Better therapeutics through microarrays

Screening differentially expressed genes between endometriosis and ovarian cancer to find new biomarkers for endometriosis

AIM

Endometriosis is one of the most common reproductive system diseases, but the mechanisms of disease progression are still unclear. Due to its high recurrence rate, searching for potential therapeutic biomarkers involved in the pathogenesis of endometriosis is an urgent issue.

METHODS

Due to the similarities between endometriosis and ovarian cancer, four endometriosis datasets and one ovarian cancer dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) analyses. Then, we validated gene expression and performed survival analysis with ovarian serous cystadenocarcinoma (OV) datasets in TCGA/GTEx database, and searched for potential drugs in the Drug-Gene Interaction Database. Finally, we explored the miRNAs of key genes to find biomarkers associated with the recurrence of endometriosis.

RESULTS

In total, 104 DEGs were identified in the endometriosis datasets, and the main enriched GO functions included cell adhesion, extracellular exosome and actin binding. Fifty DEGs were identified between endometriosis and ovarian cancer datasets including 11 consistently regulated genes, and nine DEGs with significant expression in TCGA/GTEx. Only IGHM had both significant expression and an association with survival, three module DEGs and two significantly expressed DEGs had drug associations, and 10 DEGs had druggability.

CONCLUSIONS

ITGA7, ITGBL1 and SORBS1 may help us understand the invasive nature of endometriosis, and IGHM might be related to recurrence; moreover, these genes all may be potential therapeutic targets.KEY MESSAGEThis manuscript used a bioinformatics approach to find target genes for the treatment of endometriosis.This manuscript used a new approach to find target genes by drawing on common characteristics between ovarian cancer and endometriosis.We screened relevant therapeutic agents for target genes in the drug database, and performed histological validation of target genes with both expression and survival analysis difference in cancer databases.

Precision pharmacotherapy: psychiatry's future direction in preventing, diagnosing, and treating mental disorders

Mental disorders account for around one-third of disability worldwide and cause enormous personal and societal burden. Current pharmacotherapies and nonpharmacotherapies do help many patients, but there are still high rates of partial or no response, delayed effect, and unfavorable adverse effects. The current diagnostic taxonomy of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases relies on presenting signs and symptoms, but does not reflect evidence from neurobiological and behavioral systems. However, in the last decades, the understanding of biological mechanisms underlying mental disorders has grown and can be used for the development of precision medicine, that is, to deliver a patient-tailored individual treatment. Precision medicine may incorporate genetic variants contributing to the mental disorder and the response to pharmacotherapies, but also consider gene ¥ environment interactions, blood-based markers, neuropsychological tests, data from electronic health records, early life adversity, stressful life events, and very proximal factors such as lifestyle, nutrition, and sport. Methods such as artificial intelligence and the underlying machine learning and deep learning approaches provide the framework to stratify patients, initiate specific tailored treatments and thus increase response rates, reduce adverse effects and medical errors. In conclusion, precision medicine uses measurable health parameters to identify individuals at risk of a mental disorder, to improve the diagnostic process and to deliver a patient-tailored treatment.

Pharmacogenetics and application in pediatrics

Identification of markers involved in drug disposition is crucial for drugs with a narrow therapeutic index. Individual genomic differences can affect the pharmacology of some drugs and participate to inter-individual variability in drug response. Pharmacogenetics is a useful tool in clinical practice for dosage adjustment and to limit drug toxicities. In pediatrics, physiological changes can also influence the disposition of drugs in infants, children and adolescents. The importance of ontogeny translates into different responses to the same drug in children and adults. Thus, interactions between the maturation of metabolism enzymes or transporters and genetics have a major impact on drug exposure leading to age-specific dosage requirements. This review aims to describe implementation of pharmacogenetics in personalized medicine and specifies pediatric characteristics with ethical considerations.

A systematic assessment of chemical, genetic, and epigenetic factors influencing the activity of anticancer drug KP1019 (FFC14A)

KP1019 ([trans-RuCl₄(1H-indazole)₂]; FFC14A) is one of the promising ruthenium-based anticancer drugs undergoing clinical trials. Despite the pre-clinical and clinical success of KP1019, the mode of action and various factors capable of modulating its effects are largely unknown. Here, we used transcriptomics and genetic screening approaches in budding yeast model and deciphered various genetic targets and plethora of cellular pathways including cellular signaling, metal homeostasis, vacuolar transport, and lipid homeostasis that are primarily targeted by KP1019. We also demonstrated that KP1019 modulates the effects of TOR (target of rapamycin) signaling pathway and induces accumulation of neutral lipids (lipid droplets) in both yeast and HeLa cells. Interestingly, KP1019-mediated effects were found augmented with metal ions (Al³⁺/Ca²⁺/Cd²⁺/Cu²⁺/Mn²⁺/Na⁺/Zn²⁺), and neutralized by Fe²⁺, antioxidants, osmotic stabilizer, and ethanolamine. Additionally, our comprehensive screening of yeast histone H3/H4 mutant library revealed several histone residues that could significantly modulate the KP1019-induced toxicity. Altogether, our findings in both the yeast and HeLa cells provide molecular insights into mechanisms of action of KP1019 and various factors (chemical/genetic/epigenetic) that can alter the therapeutic efficiency of this clinically important anticancer drug.

Combined Transcriptomics and Chemical-Genetics Reveal Molecular Mode of Action of Valproic acid, an Anticancer Molecule using Budding Yeast Model

Valproic acid (VA) is a pharmacologically important histone deacetylase inhibitor that recently garnered attention as an anticancer agent. Since the molecular mechanisms behind the multiple effects of VA are unclear, this study was aimed to unravel the comprehensive cellular processes affected by VA and its molecular targets in vivo using budding yeast as a model organism. Interestingly, genome-wide transcriptome analysis of cells treated with VA showed differential regulation of 30% of the genome. Functional enrichment analysis of VA transcriptome evidenced alteration of various cellular processes including cell cycle, cell wall biogenesis, DNA repair, ion homeostasis, metabolism, stress response, transport and ribosomal biogenesis, etc. Moreover, our genetic screening analysis revealed VA molecular targets belonging to oxidative and osmotic stress, DNA repair, cell wall integrity, and iron homeostasis. Further, our results demonstrated the activation of mitogen-activated protein kinases (MAPKs) Hog1 (p38) and Slt2 (p44/42) upon VA treatment. Our results also exhibited that VA acts through alteration of mitochondrial, ER architecture and functions. Especially, VA effects were neutralized in cells lacking lipid particles. Altogether, our results deciphered the novel molecular insights and mechanistic links to strengthen our knowledge on diverse cellular effects of VA along with its probable therapeutic targets and detoxification approaches.

Transcriptomics and the mechanisms of antidepressant efficacy

The mechanisms by which antidepressants have their effects are not clear and the reasons for variability in treatment outcomes are also unknown. However, there is evidence from candidate gene research that indicates gene expression changes may be involved in antidepressant action. In this study, we examined antidepressant-induced alterations in gene expression on a transcriptome-wide scale, exploring associations with treatment response. Blood samples were taken from a subset of depressed patients from the GENDEP study (n=136) before and after eight weeks of treatment with either escitalopram or nortriptyline. Transcriptomic data were obtained from these samples using Illumina HumanHT-12 v4 Expression BeadChip microarrays. When analysing individual genes, we observed that changes in the expression of two genes (MMP28 and KXD1) were associated with better response to nortriptyline. Considering connectivity between genes, we identified modules of genes that were highly coexpressed. In the whole sample, changes in one of the ten identified coexpression modules showed significant correlation with treatment response (cor=0.27, p=0.0029). Using transcriptomic approaches, we have identified gene expression correlates of the therapeutic effects of antidepressants, highlighting possible molecular pathways involved in efficacious antidepressant treatment.

Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

Background

Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism.

Results

S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets.

Conclusions

This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.

Cell microarrays

In the postgenomic era, DNA and protein arrays are increasing the speed at which knowledge is gathered on gene expression in cells and tissues. At the same time, researchers realize that a miniaturized and parallelized analysis of whole cells may equally expedite the acquisition of data describing cellular properties and function. Researchers are starting to explore means of generating and using cell microarrays to investigate cells at higher throughput. In this initial phase of exploration, cell microarrays are being developed for various cellular analyses including the effects of gene expression, cellular reactions to the biomolecular environment, and profiling of cell surface molecules. This article will provide an overview of different types of eukaryotic cell microarrays described to date, how they are generated, and their fields of application.

Gene expression: biomarker of antidepressant therapy?

While antidepressant therapy is an essential treatment of major depression, a substantial group of treated patients do not respond to therapy, or suffer from severe side effects. Moreover, the time of onset of the clinical improvement is often delayed. Antidepressants as currently available usually enhance serotonergic, noradrenergic and dopaminergic neurotransmission and may contribute to the inadequate remission rates for major depression. Therefore biomarkers enabling the identification of subgroups of patients and also finding unprecedented targets would provide the basis for personalized medication and thus improve treatment efficacy and reduce side effects. Several pharmacogenetic studies on antidepressant treatment response using single nucleotide polymorphism (SNPs) mapping have been performed but provided only modest findings. Therefore the analysis of gene expression to integrate genomic activity and environmental effects promises a new approach to cope with the complexity of factors influencing antidepressant treatment. Here gene expression studies focusing on candidate genes and genome-wide approaches using RNA derived from peripheral blood cells are reviewed. The most promising findings exist for hypothalamic-pituitary-adrenal (HPA) axis, inflammation and neuroplasticity related genes. However, straightforward translation into tailored treatment is still unlikely. Contradictory results limit the clinical use of the findings. Future studies are necessary, which could include functional analysis and consider gene-environment interactions.

Analysis of the gene expression profile of curcumin-treated kidney on endotoxin-induced renal inflammation

Acute or chronic kidney inflammation is closely related to the progress of kidney diseases. Curcumin, a yellow pigment present in the rhizome of turmeric (Curcuma longa L. Zingiberaceae), was found to be a potential anti-inflammatory agent. The present study aimed to investigate the effects and explore the protective mechanism of curcumin on lipopolysaccharide (LPS)-induced kidney inflammation in mice using gene chip and pathological technology. Nine SPF Kunming mice (aged 6-8 weeks, weighing 20-25 g) were divided into three groups. Saline and LPS were injected intraperitoneally in a normal control group and a model group, respectively. Mice in the treatment group were first injected with curcumin (5 mg/kg) for 3 days before being injected with LPS (5 mg/kg). Kidney tissues were harvested at 6 h after treatment. Parts of kidney were fixed with 10 % formaldehyde for HE, Periodic acid-Schiff staining, and immunohistochemistry. Affymetrix gene chips (mouse 430 chip) were used to detect the renal gene expression profile, and the results were analyzed using bioinformatics methods. The renal gene expression profile showed that there are 148 Affy IDs (up-down group) whose levels of gene expression were increased after LPS stimulation and decreased by curcumin treatment and that there are 133 Affy IDs (down-up group) exhibiting the opposite trend. In the differentially expressed genes of the up-down group, 21 Gene Ontology (GO) genes were selected by screening function (P ≤ 0.01). In the biological processes, most of the genes were found to be related to the genes of regulation of macrophage activation and macrophage activation-associated genes. In the cellular localization, there were four functional GO genes (P ≤ 0.01); in the molecular structure, there were seven functional GO genes (P ≤ 0.01). In the down-up group, there were functional GO genes (P ≤ 0.01) and one functional GO gene (P ≤ 0.01) in the biological process and the cellular localization, respectively. Macrophage infiltration could be observed as early as 6 h after LPS stimulation. Pretreatment with 5 mg/kg of curcumin significantly decreased the macrophage infiltration. At 6 h after LPS injection, significant decreased expression of M6PRBP-1 and NEDD-4 was observed in renal tissue. On the other hand, pretreatment with curcumin significantly increased renal M6PRBP-1 and NEDD-4 expression. In this study, we also found the signaling pathway and the possible target gene of the protective effects of curcumin on endotoxin-induced renal inflammation. The kidney gene expression profile in the inflammatory state was clarified by using gene chip technology. Furthermore, we confirmed that curcumin treatment can change the gene expression profile.

Intragastric exposure to titanium dioxide nanoparticles induced nephrotoxicity in mice, assessed by physiological and gene expression modifications

Background

Numerous studies have demonstrated that titanium dioxide nanoparticles (TiO₂ NPs) induced nephrotoxicity in animals. However, the nephrotoxic multiple molecular mechanisms are not clearly understood.

Methods

Mice were exposed to 2.5, 5 and 10 mg/kg TiO₂ NPs by intragastric administration for 90 consecutive days, and their growth, element distribution, and oxidative stress in kidney as well as kidney gene expression profile were investigated using whole-genome microarray analysis technique.

Results

Our findings suggest that TiO₂ NPs resulted in significant reduction of renal glomerulus number, apoptosis, infiltration of inflammatory cells, tissue necrosis or disorganization of renal tubules, coupled with decreased body weight, increased kidney indices, unbalance of element distribution, production of reactive oxygen species and peroxidation of lipid, protein and DNA in mouse kidney tissue. Furthermore, microarray analysis showed significant alterations in the expression of 1, 246 genes in the 10 mg/kg TiO₂ NPs-exposed kidney. Of the genes altered, 1006 genes were associated with immune/inflammatory responses, apoptosis, biological processes, oxidative stress, ion transport, metabolic processes, the cell cycle, signal transduction, cell component, transcription, translation and cell differentiation, respectively. Specifically, the vital up-regulation of Bcl6, Cfi and Cfd caused immune/ inflammatory responses, the significant alterations of Axud1, Cyp4a12a, Cyp4a12b, Cyp4a14, and Cyp2d9 expression resulted in severe oxidative stress, and great suppression of Birc5, Crap2, and Tfrc expression led to renal cell apoptosis.

Conclusions

Axud1, Bcl6, Cf1, Cfd, Cyp4a12a, Cyp4a12b, Cyp2d9, Birc5, Crap2, and Tfrc may be potential biomarkers of kidney toxicity caused by TiO₂ NPs exposure.

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.

Dexamethasone stimulated gene expression in peripheral blood is a sensitive marker for glucocorticoid receptor resistance in depressed patients

Although gene expression profiles in peripheral blood in major depression are not likely to identify genes directly involved in the pathomechanism of affective disorders, they may serve as biomarkers for this disorder. As previous studies using baseline gene expression profiles have provided mixed results, our approach was to use an in vivo dexamethasone challenge test and to compare glucocorticoid receptor (GR)-mediated changes in gene expression between depressed patients and healthy controls. Whole genome gene expression data (baseline and following GR-stimulation with 1.5 mg dexamethasone p.o.) from two independent cohorts were analyzed to identify gene expression pattern that would predict case and control status using a training (N=18 cases/18 controls) and a test cohort (N=11/13). Dexamethasone led to reproducible regulation of 2670 genes in controls and 1151 transcripts in cases. Several genes, including FKBP5 and DUSP1, previously associated with the pathophysiology of major depression, were found to be reliable markers of GR-activation. Using random forest analyses for classification, GR-stimulated gene expression outperformed baseline gene expression as a classifier for case and control status with a correct classification of 79.1 vs 41.6% in the test cohort. GR-stimulated gene expression performed best in dexamethasone non-suppressor patients (88.7% correctly classified with 100% sensitivity), but also correctly classified 77.3% of the suppressor patients (76.7% sensitivity), when using a refined set of 19 genes. Our study suggests that in vivo stimulated gene expression in peripheral blood cells could be a promising molecular marker of altered GR-functioning, an important component of the underlying pathology, in patients suffering from depressive episodes.

Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis

So far, there are no means of identifying rheumatoid arthritis (RA) patients who will fail to respond to tumour necrosis factor blocking agents (anti-TNF), prior to treatment. We set out to validate eight previously reported gene expression signatures predicting therapy outcome. Genome-wide expression profiling using Affymetrix GeneChip Exon 1.0 ST arrays was performed on RNA isolated from whole blood of 42 RA patients starting treatment with infliximab or adalimumab. Clinical response according to EULAR criteria was determined at week 14 of therapy. Genes that have been reported to be associated with anti-TNF treatment were extracted from our dataset. K-means partition clustering was performed to assess the predictive value of the gene-sets. We performed a hypothesis-driven analysis of the dataset using eight existing gene sets predictive of anti-TNF treatment outcome. The set that performed best reached a sensitivity of 71% and a specificity of 61%, for classifying the patients in the current study. We successfully validated one of eight previously reported predictive expression profile. This replicated expression signature is a good starting point for developing a prediction model for anti-TNF treatment outcome that can be used in a daily clinical setting. Our results confirm that gene expression profiling prior to treatment is a useful tool to predict anti-TNF (non) response.

Identifying inhibitors of epithelial-mesenchymal transition by connectivity map-based systems approach

BACKGROUND

Acquisition of mesenchymal phenotype by epithelial cells by means of epithelial-mesenchymal transition (EMT) is considered as an early event in the multistep process of tumor metastasis. Therefore, inhibition of EMT might be a rational strategy to prevent metastasis.

METHODS

Using the global gene expression profile from a cell culture model of transforming growth factor-β (TGF-β)-induced EMT, we identified potential EMT inhibitors. We used a publicly available database (www.broad.mit.edu/cmap) comprising gene expression profiles obtained from multiple different cell lines in response to various drugs to derive negative correlations to EMT gene expression profile using Connectivity Map, a pattern matching tool.

RESULTS

Experimental validation of the identified compounds showed rapamycin as a novel inhibitor of TGF-β signaling along with 17-AAG, a known modulator of TGF-β pathway. Both of these compounds completely blocked EMT and the associated migratory and invasive phenotype. The other identified compound, LY294002, demonstrated a selective inhibition of mesenchymal markers, cell migration and invasion, without affecting the loss of E-cadherin expression or Smad phosphorylation.

CONCLUSIONS

Our data reveal that rapamycin is a novel modulator of TGF-β signaling, and along with 17-AAG and LY294002, could be used as therapeutic agent for inhibiting EMT. This study demonstrates the potential of a systems approach in identifying novel modulators of a complex biological process.

Nanomedicine: application areas and development prospects

Nanotechnology, along with related concepts such as nanomaterials, nanostructures and nanoparticles, has become a priority area for scientific research and technological development. Nanotechnology, i.e., the creation and utilization of materials and devices at nanometer scale, already has multiple applications in electronics and other fields. However, the greatest expectations are for its application in biotechnology and health, with the direct impact these could have on the quality of health in future societies. The emerging discipline of nanomedicine brings nanotechnology and medicine together in order to develop novel therapies and improve existing treatments. In nanomedicine, atoms and molecules are manipulated to produce nanostructures of the same size as biomolecules for interaction with human cells. This procedure offers a range of new solutions for diagnoses and “smart” treatments by stimulating the body’s own repair mechanisms. It will enhance the early diagnosis and treatment of diseases such as cancer, diabetes, Alzheimer’s, Parkinson’s and cardiovascular diseases. Preventive medicine may then become a reality.

RNA and DNA microarrays

The development of microarray technology has revolutionized RNA and deoxyribonucleic acid (DNA) research. In contrast with traditional biological assays, microarrays allow the simultaneous measurement of tens of thousands of messenger RNA (mRNA) transcripts for gene expression or of genomic DNA fragments for copy number variation analysis. Over the past decade, genome-wide RNA or DNA microarray analysis has become an essential component of biology and biomedical research. The successful use of microarrays requires attention to unique issues of experimental design and execution. This chapter provides an overview of the methodology and applications of RNA and DNA microarrays in various areas of biological research.

Study of anti-inflammatory activities of the pure compounds from Andrographis paniculata (burm.f.) Nees and their effects on gene expression

In inflammation, the responses to noxious stimuli are controlled by the highly modulated interactions between various immune cells and chemical mediators. The purpose of this study is to evaluate and compare the anti-inflammatory effect of diterpenoids isolated from Andrographis paniculata, including dehydroandrographolide (AP1), andrographolide (AP2) and neoandrographolide (AP3), on the production of inflammatory cytokines and COX activities. Furthermore, the alteration of gene expression involved in this activity was investigated in the most potent compound to elucidate the other possible molecular mechanisms. AP1 (30.1 μM; 10 μg/ml) and AP2 (28.5 μM; 10 μg/ml) markedly inhibited COX-1 in ionophore A23187-induced human platelets. AP2 (28.5 μM) and AP3 (20.8 μM; 10 μg/ml) strongly suppressed the LPS-stimulated COX-2 activity in human blood. In addition, AP2 modulated the level of LPS-induced TNF-α, IL-6, IL-1β and IL-10 secretion in human blood in a concentration-dependent manner. The results revealed that AP2 exhibited the highest efficacy. Therefore, changes in the levels of mRNA transcripts by AP2 were further measured using human cDNA microarrays. The molecular response to AP2 was complex and mediated by various processes. Among the altered gene expressions, the genes involved in immune and inflammation processes were selectively down-regulated, such as cytokines and cytokine receptors (TNFSF14, TNF, TNFRSF6, and IL1A), chemokines (CCL8 and CXCL11), JAK/STAT signaling (JAK3 and STAT5A), TLRs family (TLR4 and TLR8) and NF-κB (NFKB1). Expression of some genes was validated using RT-PCR. The results demonstrated that AP1, AP2 and AP3 exhibited the anti-inflammatory effect by interfering COX and inflammatory cytokines and the underlying mechanisms of AP2 may be related to down-expression of genes involved in inflammatory cascade.

Gene expression reveals overlap between normal aging and Alzheimer's disease genes

Alzheimer's disease (AD) is a common cause of dementia with a strong genetic component and risk sharply increasing with age. We performed two parallel microarray experiments to independently identify genes involved in normal aging and genes involved in AD using RNA extracted from the temporal lobe of 22 late onset AD and 23 control brain donors. We found that AD is accompanied by significant changes in the expression of many genes with upregulation of genes involved in inflammation and in transcription regulation and downregulation of genes involved in neuronal functions. The changes with healthy aging involved multiple genes but were not as strong. Replicating and strengthening previous reports, we find a highly significant overlap between genes changing expression with age and those changing in AD, and we observe that those changes are most often in the same direction. This result supports an overlap between the biological processes of normal aging and susceptibility to AD and suggests that age related genes expression changes might increase the risk of developing AD.

The derivation of diagnostic markers of chronic myeloid leukemia progression from microarray data

Currently, limited molecular markers exist that can determine where in the spectrum of chronic myeloid leukemia (CML) progression an individual patient falls at diagnosis. Gene expression profiles can predict disease and prognosis, but most widely used microarray analytical methods yield lengthy gene candidate lists that are difficult to apply clinically. Consequently, we applied a probabilistic method called Bayesian model averaging (BMA) to a large CML microarray dataset. BMA, a supervised method, considers multiple genes simultaneously and identifies small gene sets. BMA identified 6 genes (NOB1, DDX47, IGSF2, LTB4R, SCARB1, and SLC25A3) that discriminated chronic phase (CP) from blast crisis (BC) CML. In CML, phase labels divide disease progression into discrete states. BMA, however, produces posterior probabilities between 0 and 1 and predicts patients in "intermediate" stages. In validation studies of 88 patients, the 6-gene signature discriminated early CP from late CP, accelerated phase, and BC. This distinction between early and late CP is not possible with current classifications, which are based on known duration of disease. BMA is a powerful tool for developing diagnostic tests from microarray data. Because therapeutic outcomes are so closely tied to disease phase, these probabilities can be used to determine a risk-based treatment strategy at diagnosis.

Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging

Studies employing high-throughput biological techniques have recently contributed to an improved characterization of human cancers, allowing for novel sub-classification, better diagnostic accuracy, and more precise prognostication. However, requirement of surgical procurement of tissue among other things limits the clinical application of such methods in everyday patient care. Radiographic imaging is routine in clinical practice but is currently histopathology based. The use of routine radiographic imaging provides a potential platform for linking specific imaging traits with specific gene expression patterns that inform the underlying cellular pathophysiology; imaging features could then serve as molecular surrogates that contribute to the diagnosis, prognosis, and likely gene-expression-associated treatment response of various forms of human cancer. This review focuses on high-throughput methods such as microarray analysis of gene expression, their role in cancer research, and in particular, on novel methods of associating gene expression patterns with radiographic imaging phenotypes, known as "radiogenomics." These findings underline a potential future role of both diagnostic and interventional radiologists in genetic assessment of cancer patients with radiographic imaging studies.

Mechanisms of drug combinations: interaction and network perspectives

Understanding the molecular mechanisms underlying synergistic, potentiative and antagonistic effects of drug combinations could facilitate the discovery of novel efficacious combinations and multi-targeted agents. In this article, we describe an extensive investigation of the published literature on drug combinations for which the combination effect has been evaluated by rigorous analysis methods and for which relevant molecular interaction profiles of the drugs involved are available. Analysis of the 117 drug combinations identified reveals general and specific modes of action, and highlights the potential value of molecular interaction profiles in the discovery of novel multicomponent therapies.

RNA stabilization of peripheral blood and profiling by bead chip analysis

Gene expression profiling of peripheral blood is a very attractive approach for the development of new diagnostic markers of blood-borne but also systemic diseases as well as the development of biomarkers for drug development. Since most cellular components of peripheral blood are specialized to quickly respond to exogenous stimuli, sample procurement approaches are required that reduce the overall impact of ex vivo changes in gene expression due to technical issues such as prolonged sample handling or temperature changes during transportation of the blood prior to genome-wide analysis. To address these needs, a whole blood RNA stabilization technology was combined with a bead-based oligonucleotide microarray technology for genome-wide transcriptome analysis. Cells, and thereby also RNA is immediately stabilized after the blood draw by a commercially available device (PAXgene). Total RNA is then extracted from PAXgene-stabilized blood and subjected to microarray analysis. In our hands, the Illumina BeadChip array platform outperformed other microarray platforms. Combining RNA stabilization of peripheral blood with bead-based oligonucleotide microarray technology is not only applicable to small single-center studies with optimized infrastructure but also to large scale multi-center trials that are mandatory for the development of predictive markers for disease and treatment outcome.

Development of clinical highly sensitive biosensor-based microarray system

AIM: To develop a sensitive and less instrument-dependent clinical microarray system, in which the microarray signal can be amplified in situ and identified by naked eyes.

METHODS: A group of capture probes for a specific target nucleic acid that was made according to a specific region such as hepatitis B virus (HBV) YMDD motif were arrayed on a thin-film biosensor in a well. A set of detected probes labeled with Au nanoparticle were used to take place of the fluorence labeled probes in classic microarray. The single-strand PCR product was reacted with the capture and detected probes and the deposition of capture-biotin-streptin-Au nanoparticle compound appeared on the surface of the microarray. After in situ amplification in this biosensor based system, we could read the signal on this chips by naked eyes or the digital camera. HBV YMDD mutation detection was applied to identify the sensitivity and specificity of the microarray system.

RESULTS: The signal of the biosensor microarray could be acquired by common camera or naked eyes without any instruments and we could determine the kind of mutation according to the place of the positive signal. The signal-noise ratio were high enough to make the signal absolutely yes and no both in synthesized target oligos and serum samples. The microarray could identify a single base change of selected lamivudine resistance-related mutation as well as multiple mutations at the same time with a high stability, sensitivity, and specificity. We used the biosensor system to test 23 serum samples with YMDD mutation, and the result was coincident with the sequencing result and the signal could be aquired by naked eyes.

CONCLUSION: The thin-film based microarray system which exploits nanoparticle material and biosensor technique can amplify the signal in situ that can be detected by simple instruments or even unaided eyes. Its attractive features are the nonintervention of instrumentation required to detect signal, as well as its high versatility and accuracy.

Human cardiac-specific cDNA array for idiopathic dilated cardiomyopathy: sex-related differences

Idiopathic dilated cardiomyopathy (IDCM) constitutes a large portion of patients with heart failure of unknown etiology. Up to 50% of all transplant recipients carry this clinical diagnosis. Female-specific gene expression in IDCM has not been explored. We report sex-related differences in the gene expression profile of ventricular myocardium from patients undergoing cardiac transplantation. We produced and sequenced subtractive cDNA libraries, using human left ventricular myocardium obtained from male transplant recipients with IDCM and nonfailing human heart donors. With the resulting sequence data, we generated a custom human heart failure microarray for IDCM containing 1,145 cardiac-specific oligonucleotide probes. This array was used to characterize RNA samples from female IDCM transplant recipients. We identified a female gene expression pattern that consists of 37 upregulated genes and 18 downregulated genes associated with IDCM. Upon functional analysis of the gene expression pattern, deregulated genes unique to female IDCM were those that are involved in energy metabolism and regulation of transcription and translation. For male patients we found deregulation of genes related to muscular contraction. These data suggest that 1) the gene expression pattern we have detected for IDCM may be specific for this disease and 2) there is a sex-specific profile to IDCM. Our observations further suggest for the first time ever novel targets for treatment of IDCM in women and men.

ATF3 protects against renal ischemia-reperfusion injury

Oxidative stress-induced cell death plays a major role in the progression of ischemic acute renal failure. Using microarrays, we sought to identify a stress-induced gene that may be a therapeutic candidate. Human proximal tubule (HK2) cells were treated with hydrogen peroxide (H2O2) and RNA was applied to an Affymetrix gene chip. Five genes were markedly induced in a parallel time-dependent manner by cluster analysis, including activating transcription factor 3 (ATF3), p21(WAF1/CiP1) (p21), CHOP/GADD153, dual-specificity protein phosphatase, and heme oxygenase-1. H2O2 rapidly induced ATF3 approximately 12-fold in HK2 cells and approximately 6.5-fold in a mouse model of renal ischemia-reperfusion injury. Adenovirus-mediated expression of ATF3 protected HK2 cells against H2O2-induced cell death, and this was associated with a decrease of p53 mRNA and an increase of p21 mRNA. Moreover, when ATF3 was overexpressed in mice via adenovirus-mediated gene transfer, ischemia-reperfusion injury was reduced. In conclusion, ATF3 plays a protective role in renal ischemia-reperfusion injury and the mechanism of the protection may involve suppression of p53 and induction of p21.

Quantification of mixed-phase hybridization on polymer microparticles by europium(III) ion fluorescence

A protocol for quantification of oligonucleotide hybridization on polymer microparticles by europium(III) ion fluorescence is described. The procedure involves modification of commercially available amino-functionalized microparticles in such a manner that oligonucleotide probes may be assembled in situ on these particles or, alternatively, they may be immobilized postsynthetically. The oligonucleotide-coated particles obtained are then used as the solid phase in a mixed-phase hybridization assay. The efficiency of hybridization is quantified with the aid of oligonucleotides tagged with a europium(III) chelate. Either, the fluorescently tagged probe is hybridized directly to a complementary particle-anchored oligonucleotide, or a sandwich-type assay set up, where a third oligonucleotide complementary both to the tagged and particle-bound probe mediates the attachment to the particles, is exploited. The number of europium(III) ions attached to the solid-phase is determined by the DELFIA protocol, involving release of the europium(III) ions in solution and development of the fluorescence by addition of an enhancement solution. Alternatively, the fluorescence intensity of the photoluminescent chelate may be measured directly from a single particle.

Multiplexed microsphere diagnostic tools in gene expression applications: factors and futures

Microarrays have received significant attention in recent years as scientists have firstly identified factors that can produce reduced confidence in gene expression data obtained on these platforms, and secondly sought to establish laboratory practices and a set of standards by which data are reported with integrity. Microsphere-based assays represent a new generation of diagnostics in this field capable of providing substantial quantitative and qualitative information from gene expression profiling. However, for gene expression profiling, this type of platform is still in the demonstration phase, with issues arising from comparative studies in the literature not yet identified. It is desirable to identify potential parameters that are established as important in controlling the information derived from microsphere-based hybridizations to quantify gene expression. As these evolve, a standard set of parameters will be established that are required to be provided when data are submitted for publication. Here we initiate this process by identifying a number of parameters we have found to be important in microsphere-based assays designed for the quantification of low abundant genes which are variable between studies.

Semiautomated multiplexed quantum dot-based in situ hybridization and spectral deconvolution

Gene expression profiling has identified several potentially useful gene signatures for predicting outcome or for selecting targeted therapy. However, these signatures have been developed in fresh or frozen tissue, and there is a need to apply them to routinely processed samples. Here, we demonstrate the feasibility of a potentially high-throughput methodology combining automated in situ hybridization with quantum dot-labeled oligonucleotide probes followed by spectral imaging for the detection and subsequent deconvolution of multiple signals. This method is semiautomated and quantitative and can be applied to formalin-fixed, paraffin-embedded tissues. We have combined dual in situ hybridization with immunohistochemistry, enabling simultaneous measurement of gene expression and cell lineage determination. The technique achieves levels of sensitivity and specificity sufficient for the potential application of known expression signatures to biopsy specimens in a semiquantitative way, and the semiautomated nature of the method enables application to high-throughput studies.

Applications of transcriptional profiling in antibiotics discovery and development

This chapter will review specific applications of microarray technology and related data analysis strategies in antibacterial research and development. We present examples of microarray applications spanning the entire antibiotics research and development pipeline, from target discovery, assay development, pharmacological evaluation, to compound safety studies. This review emphasizes the utility of microarrays for a systematic evaluation of novel chemistry as antibiotic agents. Transcriptional profiling has revolutionized the process of target elucidation and has the potential to offer substantial guidance in the identification of new targets. Microarrays will continue to be a workhorse of anti-infectives discovery programs ranging from efficacy assessments of antibiotics ('forward pharmacology') to drug safety evaluations ('toxicogenomics').

A Toxicogenomic Approach Revealed Hepatic Gene Expression Changes Mechanistically Linked to Drug-Induced Hemolytic Anemia

A variety of pharmaceutical compounds causes hemolytic anemia as a significant adverse effect and this toxicity restricts the clinical utility of these drugs. In this study, we applied microarray technology to investigate hepatic gene expression changes associated with drug-induced hemolytic anemia and to identify potential biomarker genes for this hematotoxicity. We treated female Sprague-Dawley rats with two hemolytic anemia-inducing compounds: phenylhydrazine and phenacetin. Hepatic gene expression profiles were obtained using a whole-genome oligonucleotide microarray with pooled RNA samples from individual rats within each dose group and analyzed in comparison with hepatic histopathology, hematology, and blood chemistry data. We identified a small subset of genes that were commonly deregulated in all the severe hemolytic conditions, some of which were considered to be involved in hepatic events characteristic of hemolytic anemia, such as hemoglobin biosynthesis, heme metabolism, and phagocytosis. Among them, we selected six upregulated genes as putative biomarkers, and their expression changes from microarray measurements were confirmed by quantitative real-time PCR using RNAs from individual animals. They were Alas2, beta-glo, Eraf, Hmox1, Lgals3, and Rhced. Expression patterns of all these genes showed high negative and positive correlation against erythrocyte counts and total bilirubin levels in circulation, respectively, suggesting that these genes may be the potential biomarkers for hemolytic anemia. These findings indicate that drug-induced hemolytic anemia may be detected based on hepatic changes in the expression of a subset of genes that are mechanistically linked to the hematotoxicity.

DNA microarrays in herbal drug research

Natural products are gaining increased applications in drug discovery and development. Being chemically diverse they are able to modulate several targets simultaneously in a complex system. Analysis of gene expression becomes necessary for better understanding of molecular mechanisms. Conventional strategies for expression profiling are optimized for single gene analysis. DNA microarrays serve as suitable high throughput tool for simultaneous analysis of multiple genes. Major practical applicability of DNA microarrays remains in DNA mutation and polymorphism analysis. This review highlights applications of DNA microarrays in pharmacodynamics, pharmacogenomics, toxicogenomics and quality control of herbal drugs and extracts.

Expression profiling of drug response--from genes to pathways

Understanding individual response to a drug -what determines its efficacy and tolerability -is the major bottleneck in current drug development and clinical trials. Intracellular response and metabolism, for example through cytochrome P-450 enzymes, may either enhance or decrease the effect of different drugs, dependent on the genetic variant. Microarrays offer the potential to screen the genetic composition of the individual patient However, experiments are «noisy» and must be accompanied by solid and robust data analysis. Furthermore, recent research aims at the combination of high-throughput data with methods of mathematical modeling, enabling problem-oriented assistance in the drug discovery process. This article will discuss state-of-the-art DNA array technology platforms and the basic elements of data analysis and bioinformatics research in drug discovery. Enhancing single-gene analysis, we will present a new method for interpreting gene expression changes in the context of entire pathways. Furthermore, we will introduce the concept of systems biology as a new paradigm for drug development and highlight our recent research - the development of a modeling and simulation platform for biomedical applications. We discuss the potentials of systems biology for modeling the drug response of the individual patient.

Ultrasensitive DNA chip: gene expression profile analysis without RNA amplification

We have developed a new DNA chip whose substrate has a unique minute columnar array structure made of plastic. The DNA chip exhibits ultrahigh sensitivity, up to 100-fold higher than that of reference DNA chips, which makes it possible to monitor gene expression profiles even with very small amounts of RNA (0.1-0.01 microg of total RNA) without amplification. Differential expression ratios obtained with the new DNA chip were validated against those obtained with quantitative real-time PCR assays. This novel microarray technology would be a powerful tool for monitoring gene expression profiles, especially for clinical diagnosis.

Genomics of Hepatitis B and C Infections: Diagnostic and Therapeutic Applications of Microarray Profiling

Microarray profiling offers many potential advances in diagnostic and therapeutic intervention in human disease because of its unparalleled ability to conduct high-throughput analysis of gene expression. However, limitations of this technique relate in part to issues regarding the various methodologies and experimental designs as well as difficulties in the interpretation of results. Despite this, microarray profiling has led to a better understanding of the molecular pathogenesis of hepatitis B virus (HBV) and hepatitis C virus (HCV) infection. Key events in clearance and the development of chronicity of HCV have been identified that may prove to have a role in the development of future treatments. In addition, pharmacogenomic studies of interferon-based treatment for chronic HCV and HBV have provided mechanistic insights into the therapeutic action of interferons. These advances have implications with respect to the development of improved therapeutic agents. New biomarkers for cancer screening and gene profiles with prognostic value for survival have also been developed for hepatocellular carcinoma, which frequently complicates chronic viral hepatitis. Thus, microarray profiling offers enormous potential for improvements in antiviral therapy and our understanding of blood-borne viral hepatitis.

The promise of a virtual lab in drug discovery

To date, the life sciences 'omics' revolution has not lived up to the expectation of boosting the drug discovery process. The major obstacle is dealing with the volume and diversity of data generated. An enhanced-science (e-science) approach based on remote collaboration, reuse of data and methods, and supported by a virtual laboratory (VL) environment promises to get the drug discovery process afloat. The creation, use and preservation of information in formalized knowledge spaces is essential to the e-science approach. VLs include Grid computation and data communication as well as generic and domain-specific tools and methods for information management, knowledge extraction and data analysis. Problem-solving environments (PSEs) are the domain-specific experimental environments of VLs. Thus, VL-PSEs can support virtual organizations, based on the changing partnerships characteristic of successful drug discovery enterprises.

A highly standardized, robust, and cost-effective method for genome-wide transcriptome analysis of peripheral blood applicable to large-scale clinical trials

The use of peripheral blood mononuclear cells (PBMC) for transcriptome analysis has already been proven valuable for assessing disease-associated and drug-response-related gene signatures. While these proof-of-principle studies have been critically important, the instability of RNA within PBMC prohibits their use in large-scale multicenter trials for which samples have to be transported for a prolonged time prior to RNA isolation. Therefore, a prerequisite for transcriptome analysis of peripheral blood in clinical trials will be a standardized and valid method to stabilize the RNA profile immediately after blood withdrawal. Moreover, to be able to perform such large-scale clinical studies routinely in several hundred patients more cost-effective array technologies are required. To address these critical issues, we have combined a whole-blood RNA stabilization technology with a method to reduce globin mRNA, followed by genome-wide transcriptome analysis using a newly introduced BeadChip oligonucleotide technology. We demonstrate that the globin mRNA reduction method results in significantly improved data quality of stabilized RNA samples with low intragroup variance and a detection rate of expressed genes similar to that in PBMC. More important, even small differences in gene expression such as are observed between females and males were detected and sufficient to predict gender in whole-blood samples. We therefore propose the combination of globin mRNA reduction after whole-blood RNA stabilization with a newly introduced cost-effective BeadChip array as the preferred approach for large-scale multicenter trials, especially when establishing predictive markers for disease and treatment outcome in peripheral blood.

DNA MICROARRAY TECHNOLOGY FOR TARGET IDENTIFICATION AND VALIDATION

1. Microarrays, a recent development, provide a revolutionary platform to analyse thousands of genes at once. They have enormous potential in the study of biological processes in health and disease and, perhaps, microarrays have become crucial tools in diagnostic applications and drug discovery. 2. Microarray based studies have provided the essential impetus for biomedical experiments, such as identification of disease-causing genes in malignancies and regulatory genes in the cell cycle mechanism. Microarrays can identify genes for new and unique potential drug targets, predict drug responsiveness for individual patients and, finally, initiate gene therapy and prevention strategies. 3. The present article reviews the principles and technological concerns, as well as the steps involved in obtaining and analysing of data. Furthermore, applications of microarray based experiments in drug target identifications and validation strategies are discussed. 4. To exemplify how this tool can be useful, in the present review we provide an overview of some of the past and potential future aspects of microarray technology and present a broad overview of this rapidly growing field.

Evaluation of experimental designs for two-color cDNA microarrays

The major goal of two-color cDNA microarray experiments is to measure the relative gene expression level (i.e., relative amount of mRNA) of each gene between samples in studies of gene expression. More specifically, given an N-sample experiment, we need all N(N - 1)/2 relative expression levels of all sample pairs of each gene for identification of the differentially expressed genes and for clustering of gene expression patterns. However, the intensities observed from two-color cDNA microarray experiments do not simply represent the relative gene expression level. They are composed of signal (gene expression level), noise, and other factors. In discussions on the experimental design of two-color cDNA microarray experiments, little attention has been given to the fact that different combinations of test and control samples will produce microarray intensities data with varying intrinsic composition of factors. As a consequence, not all experimental designs for two-color cDNA microarray experiments are able to provide all possible relative gene expression levels. This phenomenon has never been addressed. To obtain all possible relative gene expression levels, a novel method for two-color cDNA microarray experimental design evaluation is necessary that will allow the making of an accurate choice. In this study, we propose a model-based approach to illustrate how the factor composition of microarray intensities changed with different experimental designs in two-color cDNA microarray experiments. By analyzing 12 experimental designs (including 5 general forms), we demonstrate that not all experimental designs are able to provide all possible relative gene expression levels due to the differences in factor composition. Our results indicate that whether an experimental design can provide all possible relative expression levels of all sample pairs for each gene should be the first criterion to be considered in an evaluation of experimental designs for two-color cDNA microarray experiments.

Microarray Analysis of Stem Cells and Differentiation

Microarrays have revolutionized molecular biology and enabled biologists to perform global analysis on the expression of tens of thousands of genes simultaneously. They have been widely used in gene discovery, biomarker determination, disease classification, and studies of gene regulation. Microarrays have been applied in stem cell research to identify major features or expression signatures that define stem cells and characterize their differentiation programs toward specific lineages. Here we provide a review of the microarray technology, including the introduction of array platforms, experimental designs, RNA isolation and amplification, array hybridization, and data analysis. We also detail examples that apply microarray technology to address several of the main questions in stem cell biology.

VeloceGenomics: An Accelerated in Vivo Drug Discovery Approach to Rapidly Predict the Biologic, Drug-Like Activity of Compounds, Proteins, or Genes

PURPOSE

The aim of this study is to test the predictive power of in vivo multiorgan RNA expression profiling in identifying the biologic activity of molecules.

METHODS

Animals were treated with compound A or B. At the end of the treatment period, in vivo multiorgan microarray-based gene expression data were collected. Investigators masked to the identity of the compounds analyzed the transcriptome signatures to define the molecular pathways affected by treatment and to hypothesize the biologic activity and potential therapeutic indications of the blinded compounds.

RESULTS

For compound A, G-protein-coupled receptors and factors associated with cell growth were affected-growth hormone/insulin-like growth factor-1, glucagon/insulin axes, and general somatomedin-like activity. Deblinding showed the compound to be a somatostatin analog, SOM230, confirming the accuracy of the predicted biologic activity. For compound B, components of the inflammatory cascade potentially mediated by lipopolysaccharide, tumor necrosis factor, or proinflammatory cytokines were affected. The gene expression signatures were most consistent with an interleukin-6 family activity. Deblinding revealed that compound B was leukemia inhibitory factor.

CONCLUSIONS

VeloceGenomics is a strategy of coupling in vivo compound testing with genomic technologies. The process enables prediction of the mechanism of action and, coupled with other relevant data, prediction of the suitability of compounds for advancement in the drug development process.

RACE: Remote Analysis Computation for gene Expression data

The Remote Analysis Computation for gene Expression data (RACE) suite is a collection of bioinformatics web tools designed for the analysis of DNA microarray data. RACE performs probe-level data preprocessing, extensive quality checks, data visualization and data normalization for Affymetrix GeneChips. In addition, it offers differential expression analysis on normalized expression levels from any array platform. RACE estimates the false discovery rates of lists of potentially regulated genes and provides a Gene Ontology-term analysis tool for GeneChip data to support the biological interpretation and annotation of results. The analysis is fully automated but can be customized by flexible parameter settings. To offer a convenient starting point for subsequent analyses, and to provide maximum transparency, the R scripts used to generate the results can be downloaded along with the output files. RACE is freely available for use at http://race.unil.ch.

Gene expression profiling of anti-GBM glomerulonephritis model: the role of NF-kappaB in immune complex kidney disease

BACKGROUND

Immune complexes may cause an irreversible onset of chronic renal disease. Most patients with chronic renal disease undergo a final common pathway, marked by glomerulosclerosis and interstitial fibrosis. We attempted to draw a molecular map of anti-glomerular basement membrane (GBM) glomerulonephritis in mice using oligonucleotide microarray technology.

METHODS

Kidneys were harvested at days 1, 3, 7, 11, and 16 after inducing glomerulonephritis by using anti-GBM antibody. In parallel with examining the biochemical and histologic changes, gene expression profiles were acquired against five pooled control kidneys. Gene expression levels were cross-validated by either reverse transcription-polymerase chain reaction (RT-PCR), real-time PCR, or immunohistochemistry.

RESULTS

Pathologic changes in anti-GBM glomerulonephritis were confirmed in both BALB/c and C57BL/6 strains. Among the 13,680 spotted 65mer oligonucleotides, 1112 genes showing significant temporal patterns by permutation analysis of variance (ANOVA) with multiple testing correction [false discovery ratio (FDR) < 0.05] were chosen for cluster analysis. From the expression profile, acute inflammatory reactions characterized by the elevation of various cytokines, including interleukin (IL)-1 and IL-6, were identified within 3 days of disease onset. After 7 days, tissue remodeling response was prominent with highly induced extracellular-matrix (ECM) genes. Although cytokines related to lymphocyte activation were not detected, monocyte or mesangial cell proliferation-related genes were increased. Tumor necrosis factor-alpha (TNF-alpha) and nuclear factor-kappaB (NF-kappaB) pathway were consistently activated along the entire disease progression, inducing various target genes like complement 3, IL-1b, IL-6, Traf1, and Saa1.

CONCLUSION

We made a large-scale gene expression time table for mouse anti-GBM glomerulonephritis model, providing a comprehensive overview on the mechanism governing the initiation and the progression of inflammatory renal disease.