Molecular classification of cutaneous malignant melanoma by gene expression profiling

The most common human cancers are malignant neoplasms of the skin. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm. Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

Bayesian survival analysis using gene expression

This thesis developed and applied Bayesian models for the analysis of survival data. The gene expression was considered as explanatory variables within the Bayesian survival model which can be considered the new contribution in the analysis of such data. The censoring factor that is inherent of survival data has also been addressed in terms of its impact on the fitting of a finite mixture of Weibull distribution with and without covariates. To investigate this, simulation study were carried out under several censoring percentages. Censoring percentage as high as 80% is acceptable here as the work involved high dimensional data. Lastly the Bayesian model averaging approach was developed to incorporate model uncertainty in the prediction of survival.

Seed-based biclustering of gene expression data

Background Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular process is active only under a subset of conditions. Biclustering finds gene clusters that have similar expression levels across a subset of conditions. This paper proposes a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner. Methods In order to find the biclusters in a gene expression dataset, we exhaustively select combinations of genes and conditions as seeds to create candidate bicluster tables. The tables have two columns: (a) a gene set, and (b) the conditions on which the gene set have dissimilar expression levels to the seed. First, the genes with less than the maximum number of dissimilar conditions are identified and a table of these genes is created. Second, the rows that have the same dissimilar conditions are grouped together. Third, the table is sorted in ascending order based on the number of dissimilar conditions. Finally, beginning with the first row of the table, a test is run repeatedly to determine whether the cardinality of the gene set in the row is greater than the minimum threshold number of genes in a bicluster. If so, a bicluster is outputted and the corresponding row is removed from the table. Repeating this process, all biclusters in the table are systematically identified until the table becomes empty. Conclusions This paper presents a novel biclustering algorithm for the identification of additive biclusters. Since it involves exhaustively testing combinations of genes and conditions, the additive biclusters can be found more readily.

Assessment of gene expression of intracellular calcium channels, pumps and exchangers with epidermal growth factor-induced epithelial-mesenchymal transition in a breast cancer cell line

Background Epithelial-mesenchymal transition (EMT) is a process implicated in cancer metastasis that involves the conversion of epithelial cells to a more mesenchymal and invasive cell phenotype. In breast cancer cells EMT is associated with altered store-operated calcium influx and changes in calcium signalling mediated by activation of cell surface purinergic receptors. In this study, we investigated whether MDA-MB-468 breast cancer cells induced to undergo EMT exhibit changes in mRNA levels of calcium channels, pumps and exchangers located on intracellular calcium storing organelles, including the Golgi, mitochondria and endoplasmic reticulum (ER). Methods Epidermal growth factor (EGF) was used to induce EMT in MDA-MB-468 breast cancer cells. Serum-deprived cells were treated with EGF (50 ng/mL) for 12 h and gene expression was assessed using quantitative RT-PCR. Results and conclusions These data reveal no significant alterations in mRNA levels of the Golgi calcium pump secretory pathway calcium ATPases (SPCA1 and SPCA2), or the mitochondrial calcium uniporter (MCU) or Na+/Ca2+ exchanger (NCLX). However, EGF-induced EMT was associated with significant alterations in mRNA levels of specific ER calcium channels and pumps, including (sarco)-endoplasmic reticulum calcium ATPases (SERCAs), and inositol 1,4,5-trisphosphate receptor (IP3R) and ryanodine receptor (RYR) calcium channel isoforms. The most prominent change in gene expression between the epithelial and mesenchymal-like states was RYR2, which was enriched 45-fold in EGF-treated MDA-MB-468 cells. These findings indicate that EGF-induced EMT in breast cancer cells may be associated with major alterations in ER calcium homeostasis.

Edge-weighting of gene expression graphs

In recent years, considerable research efforts have been directed to micro-array technologies and their role in providing simultaneous information on expression profiles for thousands of genes. These data, when subjected to clustering and classification procedures, can assist in identifying patterns and providing insight on biological processes. To understand the properties of complex gene expression datasets, graphical representations can be used. Intuitively, the data can be represented in terms of a bipartite graph, with weighted edges corresponding to gene-sample node couples in the dataset. Biologically meaningful subgraphs can be sought, but performance can be influenced both by the search algorithm, and, by the graph-weighting scheme and both merit rigorous investigation. In this paper, we focus on edge-weighting schemes for bipartite graphical representation of gene expression. Two novel methods are presented: the first is based on empirical evidence; the second on a geometric distribution. The schemes are compared for several real datasets, assessing efficiency of performance based on four essential properties: robustness to noise and missing values, discrimination, parameter influence on scheme efficiency and reusability. Recommendations and limitations are briefly discussed. Keywords: Edge-weighting; weighted graphs; gene expression; bi-clustering

Design, construction, and analysis of cell line arrays and tissue microarrays for gene expression analysis

Cell line array (CMA) and tissue microarray (TMA) technologies are high-throughput methods for analysing both the abundance and distribution of gene expression in a panel of cell lines or multiple tissue specimens in an efficient and cost-effective manner. The process is based on Kononen's method of extracting a cylindrical core of paraffin-embedded donor tissue and inserting it into a recipient paraffin block. Donor tissue from surgically resected paraffin-embedded tissue blocks, frozen needle biopsies or cell line pellets can all be arrayed in the recipient block. The representative area of interest is identified and circled on a haematoxylin and eosin (H&E)-stained section of the donor block. Using a predesigned map showing a precise spacing pattern, a high density array of up to 1,000 cores of cell pellets and/or donor tissue can be embedded into the recipient block using a tissue arrayer from Beecher Instruments. Depending on the depth of the cell line/tissue removed from the donor block 100-300 consecutive sections can be cut from each CMA/TMA block. Sections can be stained for in situ detection of protein, DNA or RNA targets using immunohistochemistry (IHC), fluorescent in situ hybridisation (FISH) or mRNA in situ hybridisation (RNA-ISH), respectively. This chapter provides detailed methods for CMA/TMA design, construction and analysis with in-depth notes on all technical aspects including tips to deal with common pitfalls the user may encounter. © Springer Science+Business Media, LLC 2011.

Analysis of differential gene expression based on Bayesian estimation of variance

Gene expression is arguably the most important indicator of biological function. Thus identifying differentially expressed genes is one of the main aims of high throughout studies that use microarray and RNAseq platforms to study deregulated cellular pathways. There are many tools for analysing differentia gene expression from transciptomic datasets. The major challenge of this topic is to estimate gene expression variance due to the high amount of ‘background noise’ that is generated from biological equipment and the lack of biological replicates. Bayesian inference has been widely used in the bioinformatics field. In this work, we reveal that the prior knowledge employed in the Bayesian framework also helps to improve the accuracy of differential gene expression analysis when using a small number of replicates. We have developed a differential analysis tool that uses Bayesian estimation of the variance of gene expression for use with small numbers of biological replicates. Our method is more consistent when compared to the widely used cyber-t tool that successfully introduced the Bayesian framework to differential analysis. We also provide a user-friendly web based Graphic User Interface for biologists to use with microarray and RNAseq data. Bayesian inference can compensate for the instability of variance caused when using a small number of biological replicates by using pseudo replicates as prior knowledge. We also show that our new strategy to select pseudo replicates will improve the performance of the analysis. - See more at: http://www.eurekaselect.com/node/138761/article#sthash.VeK9xl5k.dpuf

An assessment of MMP and TIMP gene expression in cell lines and stroma : tumour differences in microdissected breast cancer biopsies

To examine matrix metalloproteinase (MMP) and tissue inhibitor of metalloproteinases (TIMP) mRNA levels in archival breast cancer biopsies, we employed microdissection to separate tumour tissue from the surrounding breast tissue, or stroma and RT-PCR to determine gross qualitative and small quantitative differences in the patterns of expression. In this study, a significant correlation (p < 0.05, by Mann-Whitney U analysis) between TIMP-2 expression and lymph node involvement was identified, while MMP-11 and TIMP-1 expression patterning also significantly (p < 0.05) differed between those tumours showing calcification and those that did not. When compared by Spearmans’ ρ correlation analysis, a significant association (p < 0.05, ρ = 0.404) was identified in the pattern of MMP-2 and MMP-9 gene expression. In this study, the use of microdissection and a systematic strategy of RT-PCR analysis have allowed us to investigate localized MMP and MMP inhibitor expression within breast tumours. We have identified patterns of gene expression that may further reveal aspects of breast carcinogenesis, and a robust method for examining changes in clinically important genes using archival biopsies and across stroma-tumour boundaries.

Effect of 5′UTR introns on gene expression in Arabidopsis thaliana

Background The majority of introns in gene transcripts are found within the coding sequences (CDSs). A small but significant fraction of introns are also found to reside within the untranslated regions (5′UTRs and 3′UTRs) of expressed sequences. Alignment of the whole genome and expressed sequence tags (ESTs) of the model plant Arabidopsis thaliana has identified introns residing in both coding and non-coding regions of the genome. Results A bioinformatic analysis revealed some interesting observations: (1) the density of introns in 5′UTRs is similar to that in CDSs but much higher than that in 3′UTRs; (2) the 5′UTR introns are preferentially located close to the initiating ATG codon; (3) introns in the 5′UTRs are, on average, longer than introns in the CDSs and 3′UTRs; and (4) 5′UTR introns have a different nucleotide composition to that of CDs and 3′UTR introns. Furthermore, we show that the 5′UTR intron of the A. thaliana EFIα-A3 gene affects the gene expression and the size of the 5′UTR intron influences the level of gene expression. Conclusion Introns within the 5′UTR show specific features that distinguish them from introns that reside within the coding sequence and the 3′UTR. In the EFIα-A3 gene, the presence of a long intron in the 5′UTR is sufficient to enhance gene expression in plants in a size dependent manner.

Analysis, characterisation and expression of gill-expressed carbonic anhydrase genes in the freshwater crayfish Cherax quadricarinatus

Changes in water quality parameters such as pH and salinity can have a significant effect on productivity of aquaculture species. Similarly, relative osmotic pressure influences various physiological processes and regulates expression of a number of osmoregulatory genes. Among those, carbonic anhydrase (CA) plays a key role in systemic acid–base balance and ion regulation. Redclaw crayfish (Cherax quadricarinatus) are unique in their ability to thrive in environments with naturally varied pH levels, suggesting unique adaptation to pH stress. To date, however, no studies have focused on identification and characterisation of CA or other osmoregulatory genes in C. quadricarinatus. Here, we analysed the redclaw gill transcriptome and characterized CA genes along with a number of other key osmoregulatory genes that were identified in the transcriptome. We also examined patterns of gene expression of these CA genes when exposed to three pH treatments. In total, 72,382,710 paired end Illumina reads were assembled into 36,128 contigs with an average length of 800 bp. Approximately 37% of contigs received significant BLAST hits and 22% were assigned gene ontology terms. Three full length CA isoforms; cytoplasmic CA (ChqCAc), glycosyl-phosphatidylinositol-linked CA (ChqCAg), and β-CA (ChqCA-beta) as well as two partial CA gene sequences were identified. Both partial CA genes showed high similarity to ChqCAg and appeared to be duplicated from the ChqCAg. Full length coding sequences of Na+/K+-ATPase, V-type H+-ATPase, sarcoplasmic Ca+-ATPase, arginine kinase, calreticulin and Cl− channel protein 2 were also identified. Only the ChqCAc gene showed significant differences in expression across the three pH treatments. These data provide valuable information on the gill expressed CA genes and their expression patterns in freshwater crayfish. Overall our data suggest an important role for the ChqCAc gene in response to changes in pH and in systemic acid–base balance in freshwater crayfish.

Choose wisely: Network, ontology and annotation resources for the analysis of Staphylococcus aureus omics data

Staphylococcus aureus (S. aureus) is a prominent human and livestock pathogen investigated widely using omic technologies. Critically, due to availability, low visibility or scattered resources, robust network and statistical contextualisation of the resulting data is generally under-represented. Here, we present novel meta-analyses of freely-accessible molecular network and gene ontology annotation information resources for S. aureus omics data interpretation. Furthermore, through the application of the gene ontology annotation resources we demonstrate their value and ability (or lack-there-of) to summarise and statistically interpret the emergent properties of gene expression and protein abundance changes using publically available data. This analysis provides simple metrics for network selection and demonstrates the availability and impact that gene ontology annotation selection can have on the contextualisation of bacterial omics data.

Genetic association and gene expression studies suggest that genetic variants in the SYNE1 and TNF genes are related to menstrual migraine

BACKGROUND: Menstrual migraine (MM) encompasses pure menstrual migraine (PMM) and menstrually-related migraine (MRM). This study was aimed at investigating genetic variants that are potentially related to MM, specifically undertaking genotyping and mRNA expression analysis of the ESR1, PGR, SYNE1 and TNF genes in MM cases and non-migraine controls. METHODS: A total of 37 variants distributed across 14 genes were genotyped in 437 DNA samples (282 cases and 155 controls). In addition levels of gene expression were determined in 74 cDNA samples (41 cases and 33 controls). Association and correlation analysis were performed using Plink and RStudio. RESULTS: SNPs rs3093664 and rs9371601 in TNF and SYNE1 genes respectively, were significantly associated with migraine in the MM population (p = 0.008; p = 0.009 respectively). Analysis of qPCR results found no significant difference in levels of gene expression between cases and controls. However, we found a significant correlation between the expression of ESR1 and SYNE1, ESR1 and PGR and TNF and SYNE1 in samples taken during the follicular phase of the menstrual cycle. CONCLUSIONS: Our results show that SNPs rs9371601 and rs3093664 in the SYNE1 and TNF genes respectively, are associated with MM. The present study also provides strong evidence to support the correlation of ESR1, PGR, SYNE1 and TNF gene expression in MM.