Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes

Background  

A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions.     

mRNA-Seq reveals complex patterns of gene regulation and expression in the mouse skeletal muscle transcriptome associated with calorie restriction

Sarcopenia is an age-associated loss of skeletal muscle mass and strength that increases the risk of disability. Calorie restriction (CR), the consumption of fewer calories while maintaining adequate nutrition, mitigates sarcopenia and many other age-related diseases. To identify potential mechanisms by which CR preserves skeletal muscle integrity during aging, we used mRNA-Seq for deep characterization of gene regulation and mRNA abundance in skeletal muscle of old mice compared with old mice subjected to CR. mRNA-Seq revealed complex CR-associated changes in expression of mRNA isoforms, many of which occur without a change in total message abundance and thus would not be detected by methods other than mRNA-Seq. Functional annotation of differentially expressed genes reveals CR-associated upregulation of pathways involved in energy metabolism and lipid biosynthesis, and downregulation of pathways mediating protein breakdown and oxidative stress, consistent with earlier microarray-based studies. CR-associated changes not noted in previous studies involved downregulation of genes controlling actin cytoskeletal structures and muscle development. These CR-associated changes reflect generally healthier muscle, consistent with CR's mitigation of sarcopenia. mRNA-Seq generates a rich picture of the changes in gene expression associated with CR, and may facilitate identification of genes that are primary mediators of CR's effects.     

Clustering formal concepts to discover biologically relevant knowledge from gene expression data

The production of high-throughput gene expression data has generated a crucial need for bioinformatics tools to generate biologically interesting hypotheses. Whereas many tools are available for extracting global patterns, less attention has been focused on local pattern discovery. We propose here an original way to discover knowledge from gene expression data by means of the so-called formal concepts which hold in derived Boolean gene expression datasets. We first encoded the over-expression properties of genes in human cells using human SAGE data. It has given rise to a Boolean matrix from which we extracted the complete collection of formal concepts, i.e., all the largest sets of over-expressed genes associated to a largest set of biological situations in which their over-expression is observed. Complete collections of such patterns tend to be huge. Since their interpretation is a time-consuming task, we propose a new method to rapidly visualize clusters of formal concepts. This designates a reasonable number of Quasi-Synexpression-Groups (QSGs) for further analysis. The interest of our approach is illustrated using human SAGE data and interpreting one of the extracted QSGs. The assessment of its biological relevancy leads to the formulation of both previously proposed and new biological hypotheses.     

Extracting biologically significant patterns from short time series gene expression data

Background  

Time series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult.     

Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer

MOTIVATION: Most gene-expression based studies aim to identify genes with the capability of distinguishing different phenotypes. Although analysis at the genomic level is important, results of the molecular/cellular level are essential for understanding biological mechanisms. To deliver molecular/cellular-level results, a two-stage scheme is widely employed. This scheme just evaluates biological processes/molecular activities individually, totally overlooking the relationship between processes/activities. This treatment conflicts with the fact that most biological processes/molecular activities do not work alone. In order to deliver improved results, this shortcoming should be addressed. RESULTS: We design a selection model from a novel perspective to directly detect important gene functional categories (each category represents a cellular process or a molecular activity). More importantly, the correlations between gene categories are considered. Contributed by this capability, the proposed method shows its advantages over others. AVAILABILITY: the source code in Matlab is accessible via http://www.ee.cityu.edu.hk/~twschow/category_selection/category_selection.htm     

Exploration of the gene fusion landscape of glioblastoma using transcriptome sequencing and copy number data

Background

RNA-seq has spurred important gene fusion discoveries in a number of different cancers, including lung, prostate, breast, brain, thyroid and bladder carcinomas. Gene fusion discovery can potentially lead to the development of novel treatments that target the underlying genetic abnormalities.

Results

In this study, we provide comprehensive view of gene fusion landscape in 185 glioblastoma multiforme patients from two independent cohorts. Fusions occur in approximately 30-50% of GBM patient samples. In the Ivy Center cohort of 24 patients, 33% of samples harbored fusions that were validated by qPCR and Sanger sequencing. We were able to identify high-confidence gene fusions from RNA-seq data in 53% of the samples in a TCGA cohort of 161 patients. We identified 13 cases (8%) with fusions retaining a tyrosine kinase domain in the TCGA cohort and one case in the Ivy Center cohort. Ours is the first study to describe recurrent fusions involving non-coding genes. Genomic locations 7p11 and 12q14-15 harbor majority of the fusions. Fusions on 7p11 are formed in focally amplified EGFR locus whereas 12q14-15 fusions are formed by complex genomic rearrangements. All the fusions detected in this study can be further visualized and analyzed using our website: http://ivygap.swedish.org/fusions.

Conclusions

Our study highlights the prevalence of gene fusions as one of the major genomic abnormalities in GBM. The majority of the fusions are private fusions, and a minority of these recur with low frequency. A small subset of patients with fusions of receptor tyrosine kinases can benefit from existing FDA approved drugs and drugs available in various clinical trials. Due to the low frequency and rarity of clinically relevant fusions, RNA-seq of GBM patient samples will be a vital tool for the identification of patient-specific fusions that can drive personalized therapy.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-818) contains supplementary material, which is available to authorized users.     

Synthesis of biologically relevant biflavanoids--a review

Recent investigations show that naturally occurring biflavanoids possess anti-inflammatory, anticancer, antiviral, antimicrobial, vasorelaxant, and anticlotting activities. These activities have been discovered from the small number of biflavanoid structures that have been investigated, although the natural biflavanoid library is likely to be large. Structurally, biflavanoids are polyphenolic molecules comprised of two identical or non-identical flavanoid units conjoined in a symmetrical or unsymmetrical manner through an alkyl or an alkoxy-based linker of varying length. These possibilities introduce significant structural variation in biflavanoids, which is further amplified by the positions of functional groups--hydroxy, methoxy, keto, or double bond--and stereogenic centers on the flavanoid scaffold. In combination, the class of biflavanoids represents a library of structurally diverse molecules, which remains to be fully exploited. Since the time of their discovery, several chemical approaches utilizing coupling and rearrangement strategies have been developed to synthesize biflavanoids. This review compiles these synthetic approaches into nine different methods including Ullmann coupling of halogenated flavones, biphenyl-based construction of biflavanoids, metal-catalyzed cross-coupling of flavones, Wessely-Moser rearrangements, oxidative coupling of flavones, Ullmann condensation with nucleophiles, nucleophilic substitutions for alkoxy biflavanoids, and dehydrogenation-based or hydrogenation-based synthesis. Newer, more robust synthetic approaches are necessary to realize the full potential of the structurally diverse class of biflavanoids.     

A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database

Background  

Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA.     

Mapping of biologically relevant sites on human IL-1 beta using monoclonal antibodies

mAb have been raised that recognize human IL-1 beta. Using overlapping peptide fragments expressed in yeast and bacteria, we have mapped the regions of the protein to which these antibodies bind. To assess the relevance of the different regions of IL-1 beta for the expression of its biologic activity, the ability of the antibodies to block IL-1 activity was assayed. Antibodies recognizing the regions 133-148 and 251-269 of human IL-1 beta could inhibit the activity of IL-1 beta, but not of IL-1 alpha, in two different biologic assays, the murine thymocyte proliferation and PGE2 release from human fibroblasts. Conversely, antibodies that recognize the region 218-243 have only a moderate inhibitory effect on the IL-1 beta biologic activity in both assays. Finally, an antibody mapping to the region 148-192 did not inhibit IL-1 beta activity either on thymocytes or on fibroblasts. It is suggested that IL-1 beta-induced cell activation involves different regions of the protein and that both N-terminal and C-terminal fragments are involved in the correct functioning of the IL-1 beta molecule.     

Independent component analysis reveals new and biologically significant structures in micro array data

Background  

An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS) problem. BSS attempts to separate a mixture of signals into their different sources and refers to the problem of recovering signals from several observed linear mixtures. In the context of micro array data, "sources" may correspond to specific cellular responses or to co-regulated genes.     

Meta-analysis of human methylation data for evidence of sex-specific autosomal patterns

Background

Several individual studies have suggested that autosomal CpG methylation differs by sex both in terms of individual CpG sites and global autosomal CpG methylation. However, these findings have been inconsistent and plagued by spurious associations due to the cross reactivity of CpG probes on commercial microarrays. We collectively analysed 76 published studies (n = 6,795) for sex-associated differences in both autosomal and sex chromosome CpG sites.

Results

Overall autosomal methylation profiles varied substantially by study, and we encountered substantial batch effects. We accounted for these by conducting random effects meta-analysis for individual autosomal CpG methylation associations. After excluding non-specific probes, we found 184 autosomal CpG sites differentially methylated by sex after correction for multiple testing. In line with previous studies, average beta differences were small. Many of the most significantly associated CpG probes were new. Of note was differential CpG methylation in the promoters of genes thought to be involved in spermatogenesis and male fertility, such as SLC9A2, SPESP1, CRISP2, and NUPL1. Pathway analysis revealed overrepresentation of genes differentially methylated by sex in several broad Gene Ontology biological processes, including RNA splicing and DNA repair.

Conclusions

This study represents a comprehensive analysis of sex-specific methylation patterns. We demonstrate the existence of sex-specific methylation profiles and report a large number of novel DNA methylation differences in autosomal CpG sites between sexes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-981) contains supplementary material, which is available to authorized users.