Biomedical discovery with DNA arrays

Study on the Multimodal Anticancer Mechanism of Ru(II)/Ir(III) Complexes Bearing a Poly(ADP-ribose) Polymerase 1 Inhibitor

A series of novel ruthenium(II) and iridium(III) complexes (Ru1-Ru3 and Ir1-Ir3) with different ancillary ligands and a PARP-1-inhibitory chelating ligand 2-(2,3-dibromo-4,5-dimethoxybenzylidene)hydrazine-1-carbothioamide (L1) were designed and prepared. The target complexes were structurally characterized by NMR and ESI-MS techniques. Among them, the crystal and molecular structures of Ir1 and Ir2 were also determined by X-ray crystallography. These complexes retained the PARP-1 enzyme inhibitory effect of L1 and showed potent antiproliferative activity on the tested cancer cell lines. The ruthenium(II) complexes Ru1-Ru3 were found to be more cytotoxic than the iridium(III) complexes Ir1-Ir3. Further investigations revealed that the most active complex Ru3 induced apoptosis in MCF-7 cells by multiple modes, inclusive of inducing DNA damage, suppressing DNA damage repair, disturbing cell cycle distribution, decreasing the mitochondrial membrane potential, and increasing the intracellular reactive oxygen species levels.

DNA Microarray-Based Global Gene Expression Profiling in Human Amniotic Epithelial Cells Predicts the Potential of Microalgae-Derived Squalene for the Nervous System and Metabolic Health

In recent years, perinatal stem cells, such as human amniotic epithelial cells (hAECs), have attracted increasing interest as a novel tool of stem cell-based high-throughput drug screening. In the present study, we investigated the bioactivities of squalene (SQ) derived from ethanol extract (99.5%) of a microalgae Aurantiochytrium Sp. (EEA-SQ) in hAECs using whole-genome DNA microarray analysis. Tissue enrichment analysis showed that the brain was the most significantly enriched tissue by the differentially expressed genes (DEGs) between EEA-SQ-treated and control hAECs. Further gene set enrichment analysis and tissue-specific functional analysis revealed biological functions related to nervous system development, neurogenesis, and neurotransmitter modulation. Several adipose tissue-specific genes and functions were also enriched. Gene-disease association analysis showed nervous system-, metabolic-, and immune-related diseases were enriched. Altogether, our study suggests the potential health benefits of microalgae-derived SQ and we would further encourage investigation in EEA-SQ and its derivatives as potential therapeutics for nervous system- and metabolism-related diseases.

Water-Soluble Dioxidovanadium(V) Complexes of Aroylhydrazones: DNA/BSA Interactions, Hydrophobicity, and Cell-Selective Anticancer Potential

Five new anionic aqueous dioxidovanadium(V) complexes, [{VO₂L^1,2}A(H₂O)_n]_α (1-5), with the aroylhydrazone ligands pyridine-4-carboxylic acid (3-ethoxy-2-hydroxybenzylidene)hydrazide (H₂L¹) and furan-2-carboxylic acid (3-ethoxy-2-hydroxybenzylidene)hydrazide (H₂L²) incorporating different alkali metals (A = Na⁺, K⁺, Cs⁺) as countercation were synthesized and characterized by various physicochemical techniques. The solution-phase stabilities of 1-5 were determined by time-dependent NMR and UV-vis, and also the octanol/water partition coefficients were obtained by spectroscopic techniques. X-ray crystallography of 2-4 confirmed the presence of vanadium(V) centers coordinated by two cis-oxido-O atoms and the O, N, and O atoms of a dianionic tridentate ligand. To evaluate the biological behavior, all complexes were screened for their DNA/protein binding propensity through spectroscopic experiments. Finally, a cytotoxicity study of 1-5 was performed against colon (HT-29), breast (MCF-7), and cervical (HeLa) cancer cell lines and a noncancerous NIH-3T3 cell line. The cytotoxicity was cell-selective, being more active against HT-29 than against other cells. In addition, the role of hydrophobicity in the cytotoxicity was explained in that an optimal hydrophobicity is essential for high cytotoxicity. Moreover, the results of wound-healing assays indicated antimigration in case of HT-29 cells. Remarkably, 1 with an IC₅₀ value of 5.42 ± 0.15 μM showed greater activity in comparison to cisplatin against the HT-29 cell line.

Expression profiles of long non-coding RNAs during fetal lung development

With advances in neonatology, a greater percentage of premature infants now survive and consequently, diseases of lung development, including bronchopulmonary dysplasia and neonatal respiratory distress syndrome, have become more common. However, few studies have addressed the association between fetal lung development and long non-coding RNA (lncRNA). In the present study, right lung tissue samples of fetuses at different gestational ages were collected within 2 h of the induction of labor in order to observe morphological discrepancies. An Affymetrix Human GeneChip was used to identify differentially expressed lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed. A total of 687 lncRNAs were identified to be differentially expressed among three groups of fetal lung tissue samples corresponding to the three embryonic periods. A total of 34 significantly upregulated and 12 significantly downregulated lncRNAs (fold-change, ≥1.5; P<0.05) were detected at different time points (embryonic weeks 7-16, 16-25 and 25-28) of fetal lung development and compared with healthy tissues Expression changes in lncRNAs n340848, n387037, n336823 and ENST00000445168 were validated by reverse transcription-quantitative PCR and the results were consistent with the GeneChip results. These novel identified lncRNAs may have roles in fetal lung development and the results of the present study may lay the foundation for subsequent in-depth studies into lncRNAs in fetal lung development and subsequent clarification of the pathogenesis of neonatal pulmonary diseases.

DeepNEU: Artificially Induced Stem Cell (aiPSC) and Differentiated Skeletal Muscle Cell (aiSkMC) Simulations of Infantile Onset POMPE Disease (IOPD) for Potential Biomarker Identification and Drug Discovery

Infantile onset Pompe disease (IOPD) is a rare and lethal genetic disorder caused by the deletion of the acid alpha-glucosidase (GAA) gene. This gene encodes an essential lysosomal enzyme that converts glycogen to glucose. While enzyme replacement therapy helps some, our understanding of disease pathophysiology is limited. In this project we develop computer simulated stem cells (aiPSC) and differentiated skeletal muscle cells (aiSkMC) to empower IOPD research and drug discovery. Our Artificial Intelligence (AI) platform, DeepNEU v3.6 was used to generate aiPSC and aiSkMC simulations with and without GAA expression. These simulations were validated using peer reviewed results from the recent literature. Once the aiSkMC simulations (IOPD and WT) were validated they were used to evaluate calcium homeostasis and mitochondrial function in IOPD. Lastly, we used aiSkMC IOPD simulations to identify known and novel biomarkers and potential therapeutic targets. The aiSkMC simulations of IOPD correctly predicted genotypic and phenotypic features that were reported in recent literature. The probability that these features were accurately predicted by chance alone using the binomial test is 0.0025. The aiSkMC IOPD simulation correctly identified L-type calcium channels (VDCC) as a biomarker and confirmed the positive effects of calcium channel blockade (CCB) on calcium homeostasis and mitochondrial function. These published data were extended by the aiSkMC simulations to identify calpain(s) as a novel potential biomarker and therapeutic target for IOPD. This is the first time that computer simulations of iPSC and differentiated skeletal muscle cells have been used to study IOPD. The simulations are robust and accurate based on available published literature. We also demonstrated that the IOPD simulations can be used for potential biomarker identification leading to targeted drug discovery. We will continue to explore the potential for calpain inhibitors with and without CCB as effective therapy for IOPD.

Biomarkers for detection, prognosis and therapeutic assessment of neurological disorders

Neurological disorders have aroused a significant concern among the health scientists globally, as diseases such as Parkinson's, Alzheimer's and dementia lead to disability and people have to live with them throughout the life. Recent evidence suggests that a number of environmental chemicals such as pesticides (paraquat) and metals (lead and aluminum) are also the cause of these diseases and other neurological disorders. Biomarkers can help in detecting the disorder at the preclinical stage, progression of the disease and key metabolomic alterations permitting identification of potential targets for intervention. A number of biomarkers have been proposed for some neurological disorders based on laboratory and clinical studies. In silico approaches have also been used by some investigators. Yet the ideal biomarker, which can help in early detection and follow-up on treatment and identifying the susceptible populations, is not available. An attempt has therefore been made to review the recent advancements of in silico approaches for discovery of biomarkers and their validation. In silico techniques implemented with multi-omics approaches have potential to provide a fast and accurate approach to identify novel biomarkers.

Artificial Base zT as Functional "Element" for Constructing Photoresponsive DNA Nanomolecules

In contrast to small molecules, DNA and RNA macromolecules can be accurately formulated with base "elements" abbreviated as A, T, U, C, and G. However, the development of functionally artificial bases can result in the generation of new biomaterials with unique properties and applications. Therefore, we herein report the design and synthesis of a photoresponsive base as a new functional or molecular "element" for constructing DNA nanomolecules. The new base is made by fusion of an azobenzene with a natural T base (zT). zT, a new molecular element, is not only the most size-expanded T analogue but also a photoresponsive base capable of specific self-assembly through hydrogen bonding. Our results showed that stable and selective self-assembly of double-stranded DNAs occurred through zT-A base pairing, but it could still be efficiently dissociated by light irradiation. The photoresponsive DNA bases will provide the versatility required for constructing desired DNA nanomolecules and nanodevices.

DNA Hybridization Arrays for Gene Expression Analysis of Human Oral Cancer

DNA hybridization arrays permit global gene expression profiling to be done in a single experiment. The evolution and challenges of DNA hybridization arrays are reflected in the variety of experimental platforms, probe composition, hybridization/signal detection methods, and bioinformatic interpretation. In tumor biology, DNA hybridization arrays are being used for gene/gene pathway discovery, diagnosis, and therapeutic design. Similar applications are advancing our understanding of oral cancer cell biology.

Role of Supercomputers in Bioinformatics

Due to the involvement of effective and client-friendly components (i.e. supercomputers), rapid data analysis is being accomplished. In Bioinformatics, it is expanding many areas of research such as genomics, proteomics, metabolomics, etc. Structure-based drug design is one of the major areas of research to cure human malady. This chapter initiates a discussion on supercomputing in sequence analysis with a detailed table summarizing the software and Web-based programs used for sequence analysis. A brief talk on the supercomputing in virtual screening is given where the databases like DOCK, ZINC, EDULISS, etc. are introduced. As the chapter transitions to the next phase, the intricacies of advanced Quantitative Structure-Activity Relationship technologies like Fragment-Based 2D QSAR, Multiple-Field 3D QSAR, and Amino Acid-Based Peptide Prediction are put forth in a manner similar to the concept of abstraction. The supercomputing in docking studies is stressed where docking software for Protein-Ligand docking, Protein-Protein docking, and Multi-Protein docking are provided. The chapter ends with the applications of supercomputing in widely used microarray data analysis.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.

High-Throughput siRNA-Based Functional Target Validation

The drug discovery process pursued by major pharmaceutical companies for many years starts with target identification followed by high-throughput screening (HTS) with the goal of identifying lead compounds. To accomplish this goal, significant resources are invested into automation of the screening process or HTS. Robotic systems capable of handling thousands of data points per day are implemented across the pharmaceutical sector. Many of these systems are amenable to handling cell-based screening protocols as well. On the other hand, as companies strive to develop innovative products based on novel mechanisms of action(s), one of the current bottlenecks of the industry is the target validation process. Traditionally, bioinformatics and HTS groups operate separately at different stages of the drug discovery process. The authors describe the convergence and integration of HTS and bioinformatics to perform high-throughput target functional identification and validation. As an example of this approach, they initiated a project with a functional cell-based screen for a biological process of interest using libraries of small interfering RNA (siRNA) molecules. In this protocol, siRNAs function as potent gene-specific inhibitors. siRNA-mediated knockdown of the target genes is confirmed by TaqMan analysis, and genes with impacts on biological functions of interest are selected for further analysis. Once the genes are confirmed and further validated, they may be used for HTS to yield lead compounds.

An anti-fouling aptasensor for detection of thrombin by dual polarization interferometry

An anti-fouling surface was designed to effectively resist nonspecific protein adsorption using dual polarization interferometry, based on which the aptasensor for detection of thrombin was fabricated according to the specific interaction between thrombin and its 15-mer aptamer.

Pediatric asthma and autism-genomic perspectives

High-throughput technologies, ranging from microarrays to NexGen sequencing of RNA and genomic DNA, have opened new avenues for exploration of the pathobiology of human disease. Comparisons of the architecture of the genome, identification of mutated or modified sequences, and pre-and post- transcriptional regulation of gene expression as disease specific biomarkers are revolutionizing our understanding of the causes of disease and are guiding the development of new therapies. There is enormous heterogeneity in types of genomic variation that occur in human disease. Some are inherited, while others are the result of new somatic or germline mutations or errors in chromosomal replication. In this review, we provide examples of changes that occur in the human genome in two of the most common chronic pediatric disorders, autism and asthma. The incidence and economic burden of both of these disorders are increasing worldwide. Genomic variations have the potential to serve as biomarkers for personalization of therapy and prediction of outcomes.

Differentially expressed genes in the fat body of Bombyx mori in response to phoxim insecticide

The silkworm, Bombyx mori, is an economically important insect. However, poisoning of silkworms by organophosphate pesticides causes tremendous loss to the sericulture. The fat body is the major tissue involved in detoxification and produces antimicrobial peptides and regulates hormones. In this study, a microarray system comprising 22,987 oligonucluotide 70-mer probes was employed to examine differentially expressed genes in the fat body of B. mori exposed to phoxim insecticide. The results showed that a total of 774 genes were differentially expressed upon phoxim exposure, including 500 up-regulated genes and 274 down-regulated genes. The expression levels of eight detoxification-related genes were up-regulated upon phoxim exposure, including six cytochrome P450s and two glutathione-S-transferases. It was firstly found that eight antimicrobial peptide genes were down-regulated, which might provide important references for studying the larvae of B. mori become more susceptible to microbial infections after phoxim treatment. In addition, we firstly detected the expression level of metamorphosis-related genes after phoxim exposure, which may lead to impacted reproduction. Our results may facilitate the overall understanding of the molecular mechanism of multiple pathways following exposure to phoxim insecticide in the fat body of B. mori.

A novel role for lncRNAs in cell cycle control during stress adaptation

Eukaryotic cells have developed sophisticated systems to constantly monitor changes in the extracellular environment and to orchestrate a proper cellular response. To maximize survival, cells delay cell-cycle progression in response to environmental changes. In response to extracellular insults, stress-activated protein kinases (SAPKs) modulate cell-cycle progression and gene expression. In yeast, osmostress induces activation of the p38-related SAPK Hog1, which plays a key role in reprogramming gene expression upon osmostress. Genomic analysis has revealed the existence of a large number of long non-coding RNAs (lncRNAs) with different functions in a variety of organisms, including yeast. Upon osmostress, hundreds of lncRNAs are induced by the SAPK p38/Hog1. One gene that expresses Hog1-dependent lncRNA in an antisense orientation is the CDC28 gene, which encodes CDK1 kinase that controls the cell cycle in yeast. Cdc28 lncRNA mediates the induction of CDC28 expression and this increase in the level of Cdc28 results in more efficient re-entry of the cells into the cell cycle after stress. Thus, the control of lncRNA expression as a new mechanism for the regulation of cell-cycle progression opens new avenues to understand how stress adaptation can be accomplished in response to changing environments.

Identification of DMSA-Coated Fe3O4 Nanoparticles Induced-Apoptosis Response Genes in Human Monocytes by cDNA Microarrays

This study investigated the cell apoptosis and gene expression profiles of human THP-1 monocytes in order to identify the molecular mechanism of cell apoptosis induced by meso-2,-3-dimercaptosuccinnic acid-coated Fe3O4magnetic nanoparticles. Cell apoptosis was visualized with flow cytometry after treated by 50 and 100 μg/ml Fe3O4nanoparticles, and the gene expression profiles were detected with Affymetrix Human Genome U133 Plus 2.0 GeneChips® microarrays. The transmission electron microscopy obserbation revealed that THP-1 cells were effectively labeled by the Fe3O4nanoparticles. The internalized Fe3O4nanoparticles increased cell apoptosis in a dose-dependent manner, but not decreased cell viability significantly. The cDNA microarray results showed that hundreds of genes were significantly regulated at the concentration of 50 and 100 μg/ml, and the level of these genes exhibited a dose response, includingCD14,CD86,CFLAR,IL-1,NFKBIA,NLRC4,NAIPandAIP3. The Fe3O4nanoparticles treatments resulted in significantly altered in Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, and Cell apoptosis signaling pathway. Gene ontology analysis of these differentially expressed genes demonstrated that mainly up-regulated genes were related to cytokine production and cell apoptosis. These results showed that the Fe3O4nanoparticles induced THP-1 cells apoptosis and the level of lots of genes involved in extrinsic apoptosis pathway differentially expressed, which further revealed demonstrated the relation between Fe3O4MNPs treatment and cell apoptosis.

Regioselective covalent immobilization of recombinant antibody-binding proteins A, G, and L for construction of antibody arrays

Immobilized antibodies are useful for the detection of antigens in highly sensitive microarray diagnostic applications. Arrays with the antibodies attached regioselectively in a uniform orientation are typically more sensitive than those with random orientations. Direct regioselective immobilization of antibodies on a solid support typically requires a modified form of the protein. We now report a general approach for the regioselective attachment of antibodies to a surface using truncated forms of antibody-binding proteins A, G, and L that retain the structural motifs required for antibody binding. The recombinant proteins have a C-terminal CVIX protein farnesyltransferase recognition motif that allows us to append a bioorthogonal azide or alkyne moiety and use the Cu(I)-catalyzed Huisgen cycloaddition to attach the binding proteins to a suitably modified glass surface. This approach offers several advantages. The recombinant antibody-binding proteins are produced in Escherichia coli, chemoselectively modified posttranslationally in the cell-free homogenate, and directly attached to the glass surface without the need for purification at any stage of the process. Complexes between immobilized recombinant proteins A, G, and L and their respective strongly bound antibodies were stable to repeated washing with PBST buffer at pH 7.2. However, the antibodies could be stripped from the slides by treatment with 0.1 M glycine·HCl buffer, pH 2.6, for 30 min and regenerated by shaking with PBS buffer, pH 7.2, at 4 °C overnight. The recombinant forms of proteins A, G, and L can be used separately or in combination to give glass surfaces capable of binding a wide variety of antibodies.

Comparison of feature selection methods for cross-laboratory microarray analysis

The amount of gene expression data of microarray has grown exponentially. To apply them for extensive studies, integrated analysis of cross-laboratory (cross-lab) data becomes a trend, and thus, choosing an appropriate feature selection method is an essential issue. This paper focuses on feature selection for Affymetrix (Affy) microarray studies across different labs. We investigate four feature selection methods: $(t)$-test, significance analysis of microarrays (SAM), rank products (RP), and random forest (RF). The four methods are applied to acute lymphoblastic leukemia, acute myeloid leukemia, breast cancer, and lung cancer Affy data which consist of three cross-lab data sets each. We utilize a rank-based normalization method to reduce the bias from cross-lab data sets. Training on one data set or two combined data sets to test the remaining data set(s) are both considered. Balanced accuracy is used for prediction evaluation. This study provides comprehensive comparisons of the four feature selection methods in cross-lab microarray analysis. Results show that SAM has the best classification performance. RF also gets high classification accuracy, but it is not as stable as SAM. The most naive method is $(t)$-test, but its performance is the worst among the four methods. In this study, we further discuss the influence from the number of training samples, the number of selected genes, and the issue of unbalanced data sets.

Transcriptional characteristics of gene expression in the midgut of domestic silkworms (Bombyx mori) exposed to phoxim

Silkworm (Bombyx mori) is not only an economically important insect but also a model system for lepidoptera. As a vital organ of digestion and nutrient absorption, the midgut of insects also serves as the first physiological barrier to chemical pesticides. In this study, microarray was performed to profile the gene expression changes in the midgut of silkworms exposed to phoxim. After 24h of phoxim exposure (4.0μg/mL), 266 genes displayed at least 2.0-fold changes in expression levels. Among them, 192 genes were up-regulated, and 74 genes were down-regulated. The most significant changes were 14.88-fold up-regulation and 23.36-fold down-regulation. According to gene ontology annotation and pathway analysis, differentially expressed genes were mainly classified into different groups based on their potential involvements in detoxification, immunne response, stress response, energy metabolism and transport. Particularly, the transcription levels of detoxification-related genes were up-regulated, such as cytochrome P450s, esterases and glutathione-S-transferase (GST), indicating increased detoxification activity in the midgut. Our study provides new insights into the molecular mechanism of pesticide metabolism in the midgut of insects, which may promote the development of highly efficient insecticides.

Effects of DMSA-coated Fe3O4 nanoparticles on the transcription of genes related to iron and osmosis homeostasis

In this article, we checked the effect of 2,3-dimercaptosuccinic acid-coated Fe(3)O(4) nanoparticles on gene expression of mouse macrophage RAW264.7 cells and found that the transcription of several important genes related to intracellular iron homeostasis were significantly changed. We thus speculated that the cellular iron homeostasis might be disturbed by this nanoparticle through releasing iron ion in cells. To verify this speculation, we first confirmed the transcriptional changes of several key iron homeostasis- related genes, such as Tfrc, Trf, and Lcn2, using quantitative PCR, and found that an iron ion chelator, desferrioxamine, could alleviate the transcriptional alterations of two typical genes, Tfrc and Lcn2. Then, we designed and validated a method based on centrifugation for assaying intracellular irons in ion and nanoparticle state. After extensive measures of intracellular iron in two forms and total iron, we found that the intracellular iron ion significantly increased with intracellular total iron and nanoparticle iron, demonstrating degradation of this nanoparticle into iron ion in cells. We next mimicked the intralysosomal environment in vitro and verified that the internalized iron nanoparticle could release iron ion in lysosome. We found that as another important compensatory response to intracellular overload of iron ion, cells significantly downregulated the expressions of genes belonging to solute carrier family which are responsible for transferring many organic solutes into cells, such as Slc5a3 and Slc44a1, in order to prevent more organic solutes into cells and thus lower the intracellular osmosis. Based on these findings, we profiled a map of gene effects after cells were treated with this iron nanoparticle and concluded that the iron nanoparticles might be more detrimental to cell than iron ion due to its intracellular internalization fashion, nonspecific endocytosis.

Volcano plots in analyzing differential expressions with mRNA microarrays

A volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log(10)(p-value) from the t-test). We review the basic and interactive use of the volcano plot and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide a unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility of applying volcano plots to other fields beyond microarray.

Differential gene expression from genome-wide microarray analyses distinguishes Lohmann Selected Leghorn and Lohmann Brown layers

The Lohmann Selected Leghorn (LSL) and Lohmann Brown (LB) layer lines have been selected for high egg production since more than 50 years and belong to the worldwide leading commercial layer lines. The objectives of the present study were to characterize the molecular processes that are different among these two layer lines using whole genome RNA expression profiles. The hens were kept in the newly developed small group housing system Eurovent German with two different group sizes. Differential expression was observed for 6,276 microarray probes (FDR adjusted P-value <0.05) among the two layer lines LSL and LB. A 2-fold or greater change in gene expression was identified on 151 probe sets. In LSL, 72 of the 151 probe sets were up- and 79 of them were down-regulated. Gene ontology (GO) enrichment analysis accounting for biological processes evinced 18 GO-terms for the 72 probe sets with higher expression in LSL, especially those taking part in immune system processes and membrane organization. A total of 32 enriched GO-terms were determined among the 79 down-regulated probe sets of LSL. Particularly, these terms included phosphorus metabolic processes and signaling pathways. In conclusion, the phenotypic differences among the two layer lines LSL and LB are clearly reflected in their gene expression profiles of the cerebrum. These novel findings provide clues for genes involved in economically important line characteristics of commercial laying hens.

Paradigm of Time-sequence Development of the Intestine of Suckling Piglets with Microarray

The interaction of the genes involved in intestinal development is the molecular basis of the regulatory mechanisms of intestinal development. The objective of this study was to identify the significant pathways and key genes that regulate intestinal development in Landrace piglets, and elucidate their rules of operation. The differential expression of genes related to intestinal development during suckling time was investigated using a porcine genome array. Time sequence profiles were analyzed for the differentially expressed genes to obtain significant expression profiles. Subsequently, the most significant profiles were assayed using Gene Ontology categories, pathway analysis, network analysis, and analysis of gene co-expression to unveil the main biological processes, the significant pathways, and the effective genes, respectively. In addition, quantitative real-time PCR was carried out to verify the reliability of the results of the analysis of the array. The results showed that more than 8000 differential expression transcripts were identified using microarray technology. Among the 30 significant obtained model profiles, profiles 66 and 13 were the most significant. Analysis of profiles 66 and 13 indicated that they were mainly involved in immunity, metabolism, and cell division or proliferation. Among the most effective genes in these two profiles, CN161469, which is similar to methylcrotonoyl-Coenzyme A carboxylase 2 (beta), and U89949.1, which encodes a folate binding protein, had a crucial influence on the co-expression network.

Microarray analysis of 1,25(OH)₂D₃ regulated gene expression in human primary osteoblasts

Though extensive studies have been conducted, questions regarding the molecular effectors and pathways underlying the regulatory role of 1,25(OH)(2)D(3) in human osteoblasts other than cell differentiation and matrix protein production remain unanswered. This study aims to identify genes and pathways that are modulated by 1,25(OH)(2)D(3) treatment in human osteoblasts. Primary osteoblast cultures obtained from human bone tissue samples were treated with 1,25(OH)(2)D(3) (10(-7)  M) for 24 h and their transcritptomes were profiled by microarray analysis using the Affymetrix GeneChip. Statistical analysis was conducted to identify genes whose expression is significantly modulated following 1,25(OH)(2)D(3) treatment. One hundred and fifty-eight genes were found to be differentially expressed. Of these, 136 were upregulated, indicating clear transcriptional activation by 1,25(OH)(2)D(3). Biostatistical evaluation of microarray data by Ingenuity Pathways Analysis (IPA) revealed a relevant modulation of genes involved in vitamin D metabolism (CYP24), immune functions (CD14), neurotransmitter transporters (SLC1A1, SLC22A3), and coagulation [thrombomodulin (THBD), tissue plasminogen activator (PLAT), endothelial protein C receptor (PROCR), thrombin receptor (F2R)]. We identified a restricted number of highly regulated genes and confirmed their differential expression by real-time quantitative PCR (RT qPCR). The present genome-wide microarray analysis on 1,25(OH)(2)D(3) -treated human osteoblasts reveals an interplay of critical regulatory and metabolic pathways and supports the hypothesis that 1,25(OH)(2)D(3) can modulate the coagulation process through osteoblasts, activates osteoclastogenesis through inflammation signaling, modulates the effects of monoamines by affecting their reuptake.

Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry

An efficient means of identifying protein biomarkers is essential to proper cancer management. A well-characterized proteome resource holds special promise for the discovery of novel biomarkers. However, quantification of the differences between physiological conditions together with deep down profiling has become increasingly challenging in proteomics. Here, we perform expression profiling of the colorectal cancer (CRC) proteome by stable isotope labeling and mass spectrometry. Quantitative analysis included performing mTRAQ and cICAT labeling in a pooled sample of three microsatellite stable (MSS) type CRC tissues and a pooled sample of their matched normal tissues. We identified and quantified a total of 3688 proteins. Among them, 1487 proteins were expressed differentially between normal and cancer tissues by higher than 2-fold; 1009 proteins showed increased expression in cancer tissue, whereas 478 proteins showed decreased expression. Bioinformatic analysis revealed that our data were largely consistent with known CRC relevant signaling pathways, such as the Wnt/β-catenin, caveolar-mediated endocytosis, and RAN signaling pathways. Mitochondrial dysfunction, known as the Waburg hypothesis, was also confirmed. Therefore, our data showing alterations in the proteomic profile of CRC constitutes a useful resource that may provide insights into tumor progression with later goal of identifying biologically and clinically relevant marker proteins. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Kernel machine approach to testing the significance of multiple genetic markers for risk prediction

There is growing evidence that genomic and proteomic research holds great potential for changing irrevocably the practice of medicine. The ability to identify important genomic and biological markers for risk assessment can have a great impact in public health from disease prevention, to detection, to treatment selection. However, the potentially large number of markers and the complexity in the relationship between the markers and the outcome of interest impose a grand challenge in developing accurate risk prediction models. The standard approach to identifying important markers often assesses the marginal effects of individual markers on a phenotype of interest. When multiple markers relate to the phenotype simultaneously via a complex structure, such a type of marginal analysis may not be effective. To overcome such difficulties, we employ a kernel machine Cox regression framework and propose an efficient score test to assess the overall effect of a set of markers, such as genes within a pathway or a network, on survival outcomes. The proposed test has the advantage of capturing the potentially nonlinear effects without explicitly specifying a particular nonlinear functional form. To approximate the null distribution of the score statistic, we propose a simple resampling procedure that can be easily implemented in practice. Numerical studies suggest that the test performs well with respect to both empirical size and power even when the number of variables in a gene set is not small compared to the sample size.

Molecular diagnosis and prognosis with DNA microarrays

Microarray analysis makes it possible to determine thousands of gene expression values simultaneously. Changes in gene expression, as a response to diseases, can be detected allowing a better understanding and differentiation of diseases at a molecular level. By comparing different kinds of tissue, for example healthy tissue and cancer tissue, the microarray analysis indicates induced gene activity, repressed gene activity or when there is no change in the gene activity level. Fundamental patterns in gene expression are extracted by several clustering and machine learning algorithms. Certain kinds of cancer can be divided into subtypes, with different clinical outcomes, by their specific gene expression patterns. This enables a better diagnosis and tailoring of individual patient treatments.

Effects of DMSA-coated Fe3O4 magnetic nanoparticles on global gene expression of mouse macrophage RAW264.7 cells

Fe(3)O(4) magnetic nanoparticles (MNPs) coated with 2,3-dimercaptosuccinnic acid (DMSA) are considered to be a promising nanomaterial with biocompatibility. In the present study, the effects of DMSA-coated Fe(3)O(4) MNPs on the expression of all identified mouse genes, which regulate various cellular biological processes, were determined to establish whether this nanoparticle is cytotoxic to mammalian cells. Mouse macrophage RAW264.7 cells were treated with 100μg/ml of DMSA-coated Fe(3)O(4) MNPs for 4, 24 and 48h, and the global gene expression was detected via Affymetrix Mouse Genome 430 2.0 GeneChips(®) microarrays. It was found that gene expression of 711, 545 and 434 transcripts was significantly altered by 4-, 24- and 48-h treatments, respectively. Of these genes, 27 were consistently upregulated and 6 were consistently downregulated at the three treatment durations. Bioinformatic analysis of all differentially expressed genes revealed that this nanoparticle can strongly activate inflammatory and immune responses and can inhibit the biosynthesis and metabolism of RAW264.7 cells at a dose of 100μg/ml. These results demonstrated that DMSA-coated Fe(3)O(4) MNPs display cytotoxicity in this type of macrophage at high doses.

Genomic and proteomic analysis of the impact of mitotic quiescence on the engraftment of human CD34+ cells

It is well established that in adults, long-term repopulating hematopoietic stem cells (HSC) are mitotically quiescent cells that reside in specialized bone marrow (BM) niches that maintain the dormancy of HSC. Our laboratory demonstrated that the engraftment potential of human HSC (CD34⁺ cells) from BM and mobilized peripheral blood (MPB) is restricted to cells in the G0 phase of cell cycle but that in the case of umbilical cord blood (UCB) -derived CD34⁺ cells, cell cycle status is not a determining factor in the ability of these cells to engraft and sustain hematopoiesis. We used this distinct in vivo behavior of CD34⁺ cells from these tissues to identify genes associated with the engraftment potential of human HSC. CD34⁺ cells from BM, MPB, and UCB were fractionated into G0 and G1 phases of cell cycle and subjected in parallel to microarray and proteomic analyses. A total of 484 target genes were identified to be associated with engraftment potential of HSC. System biology modeling indicated that the top four signaling pathways associated with these genes are Integrin signaling, p53 signaling, cytotoxic T lymphocyte-mediated apoptosis, and Myc mediated apoptosis signaling. Our data suggest that a continuum of functions of hematopoietic cells directly associated with cell cycle progression may play a major role in governing the engraftment potential of stem cells. While proteomic analysis identified a total of 646 proteins in analyzed samples, a very limited overlap between genomic and proteomic data was observed. These data provide a new insight into the genetic control of engraftment of human HSC from distinct tissues and suggest that mitotic quiescence may not be the requisite characteristic of engrafting stem cells, but instead may be the physiologic status conducive to the expression of genetic elements favoring engraftment.

RRAS: A key regulator and an important prognostic biomarker in biliary atresia

AIM: To characterize the differentially expressed gene profiles in livers from biliary atresia (BA) patients including, ascertain genes, functional categories and pathways that play a central role in the pathogenesis of BA, and identify the novel prognostic markers for BA.

METHODS: Liver tissue samples from control patients, neonatal cholestasis patients, and BA patients at the age of < 60 d, 60-90 d, and > 90 d were pooled for DNA microarray analysis. Bioinformatics analysis was performed using, series test cluster of gene ontology, and Pathway-Finder software. Reverse-transcription polymerase chain reaction was performed to confirm changes in selected genes. Relation between RRAS gene expression and prognosis of 40 BA patients was analyzed in a 2-year follow-up study.

RESULTS: The 4 identified significant gene expression profiles could confidently separate BA liver tissue from normal and other diseased liver tissues. The included genes were mainly involved in inflammation response and reconstruction of cellular matrix. The significant pathways associated with BA were primarily involved in autoimmune response, activation of T lymphocytes and its related cytokines. The RRAS, POMC, SLC26A6 and STX3 genes were important regulatory modules in pathogenesis of BA. The expression of RRAS was negatively correlated with the elimination rate of jaundice and positively correlated with the occurrence rate of cholangitis.

CONCLUSION: Autoimmune response mediated by T lymphocytes may play a vital role in the pathogenesis of BA. The RRAS gene is an important regulatory module in the pathogenesis of BA, which may serve as a novel prognostic marker for BA.

Gene expression profiling: changing face of breast cancer classification and management

Epithelial breast malignancies are a group of several disease entities that vary in their biology and response to specific therapies. Historically, classification of different molecular types of breast cancer was done through the use of conventional methods such as tumor morphology, grade, and immunophenotyping for estrogen, progesterone, and HER-2/neu receptor expression. Such techniques, although helpful, are not sufficient to accurately predict biologic behavior of breast cancers. Over the last several years, much progress has been made in more precise identification of molecular breast cancer subtypes. Such advances hold a great promise in improving estimation of prognosis and assigning most appropriate therapies. Thanks to use of cDNA microarrays expression technology and quantitative reverse transcriptase polymerase chain reaction (RT-PCR), tumors with specific gene expression patterns can now be identified. This process is presently reshaping perceptions of how breast cancer should be classified and treated. Categorization of breast cancers by gene expression is only beginning to make its way into the daily clinical practice and likely will complement, but not replace, the conventional methods of classification.

Lectin microarrays for glycomic analysis

Glycomics is the study of comprehensive structural elucidation and characterization of all glycoforms found in nature and their dynamic spatiotemporal changes that are associated with biological processes. Glycocalyx of mammalian cells actively participate in cell-cell, cell-matrix, and cell-pathogen interactions, which impact embryogenesis, growth and development, homeostasis, infection and immunity, signaling, malignancy, and metabolic disorders. Relative to genomics and proteomics, glycomics is just growing out of infancy with great potential in biomedicine for biomarker discovery, diagnosis, and treatment. However, the immense diversity and complexity of glycan structures and their multiple modes of interactions with proteins pose great challenges for development of analytical tools for delineating structure function relationships and understanding glyco-code. Several tools are being developed for glycan profiling based on chromatography, mass spectrometry, glycan microarrays, and glyco-informatics. Lectins, which have long been used in glyco-immunology, printed on a microarray provide a versatile platform for rapid high throughput analysis of glycoforms of biological samples. Herein, we summarize technological advances in lectin microarrays and critically review their impact on glycomics analysis. Challenges remain in terms of expansion to include nonplant derived lectins, standardization for routine clinical use, development of recombinant lectins, and exploration of plant kingdom for discovery of novel lectins.

Toxicity of the herbicide atrazine: effects on lipid peroxidation and activities of antioxidant enzymes in the freshwater fish Channa punctatus (Bloch)

The present study was undertaken to evaluate the toxicity and effects of a commercial formulation of the herbicide atrazine (Rasayanzine) on lipid peroxidation and antioxidant enzyme system in the freshwater air breathing fish Channa punctatus. The 12, 24, 48, 72 and 96 h LC(50) of atrazine, calculated by probit analysis, were determined to be 77.091, 64.053, 49.100, 44.412 and 42.381 mg·L(-1), respectively, in a semi static system with significant difference (p < 0.05) in LC(10-90) values obtained for different times of exposure. In addition to concentration and time dependent decrease in mortality rate, stress signs in the form of behavioral changes were also observed in response to the test chemical. In fish exposed for 15 days to different sublethal concentrations of the herbicide (1/4 LC(50) = ∼10.600 mg·L(-1), 1/8 LC(50) = ∼5.300 mg·L(-1) and 1/10 LC(50) = ∼4.238 mg·L(-1)) induction of oxidative stress in the liver was evidence by increased lipid peroxidation levels. The antioxidants superoxide dismutase (SOD), catalase (CAT) and glutathione reductase (GR) responded positively in a concentration dependent pattern, thus, suggesting the use of these antioxidants as potential biomarkers of toxicity associated with contaminations exposure in freshwater fishes.

Proteomic analysis of proteins expressing in regions of rat brain by a combination of SDS-PAGE with nano-liquid chromatography-quadrupole-time of flight tandem mass spectrometry

BACKGROUND

Most biological functions controlled by the brain and their related disorders are closely associated with activation in specific regions of the brain. Neuroproteomics has been applied to the analysis of whole brain, and the general pattern of protein expression in all regions has been elucidated. However, the comprehensive proteome of each brain region remains unclear.

RESULTS

In this study, we carried out comparative proteomics of six regions of the adult rat brain: thalamus, hippocampus, frontal cortex, parietal cortex, occipital cortex, and amygdala using semi-quantitative analysis by Mascot Score of the identified proteins. In order to identify efficiently the proteins that are present in the brain, the proteins were separated by a combination of SDS-PAGE on a C18 column-equipped nano-liquid chromatograph, and analyzed by quadrupole-time of flight-tandem-mass spectrometry. The proteomic data show 2,909 peptides in the rat brain, with more than 200 identified as region-abundant proteins by semi-quantitative analysis. The regions containing the identified proteins are membrane (20.0%), cytoplasm (19.5%), mitochondrion (17.1%), cytoskeleton (8.2%), nucleus (4.7%), extracellular region (3.3%), and other (18.0%). Of the identified proteins, the expressions of glial fibrillary acidic protein, GABA transporter 3, Septin 5, heat shock protein 90, synaptotagmin, heat shock protein 70, and pyruvate kinase were confirmed by immunoblotting. We examined the distributions in rat brain of GABA transporter 3, glial fibrillary acidic protein, and heat shock protein 70 by immunohistochemistry, and found that the proteins are localized around the regions observed by proteomic analysis and immunoblotting. IPA analysis indicates that pathways closely related to the biological functions of each region may be activated in rat brain.

CONCLUSIONS

These observations indicate that proteomics in each region of adult rat brain may provide a novel way to elucidate biological actions associated with the activation of regions of the brain.

Quantifying cytosolic messenger RNA concentrations in Escherichia coli using real-time polymerase chain reaction for a systems biology approach

Current messenger RNA (mRNA) quantification methods are sophisticated tools for the analysis of gene regulation. However, these methods are not suitable for more complex quantitative approaches such as the mathematical modeling of the in vivo regulation of transcription where dynamic cytosolic mRNA concentrations need to be taken into consideration. In the current study, the "standard curve method" for quantitative reverse transcription real-time polymerase chain reaction (qRT-PCR) was extended by including an internal RNA standard. This standard enables transcript losses that occur during the process, as well as variations resulting from nonquantitative processes, to be accounted for. The use of an internal standard yielded transcript concentration estimates that were on average seven times higher than those in cases where an internal standard is omitted. Choosing the cra modulon in Escherichia coli as an example, the method applied shows that the regulation of the Cra protein, as well as the growth rate-dependent regulation, need to be taken into consideration. The new method, which enables the determination of cytosolic mRNA concentrations, allows the quantitative representation of transcriptional dynamics. This is an important aspect of the analysis of the complex interactions of metabolism and regulation and in the application of mathematical modeling for systems biology.

Simultaneous class discovery and classification of microarray data using spectral analysis

Classification methods are commonly divided into two categories: unsupervised and supervised. Unsupervised methods have the ability to discover new classes by grouping data into clusters or tree structures without using the class labels, but they carry the risk of producing noninterpretable results. On the other hand, supervised methods always find decision rules that discriminate samples with different class labels. However, the class label information plays such an important role that it confines supervised methods by defining the possible classes. Consequently, supervised methods do not have the ability to discover new classes. To overcome the limitations of unsupervised and supervised methods, we propose a new method, which utilizes the class labels to a less important role so as to perform class discovery and classification simultaneously. The proposed method is called SPACC (SPectral Analysis for Class discovery and Classification). In SPACC, the training samples are nodes of an undirected weighted network. Using spectral analysis, SPACC iteratively partitions the network into a top-down binary tree. Each partitioning step is unsupervised, and the class labels are only used to define the stopping criterion. When the partitioning ends, the training samples have been divided into several subsets, each corresponding to one class label. Because multiple subsets can correspond to the same class label, SPACC may identify biologically meaningful subclasses, and minimize the impact of outliers and mislabeled data. We demonstrate the effectiveness of SPACC for class discovery and classification on microarray data of lymphomas and leukemias. SPACC software is available at http://icbp.stanford.edu/software/SPACC/.

Repression of DNA-binding dependent glucocorticoid receptor-mediated gene expression

The glucocorticoid receptor (GR) affects the transcription of genes involved in diverse processes, including energy metabolism and the immune response, through DNA-binding dependent and independent mechanisms. The DNA-binding dependent mechanism occurs by direct binding of GR to glucocorticoid response elements (GREs) at regulatory regions of target genes. The DNA-binding independent mechanism involves binding of GR to transcription factors and coactivators that, in turn, contact DNA. A small molecule that competes with GR for binding to GREs could be expected to affect the DNA-dependent pathway selectively by interfering with the protein-DNA interface. We show that a DNA-binding polyamide that targets the consensus GRE sequence binds the glucocorticoid-induced zipper (GILZ) GRE, inhibits expression of GILZ and several other known GR target genes, and reduces GR occupancy at the GILZ promoter. Genome-wide expression analysis of the effects of this polyamide on a set of glucocorticoid-induced and -repressed genes could help to elucidate the mechanism of GR regulation for these genes.

Strategy for glycoproteomics: identification of glyco-alteration using multiple glycan profiling tools

Glycan alterations of proteins, a common feature of cancer cells, are associated with carcinogenesis, invasion and metastasis. Glycomics, the study of glycans and glycan-binding proteins in various biological systems, is an emerging field in the postgenome and postproteomics era. However, systematic and robust strategies for glycomics are still not fully established because the structural analysis of glycans, which comprise different patterns of branching, various possible linkage positions as well as monomer anomericity, is technically difficult. Here, we introduce a new strategy for glyco-alteration analysis of glycoproteins by using multiple glycan profiling tools. To understand glycan alterations of proteins by correlating the glycosyltransferase expression profile with the actual glycan structure, we systematically used three glycan profiling tools: (1) multiplex quantitative PCR (qPCR) array format for profiling the expression pattern of glycogenes, (2) lectin microarray as a multiplex glycan-lectin interaction analysis system for profiling either a pool of cell glycoproteins or a target glycoprotein, and (3) tandem mass spectrometry for identifying the glycan structure connected to a target glycoprotein. Using our system, we successfully identified glycan alterations on alpha-fetoprotein (AFP), including a novel LacdiNAc structure in addition to previously reported alterations such as alpha1,6 fucosylation.

Adaptable gene-specific dye bias correction for two-channel DNA microarrays

DNA microarray technology is a powerful tool for monitoring gene expression or for finding the location of DNA-bound proteins. DNA microarrays can suffer from gene-specific dye bias (GSDB), causing some probes to be affected more by the dye than by the sample. This results in large measurement errors, which vary considerably for different probes and also across different hybridizations. GSDB is not corrected by conventional normalization and has been difficult to address systematically because of its variance. We show that GSDB is influenced by label incorporation efficiency, explaining the variation of GSDB across different hybridizations. A correction method (Gene- And Slide-Specific Correction, GASSCO) is presented, whereby sequence-specific corrections are modulated by the overall bias of individual hybridizations. GASSCO outperforms earlier methods and works well on a variety of publically available datasets covering a range of platforms, organisms and applications, including ChIP on chip. A sequence-based model is also presented, which predicts which probes will suffer most from GSDB, useful for microarray probe design and correction of individual hybridizations. Software implementing the method is publicly available.

Making nanoflowerbeds: reaction pathways involved in the selective chemical bath deposition of ZnS on functionalized alkanethiolate self-assembled monolayers

We have investigated the chemical bath deposition (CBD) of ZnS on functionalized alkanethiolate self-assembled monolayers (SAMs) using time-of-flight secondary ion mass spectrometry and scanning electron microscopy. The reaction mechanism involves both cluster-by-cluster and ion-by-ion growth. The dominant reaction pathway is dependent on both the SAM terminal group and the experimental conditions. On -COOH-terminated SAMs, two types of crystallites are observed: approximately 500 nm nanoflowers formed by ion-by-ion growth, and larger approximately 2 mum crystallites formed by cluster-by-cluster deposition. The nanoflowers nucleate at Zn(2+)- carboxylate surface complexes. On -OH- and -CH(3)-terminated SAMs, only the larger crystallites are formed. These do not adhere strongly to the SAM surface and can be easily removed. Finally, we demonstrate that under appropriate experimental conditions ZnS selectively deposits on the -COOH-terminated SAM regions of -COOH/-CH(3)-patterned SAM surfaces, forming nanoscale "flowerbeds".

Aluminum nanoparticles as substrates for metal-enhanced fluorescence in the ultraviolet for the label-free detection of biomolecules

We use finite-difference time-domain calculations to show that aluminum nanoparticles are efficient substrates for metal-enhanced fluorescence (MEF) in the ultraviolet (UV) for the label-free detection of biomolecules. The radiated power enhancement of the fluorophores in proximity to aluminum nanoparticles is strongly dependent on the nanoparticle size, fluorophore-nanoparticle spacing, and fluorophore orientation. Additionally, the enhancement is dramatically increased when the fluorophore is between two aluminum nanoparticles of a dimer. Finally, we present experimental evidence that functionalized forms of amino acids tryptophan and tyrosine exhibit MEF when spin-coated onto aluminum nanostructures.

Methods to reconstruct and compare transcriptional regulatory networks

The availability of completely sequenced genomes and the wealth of literature on gene regulation have enabled researchers to model the transcriptional regulation system of some organisms in the form of a network. In order to reconstruct such networks in non-model organisms, three principal approaches have been taken. First, one can transfer the interactions between homologous components from a model organism to the organism of interest. Second, microarray experiments can be used to detect patterns in gene expression that stem from regulatory interactions. Finally, knowledge of experimentally characterized transcription factor binding sites can be used to analyze the promoter sequences in a genome in order to identify potential binding sites. In this chapter, we will focus in detail on the first approach and describe methods to reconstruct and analyze the transcriptional regulatory networks of uncharacterized organisms by using a known regulatory network as a template.

An in silico strategy identified the target gene candidates regulated by dehydration responsive element binding proteins (DREBs) in Arabidopsis genome

Identification of downstream target genes of stress-relating transcription factors (TFs) is desirable in understanding cellular responses to various environmental stimuli. However, this has long been a difficult work for both experimental and computational practices. In this research, we presented a novel computational strategy which combined the analysis of the transcription factor binding site (TFBS) contexts and machine learning approach. Using this strategy, we conducted a genome-wide investigation into novel direct target genes of dehydration responsive element binding proteins (DREBs), the members of AP2-EREBPs transcription factor super family which is reported to be responsive to various abiotic stresses in Arabidopsis. The genome-wide searching yielded in total 474 target gene candidates. With reference to the microarray data for abiotic stresses-inducible gene expression profile, 268 target gene candidates out of the total 474 genes predicted, were induced during the 24-h exposure to abiotic stresses. This takes about 57% of total predicted targets. Furthermore, GO annotations revealed that these target genes are likely involved in protein amino acid phosphorylation, protein binding and Endomembrane sorting system. The results suggested that the predicted target gene candidates were adequate to meet the essential biological principle of stress-resistance in plants.

Gene expression in early and progression phases of autosomal dominant polycystic kidney disease

Background

Little is known about the genes involved in the initial cyst formation and disease progression in autosomal dominant polycystic kidney disease (ADPKD); however, such knowledge is necessary to explore therapeutic avenues for this common inherited kidney disease.

Findings

To uncover the genetic determinants and molecular mechanisms of ADPKD, we analyzed 4-point time-series DNA microarrays from Pkd1^L3/L3 mice to generate high resolution gene expression profiles at different stages of disease progression. We found different characteristic gene expression signatures in the kidneys of Pkd1^L3/L3 mice compared to age-matched controls during the initial phase of the disease. By postnatal week 1, the Pkd1^L3/L3 kidney already had a distinctive gene expression pattern different from the corresponding normal controls.

Conclusion

The genes differentially expressed, either induced or repressed, in ADPKD are important in immune defense, cell structure and motility, cellular proliferation, apoptosis and metabolic processes, and include members of three pathways (Wnt, Notch, and BMP) involved in morphogenetic signaling. Further analysis of the gene expression profiles from the early stage of cystogenesis to end stage disease identified a possible gene network involved in the pathogenesis of ADPKD.

Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray

Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research because they enable massively-parallel assays and simultaneous monitoring of thousands of gene expression of biological samples. However, a simple microarray experiment often leads to very high-dimensional data and a huge amount of information, the vast amount of data challenges researchers into extracting the important features and reducing the high dimensionality. In this paper, a nonlinear dimensionality reduction kernel method based locally linear embedding(LLE) is proposed, and fuzzy K-nearest neighbors algorithm which denoises datasets will be introduced as a replacement to the classical LLE's KNN algorithm. In addition, kernel method based support vector machine (SVM) will be used to classify genomic microarray data sets in this paper. We demonstrate the application of the techniques to two published DNA microarray data sets. The experimental results confirm the superiority and high success rates of the presented method.

Gene expression profiles in liver of zebrafish treated with microcystin-LR

Microcystin-LR (MC-LR) is the most frequently studied cyclic heptatoxin produced by cyanobacteria, which has tremendous negative impacts on fish, while its molecular mechanism behind remained unclear at present. Here, Affymetrix Zebrafish GeneChip was used to identify alterations in gene expression of zebrafish (Danio rerio) after MC-LR exposure. Among the 14,900 transcripts in the microarray, 273 genes were differentially expressed, in which 243 genes were elevated and 30 were decreased. According to GOstat analysis, MC-LR mainly influenced the cell cycle and mitogen-activated protein kinases (MAPK) signaling pathways. In addition, many immune-related genes were also influenced. These data suggest that MC-LR could promote tumorigenesis and cause immunotoxicity in fish.