Experimental Microarray Data Research Articles

Reconstruction of a genome-scale metabolic model and in-silico flux analysis of Aspergillus tubingensis: a non-mycotoxinogenic citric acid-producing fungus

BackgroundAspergillus tubingensis is a citric acid-producing fungus that can utilize sugars in hydrolysate of lignocellulosic biomass such as sugarcane bagasse and, unlike A. niger, does not produce mycotoxins. To date, no attempt has been made to model its metabolism at genome scale.ResultsHere, we utilized the whole-genome sequence (34.96 Mb length) and the measured biomass composition to reconstruct a genome-scale metabolic model (GSMM) of A. tubingensis DJU120 strain. The model, named iMK1652, consists of 1652 genes, 1657 metabolites and 2039 reactions distributed over four cellular compartments. The model has been extensively curated manually. This included removal of dead-end metabolites and generic reactions, addition of secondary metabolite pathways and several transporters. Several mycotoxin synthesis pathways were either absent or incomplete in the genome, providing a genomic basis for the non-toxinogenic nature of this species. The model was further refined based on the experimental phenotypic microarray (Biolog) data. The model closely captured DJU120 fermentative data on glucose, xylose, and phosphate consumption, as well as citric acid and biomass production, showing its applicability to capture citric acid fermentation of lignocellulosic biomass hydrolysate.ConclusionsThe model offers a framework to conduct metabolic systems biology investigations and can act as a scaffold for integrative modelling of A. tubingensis.

Summarising multiple clustering-centric estimates with OWA operators for improved KNN imputation on microarray data

As part of celebrating the success of OWA operators and their contributions over the past decades, this work presents an original investigation of exploiting OWA in dealing with missing value imputation witnessed in microarray experimental data. This task is significant in life science and its realisation to humanity. Both argument-independent and -dependent variants of such operators are applied to summarise a collection of estimates, determined through the concept of clustering-centric KNN imputation. This provides an innovative alternative to the state-of-the-art model that makes use of a single clustering to identify neighbours of a particular instance of interest. Instead of manually specify data partition, the proposed approach works by selecting a subset of diverse clusterings or committees from a candidate pool, which has been prepared using k-means and different (and popular) generation strategies invented for ensemble clustering. It is automated through a greedy forward-search looking for a desired number of committee members. Based on published gene expression datasets and different experimental settings, the resulting model generally outperforms its baselines, being competitive to related methods found in the literature. Further extensions to iterative refinement and supervised imputation are also discussed in addition to the analysis of algorithmic parameters.

α-Synuclein Induces Neuroinflammation Injury through the IL6ST-AS/STAT3/HIF-1α Axis.

The aggregation of α-synuclein (α-syn) promotes neuroinflammation and neuronal apoptosis, which eventually contribute to the pathogenesis of Parkinson's disease (PD). Our microarray analysis and experimental data indicated a significant expression difference of the long noncoding RNA IL6ST-AS and its anti-sense strand, IL6ST, in α-synuclein-induced microglia, compared with unstimulated microglia. IL6ST is a key component of the IL6R/IL6ST complex in the microglial membrane, which recognizes extracellular inflammatory factors, such as IL6. Studies have shown that the binding of IL6 to the IL6R/IL6ST complex could activate the JAK2-STAT3 pathway and promote an excessive immune response in glia cells. Meanwhile, the phosphorylation and activation of STAT3 could increase the transcription of HIF1A, encoding a hypoxia-inducible factor related to cytotoxic damage. Our results indicated that the overexpression of IL6ST-AS induced by exogenous α-synuclein could inhibit the expression of IL6ST and the activation of JAK2-STAT3 pathway in HMC3 cells. In addition, a reduction in STAT3 resulted in the transcription inhibition of HIF1A and the acceleration of oxidative stress injury in SH-SY5Y cells co-cultured with α-synuclein-induced HMC3 cells. Our findings indicate that IL6ST-AS is an important factor that regulates microglia activation and neuronal necrosis in the progression of PD. In the HMC3 and SH-SY5Y cell co-culture system, the overexpression of IL6ST-AS led to microglial dysfunction and neurotoxicology through the IL6ST-AS/STAT3/HIF-1α axis. Our research revealed the relationships among α-synuclein, IL6ST, STAT3, and HIF-1α in the pathological process of PD and provided a new inflammation hypothesis for the pathogenesis of PD.

Microarray Analysis Workflow Based on a Genetic Algorithm to Discover Potential Hub Genes

Abstract: This paper presents a sequence of steps oriented to gain biological knowledge from microarray gene expression data. The pipeline's core is a canonical multi-objective Genetic Algorithm (GA), which takes a gene expression matrix and a factor as input. The factor groups samples according to different criteria, e.g. healthy tissue and diseased tissue samples. The result of one run of the GA is a gene set with good properties both at the individual level, in terms of differential expression, and at the aggregate level, in terms of correlation between expression profiles. Microarray experiment data are obtained from GEO (Gene Expression Omnibus dataset). As for the pipeline structure, independent runs of the GA are analyzed, genes in common between all the runs are collected, and over-representation analysis is performed. At the end of the process, a small number of genes of interest arise. The methodology is exemplified with a leukemia benchmark dataset, and a group of genes of interest is obtained for the illustrative example.

Arabidopsis Coexpression Tool: a tool for gene coexpression analysis in Arabidopsis thaliana

SummaryGene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions.

Integration of microarray data and literature mining identifies a sex bias in DPP4+CD4+ T cells in HIV-1 infection.

HIV-1 infection exhibits a significant sex bias. This study aimed at identifying and examining lymphocyte associated sex differences in HIV-1 pathogenesis using a data-driven approach. To select targets for investigating sex differences in lymphocytes, data of microarray experiments and literature mining were integrated. Data from three large-scale microarray experiments were obtained from NCBI/GEO and screened for sex differences in gene expression. Literature mining was employed to identify sex biased genes in the microarray data, which were relevant to HIV-1 pathogenesis and lymphocyte biology. Sex differences in gene expression of selected genes were investigated by RT-qPCR and flowcytometry in healthy individuals and persons living with HIV-1. A significant and consistent sex bias was identified in 31 genes, the majority of which were related to immunity and expressed at higher levels in women. Using literature mining, three genes (DPP4, FCGR1A and SOCS3) were selected for analysis by qPCR because of their relevance to HIV, as well as, B and T cell biology. DPP4 exhibited the most significant sex bias in mRNA expression (p = 0.00029). Therefore, its expression was further analyzed on B and T cells using flowcytometry. In HIV-1 infected controllers and healthy individuals, frequencies of CD4+DPP4+ T cells were higher in women compared to men (p = 0.037 and p = 0.027). In women, CD4 T cell counts correlated with a predominant decreased in DPP4+CD4+ T cells (p = 0.0032). Sex differences in DPP4 expression abrogated in progressive HIV-1 infection. In conclusion, we found sex differences in the pathobiology of T cells in HIV-1 infection using a data-driven approach. Our results indicate that DPP4 expression on CD4+ T cells might contribute to the immunological sex differences observed in chronic HIV‑1 infection.

Identification of Monotonically Differentially Expressed Genes for IFN-β-Treated Multiple Sclerosis Patients.

Multiple sclerosis (MS) is a common neurological disability of the central nervous system. Immune-modulatory therapy with interferon-β (IFN-β) has been used as a first-line treatment to prevent relapses in MS patients. While the therapeutic mechanism of IFN-β has not been fully elucidated, the data of microarray experiments that collected longitudinal gene expression profiles to evaluate the long-term response of IFN-β treatment have been analyzed using statistical methods that were incapable of dealing with such data. In this study, the GeneRank method was applied to generate weighted gene expression values and the monotonically expressed genes (MEGs) for both IFN-β treatment responders and nonresponders were identified. The proposed procedure identified 13 MEGs for the responders and 2 MEGs for the nonresponders, most of which are biologically relevant to MS. Our work here provides some useful insight into the mechanism of IFN-β treatment for MS patients. A full understanding of the therapeutic mechanism will enable a more personalized treatment strategy possible.

Establishment of resveratrol and its derivatives as neuroprotectant against monocrotophos-induced alteration in NIPBL and POU4F1 protein through molecular docking studies.

Monocrotophos (MCP) is a broad spectrum organophosphorus insecticide, which is widely used as foliar spray to the different important crops. MCP may reach the soil and the aquatic environment directly or indirectly during and after the application, which leads to the different environmental issues. MCP is found to be associated with neurotoxicity and its toxic effects have been monitored during different stages of neuronal development. Identification of gene expression in MCP-induced neurotoxicity during neuronal developmental stage is a major area of genomic research interest. In accordance with this identification, screening of potential neuroprotective, natural resources are also required as a preventive aspects by targeting the impaired genes. In this current course of work, microarray experiment has been used to identify genes that were expressed in monocrotophos (MCP)-induced mesenchymal stem cells (MSC) and also the neuroprotectant activity of RV on MCP-exposed MSCs. Microarray experiment data have been deposited in NCBI's Gene Expression Omnibus database and are accessible through GEO Series accession number GSE121261. In this paper, we have discussed two important genes NIPBL (nipped-B-like protein) and POU4F1 (POU domain, class 4, transcription factor 1). These genes were found to be significantly expressed in MCP-exposed MSC and show minimum expression in presence of RV. Homology modelling and docking study was done to identify the interaction and binding affinity of resveratrol and its derivatives with NIPBL and POU4F1 protein. Docking analysis shows that RV and its derivatives have strong interaction with NIPBL and POU4F1 protein hence proves the significance of resveratrol as potential neuroprotectant. This paper highlights the hazardous impact of MCP on neuronal development disorders and repairing potentiality of RV and its derivatives on altered genes involved in neuronal diseases. Graphical Abstract.

A microarray study of radiation-induced transcriptional responses and the role of Jagged 1 in two closely-related lung cancer cell lines

Background: Lung cancer is the most lethal and common cancer in the world. Non-small cell lung cancer accounts for 80% lung cancer cases and its five-year survival rate after radiotherapy is approximately 5%. Patients with lung cancer in the advanced stages are suitable for radiotherapy rather than surgical resection. Unfortunately, radiotherapy alone or in combination with chemotherapy does not significantly improve 5-year survival rates of lung cancer patients. The mechanism by which lung cancer responds to radiation is unclear and needed to be investigated. Methods: Two lung adenocarcinoma cell lines, CL1-0 and CL1-5, that have exhibited different metastasis capacities in previous research, were utilized herein to investigate cellular radio-sensitivity. Clonogenic assay was used to evaluate the radio-sensitivity of CL1-0 and CL1-5. Flow cytometry experiments were performed on both cell lines to understand the change in the cell cycle profile that followed radiation. Gene expression data with the untreated and radiation treated lung cancer cell samples was obtained using Illumina microarrays. Tight hierarchical clustering was conducted to group genes with similar expression profiles. Clonogenic assay and cell cycle distribution were performed with Jagged 1-overexpressed CL1-0 to investigate the role of Jagged 1 in the radio-sensitivity of the lung cancer cells. Results: Clonogenic assay results showed that CL1-5 was more radio-sensitive than CL1-0 after exposure to 10 Gy radiation. The proportion of the CL1-5 cell population in the G2/M phase was found to be markedly higher than that with the CL1-0 cell population following exposure to radiation. Based on the microarray experimental data, 1,595 genes were identified as differentially expressed and sub-grouped into six clusters by hierarchical clustering. The functions of the identified genes in the tight clusters were associated with cell death, cell cycle, cell growth and proliferation, cellular function and maintenance. Moreover, our microarray data suggested that the gene expression levels of Jagged 1 , a Notch ligand, differed significantly between CL1-0 and CL1-5. This finding was further verified by real-time PCR and western blot. Clonogenic assay results further revealed a lower survival rate of Jagged 1-overexpressed CL1-0 than that of CL1-0 following exposure to radiation. Conclusions: CL1-5 was shown to be more radio-sensitive than CL1-0 from our clonogenic assay data. The radiation-induced transcriptional responses in lung cancer cells were evaluated with gene expression microarrays in this work. Jagged 1, the Notch receptor ligand, may have an important role in increasing the radio-sensitivity of lung adenocarcinomas.

FFcPsA: a fast finite conventional state using prefix pattern gene search algorithm for large sequence identification

Gnomic information continues to flood, and this trend comes in the wake of the life sciences’ rapid development. The eventuality has been an increase in the demand for more scalable and faster searching techniques, with the demand also proving urgent. Whereas a faster algorithm could be used to search biomedical data, the process of making gene prediction remains challenging. Particularly, the searching of biomedical data has been affirmed to be a simple gradient base approach. Therefore, indexing has been investigated with the aim of achieving a fast finite conventional rate. With biomedical expressed datasheet at hand, data-based large sequence identification has been achieved via the prefix pattern gene search algorithm. Imperative to note is that real-value expression matrices can replace microarray experimental gene expression data. To ensure that the genomic dataset’s querying exhibits reductions in the overall retrieval time and that the time used for pattern array building is sped up, parallel partitioned methods have gained application. Notably, the central merit accruing from the latter method is that the majority of unrelated sequences are skipped. Also, these methods ensure that the real search problems are only decomposed to establish original database fractions. To ensure that the establishment of the gene’s hidden information and similar characteristics is enhanced, large genetic data patterns are required.

Evolutionary Computational Algorithm by Blending of PPCA and EP-Enhanced Supervised Classifier for Microarray Gene Expression Data

In DNA microarray technology, gene classification is considered to be difficult because the attributes of the data, are characterized by high dimensionality and small sample size. Classification of tissue samples in such high dimensional problems is a complicated task. Furthermore, there is a high redundancy in microarray data and several genes comprise inappropriate information for accurate classification of diseases or phenotypes. Consequently, an efficient classification technique is necessary to retrieve the gene information from the microarray experimental data. In this paper, a classification technique is proposed that classifies the microarray gene expression data well. In the proposed technique, the dimensionality of the gene expression dataset is reduced by Probabilistic PCA. Then, an Artificial Neural Network (ANN) is selected as the supervised classifier and it is enhanced using Evolutionary programming (EP) technique. The enhancement of the classifier is accomplished by optimizing the dimension of the ANN. The enhanced classifier is trained using the Back Propagation (BP) algorithm and so the BP error gets minimized. The well-trained ANN has the capacity of classifying the gene expression data to the associated classes. The proposed technique is evaluated by classification performance over the cancer classes, Acute myeloid leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). The classification performance of the enhanced ANN classifier is compared over the existing ANN classifier and SVM classifier.

Complex networks of interaction of genes located in the critical region of Down syndrome expressed in the normal human brain

The quantification and analysis of the human transcriptome allows expanding the knowledge of the genomic functioning, especially in body’s parts as complex and important as the human brain. In this way, in silico studies offer the possibility to extract and analyse information contained in databases, at the level of gene expression along the different brain structures. This study aimed to correlate the transcription levels of 38 genes located in the critical region of the chromosome 21 associated with Down syndrome with the cerebral localization and its intervention in the correct operation of different brain substructures. To carry out this, the expression profiles of these genes along 24 substructures of the brain cores and 18 of the Limbic lobe were done, from gene expression data of microarray experiments of DNA, available in the database of the Atlas of the brain of the "Allen Institute for Brain Sciences". It was determined a differential expression of these genes along the analysed structures, in addition to register higher levels of overall transcription in certain areas of the brain, which appear to be associated with different processes of learning and memory. The differential transcription was correlated with the cerebral localization and its potential functional role.

The Age-Related Effect of Nicotine on the Expression of Neuroprotective Genes in Ventral Tegmental Area and Substantia Nigra

Selective degeneration of dopaminergic neurons, which are predominantly located in the substantia nigra (SN) of the midbrain, is one of the hallmarks of Parkinson’s disease (PD). Large-scale microarray experimental data revealed several genes with significantly differential expression between the ventral tegmental area (VTA) and the SN dopamine (DA) neurons. Several epidemiological studies have additionally indicated nicotine-mediated neuroprotection in PD patients. Based on the strong evidence implicating lipoprotein lipase (LPL), pituitary adenylate cyclase-activating polypeptide and gastrin-releasing peptide genes in neuroprotection, this letter investigates area- and age-specific nicotine regulation of these genes’ expression in the VTA and SN of different age groups (3 months, 12 months, and 24 months) in an in vivo animal model. Our in vivo rat model results suggest that out of these genes, only the LPL gene has significantly differential expression between the VTA and SN in the senior age group (24 months). Nicotine treatment did not upregulate the neuroprotective genes in adult and senior groups (12 and 24 months). Differential expression of the LPL gene in the senior population may contribute to the different survival rates of DA neurons within the VTA and SN. However, downregulation by nicotine suggests that these genes may not be related to the nicotine-mediated neuroprotection known to reduce the risks of PD. Our results suggest that nicotine may not play an important role in the regulation of neuroprotective gene expressions, while providing new insights into the role of nicotine in PD.

Array Analysis Manager-An automated DNA microarray analysis tool simplifying microarray data filtering, bias recognition, normalization, and expression analysis.

Desoxyribonucleic acid (DNA) microarray experiments generate big datasets. To successfully harness the potential information within, multiple filtering, normalization, and analysis methods need to be applied. An in-depth knowledge of underlying physical, chemical, and statistical processes is crucial to the success of this analysis. However, due to the interdisciplinarity of DNA microarray applications and experimenter backgrounds, the published analyses differ greatly, for example, in methodology. This severely limits the comprehensibility and comparability among studies and research fields. In this work, we present a novel end-user software, developed to automatically filter, normalize, and analyze two-channel microarray experiment data. It enables the user to analyze single chip, dye-swap, and loop experiments with an extended dynamic intensity range using a multiscan approach. Furthermore, to our knowledge, this is the first analysis software solution, that can account for photobleaching, automatically detected by an artificial neural network. The user gets feedback on the effectiveness of each applied normalization regarding bias minimization. Standardized methods for expression analysis are included as well as the possibility to export the results in the Gene Expression Omnibus (GEO) format. This software was designed to simplify the microarray analysis process and help the experimenter to make educated decisions about the analysis process to contribute to reproducibility and comparability.

Common Gene Expression Patterns in the Generation of Induced Neurons From Fibroblasts

In the current study, we analyzed ten gene expression data sets including RNA-sequencing and microarray experiment data during the direct reprogramming of mouse and human fibroblasts to induced neurons and found common gene expression pattern across all data sets for this conversion.

A statistical method for the conservative adjustment of false discovery rate (q-value)

Backgroundq-value is a widely used statistical method for estimating false discovery rate (FDR), which is a conventional significance measure in the analysis of genome-wide expression data. q-value is a random variable and it may underestimate FDR in practice. An underestimated FDR can lead to unexpected false discoveries in the follow-up validation experiments. This issue has not been well addressed in literature, especially in the situation when the permutation procedure is necessary for p-value calculation.ResultsWe proposed a statistical method for the conservative adjustment of q-value. In practice, it is usually necessary to calculate p-value by a permutation procedure. This was also considered in our adjustment method. We used simulation data as well as experimental microarray or sequencing data to illustrate the usefulness of our method.ConclusionsThe conservativeness of our approach has been mathematically confirmed in this study. We have demonstrated the importance of conservative adjustment of q-value, particularly in the situation that the proportion of differentially expressed genes is small or the overall differential expression signal is weak.

A Benchmark of the Marker Gene Tool: MGFM

Large amounts of microarray experimental data are available in public repositories. Although a variety of tools have been developed to make use of these data, the number of tools that detect marker genes is limited. Identification of marker genes associated with a specific tissue/cell type is a fundamental challenge in genetic and genomic research. In addition to other genes, marker genes are of great importance for understanding the gene function, the molecular mechanisms underlying complex diseases, and may lead to the development of new drug targets. We have previously developed a Bioconductor R package (MGFM) for marker gene detection from microarray data. The tool is freely available from the Bioconductor web site (https://www.bioconductor.org/packages/release/bioc/html/MGFM.html), and it is also provided as an online application integrated into the CellFinder platform (http://cellfinder.org/analysis/marker). In this work, we applied our tool to a public microarray data set from the NCBI’s Gene Expression Omnibus public repository encompassing samples for 12 human tissues. We compared the set of predicted marker genes to a set of tissue-specific genes obtained from the Tissue-specific Gene Expression and Regulation (TiGER) database. Furthermore, we tested the performance of the tool using two normalization methods, RMA and YuGene. YuGene performed slightly better than RMA. Our tool identified 38,4 % or 37,9 % of the TiGER derived tissue-specific genes using YuGene or RMA, respectively.

Multivariate Surprisal Analysis of Gene Expression Levels

We consider here multivariate data which we understand as the problem where each data point i is measured for two or more distinct variables. In a typical situation there are many data points i while the range of the different variables is more limited. If there is only one variable then the data can be arranged as a rectangular matrix where i is the index of the rows while the values of the variable label the columns. We begin here with this case, but then proceed to the more general case with special emphasis on two variables when the data can be organized as a tensor. An analysis of such multivariate data by a maximal entropy approach is discussed and illustrated for gene expressions in four different cell types of six different patients. The different genes are indexed by i, and there are 24 (4 by 6) entries for each i. We used an unbiased thermodynamic maximal-entropy based approach (surprisal analysis) to analyze the multivariate transcriptional profiles. The measured microarray experimental data is organized as a tensor array where the two minor orthogonal directions are the different patients and the different cell types. The entries are the transcription levels on a logarithmic scale. We identify a disease signature of prostate cancer and determine the degree of variability between individual patients. Surprisal analysis determined a baseline expression level common for all cells and patients. We identify the transcripts in the baseline as the “housekeeping” genes that insure the cell stability. The baseline and two surprisal patterns satisfactorily recover (99.8%) the multivariate data. The two patterns characterize the individuality of the patients and, to a lesser extent, the commonality of the disease. The immune response was identified as the most significant pathway contributing to the cancer disease pattern. Delineating patient variability is a central issue in personalized diagnostics and it remains to be seen if additional data will confirm the power of multivariate analysis to address this key point. The collapsed limits where the data is compacted into two dimensional arrays are contained within the proposed formalism.

Transcriptional profiling of HOXA9-regulated genes in human glioblastoma cell models.

The data here described pertain to the article by Pojo et al. (2015) [10] titled “A transcriptomic signature mediated by HOXA9 promotes human glioblastoma initiation, aggressiveness and resistance to temozolomide” (Pojoet al., 2015 [10]). HOX genes are part of the homeobox gene family, which encodes transcription factors crucial during embryonic development (Grier et al., 2005; Pearson et al., 2005 [6,9]) and also in post developmental regulation(Neville et al., 2002; Yamamoto et al., 2003; Takahashi et al., 2004;Morgan 2006 [8,14,13,7]). Alterations interfering with the regulation of these genes may lead to tumorigenesis in adults. Due to their contributions in the control of important cellular processes, the deregulation of HOX genes is ultimately correlated with cancer treatment failure and patients' poor prognosis (Golub et al., 1999; Abdel-Fattah et al., 2006 [5,1]; Costa et al.,2010 [4]; Pojo et al., 2015 [10]). Recently, our studies showed that HOXA9 overexpression is associated with poor prognosis in patients with glioblastoma (GBM), the most common and most malignant primary brain tumor. Mechanistically, HOXA9 is associated with resistance to chemotherapy and with pro-proliferative, pro invasive and anti-apoptotic features (Costa et al., 2010 [4]; Pojo et al., 2015 [10]) in GBM in vitro models. Since HOXA9 is a transcription factor, its target genes can be the true biological effectors of its aggressiveness. In this context, whole genome Agilent's microarrays were used to obtain the full transcriptome of HOXA9 in a variety of GBM cell models, including human immortalized astrocytes, established GBM cell lines, and GBM patient derived cell cultures. Here, we provide detailed methods, including experimental design and microarray data analyses,which can be accessed in Gene Expression Omnibus (GEO) under the accession number GSE56517. Additional interpretation of the data is included and supplemented in (Pojo et al., 2015 [10]).

Application of Comparative Transcriptional Genomics to Identify Molecular Targets for Pediatric IBD.

Experimental models of colitis in mice have been used extensively for analyzing the molecular events that occur during inflammatory bowel disease (IBD) development. However, it is uncertain to what extent the experimental models reproduce features of human IBD. This is largely due to the lack of precise methods for direct and comprehensive comparison of mouse and human inflamed colon tissue at the molecular level. Here, we use global gene expression patterns of two sets of pediatric IBD and two mouse models of colitis to obtain a direct comparison of the genome signatures of mouse and human IBD. By comparing the two sets of pediatric IBD microarray data, we found 83 genes were differentially expressed in a similar manner between pediatric Crohn’s disease and ulcerative colitis. Up-regulation of the chemokine (C–C motif) ligand 2 (CCL2) gene that maps to 17q12, a confirmed IBD susceptibility loci, indicates that our comparison study can reveal known genetic associations with IBD. In comparing pediatric IBD and experimental colitis microarray data, we found common signatures amongst them including: (1) up-regulation of CXCL9 and S100A8; (2) cytokine–cytokine receptor pathway dysregulation; and (3) over-represented IRF1 and IRF2 transcription binding sites in the promoter region of up-regulated genes, and HNF1A and Lhx3 binding sites were over-represented in the promoter region of the down-regulated genes. In summary, this study provides a comprehensive view of transcriptome changes between different pediatric IBD populations in comparison with different colitis models. These findings reveal several new molecular targets for further study in the regulation of colitis.