Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme

Background

Despite recent discoveries of new molecular targets and pathways, the search for an effective therapy for Glioblastoma Multiforme (GBM) continues. A newly emerged field, radiogenomics, links gene expression profiles with MRI phenotypes. MRI-FLAIR is a noninvasive diagnostic modality and was previously found to correlate with cellular invasion in GBM. Thus, our radiogenomic screen has the potential to reveal novel molecular determinants of invasion. Here, we present the first comprehensive radiogenomic analysis using quantitative MRI volumetrics and large-scale gene- and microRNA expression profiling in GBM.

Methods

Based on The Cancer Genome Atlas (TCGA), discovery and validation sets with gene, microRNA, and quantitative MR-imaging data were created. Top concordant genes and microRNAs correlated with high FLAIR volumes from both sets were further characterized by Kaplan Meier survival statistics, microRNA-gene correlation analyses, and GBM molecular subtype-specific distribution.

Results

The top upregulated gene in both the discovery (4 fold) and validation (11 fold) sets was PERIOSTIN (POSTN). The top downregulated microRNA in both sets was miR-219, which is predicted to bind to POSTN. Kaplan Meier analysis demonstrated that above median expression of POSTN resulted in significantly decreased survival and shorter time to disease progression (P<0.001). High POSTN and low miR-219 expression were significantly associated with the mesenchymal GBM subtype (P<0.0001).

Conclusion

Here, we propose a novel diagnostic method to screen for molecular cancer subtypes and genomic correlates of cellular invasion. Our findings also have potential therapeutic significance since successful molecular inhibition of invasion will improve therapy and patient survival in GBM.     

The leukemia-specific fusion gene ETV6/RUNX1 perturbs distinct key biological functions primarily by gene repression

Background

ETV6/RUNX1 (E/R) (also known as TEL/AML1) is the most frequent gene fusion in childhood acute lymphoblastic leukemia (ALL) and also most likely the crucial factor for disease initiation; its role in leukemia propagation and maintenance, however, remains largely elusive. To address this issue we performed a shRNA-mediated knock-down (KD) of the E/R fusion gene and investigated the ensuing consequences on genome-wide gene expression patterns and deducible regulatory functions in two E/R-positive leukemic cell lines.

Findings

Microarray analyses identified 777 genes whose expression was substantially altered. Although approximately equal proportions were either up- (KD-UP) or down-regulated (KD-DOWN), the effects on biological processes and pathways differed considerably. The E/R KD-UP set was significantly enriched for genes included in the “cell activation”, “immune response”, “apoptosis”, “signal transduction” and “development and differentiation” categories, whereas in the E/R KD-DOWN set only the “PI3K/AKT/mTOR signaling” and “hematopoietic stem cells” categories became evident. Comparable expression signatures obtained from primary E/R-positive ALL samples underline the relevance of these pathways and molecular functions. We also validated six differentially expressed genes representing the categories “stem cell properties”, “B-cell differentiation”, “immune response”, “cell adhesion” and “DNA damage” with RT-qPCR.

Conclusion

Our analyses provide the first preliminary evidence that the continuous expression of the E/R fusion gene interferes with key regulatory functions that shape the biology of this leukemia subtype. E/R may thus indeed constitute the essential driving force for the propagation and maintenance of the leukemic process irrespective of potential consequences of associated secondary changes. Finally, these findings may also provide a valuable source of potentially attractive therapeutic targets.     

Stochastic resonance reveals "pilot light" expression in mammalian genes

Background

Microarrays are widely used for estimation of expression of thousands of genes in a biological sample. The resolution ability of this method is limited by the background noise. Low expressed genes are detected with insufficient reliability and expression of many genes is never detected at all.

Methodology/Principal Findings

We have applied the principles of stochastic resonance to detect expression of genes from microarray signals below the background noise level. We report the periodic pattern detected in genes called “Absent” by traditional analysis. The pattern is consistent with expression of the conventionally detected genes and specific to the tissue of origin. This effect is corroborated by the analysis of oscillating gene expression in mouse (M.musculus) and yeast (S. cerevisae).

Conclusion/Significance

Most genes usually considered silent are in fact expressed at a very low level. Stochastic resonance can be applied to detect changes in expression pattern of low-expressed genes as well as for the validation of the probe performance in microarrays.     

Cloning and characterization of a putative TAC1 ortholog associated with leaf angle in maize (Zea mays L.)

Background

Modifying plant architecture to increase photosynthesis efficiency and reduce shade avoidance response is very important for further yield improvement when crops are grown in high density. Identification of alleles controlling leaf angle in maize is needed to provide insight into molecular mechanism of leaf development and achieving ideal plant architecture to improve grain yield.

Methodology/Principal Findings

The gene cloning was done by using comparative genomics, and then performing real-time polymerase chain reaction (RT-PCR) analysis to assay gene expression. The gene function was validated by sequence dissimilarity analysis and QTL mapping using a functional cleaved amplified polymorphism (CAP).

Conclusions

The leaf angle is controlled by a major quantitative trait locus, ZmTAC1 (Zea mays L. Leaf Angle Control 1). ZmTAC1 has 4 exons encoding a protein with 263 amino acids, and its domains are the same as those of the rice OsTAC1 protein. ZmTAC1 was found to be located in the region of qLA2 by using the CAP marker and the F_2:3 families from the cross between Yu82 and Shen137. Real-time PCR analysis revealed ZmTAC1 expression was the highest in the leaf-sheath pulvinus, less in the leaf and shoot apical meristem, and the lowest in the root. A nucleotide difference in the 5′-untranslated region (UTR) between the compact inbred line Yu82 (“CTCC”) and the expanded inbred line Shen137 (“CCCC”) influences the expression level of ZmTAC1, further controlling the size of the leaf angle. Sequence verification of the change in the 5′-UTR revealed ZmTAC1 with “CTCC” was present in 13 compact inbred lines and ZmTAC1 with “CCCC” was present in 18 expanded inbred lines, indicating ZmTAC1 had been extensively utilized in breeding with regard to the improvement of the maize plant architecture.     

An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes

Background

Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”.

Results

Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer.

Conclusion

The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy.     

Using ribosomal protein genes as reference: a tale of caution

Background

Housekeeping genes are needed in every tissue as their expression is required for survival, integrity or duplication of every cell. Housekeeping genes commonly have been used as reference genes to normalize gene expression data, the underlying assumption being that they are expressed in every cell type at approximately the same level. Often, the terms “reference genes” and “housekeeping genes” are used interchangeably. In this paper, we would like to distinguish between these terms. Consensus is growing that housekeeping genes which have traditionally been used to normalize gene expression data are not good reference genes. Recently, ribosomal protein genes have been suggested as reference genes based on a meta-analysis of publicly available microarray data.

Methodology/Principal Findings

We have applied several statistical tools on a dataset of 70 microarrays representing 22 different tissues, to assess and visualize expression stability of ribosomal protein genes. We confirmed the housekeeping status of these genes, but further estimated expression stability across tissues in order to assess their potential as reference genes. One- and two-way ANOVA revealed that all ribosomal protein genes have significant expression variation across tissues and exhibit tissue-dependent expression behavior as a group. Via multidimensional unfolding analysis, we visualized this tissue-dependency. In addition, we explored mechanisms that may cause tissue dependent effects of individual ribosomal protein genes.

Conclusions/Significance

Here we provide statistical and biological evidence that ribosomal protein genes exhibit important tissue-dependent variation in mRNA expression. Though these genes are most stably expressed of all investigated genes in a meta-analysis they cannot be considered true reference genes.     

Astrocyte-specific expression patterns associated with the PDGF-induced glioma microenvironment

Background

The tumor microenvironment contains normal, non-neoplastic cells that may contribute to tumor growth and maintenance. Within PDGF-driven murine gliomas, tumor-associated astrocytes (TAAs) are a large component of the tumor microenvironment. The function of non-neoplastic astrocytes in the glioma microenvironment has not been fully elucidated; moreover, the differences between these astrocytes and normal astrocytes are unknown. We therefore sought to identify genes and pathways that are increased in TAAs relative to normal astrocytes and also to determine whether expression of these genes correlates with glioma behavior.

Methodology/Principal Findings

We compared the gene expression profiles of TAAs to normal astrocytes and found the Antigen Presentation Pathway to be significantly increased in TAAs. We then identified a gene signature for glioblastoma (GBM) TAAs and validated the expression of some of those genes within the tumor. We also show that TAAs are derived from the non-tumor, stromal environment, in contrast to the Olig2+ tumor cells that constitute the neoplastic elements in our model. Finally, we validate this GBM TAA signature in patients and show that a TAA-derived gene signature predicts survival specifically in the human proneural subtype of glioma.

Conclusions/Significance

Our data identifies unique gene expression patterns between populations of TAAs and suggests potential roles for stromal astrocytes within the glioma microenvironment. We show that certain stromal astrocytes in the tumor microenvironment express a GBM-specific gene signature and that the majority of these stromal astrocyte genes can predict survival in the human disease.     

Distance-based assessment of the localization of functional annotations in 3D genome reconstructions

Background

Recent studies used the contact data or three-dimensional (3D) genome reconstructions from Hi-C (chromosome conformation capture with next-generation sequencing) to assess the co-localization of functional genomic annotations in the nucleus. These analyses dichotomized data point pairs belonging to a functional annotation as “close” or “far” based on some threshold and then tested for enrichment of “close” pairs. We propose an alternative approach that avoids dichotomization of the data and instead directly estimates the significance of distances within the 3D reconstruction.

Results

We applied this approach to 3D genome reconstructions for Plasmodium falciparum, the causative agent of malaria, and Saccharomyces cerevisiae and compared the results to previous approaches. We found significant 3D co-localization of centromeres, telomeres, virulence genes, and several sets of genes with developmentally regulated expression in P. falciparum; and significant 3D co-localization of centromeres and long terminal repeats in S. cerevisiae. Additionally, we tested the experimental observation that telomeres form three to seven clusters in P. falciparum and S. cerevisiae. Applying affinity propagation clustering to telomere coordinates in the 3D reconstructions yielded six telomere clusters for both organisms.

Conclusions

Distance-based assessment replicated key findings, while avoiding dichotomization of the data (which previously yielded threshold-sensitive results).

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-992) contains supplementary material, which is available to authorized users.