Accessing genetic information with high-density DNA arrays

The evolution of bioinformatics in toxicology: advancing toxicogenomics

As one reflects back through the past 50 years of scientific research, a significant accomplishment was the advance into the genomic era. Basic research scientists have uncovered the genetic code and the foundation of the most fundamental building blocks for the molecular activity that supports biological structure and function. Accompanying these structural and functional discoveries is the advance of techniques and technologies to probe molecular events, in time, across environmental and chemical exposures, within individuals, and across species. The field of toxicology has kept pace with advances in molecular study, and the past 50 years recognizes significant growth and explosive understanding of the impact of the compounds and environment to basic cellular and molecular machinery. The advancement of molecular techniques applied in a whole-genomic capacity to the study of toxicant effects, toxicogenomics, is no doubt a significant milestone for toxicological research. Toxicogenomics has also provided an avenue for advancing a joining of multidisciplinary sciences including engineering and informatics in traditional toxicological research. This review will cover the evolution of the field of toxicogenomics in the context of informatics integration its current promise, and limitations.

Localized nanoscopic surface measurements of nickel-modified mica for single-molecule DNA sequence sampling

Cleaved, cation-derivatized Muscovite mica is utilized extensively in atomic force microscopy (AFM) imaging because of its flatness over large areas (millimeter cleavage planes with local root-mean-square roughness < 0.3 nm), ease of preparation, and ability to adsorb charged biomolecules such as DNA (work by Hansma and Laney, Guthold et al., and McMaster et al.). In particular, NiCl(2) treatment has become a common method for controlling DNA adsorption on mica substrates while retaining the mica's ultraflat surface (work by Pietrement et al.). While several studies have modeled the mica/metal ion/DNA system using macroscopic colloidal theory (DLVO, etc.; Pietrement et al., Sushko et al., Pastre et al., and Cheng et al.), nickel/mica's physicochemical properties have not been well characterized on the nanoscale. Efforts to manipulate and engineer DNA nanostructures would benefit greatly from a better understanding of the surface chemistry of nickel/mica. Here we present in situ nanometer- and attogram-scale measurements and thermodynamic simulation results that show that the surface chemistry of nickel-treated mica is more complex than generally appreciated by AFM practitioners because of metal-ion speciation effects present at neutral pH. We also show that, under certain preparations, nickel/mica allows in situ nanoscopic nucleotide sequence mapping within individual surface-adsorbed DNA molecules by permitting localized, controlled desorption of the double helix by soluble DNA binding enzymes. These results should aid efforts to precisely control the DNA/mica binding affinity, particularly at the physiological pH ranges required by enzymatic biochemistry (pH 7.0-8.5), and facilitate the development of more complex and useful biochemical manipulations of adsorbed DNA, such as single-molecule sequencing.

Elucidation of beta-oxidation pathways in Ralstonia eutropha H16 by examination of global gene expression

Ralstonia eutropha H16 is capable of growth and polyhydroxyalkanoate production on plant oils and fatty acids. However, little is known about the triacylglycerol and fatty acid degradation pathways of this bacterium. We compare whole-cell gene expression levels of R. eutropha H16 during growth and polyhydroxyalkanoate production on trioleate and fructose. Trioleate is a triacylglycerol that serves as a model for plant oils. Among the genes of note, two potential fatty acid β-oxidation operons and two putative lipase genes were shown to be upregulated in trioleate cultures. The genes of the glyoxylate bypass also exhibit increased expression during growth on trioleate. We observed that single β-oxidation operon deletion mutants of R. eutropha could grow using palm oil or crude palm kernel oil as the sole carbon source, regardless of which operon was present in the genome, but a double mutant was unable to grow under these conditions. A lipase deletion mutant did not exhibit a growth defect in emulsified oil cultures but did exhibit a phenotype in cultures containing nonemulsified oil. Mutants of the glyoxylate shunt gene for isocitrate lyase were able to grow in the presence of oils, while a malate synthase (aceB) deletion mutant grew more slowly than wild type. Gene expression under polyhydroxyalkanoate storage conditions was also examined. Many findings of this analysis confirm results from previous studies by our group and others. This work represents the first examination of global gene expression involving triacylglycerol and fatty acid catabolism genes in R. eutropha.

Profiling the cancer genome

Cancer profiling studies have had a profound impact on our understanding of the biology of cancers in a number of ways, including providing insights into the biological heterogeneity of specific cancer types, identification of novel oncogenes and tumor suppressors, and defining pathways that interact to drive the growth of individual cancers. Several large-scale genomic studies are underway that aim to catalog all biologically significant mutational events in each cancer type, and these findings will allow researchers to understand how mutational networks function within individual tumors. The identification of molecular predictive and prognostic tools to facilitate treatment decisions is an important step for individualized patient therapy and, ultimately, in improving patient outcomes. Whereas there are still significant challenges to implementing genomic testing and targeted therapy into routine clinical practice, rapid technological advancements provide hope for overcoming these obstacles.

Identification of rare DNA variants in mitochondrial disorders with improved array-based sequencing

A common goal in the discovery of rare functional DNA variants via medical resequencing is to incur a relatively lower proportion of false positive base-calls. We developed a novel statistical method for resequencing arrays (SRMA, sequence robust multi-array analysis) to increase the accuracy of detecting rare variants and reduce the costs in subsequent sequence verifications required in medical applications. SRMA includes single and multi-array analysis and accounts for technical variables as well as the possibility of both low- and high-frequency genomic variation. The confidence of each base-call was ranked using two quality measures. In comparison to Sanger capillary sequencing, we achieved a false discovery rate of 2% (false positive rate 1.2 × 10⁻⁵, false negative rate 5%), which is similar to automated second-generation sequencing technologies. Applied to the analysis of 39 nuclear candidate genes in disorders of mitochondrial DNA (mtDNA) maintenance, we confirmed mutations in the DNA polymerase gamma POLG in positive control cases, and identified novel rare variants in previously undiagnosed cases in the mitochondrial topoisomerase TOP1MT, the mismatch repair enzyme MUTYH, and the apurinic-apyrimidinic endonuclease APEX2. Some patients carried rare heterozygous variants in several functionally interacting genes, which could indicate synergistic genetic effects in these clinically similar disorders.

Have advanced research technologies made real impact on respiratory medicine?

Tremendous sophistication in our ability to detect, measure and manipulate the genes and proteins involved in lung disease has provided unique insights to the pathobiology of a number of lung disorders affecting humans today. Gene array analysis and genetic manipulation of animals have helped to identify novel pathways and their potential role in disease initiation and progression. Proteomics is a relatively novel tool in molecular research in respiratory medicine. Silencing gene expression by using small interfering RNA may, sooner than later, become part of novel therapies. Despite the excitement, to date, there has been little direct impact on therapeutic interventions available to clinicians. However, the rapidity with which these early data have accumulated, and the expected advances in bioinformatics and systems biology, should bring forward tangible therapeutic benefits for many acute and chronic lung diseases within the next ten years.

Advances in pharmacogenomics technologies

The past decade has seen major advances in our understanding of genomics, with the development of newer and refined technologies. Improvements in the sensitivity and precision of DNA microarrays coupled with the emergence of massively parallel sequencing techniques have redefined how we interrogate the human genome. These tools have facilitated rapid biomarker discovery, uncovered novel targets for therapeutic intervention and enabled mechanistic studies that have helped unravel the mode of drug action. The assessment of drug toxicity based on genomic signatures associated with disease and high-throughput screening for polymorphic interindividual variability in drug responses are procedures that have greatly aided the quality of patient care. The integration of pharmacogenetic approaches for therapeutic monitoring, and health management approaches for advance clinical treatments, will be based on unique patient phenotype profiles and aimed at minimizing adverse drug reactions. In this brief commentary, the evolution and development of key genomics technologies over the past decade is reviewed.

Application of equilibrium models of solution hybridization to microarray design and analysis

Background

The probe percent bound value, calculated using multi-state equilibrium models of solution hybridization, is shown to be useful in understanding the hybridization behavior of microarray probes having 50 nucleotides, with and without mismatches. These longer oligonucleotides are in widespread use on microarrays, but there are few controlled studies of their interactions with mismatched targets compared to 25-mer based platforms.

Principal Findings

50-mer oligonucleotides with centrally placed single, double and triple mismatches were spotted on an array. Over a range of target concentrations it was possible to discriminate binding to perfect matches and mismatches, and the type of mismatch could be predicted accurately in the concentration midrange (100 pM to 200 pM) using solution hybridization modeling methods. These results have implications for microarray design, optimization and analysis methods.

Conclusions

Our results highlight the importance of incorporating biophysical factors in both the design and the analysis of microarrays. Use of the probe “percent bound” value predicted by equilibrium models of hybridization is confirmed to be important for predicting and interpreting the behavior of long oligonucleotide arrays, as has been shown for short oligonucleotide arrays.

Computational modeling with forward and reverse engineering links signaling network and genomic regulatory responses: NF-kappaB signaling-induced gene expression responses in inflammation

Background

Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Diverse cellular stimuli create a wide variety of transcription factor activities through signal transduction pathways, resulting in different gene expression patterns. Understanding the relationship between external stimuli and the corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach is needed to integrate experimental data and theoretical hypotheses to identify the physiological consequences of environmental stimuli.

Results

We proposed a systematic approach that combines forward and reverse engineering to link the signal transduction cascade with the gene responses. To demonstrate the feasibility of our strategy, we focused on linking the NF-κB signaling pathway with the inflammatory gene regulatory responses because NF-κB has long been recognized to play a crucial role in inflammation. We first utilized forward engineering (Hybrid Functional Petri Nets) to construct the NF-κB signaling pathway and reverse engineering (Network Components Analysis) to build a gene regulatory network (GRN). Then, we demonstrated that the corresponding IKK profiles can be identified in the GRN and are consistent with the experimental validation of the IKK kinase assay. We found that the time-lapse gene expression of several cytokines and chemokines (TNF-α, IL-1, IL-6, CXCL1, CXCL2 and CCL3) is concordant with the NF-κB activity profile, and these genes have stronger influence strength within the GRN. Such regulatory effects have highlighted the crucial roles of NF-κB signaling in the acute inflammatory response and enhance our understanding of the systemic inflammatory response syndrome.

Conclusion

We successfully identified and distinguished the corresponding signaling profiles among three microarray datasets with different stimuli strengths. In our model, the crucial genes of the NF-κB regulatory network were also identified to reflect the biological consequences of inflammation. With the experimental validation, our strategy is thus an effective solution to decipher cross-talk effects when attempting to integrate new kinetic parameters from other signal transduction pathways. The strategy also provides new insight for systems biology modeling to link any signal transduction pathways with the responses of downstream genes of interest.

Mitochondrial gene profiling: translational perspectives

The last decade has witnessed the development of multiple microarray platforms designed to study, in a comprehensive fashion, the expression and sequence of both mitochondrial and nuclear genes that encode mitochondrial proteins. Mitochondrial dysfunction has been implicated in a number of severe medical conditions including cancer, metabolic diseases (i.e., cardiovascular, diabetes and obesity) and neurodegenerative disorders and it is responsible for the adverse effects of numerous drugs. Profiling of the genetic and genomic status of mitochondria with focused microarrays offers the promise of rapidly and robustly identifying novel biomarkers for early disease diagnoses and prognoses, predicting of drug safety, liability, and selecting and stratifying of patients in clinical trials.

Liposome-based chemical barcodes for single molecule DNA detection using imaging mass spectrometry

We report on a mass-spectrometry (time-of-flight secondary ion mass spectrometry, TOF-SIMS) based method for multiplexed DNA detection utilizing a random array, where the lipid composition of small unilamellar liposomes act as chemical barcodes to identify unique DNA target sequences down to the single molecule level. In a sandwich format, suspended target-DNA to be detected mediates the binding of capture-DNA modified liposomes to surface-immobilized probe-DNA. With the lipid composition of each liposome encoding a unique target-DNA sequence, TOF-SIMS analysis was used to determine the chemical fingerprint of the bound liposomes. Using high-resolution TOF-SIMS imaging, providing sub-200 nm spatial resolution, single DNA targets could be detected and identified via the chemical fingerprint of individual liposomes. The results also demonstrate the capability of TOF-SIMS to provide multiplexed detection of DNA targets on substrate areas in the micrometer range. Together with a high multiplexing capacity, this makes the concept an interesting alternative to existing barcode concepts based on fluorescence, Raman, or graphical codes for small-scale bioanalysis.

Feature-level MALDI-MS characterization of in situ-synthesized peptide microarrays

Characterizing the chemical composition of microarray features is a difficult yet important task in the production of in situ-synthesized microarrays. Here, we describe a method to determine the chemical composition of microarray features, directly on the feature. This method utilizes nondiffusional chemical cleavage from the surface along with techniques from MALDI-MS tissue imaging, thereby making the chemical characterization of high-density microarray features simple, accurate, and amenable to high-throughput.

Challenges of genomics and proteomics in nephrology

An increasing number of patients suffering from renal diseases and limitations in standard diagnostic and therapeutic approaches has created an intense interest in applying genomics and proteomics in the field of nephrology. Genomics has provided a vast amount of information, linking the gene activity with disease. However, proteomic technologies allow us to understand proteins and their modifications, elucidating properties of cellular behavior that may not be reflected in analysis of gene expression. The application of these innovative approaches has recently yielded the promising new urinary biomarkers for acute kidney injury and chronic kidney disease, thus providing a better insight in renal pathophysiology and establishing the basis for new therapeutic strategies. Despite significant improvements in therapeutics, the mortality and morbidity associated with acute renal failure (ARF) remain high. The lack of early markers for ARF causes an unacceptable delay in initiating therapy. These biomarker panels will probably be useful for assessing the duration and severity of ARF, and for predicting progression and adverse clinical outcomes. Kidney failure leads to the uremic syndrome characterized by accumulation of uremic toxins, which are normally cleared by the kidneys. Proteomics has gained considerable interest in this field, as a new and promising analytical approach to identify new uremic toxins. The urinary proteome as a tool for biomarker discovery is still in its early phase. A major challenge will be the integration of proteomics with genomics data and their functional interpretation in conjunction with clinical results and epidemiology.

Feature selection for gene expression using model-based entropy

Gene expression data usually contain a large number of genes but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. Using machine learning techniques, traditional gene selection based on empirical mutual information suffers the data sparseness issue due to the small number of samples. To overcome the sparseness issue, we propose a model-based approach to estimate the entropy of class variables on the model, instead of on the data themselves. Here, we use multivariate normal distributions to fit the data, because multivariate normal distributions have maximum entropy among all real-valued distributions with a specified mean and standard deviation and are widely used to approximate various distributions. Given that the data follow a multivariate normal distribution, since the conditional distribution of class variables given the selected features is a normal distribution, its entropy can be computed with the log-determinant of its covariance matrix. Because of the large number of genes, the computation of all possible log-determinants is not efficient. We propose several algorithms to largely reduce the computational cost. The experiments on seven gene data sets and the comparison with other five approaches show the accuracy of the multivariate Gaussian generative model for feature selection, and the efficiency of our algorithms.

Using expression genetics to study the neurobiology of ethanol and alcoholism

Recent simultaneous progress in human and animal model genetics and the advent of microarray whole genome expression profiling have produced prodigious data sets on genetic loci, potential candidate genes, and differential gene expression related to alcoholism and ethanol behaviors. Validated target genes or gene networks functioning in alcoholism are still of meager proportions. Genetical genomics, which combines genetic analysis of both traditional phenotypes and whole genome expression data, offers a potential methodology for characterizing brain gene networks functioning in alcoholism. This chapter will describe concepts, approaches, and recent findings in the field of genetical genomics as it applies to alcohol research.

Transcriptome profiling of host-microbe interactions by differential display RT-PCR

In recent years, DNA microarray has become increasingly popular as a tool to investigate global expression patterns compared to differential display RT-PCR. Although differential display RT-PCR can be labour-intensive, it has its own merits over those of DNA microarray. While the latter usually consists of a well-defined set of species-specific genes, differential display RT-PCR allows the investigation of host-microbe interactions without bias towards any mRNA transcripts. This means that the regulated transcript expression of both host and pathogen can be analysed simultaneously. In addition, novel transcripts and alternate splicing variants pertaining to the infection can also be discovered. We have investigated the response of rhabdomyosarcoma cells to infection with a neurovirulent strain of enterovirus 71 (EV71) at different time-points during the infection process compared with uninfected cells. Using differential display RT-PCR, we identified mRNAs that were up- or down-regulated. Less than half of the clones match known genes including those involved in mediating the cytoskeleton, cell cycle, cell death, protein translational machinery and cellular transport. The rest of the clones do not match any known genes, of which several are novel genes. Noteworthy is the discovery of an alternate splicing form of TRIP7, which is down-regulated during EV71 infection. The differential display technique has potentially wide applicability to elucidate the gene expression or transcriptomic profiles of host-microbe interactions, which can provide a better understanding of microbial pathogenesis.

An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays

Background

Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches.

Results

In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA) will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay.

Conclusions

By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at http://www.laurenzi.net.

Automated fluorescent differential display for cancer gene profiling

Since its invention in 1992, differential display (DD) has become the most commonly used technique for identifying differentially expressed genes because of its many advantages over competing technologies such as DNA microarray, serial analysis of gene expression (SAGE), and subtractive hybridization. A large number of these publications have been in the field of cancer, specifically on p53 target genes. Despite the great impact of the method on biomedical research, there had been a lack of automation of DD technology to increase its throughput and accuracy for systematic gene expression analysis. Many previous DD work has taken a "shotgun" approach of identifying one gene at a time, with a limited number of polymerase chain reactions (PCRs) set up manually, giving DD a low-tech and low-throughput image. We have optimized the DD process with a platform that incorporates fluorescent digital readout, automated liquid handling, and large-format gels capable of running entire 96-well plates. The resulting streamlined fluorescent DD (FDD) technology offers an unprecedented accuracy, sensitivity, and throughput in comprehensive and quantitative analysis of gene expression. These major improvements will allow researchers to find differentially expressed genes of interest, both known and novel, quickly and easily.

An enhanced single base extension technique for the analysis of complex viral populations

Many techniques for the study of complex populations provide either specific information on a small number of variants or general information on the entire population. Here we describe a powerful new technique for elucidating mutation frequencies at each genomic position in a complex population. This single base extension (SBE) based microarray platform was designed and optimized using poliovirus as the target genotype, but can be easily adapted to assay populations derived from any organism. The sensitivity of the method was demonstrated by accurate and consistent readouts from a controlled population of mutant genotypes. We subsequently deployed the technique to investigate the effects of the nucleotide analog ribavirin on a typical poliovirus population through two rounds of passage. Our results show that this economical platform can be used to investigate dynamic changes occurring at frequencies below 1% within a complex nucleic acid population. Given that many key aspects of the study and treatment of disease are intimately linked to population-level genomic diversity, our SBE-based technique provides a scalable and cost-effective complement to both traditional and next generation sequencing methodologies.

Label-free detection of specific DNA sequence-telomere using unmodified gold nanoparticles as colorimetric probes

A simple and sensitive label-free colorimetric detection of telomere DNA has been developed. It was based on the color change of gold nanoparticles (AuNPs) due to DNA hybridization. UV-vis spectra and transmission electron microscopy (TEM) were used to investigate the change of AuNPs. Under the optimized conditions, the linear range for determination of telomere DNA was 5.7 x 10(-13) to 4.5 x 10(-6)mol/L. The detection limit (3 sigma) of this method has decreased to pico-molar level.

Synthesis at the interface of chemistry and biology

As the focus of synthesis increasingly shifts from its historical emphasis on molecular structure to function, improved strategies are clearly required for the generation of molecules with defined physical, chemical, and biological properties. In contrast, living organisms are remarkably adept at producing molecules and molecular assemblies with an impressive array of functions - from enzymes and antibodies to the photosynthetic center. Thus, the marriage of Nature's synthetic strategies, molecules, and biosynthetic machinery with more traditional synthetic approaches might enable the generation of molecules with properties difficult to achieve by chemical strategies alone. Here we illustrate the potential of this approach and overview some opportunities and challenges in the coming years.

Real-time DNA microarray analysis

We present a quantification method for affinity-based DNA microarrays which is based on the real-time measurements of hybridization kinetics. This method, i.e. real-time DNA microarrays, enhances the detection dynamic range of conventional systems by being impervious to probe saturation in the capturing spots, washing artifacts, microarray spot-to-spot variations, and other signal amplitude-affecting non-idealities. We demonstrate in both theory and practice that the time-constant of target capturing in microarrays, similar to all affinity-based biosensors, is inversely proportional to the concentration of the target analyte, which we subsequently use as the fundamental parameter to estimate the concentration of the analytes. Furthermore, to empirically validate the capabilities of this method in practical applications, we present a FRET-based assay which enables the real-time detection in gene expression DNA microarrays.

A dynamic network of transcription in LPS-treated human subjects

BACKGROUND

Understanding the transcriptional regulatory networks that map out the coordinated dynamic responses of signaling proteins, transcription factors and target genes over time would represent a significant advance in the application of genome wide expression analysis. The primary challenge is monitoring transcription factor activities over time, which is not yet available at the large scale. Instead, there have been several developments to estimate activities computationally. For example, Network Component Analysis (NCA) is an approach that can predict transcription factor activities over time as well as the relative regulatory influence of factors on each target gene.

RESULTS

In this study, we analyzed a gene expression data set in blood leukocytes from human subjects administered with lipopolysaccharide (LPS), a prototypical inflammatory challenge, in the context of a reconstructed regulatory network including 10 transcription factors, 99 target genes and 149 regulatory interactions. We found that the computationally estimated activities were well correlated to their coordinated action. Furthermore, we found that clustering the genes in the context of regulatory influences greatly facilitated interpretation of the expression data, as clusters of gene expression corresponded to the activity of specific factors or more interestingly, factor combinations which suggest coordinated regulation of gene expression. The resulting clusters were therefore more biologically meaningful, and also led to identification of additional genes under the same regulation.

CONCLUSION

Using NCA, we were able to build a network that accounted for between 8-11% genes in the known transcriptional response to LPS in humans. The dynamic network illustrated changes of transcription factor activities and gene expressions as well as interactions of signaling proteins, transcription factors and target genes.

Immobilization of self-quenched DNA hairpin probe with a heterobifunctional reagent on a glass surface for sensitive detection of oligonucleotides

A new sensitive method for the detection of nucleic acids on a glass surface has been described. The self-quenched DNA hairpin probe is immobilized on a glass surface utilizing heterobifunctional reagent, N-(3-triethoxysilylpropyl)-4-(isothiocyanatomethyl)-cyclohexane-1-carboxamide (TPICC). In the closed state fluorescence intensity was quenched due to the presence of guanosine residues in close vicinity of fluorophore while on hybridization with perfectly matched complementary target strand fluorescence was restored. Efficiency and specificity of immobilization as well as thermal stability at variable temperature and pH conditions have been discussed in detail. The method employed has potential for the detection of single nucleotide variations and other diagnostic studies.

Candidate protein biodosimeters of human exposure to ionizing radiation

PURPOSE

To conduct a literature review of candidate protein biomarkers for individual radiation biodosimetry of exposure to ionizing radiation.

MATERIALS AND METHODS

Reviewed approximately 300 publications (1973 - April 2006) that reported protein effects in mammalian systems after either in vivo or in vitro radiation exposure.

RESULTS

We found 261 radiation-responsive proteins including 173 human proteins. Most of the studies used high doses of ionizing radiation (>4 Gy) and had no information on dose- or time-responses. The majority of the proteins showed increased amounts or changes in phosphorylation states within 24 h after exposure (range: 1.5- to 10-fold). Of the 47 proteins that are responsive at doses of 1 Gy and below, 6 showed phosphorylation changes at doses below 10 cGy. Proteins were assigned to 9 groups based on consistency of response across species, dose- and time-response information and known role in the radiation damage response.

CONCLUSIONS

ATM (Ataxia telengiectasia mutated), H2AX (histone 2AX), CDKN1A (Cyclin-dependent kinase inhibitor 1A), and TP53 (tumor protein 53) are top candidate radiation protein biomarkers. Furthermore, we recommend a panel of protein biomarkers, each with different dose and time optima, to improve individual radiation biodosimetry for discriminating between low-, moderate-, and high-dose exposures. Our findings have applications for early triage and follow-up medical assessments.

Kinetic and thermodynamic characterization of single-mismatch discrimination using single-molecule imaging

A single-molecule detection setup based on total internal reflection fluorescence (TIRF) microscopy has been used to investigate association and dissociation kinetics of unlabeled 30mer DNA strands. Single-molecule sensitivity was accomplished by letting unlabeled DNA target strands mediate the binding of DNA-modified and fluorescently labeled liposomes to a DNA-modified surface. The liposomes, acting as signal enhancer elements, enabled the number of binding events as well as the residence time for high affinity binders (K_d < 1 nM, k_off < 0.01 s⁻¹) to be collected under equilibrium conditions at low pM concentrations. The mismatch discrimination obtained from the residence time data was shown to be concentration and temperature independent in intervals of 1–100 pM and 23–46°C, respectively. This suggests the method as a robust means for detection of point mutations at low target concentrations in, for example, single nucleotide polymorphism (SNP) analysis.

Multiplexed DNA detection using spectrally encoded porous SiO2 photonic crystal particles

A particle-based multiplexed DNA assay based on encoded porous SiO(2) photonic crystal disks is demonstrated. A "spectral barcode" is generated by electrochemical etch of a single-crystal silicon wafer using a programmed current-time waveform. A lithographic procedure is used to isolate cylindrical microparticles 25 microm in diameter and 10 microm thick, which are then oxidized, modified with a silane linker, and conjugated to various amino-functionalized oligonucleotide probes via cyanuric chloride. It is shown that the particles can be decoded based on their reflectivity spectra and that a multiple analyte assay can be performed in a single sample with a modified fluorescence microscope. The homogeneity of the reflectivity and fluorescence spectra, both within and across the microparticles, is also reported.

Overview of electrochemical DNA biosensors: new approaches to detect the expression of life

DNA microarrays are an important tool with a variety of applications in gene expression studies, genotyping, pharmacogenomics, pathogen classification, drug discovery, sequencing and molecular diagnostics. They are having a strong impact in medical diagnostics for cancer, toxicology and infectious disease applications. A series of papers have been published describing DNA biochips as alternative to conventional microarray platforms to facilitate and ameliorate the signal readout. In this review, we will consider the different methods proposed for biochip construction, focusing on electrochemical detection of DNA. We also introduce a novel single-stranded DNA platform performing high-throughput SNP detection and gene expression profiling.

Development and application of the human intestinal tract chip, a phylogenetic microarray: analysis of universally conserved phylotypes in the abundant microbiota of young and elderly adults

In this paper we present the in silico assessment of the diversity of variable regions of the small subunit ribosomal RNA (SSU rRNA) gene based on an ecosystem-specific curated database, describe a probe design procedure based on two hypervariable regions with minimal redundancy and test the potential of such probe design strategy for the design of a flexible microarray platform. This resulted in the development and application of a phylogenetic microarray for studying the human gastrointestinal microbiota – referred as the human intestinal tract chip (HITChip). Over 4800 dedicated tiling oligonucleotide probes were designed based on two hypervariable regions of the SSU rRNA gene of 1140 unique microbial phylotypes (< 98% identity) following analysis of over 16 000 human intestinal SSU rRNA sequences. These HITChip probes were hybridized to a diverse set of human intestinal samples and SSU rRNA clones to validate its fingerprinting and quantification potential. Excellent reproducibility (median Pearson's correlation of 0.99) was obtained following hybridization with T7 polymerase transcripts generated in vitro from SSU rRNA gene amplicons. A linear dose–response was observed with artificial mixtures of 40 different representative amplicons with relative abundances as low as 0.1% of total microbiota. Analysis of three consecutively collected faecal samples from ten individuals (five young and five elderly adults) revealed temporal dynamics and confirmed that the adult intestinal microbiota is an individual-specific and relatively stable ecosystem. Further analysis of the stable part allowed for the identification of a universal microbiota core at the approximate genus level (90% sequence similarity). This core consists of members of Actinobacteria, Bacteroidetes and Firmicutes. Used as a phylogenetic fingerprinting tool with the possibility for relative quantification, the HITChip has the potential to bridge the gaps in our knowledge in the quantitative and qualitative description of the human gastrointestinal microbiota composition.

Microfludic device for creating ionic strength gradients over DNA microarrays for efficient DNA melting studies and assay development

The development of DNA microarray assays is hampered by two important aspects: processing of the microarrays is done under a single stringency condition, and characteristics such as melting temperature are difficult to predict for immobilized probes. A technical solution to these limitations is to use a thermal gradient and information from melting curves, for instance to score genotypes. However, application of temperature gradients normally requires complicated equipment, and the size of the arrays that can be investigated is restricted due to heat dissipation. Here we present a simple microfluidic device that creates a gradient comprising zones of defined ionic strength over a glass slide, in which each zone corresponds to a subarray. Using this device, we demonstrated that ionic strength gradients function in a similar fashion as corresponding thermal gradients in assay development. More specifically, we noted that (i) the two stringency modulators generated melting curves that could be compared, (ii) both led to increased assay robustness, and (iii) both were associated with difficulties in genotyping the same mutation. These findings demonstrate that ionic strength stringency buffers can be used instead of thermal gradients. Given the flexibility of design of ionic gradients, these can be created over all types of arrays, and encompass an attractive alternative to temperature gradients, avoiding curtailment of the size or spacing of subarrays on slides associated with temperature gradients.

Data-driven methods to discover molecular determinants of serious adverse drug events

The dangers of serious adverse drug reactions (SADRs) are well known to clinicians, pharmacologists, and the lay public. Efforts to elucidate the molecular mechanisms behind SADRs have made significant progress through genetics and gene expression measurements. However, as the field of pharmacology adopts the same novel higher-density measurement modalities that have proven successful in other areas of biology, one wonders whether there can be more ways to benefit from the explosion of data created by these tools. The development of analytic tools and algorithms to interpret these biological data to create tools for medicine is central to the field of translational bioinformatics. In this review we introduce some of the types of SADR predictors that are required, and we discuss several databases that are publicly available for the study of SADRs, ranging from clinical to molecular measurements. We also describe recent examples of how bioinformatics methods coupled with data repositories can advance the science of SADRs.

Introduction to microarray technology

DNA microarrays can be used for large number of application where high-throughput is needed. The ability to probe a sample for hundred to million different molecules at once has made DNA microarray one of the fastest growing techniques since its introduction about 15 years ago. Microarray technology can be used for large scale genotyping, gene expression profiling, comparative genomic hybridization and resequencing among other applications. Microarray technology is a complex mixture of numerous technology and research fields such as mechanics, microfabrication, chemistry, DNA behaviour, microfluidics, enzymology, optics and bioinformatics. This chapter will give an introduction to each five basic steps in microarray technology that includes fabrication, target preparation, hybridization, detection and data analysis. Basic concepts and nomenclature used in the field of microarray technology and their relationships will also be explained.

Positional cloning of deafness genes

The identification of the majority of the known causative genes involved in nonsyndromic sensorineural hearing loss (NSHL) started with linkage analysis as part of a positional cloning procedure. The human and mouse genome projects in combination with technical developments on genotyping, transcriptomics, proteomics, and the creation of animal models have greatly enhanced the speed of disease gene identification. In the present chapter, we first discuss the possibilities for exclusion of known NSHL loci and genes. Subsequently, methods are described to determine the genomic regions that contain the genetic defects. These include linkage analysis with genotyping and statistical evaluation and the determination of copy number variations. In the case of a large genomic region, candidate genes are selected and prioritized using gene expression analysis, protein network data, and phenotypes of animal models. A number of algorithms are described to automate the process of candidate gene selection. The novel high-throughput sequencing techniques might make gene selection and prioritization unnecessary in the near future. Once genetic variants are identified, questions on pathogenicity need to be addressed, which is the topic of the last section of this chapter.

In praise of arrays

Microarray technologies have both fascinated and frustrated the transplant community since their introduction roughly a decade ago. Fascination arose from the possibility offered by the technology to gain a profound insight into the cellular response to immunogenic injury and the potential that this genomic signature would be indicative of the biological mechanism by which that stress was induced. Frustrations have arisen primarily from technical factors such as data variance, the requirement for the application of advanced statistical and mathematical analyses, and difficulties associated with actually recognizing signature gene-expression patterns and discerning mechanisms. To aid the understanding of this powerful tool, its versatility, and how it is dramatically changing the molecular approach to biomedical and clinical research, this teaching review describes the technology and its applications, as well as the limitations and evolution of microarrays, in the field of organ transplantation. Finally, it calls upon the attention of the transplant community to integrate into multidisciplinary teams, to take advantage of this technology and its expanding applications in unraveling the complex injury circuits that currently limit transplant survival.

ChIP on chip assays: genome-wide analysis of transcription factor binding and histone modifications

Deregulation of transcriptional activity of many genes has been causatively linked to human diseases including cancer. Altered patterns of gene expression in normal and cancer cells are the result of inappropriate expression of transcription factors and chromatin-modifying proteins. Chromatin immunoprecipitation assay is a well-established tool for investigating the interactions between regulatory proteins and DNA at distinct stages of gene activation. ChIP coupled with DNA microarrays, known as ChIP on chip, allow us to determine the entire spectrum of in vivo DNA-binding sites for a given protein. This has been of immense value because ChIP on chip assays can provide a snapshot of the transcriptional regulatory mechanisms on a genome-wide scale. This article outlines the general strategies used to carry out ChIP-chip assays to study the differential recruitment of regulatory molecules based on the studies conducted in our lab as well as other published protocols.

Ovarian development in mice requires the GATA4-FOG2 transcription complex

We have demonstrated previously that mammalian sexual differentiation requires both the GATA4 and FOG2 transcriptional regulators to assemble the functioning testis. Here we have determined that the sexual development of female mice is profoundly affected by the loss of GATA4-FOG2 interaction. We have also identified the Dkk1 gene, which encodes a secreted inhibitor of canonical β-catenin signaling, as a target of GATA4-FOG2 repression in the developing ovary. The tissue-specific ablation of theβ-catenin gene in the gonads disrupts female development. In Gata4ki/ki; Dkk1-/- or Fog2-/-;Dkk1-/- embryos, the normal ovarian gene expression pattern is partially restored. Control of ovarian development by the GATA4-FOG2 complex presents a novel insight into the cross-talk between transcriptional regulation and extracellular signaling that occurs in ovarian development.

Genome-wide transcriptome analysis of 150 cell samples

A major challenge in molecular biology is interrogating the human transcriptome on a genome wide scale when only a limited amount of biological sample is available for analysis. Current methodologies using microarray technologies for simultaneously monitoring mRNA transcription levels require nanogram amounts of total RNA. To overcome the sample size limitation of current technologies, we have developed a method to probe the global gene expression in biological samples as small as 150 cells, or the equivalent of approximately 300 pg total RNA. The new method employs microfluidic devices for the purification of total RNA from mammalian cells and ultra-sensitive whole transcriptome amplification techniques. We verified that the RNA integrity is preserved through the isolation process, accomplished highly reproducible whole transcriptome analysis, and established high correlation between repeated isolations of 150 cells and the same cell culture sample. We validated the technology by demonstrating that the combined microfluidic and amplification protocol is capable of identifying biological pathways perturbed by stimulation, which are consistent with the information recognized in bulk-isolated samples.

"Sequencing-grade" screening for BRCA1 variants by oligo-arrays

The need for fast, efficient, and less costly means to screen genetic variants associated with disease predisposition led us to develop an oligo-nucleotide array-based process for gene-specific single nucleotide polymorphism (SNP) genotyping. This cost-effective, high-throughput strategy has high sensitivity and the same degree of accuracy as direct sequencing, the current gold standard for genetic screening. We used the BRCA1 breast and ovarian cancer predisposing gene model for the validation of the accuracy and efficiency of our strategy. This process could detect point mutations, insertions or deletions of any length, of known and unknown variants even in heterozygous conditions without affecting sensitivity and specificity. The system could be applied to other disorders and can also be custom-designed to include a number of genes related to specific clinical conditions. This system is particularly useful for the screening of long genomic regions with relatively infrequent but clinically relevant variants, while drastically cutting time and costs in comparison to high-throughput sequencing.