarray platform. After systematic analysis, we determined an optimized hybridization condition with high sensitivity and specificity for miRNA detection. Our results would be helpful for other hybridization-based miRNA detection methods, such as northern blot and nuclease protection assay.     

Novel algorithm for coexpression detection in time-varying microarray data sets

When analyzing the results of microarray experiments, biologists generally use unsupervised categorization tools. However, such tools regard each time point as an independent dimension and utilize the Euclidean distance to compute the similarities between expressions. Furthermore, some of these methods require the number of clusters to be determined in advance, which is clearly impossible in the case of a new dataset. Therefore, this study proposes a novel scheme, designated as the Variation-based Coexpression Detection (VCD) algorithm, to analyze the trends of expressions based on their variation over time. The proposed algorithm has two advantages. First, it is unnecessary to determine the number of clusters in advance since the algorithm automatically detects those genes whose profiles are grouped together and creates patterns for these groups. Second, the algorithm features a new measurement criterion for calculating the degree of change of the expressions between adjacent time points and evaluating their trend similarities. Three real-world microarray datasets are employed to evaluate the performance of the proposed algorithm.     

桉树基因组和功能基因组

本文就桉树基因组和功能基因组的研究进展作介绍。     

Direct detection of double-stranded DNA: Molecular methods and applications for DNA diagnostics

Methodologies to detect DNA sequences with high sensitivity and specificity have tremendous potential as molecular diagnostic agents. Most current methods exploit the ability of single-stranded DNA (ssDNA) to base pair with high specificity to a complementary molecule. However, recent advances in robust techniques for recognition of DNA in the major and minor groove have made possible the direct detection of double-stranded DNA (dsDNA), without the need for denaturation, renaturation, or hybridization. This review will describe the progress in adapting polyamides, triplex DNA, and engineered zinc finger DNA-binding proteins as dsDNA diagnostic systems. In particular, the sequence-enabled reassembly (SEER) method, involving the use of custom zinc finger proteins, offers the potential for direct detection of dsDNA in cells, with implications for cell-based diagnostics and therapeutics.     

Unbiased pattern detection in microarray data series

MOTIVATION: Following the advent of microarray technology in recent years, the challenge for biologists is to identify genes of interest from the thousands of genetic expression levels measured in each microarray experiment. In many cases the aim is to identify pattern in the data series generated by successive microarray measurements. RESULTS: Here we introduce a new method of detecting pattern in microarray data series which is independent of the nature of this pattern. Our approach provides a measure of the algorithmic compressibility of each data series. A series which is significantly compressible is much more likely to result from simple underlying mechanisms than series which are incompressible. Accordingly, the gene associated with a compressible series is more likely to be biologically significant. We test our method on microarray time series of yeast cell cycle and show that it blindly selects genes exhibiting the expected cyclic behaviour as well as detecting other forms of pattern. Our results successfully predict two independent non-microarray experimental studies.     

Optimization and clinical validation of a pathogen detection microarray

DNA microarrays used as 'genomic sensors' have great potential in clinical diagnostics. Biases inherent in random PCR-amplification, cross-hybridization effects, and inadequate microarray analysis, however, limit detection sensitivity and specificity. Here, we have studied the relationships between viral amplification efficiency, hybridization signal, and target-probe annealing specificity using a customized microarray platform. Novel features of this platform include the development of a robust algorithm that accurately predicts PCR bias during DNA amplification and can be used to improve PCR primer design, as well as a powerful statistical concept for inferring pathogen identity from probe recognition signatures. Compared to real-time PCR, the microarray platform identified pathogens with 94% accuracy (76% sensitivity and 100% specificity) in a panel of 36 patient specimens. Our findings show that microarrays can be used for the robust and accurate diagnosis of pathogens, and further substantiate the use of microarray technology in clinical diagnostics.     

Methylation detection oligonucleotide microarray analysis: a high-resolution method for detection of CpG island methylation

Methylation of CpG islands associated with genes can affect the expression of the proximal gene, and methylation of non-associated CpG islands correlates to genomic instability. This epigenetic modification has been shown to be important in many pathologies, from development and disease to cancer. We report the development of a novel high-resolution microarray that detects the methylation status of over 25 000 CpG islands in the human genome. Experiments were performed to demonstrate low system noise in the methodology and that the array probes have a high signal to noise ratio. Methylation measurements between different cell lines were validated demonstrating the accuracy of measurement. We then identified alterations in CpG islands, both those associated with gene promoters, as well as non-promoter-associated islands in a set of breast and ovarian tumors. We demonstrate that this methodology accurately identifies methylation profiles in cancer and in principle it can differentiate any CpG methylation alterations and can be adapted to analyze other species.