GEDI: a user-friendly toolbox for analysis of large-scale gene expression data

Relapsed diffuse large B-cell lymphoma present different genomic profiles between early and late relapses

Despite major advances in first-line treatment, a significant proportion of patients with diffuse large B-cell lymphoma (DLBCL) will experience treatment failure. Prognosis is particularly poor for relapses occurring less than one year after the end of first-line treatment (early relapses/ER) compared to those occurring more than one year after (late relapses/LR). To better understand genomic alterations underlying the delay of relapse, we identified copy number variations (CNVs) on 39 tumor samples from a homogeneous series of patients included in the Collaborative Trial in Relapsed Aggressive Lymphoma (CORAL) prospective study. To identify CNVs associated with ER or LR, we devised an original method based on Significance Analysis of Microarrays, a permutation-based method which allows control of false positives due to multiple testing. Deletions of CDKN2A/B (28%) and IBTK (23%) were frequent events in relapsed DLBCLs. We identified 56 protein-coding genes and 25 long non-coding RNAs with significantly differential CNVs distribution between ER and LR DLBCLs, with a false discovery rate < 0.05. In ER DLBCLs, CNVs were related to transcription regulation, cell cycle and apoptosis, with duplications of histone H1T (31%), deletions of DIABLO (26%), PTMS (21%) and CK2B (15%). In LR DLBCLs, CNVs were related to immune response, with deletions of B2M (20%) and CD58 (10%), cell proliferation regulation, with duplications of HES1 (25%) and DVL3 (20%), and transcription regulation, with MTERF4 deletions (20%). This study provides new insights into the genetic aberrations in relapsed DLBCLs and suggest pathway-targeted therapies in ER and LR DLBCLs.

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.

XiP: a computational environment to create, extend and share workflows

XiP (eXtensible integrative Pipeline) is a flexible, editable and modular environment with a user-friendly interface that does not require previous advanced programming skills to run, construct and edit workflows. XiP allows the construction of workflows by linking components written in both R and Java, the analysis of high-throughput data in grid engine systems and also the development of customized pipelines that can be encapsulated in a package and distributed. XiP already comes with several ready-to-use pipeline flows for the most common genomic and transcriptomic analysis and ∼300 computational components.

AVAILABILITY

XiP is open source, freely available under the Lesser General Public License (LGPL) and can be downloaded from http://xip.hgc.jp.

Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection

BACKGROUND

The immune response to viral infection is a temporal process, represented by a dynamic and complex network of gene and protein interactions. Here, we present a reverse engineering strategy aimed at capturing the temporal evolution of the underlying Gene Regulatory Networks (GRN). The proposed approach will be an enabling step towards comprehending the dynamic behavior of gene regulation circuitry and mapping the network structure transitions in response to pathogen stimuli.

RESULTS

We applied the Time Varying Dynamic Bayesian Network (TV-DBN) method for reconstructing the gene regulatory interactions based on time series gene expression data for the mouse C57BL/6J inbred strain after infection with influenza A H1N1 (PR8) virus. Initially, 3500 differentially expressed genes were clustered with the use of k-means algorithm. Next, the successive in time GRNs were built over the expression profiles of cluster centroids. Finally, the identified GRNs were examined with several topological metrics and available protein-protein and protein-DNA interaction data, transcription factor and KEGG pathway data.

CONCLUSIONS

Our results elucidate the potential of TV-DBN approach in providing valuable insights into the temporal rewiring of the lung transcriptome in response to H1N1 virus.

Unveiling novel genes upregulated by both rhBMP2 and rhBMP7 during early osteoblastic transdifferentiation of C2C12 cells

FINDINGS

We set out to analyse the gene expression profile of pre-osteoblastic C2C12 cells during osteodifferentiation induced by both rhBMP2 and rhBMP7 using DNA microarrays. Induced and repressed genes were intercepted, resulting in 1,318 induced genes and 704 repressed genes by both rhBMP2 and rhBMP7. We selected and validated, by RT-qPCR, 24 genes which were upregulated by rhBMP2 and rhBMP7; of these, 13 are related to transcription (Runx2, Dlx1, Dlx2, Dlx5, Id1, Id2, Id3, Fkhr1, Osx, Hoxc8, Glis1, Glis3 and Cfdp1), four are associated with cell signalling pathways (Lrp6, Dvl1, Ecsit and PKCδ) and seven are associated with the extracellular matrix (Ltbp2, Grn, Postn, Plod1, BMP1, Htra1 and IGFBP-rP10). The novel identified genes include: Hoxc8, Glis1, Glis3, Ecsit, PKCδ, LrP6, Dvl1, Grn, BMP1, Ltbp2, Plod1, Htra1 and IGFBP-rP10.

BACKGROUND

BMPs (bone morphogenetic proteins) are members of the TGFβ (transforming growth factor-β) super-family of proteins, which regulate growth and differentiation of different cell types in various tissues, and play a critical role in the differentiation of mesenchymal cells into osteoblasts. In particular, rhBMP2 and rhBMP7 promote osteoinduction in vitro and in vivo, and both proteins are therapeutically applied in orthopaedics and dentistry.

CONCLUSION

Using DNA microarrays and RT-qPCR, we identified both previously known and novel genes which are upregulated by rhBMP2 and rhBMP7 during the onset of osteoblastic transdifferentiation of pre-myoblastic C2C12 cells. Subsequent studies of these genes in C2C12 and mesenchymal or pre-osteoblastic cells should reveal more details about their role during this type of cellular differentiation induced by BMP2 or BMP7. These studies are relevant to better understanding the molecular mechanisms underlying osteoblastic differentiation and bone repair.

Analyzing the connectivity between regions of interest: an approach based on cluster Granger causality for fMRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single "representative" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI.

Analysis of metagene portraits reveals distinct transitions during kidney organogenesis

Organogenesis is a multistage process, but it has been difficult, by conventional analysis, to separate stages and identify points of transition in developmentally complex organs or define genetic pathways that regulate pattern formation. We performed a detailed time-series examination of global gene expression during kidney development and then represented the resulting data as self-organizing maps (SOMs), which reduced more than 30,000 genes to 650 metagenes. Further clustering of these maps identified potential stages of development and suggested points of stability and transition during kidney organogenesis that are not obvious from either standard morphological analyses or conventional microarray clustering algorithms. We also performed entropy calculations of SOMs generated for each day of development and found correlations with morphometric parameters and expression of candidate genes that may help in orchestrating the transitions between stages of kidney development, as well as macro- and micropatterning of the organ.