On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples

This paper considers a model-based approach to the clustering of tissue samples of a very large number of genes from microarray experiments. It is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. Frequently in practice, there are also clinical data available on those cases on which the tissue samples have been obtained. Here we investigate how to use the clinical data in conjunction with the microarray gene expression data to cluster the tissue samples. We propose two mixture model-based approaches in which the number of components in the mixture model corresponds to the number of clusters to be imposed on the tissue samples. One approach specifies the components of the mixture model to be the conditional distributions of the microarray data given the clinical data with the mixing proportions also conditioned on the latter data. Another takes the components of the mixture model to represent the joint distributions of the clinical and microarray data. The approaches are demonstrated on some breast cancer data, as studied recently in van't Veer et al. (2002).

Upregulation of beta catenin in superior frontal cortex of chronic alcoholics

Chronic alcohol abuse causes neurotoxicity and the development of tolerance and dependence. At the molecular level, however, knowledge about mechanisms underlying alcoholism remains limited. In this study we examined the superior frontal cortex, one of the most vulnerable brain regions, of alcoholics and of age- and gender-matched control subjects by means of antibody microarrays and Western blot analyses, and identified an up-regulation of beta-catenin level in the superior frontal cortex of alcoholics. Beta-catenin is the orthologue of the Drosophila armadillo segment polarity gene and a down stream component of the Wnt and Akt signaling pathway. Beta-catenin was identified as a cell adhesion molecule of the cadherin family which binds to the actin cytoskeleton. Genetic and biochemical analyses also found that beta-catenin can be translocated from the cytoplasm to the nucleus and acts as a transcription factor. In addition, electron microscopy performed on rat brain tissue sections has localized the beta-catenin and cadherin complexes to the synapses where they border the active zone. Because of the multi-functional role of beta-catenin in the nervous system, this study provides the premise for further investigation of mechanisms underlying the up-regulation of beta-catenin in alcoholism, which may have considerable pathogenic and therapeutic relevance.

Genomic and proteomic analysis of synaptic protein expression in the brains of human alcoholics

Alcoholism results in changes in the human brain which reinforce the cycle of craving and dependency, and these changes are manifest in the pattern of expression of mRNA and proteins in key cells and brain areas. Long-term alcohol abuse also results in damage to selected regions of the cortex. We have used cDNA microarrays to show that less than 1% of mRNA transcripts differ signifi cantly between cases and controls in the susceptible area and that the expression profi le of a subset of these transcripts is suffi cient to distinguish alcohol abusers from controls. In addition, we have utilized a 2D gel proteomics based approach to determine the identity of proteins in the superior frontal cortex (SFC) of the human brain that show differential expression in controls and long term alcohol abusers. Overall, 182 proteins differed by the criterion of > 2-fold between case and control samples. Of these, 139 showed signifi cantly lower expression in alcoholics, 35 showed signifi cantly higher expression, and 8 were new or had disappeared. To date 63 proteins have been identifi ed. The expression of one family of proteins, the synucleins, has been further characterized using Real Time PCR and Western Blotting. The expression of alpha-synuclein mRNA was signifi cantly lower in the SFC of alcoholics compared with the same area in controls (P = 0.01) whereas no such difference in expression was found in the motor cortex. The expression of beta- and gamma- synuclein were not signifi cantly different between alcoholics and controls. In contrast, the pattern of alphasynuclein protein expression differs from that of the corresponding RNA transcript. Because of the key role of synaptic proteins in the pathogenesis of alcoholism, we are developing 2-D DIGE based techniques to quantify expression changes in synaptosomes prepared from the SFC of controls and alcoholics.

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

Latest developments in the microarray analysis of human brain mRNA

Microarrays are used to monitor the expression of thousands of gene transcripts. This technique requires high-quality RNA, which can be extracted from a variety sources, including autopsy brain tissue. Most nucleic acids and proteins are reasonably stable post mortem. However, their abundance and integrity can exhibit marked intraand inter-subject variability, so care must be taken when comparisons between case-groups are made. We will review issues associated with the sampling of RNA from autopsy brain tissue in relation to various ante- and post-mortem factors.

Systems biology approach to study permeability of paracetamol and its solid dispersion

Physiological changes that take place at cellular level are usually reflective of their level of gene expression. Different formulation excipients have an impact on physiological behavior of the exposed cells and in turn affect transporter genes, enterocyte-mediated metabolism and toxicity biomarkers. The aim of this study was to prepare solid dispersion of paracetamol and evaluate genetic changes that occur in Caco-2 cell lines during the permeability of paracetamol alone and paracetamol solid dispersion formulations. Paracetamol-PEG 8000 solid dispersion was prepared by melt fusion method and the formulation was characterised using differential scanning calorimetry (DSC), scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). Formulation of solid dispersion resulted in the conversion of crystalline drug into an amorphous form. Permeability studies showed that paracetamol absorption was higher from the solid dispersion formulation. DNA microarrays analysis was carried out in order to investigate the involvement of any efflux/uptake transporters in paracetamol or its solid dispersion permeability. Neither transporter carriers nor efflux proteins were found to be involved in the absorption of paracetamol or its PEG solid dispersion. Gene expression analysis established that paracetamol toxicity was potentially reduced upon formulation into solid dispersion when ATP binding cassette (ABC) and solute carrier transporter (SLC) genes were analyzed.

Microscale mesoarrays created by dip-pen nanolithography for screening of protein-protein interactions

Using microarrays to probe protein-protein interactions is becoming increasingly attractive due to their compatibility with highly sensitive detection techniques, selectivity of interaction, robustness and capacity for examining multiple proteins simultaneously. The major drawback to using this approach is the relatively large volumes and high concentrations necessary. Reducing the protein array spot size should allow for smaller volumes and lower concentrations to be used as well as opening the way for combination with more sensitive detection technologies. Dip-Pen Nanolithography (DPN) is a recently developed technique for structure creation on the nano to microscale with the capacity to create biological architectures. Here we describe the creation of miniaturised microarrays, 'mesoarrays', using DPN with protein spots 400× smaller by area compared to conventional microarrays. The mesoarrays were then used to probe the ERK2-KSR binding event of the Ras/Raf/MEK/ERK signalling pathway at a physical scale below that previously reported. Whilst the overall assay efficiency was determined to be low, the mesoarrays could detect KSR binding to ERK2 repeatedly and with low non-specific binding. This study serves as a first step towards an approach that can be used for analysis of proteins at a concentration level comparable to that found in the cellular environment.

Unique reporter-based sensor platforms to monitor signalling in cells

Introduction - In recent years much progress has been made in the development of tools for systems biology to study the levels of mRNA and protein, and their interactions within cells. However, few multiplexed methodologies are available to study cell signalling directly at the transcription factor level. Methods - Here we describe a sensitive, plasmid-based RNA reporter methodology to study transcription factor activation in mammalian cells, and apply this technology to profiling 60 transcription factors in parallel. The methodology uses two robust and easily accessible detection platforms; quantitative real-time PCR for quantitative analysis and DNA microarrays for parallel, higher throughput analysis. Findings - We test the specificity of the detection platforms with ten inducers and independently validate the transcription factor activation. Conclusions - We report a methodology for the multiplexed study of transcription factor activation in mammalian cells that is direct and not theoretically limited by the number of available reporters.

Amino acids in oral drug delivery:salts, ion-pairs and transcriptomics

Oral drug delivery is considered the most popular route of delivery because of the ease of administration, availability of a wide range of dosage forms and the large surface area for drug absorption via the intestinal membrane. However, besides the unfavourable biopharmaceutical properties of the therapeutic agents, efflux transporters such as Pglycoprotein (P-gp) and multiple resistance proteins (MRP) decrease the overall drug uptake by extruding the drug from the cells. Although, prodrugs have been investigated to improve drug partitioning by masking the polar groups covalently with pre-moieties promoting increased uptake, they present significant challenges including reduced solubility and increased toxicity. The current work investigates the use of amino acids as ion-pairs for three model drugs: indomethacin (weak acid), trimethoprim (weak base) and ciprofloxacin (zwitter ion) in an attempt to improve both solubility and uptake. Solubility was studied by salt formation while creating new routes for uptake across the membranes via amino acids transporter proteins or dipeptidyl transporters was the rationale to enhance absorption. New salts were prepared for the model drugs and the oppositely charged amino acids by freeze drying and they were characterised using FTIR, 1HNMR, DSC, SEM, pH solubility profile, solubility and dissolution. Permeability profiles were assessed using an in vitro cell based method; Caco-2 cells and the genetic changes occurring across the transporter genes and various pathways involved in the cellular activities were studied using DNA microarrays. Solubility data showed a significant increase in drug solubility upon preparing the new salts with the oppositely charged counter ions (ciprofloxacin glutamate salt exhibiting 2.9x103 fold enhancement when compared to the free drug). Moreover, permeability studies showed a 3 fold increase in trimethoprim and indomethacin permeabilities upon ion-pairing with amino acids and more than 10 fold when the zwitter ionic drug was paired with glutamic acid. Microarray data revealed that trimethoprim was absorbed actively via OCTN1 transporters while MRP7 is the main transporter gene that mediates its efflux. The absorption of trimethoprim from trimethoprim glutamic acid ion-paired formulations was affected by the ratio of glutamic acid in the formulation which was inversely proportional to the degree of expression of OCTN1. Interestingly, ciprofloxacin glutamic acid ion-pairs were found to decrease the up-regulation of ciprofloxacin efflux proteins (P-gp and MRP4) and over-express two solute carrier transporters; (PEPT2 and SLCO1A2) suggesting that a high aqueous binding constant (K11aq) enables the ion-paired formulations to be absorbed as one entity. In conclusion, formation of ion-pairs with amino acids can influence in a positive way solubility, transfer and gene expression effects of drugs.

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

Functionalized carbon micro/nanostructures for biomolecular detection

Advancements in the micro-and nano-scale fabrication techniques have opened up new avenues for the development of portable, scalable and easier-to-use biosensors. Over the last few years, electrodes made of carbon have been widely used as sensing units in biosensors due to their attractive physiochemical properties. The aim of this research is to investigate different strategies to develop functionalized high surface carbon micro/nano-structures for electrochemical and biosensing devices. High aspect ratio three-dimensional carbon microarrays were fabricated via carbon microelectromechanical systems (C-MEMS) technique, which is based on pyrolyzing pre-patterned organic photoresist polymers. To further increase the surface area of the carbon microstructures, surface porosity was introduced by two strategies, i.e. (i) using F127 as porogen and (ii) oxygen reactive ion etch (RIE) treatment. Electrochemical characterization showed that porous carbon thin film electrodes prepared by using F127 as porogen had an effective surface area (Aeff 185%) compared to the conventional carbon electrode. To achieve enhanced electrochemical sensitivity for C-MEMS based functional devices, graphene was conformally coated onto high aspect ratio three-dimensional (3D) carbon micropillar arrays using electrostatic spray deposition (ESD) technique. The amperometric response of graphene/carbon micropillar electrode arrays exhibited higher electrochemical activity, improved charge transfer and a linear response towards H2O2 detection between 250&mgr;M to 5.5mM. Furthermore, carbon structures with dimensions from 50 nano-to micrometer level have been fabricated by pyrolyzing photo-nanoimprint lithography patterned organic resist polymer. Microstructure, elemental composition and resistivity characterization of the carbon nanostructures produced by this process were very similar to conventional photoresist derived carbon. Surface functionalization of the carbon nanostructures was performed using direct amination technique. Considering the need for requisite functional groups to covalently attach bioreceptors on the carbon surface for biomolecule detection, different oxidation techniques were compared to study the types of carbon-oxygen groups formed on the surface and their percentages with respect to different oxidation pretreatment times. Finally, a label-free detection strategy using signaling aptamer/protein binding complex for platelet-derived growth factor oncoprotein detection on functionalized three-dimensional carbon microarrays platform was demonstrated. The sensor showed near linear relationship between the relative fluorescence difference and protein concentration even in the sub-nanomolar range with an excellent detection limit of 5 pmol.

High natural gene expression variation in the reef-building coral Acropora millepora: potential for acclimative and adaptive plasticity

Background: Ecosystems worldwide are suffering the consequences of anthropogenic impact. The diverse ecosystem of coral reefs, for example, are globally threatened by increases in sea surface temperatures due to global warming. Studies to date have focused on determining genetic diversity, the sequence variability of genes in a species, as a proxy to estimate and predict the potential adaptive response of coral populations to environmental changes linked to climate changes. However, the examination of natural gene expression variation has received less attention. This variation has been implicated as an important factor in evolutionary processes, upon which natural selection can act. Results: We acclimatized coral nubbins from six colonies of the reef-building coral Acropora millepora to a common garden in Heron Island (Great Barrier Reef, GBR) for a period of four weeks to remove any site-specific environmental effects on the physiology of the coral nubbins. By using a cDNA microarray platform, we detected a high level of gene expression variation, with 17% (488) of the unigenes differentially expressed across coral nubbins of the six colonies (jsFDR-corrected, p < 0.01). Among the main categories of biological processes found differentially expressed were transport, translation, response to stimulus, oxidation-reduction processes, and apoptosis. We found that the transcriptional profiles did not correspond to the genotype of the colony characterized using either an intron of the carbonic anhydrase gene or microsatellite loci markers. Conclusion: Our results provide evidence of the high inter-colony variation in A. millepora at the transcriptomic level grown under a common garden and without a correspondence with genotypic identity. This finding brings to our attention the importance of taking into account natural variation between reef corals when assessing experimental gene expression differences. The high transcriptional variation detected in this study is interpreted and discussed within the context of adaptive potential and phenotypic plasticity of reef corals. Whether this variation will allow coral reefs to survive to current challenges remains unknown.

Pseudomonas aeruginosa AMPR transcriptional regulatory network

In Enterobacteriaceae, the transcriptional regulator AmpR, a member of the LysR family, regulates the expression of a chromosomal β-lactamase AmpC. The regulatory repertoire of AmpR is broader in Pseudomonas aeruginosa, an opportunistic pathogen responsible for numerous acute and chronic infections including cystic fibrosis. Previous studies showed that in addition to regulating ampC, P. aeruginosa AmpR regulates the sigma factor AlgT/U and production of some quorum sensing (QS)-regulated virulence factors. In order to better understand the ampR regulon, the transcriptional profiles generated using DNA microarrays and RNA-Seq of the prototypic P. aeruginosa PAO1 strain with its isogenic ampR deletion mutant, PAOΔampR were analyzed. Transcriptome analysis demonstrates that the AmpR regulon is much more extensive than previously thought influencing the differential expression of over 500 genes. In addition to regulating resistance to β-lactam antibiotics via AmpC, AmpR also regulates non-β-lactam antibiotic resistance by modulating the MexEF-OprN efflux pump. Virulence mechanisms including biofilm formation, QS-regulated acute virulence, and diverse physiological processes such as oxidative stress response, heat-shock response and iron uptake are AmpR-regulated. Real-time PCR and phenotypic assays confirmed the transcriptome data. Further, Caenorhabditis elegans model demonstrates that a functional AmpR is required for full pathogenicity of P. aeruginosa. AmpR, a member of the core genome, also regulates genes in the regions of genome plasticity that are acquired by horizontal gene transfer. The extensive AmpR regulon included other transcriptional regulators and sigma factors, accounting for the extensive AmpR regulon. Gene expression studies demonstrate AmpR-dependent expression of the QS master regulator LasR that controls expression of many virulence factors. Using a chromosomally tagged AmpR, ChIP-Seq studies show direct AmpR binding to the lasR promoter. The data demonstrates that AmpR functions as a global regulator in P. aeruginosa and is a positive regulator of acute virulence while negatively regulating chronic infection phenotypes. In summary, my dissertation sheds light on the complex regulatory circuit in P. aeruginosa to provide a better understanding of the bacterial response to antibiotics and how the organism coordinately regulates a myriad of virulence factors.

Comparative transcriptome analyses of Pseudomonas aeruginosa

One of the hallmarks of bacterial survival is their ability to adapt rapidly to changing environmental conditions. Niche adaptation is a response to the signals received that are relayed, often to regulators that modulate gene expression. In the post-genomic era, DNA microarrays are used to study the dynamics of gene expression on a global scale. Numerous studies have used Pseudomonas aeruginosa--a Gram-negative environmental and opportunistic human pathogenic bacterium--as the model organism in whole-genome transcriptome analysis. This paper reviews the transcriptome studies that have led to immense advances in our understanding of the biology of this intractable human pathogen. Comparative analysis of 23 P. aeruginosa transcriptome studies has led to the identification of a unique set of genes that are signal specific and a core set that is differentially regulated. The 303 genes in the core set are involved in bacterial homeostasis, making them attractive therapeutic targets.

Evaluation of Some Statistical Methods for the Identification of Differentially Expressed Genes

Microarray platforms have been around for many years and while there is a rise of new technologies in laboratories, microarrays are still prevalent. When it comes to the analysis of microarray data to identify differentially expressed (DE) genes, many methods have been proposed and modified for improvement. However, the most popular methods such as Significance Analysis of Microarrays (SAM), samroc, fold change, and rank product are far from perfect. When it comes down to choosing which method is most powerful, it comes down to the characteristics of the sample and distribution of the gene expressions. The most practiced method is usually SAM or samroc but when the data tends to be skewed, the power of these methods decrease. With the concept that the median becomes a better measure of central tendency than the mean when the data is skewed, the tests statistics of the SAM and fold change methods are modified in this thesis. This study shows that the median modified fold change method improves the power for many cases when identifying DE genes if the data follows a lognormal distribution.