Pathway level analysis of gene expression using singular value decomposition

Background  

A promising direction in the analysis of gene expression focuses on the changes in expression of specific predefined sets of genes that are known in advance to be related (e.g., genes coding for proteins involved in cellular pathways or complexes). Such an analysis can reveal features that are not easily visible from the variations in the individual genes and can lead to a picture of expression that is more biologically transparent and accessible to interpretation. In this article, we present a new method of this kind that operates by quantifying the level of 'activity' of each pathway in different samples. The activity levels, which are derived from singular value decompositions, form the basis for statistical comparisons and other applications.     

Test method development to evaluate hot,humid air decontamination of materials contaminated with Bacillus anthracis ∆Sterne and B. thuringiensis Al Hakam spores

Aims

To develop test methods and evaluate the survival of Bacillus anthracis ?Sterne and Bacillus thuringiensis Al Hakam spores after exposure to hot, humid air.

Methods and Results

Spores (>7 logs) of both strains were dried on six different test materials. Response surface methodology was employed to identify the limits of spore survival at optimal test combinations of temperature (60, 68, 77°C), relative humidity (60, 75, 90%) and time (1, 4, 7 days). No spores survived the harshest test run (77°C, 90% r.h., 7 days), while > 6·5 logs of spores survived the mildest test run (60°C, 60% r.h., 1 day). Spores of both strains inoculated on nylon webbing and polypropylene had greater survival rates at 68°C, 75% r.h., 4 days than spores on other materials. Electron microscopy showed no obvious physical damage to spores using hot, humid air, which contrasted with pH‐adjusted bleach decontamination.

Conclusions

Test methods were developed to show that hot, humid air effectively inactivates B. anthracis ?Sterne and B. thuringiensis Al Hakam spores with similar kinetics.

Significance and Impact of the Study

Hot, humid air is a potential alternative to conventional chemical decontamination.     

Sequential interim analyses of survival data in DNA microarray experiments

Background  

Discovery of biomarkers that are correlated with therapy response and thus with survival is an important goal of medical research on severe diseases, e.g. cancer. Frequently, microarray studies are performed to identify genes of which the expression levels in pretherapeutic tissue samples are correlated to survival times of patients. Typically, such a study can take several years until the full planned sample size is available.     

Improving the statistical detection of regulated genes from microarray data using intensity-based variance estimation

Background  

Gene microarray technology provides the ability to study the regulation of thousands of genes simultaneously, but its potential is limited without an estimate of the statistical significance of the observed changes in gene expression. Due to the large number of genes being tested and the comparatively small number of array replicates (e.g., N = 3), standard statistical methods such as the Student's t-test fail to produce reliable results. Two other statistical approaches commonly used to improve significance estimates are a penalized t-test and a Z-test using intensity-dependent variance estimates.     

Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein

Background  

A large number of papers have been published on analysis of microarray data with particular emphasis on normalization of data, detection of differentially expressed genes, clustering of genes and regulatory network. On other hand there are only few studies on relation between expression level and composition of nucleotide/protein sequence, using expression data. There is a need to understand why particular genes/proteins express more in particular conditions. In this study, we analyze 3468 genes of Saccharomyces cerevisiae obtained from Holstege et al., (1998) to understand the relationship between expression level and amino acid composition.     

Improving signal intensities for genes with low-expression on oligonucleotide microarrays

Background  

DNA microarrays using long oligonucleotide probes are widely used to evaluate gene expression in biological samples. These oligonucleotides are pre-synthesized and sequence-optimized to represent specific genes with minimal cross-hybridization to homologous genes. Probe length and concentration are critical factors for signal sensitivity, particularly when genes with various expression levels are being tested. We evaluated the effects of oligonucleotide probe length and concentration on signal intensity measurements of the expression levels of genes in a target sample.     

Extracting the abstraction pyramid from complex networks

Background  

At present, the organization of system modules is typically limited to either a multilevel hierarchy that describes the "vertical" relationships between modules at different levels (e.g., module A at level two is included in module B at level one), or a single-level graph that represents the "horizontal" relationships among modules (e.g., genetic interactions between module A and module B). Both types of organizations fail to provide a broader and deeper view of the complex systems that arise from an integration of vertical and horizontal relationships.     

A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism

Background  

Graphical models (e.g., Bayesian networks) have been used frequently to describe complex interaction patterns and dependent structures among genes and other phenotypes. Estimation of such networks has been a challenging problem when the genes considered greatly outnumber the samples, and the situation is exacerbated when one wishes to consider the impact of polymorphisms (SNPs) in genes.     

Modulation of intracellular calcium and proliferative activity of invertebrate and vertebrate cells by ethylene

Background  

Ethylene is a widely distributed alkene product which is formed enzymatically (e.g., in plants) or by photochemical reactions (e.g., in the upper oceanic layers from dissolved organic carbon). This gaseous compound was recently found to induce in cells from the marine sponge Suberites domuncula, an increase in intracellular Ca²⁺ level ([Ca²⁺]_i) and an upregulation of the expression of two genes, the potential ethylene-responsive gene, SDERR, and a Ca²⁺/calmodulin-dependent protein kinase.     

Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR

Background  

Control genes, which are often referred to as housekeeping genes, are frequently used to normalise mRNA levels between different samples. However, the expression level of these genes may vary among tissues or cells and may change under certain circumstances. Thus, the selection of housekeeping genes is critical for gene expression studies. To address this issue, 7 candidate housekeeping genes including several commonly used ones were investigated in isolated human reticulocytes. For this, a simple ΔCt approach was employed by comparing relative expression of 'pairs of genes' within each sample. On this basis, stability of the candidate housekeeping genes was ranked according to repeatability of the gene expression differences among 31 samples.     

Ultraviolet-A radiation induces changes in cyclin G gene expression in mouse melanoma B16-F1 cells

Background  

We have previously shown that ultraviolet-A (UVA) radiation enhances metastatic lung colonization capacity of B16-F1 melanoma cells. The aim of this study was to examine changes in expression profile of genes in mouse melanoma B16-F1 cells exposed to UVA radiation.     

Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments

Background  

The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the variance of gene expression measurements into the general empirical Bayes framework.     

Modeling Sage data with a truncated gamma-Poisson model

Background  

Serial Analysis of Gene Expressions (SAGE) produces gene expression measurements on a discrete scale, due to the finite number of molecules in the sample. This means that part of the variance in SAGE data should be understood as the sampling error in a binomial or Poisson distribution, whereas other variance sources, in particular biological variance, should be modeled using a continuous distribution function, i.e. a prior on the intensity of the Poisson distribution. One challenge is that such a model predicts a large number of genes with zero counts, which cannot be observed.     

Feature selection environment for genomic applications

Background  

Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e.g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application.     

Leukocyte infiltrate in gastrointestinal adenocarcinomas is strongly associated with tumor microsatellite instability but not with tumor immunogenicity

Purpose  

To analyze the correlation of genomic instability with leukocyte infiltrate in gastrointestinal carcinomas (GIACs) and with tumor immunogenicity, e.g., HLA class I cell surface expression defects and galectin-3 and PDL-1 expression.