On entropy and information in gene interaction networks.

MOTIVATION: Modern biological experiments often produce candidate lists of genes presumably related to the studied phenotype. One can ask if the gene list as a whole makes sense in the context of existing knowledge: Are the genes in the list reasonably related to each other or do they look like a random assembly? There are also situations when one wants to know if two or more gene sets are closely related. Gene enrichment tests based on counting the number of genes two sets have in common are adequate if we presume that two genes are related only when they are in fact identical. If by related we mean well connected in the interaction network space, we need a new measure of relatedness for gene sets. RESULTS: We derive entropy, interaction information and mutual information for gene sets on interaction networks, starting from a simple phenomenological model of a living cell. Formally, the model describes a set of interacting linear harmonic oscillators in thermal equilibrium. Because the energy function is a quadratic form of the degrees of freedom, entropy and all other derived information quantities can be calculated exactly. We apply these concepts to estimate the probability that genes from several independent genome-wide association studies are not mutually informative; to estimate the probability that two disjoint canonical metabolic pathways are not mutually informative; and to infer relationships among human diseases based on their gene signatures. We show that the present approach is able to predict observationally validated relationships not detectable by gene enrichment methods. The converse is also true; the two methods are therefore complementary. AVAILABILITY AND IMPLEMENTATION: The functions defined in this paper are available in an R package, gsia, available for download at https://github.com/ucsd-ccbb/gsia.

Teleost fish (Solea solea): a novel model for ecotoxicological assay of contaminated sediments.

Chemical analysis of sediment is not indicative of the downstream biological effects on aquatic organisms. In this study, the biological effects of sediment were examined using: Teleost fish (Solea solea), Artemia and rotifers. Although chemicals levels were below the limits permissible by Italian law, S. solea juveniles exposed to sediment (0.3%, w/v) for 96 h, revealed significant induction in the expression levels of HSP70, ERα, TRα, RXRα, PPARα, PPARß, CYP4501A1 and CYP3A mRNAs, suggesting the utility of this species as a novel biosensor. The bio-toxicity of the sediment was further validated by exposing Artemia and rotifers to concentrations of elutriate (derived from the sediment) from 10 to 100% (v/v) (with a 50% mortality rate). These results suggest that sediment defined as moderately contaminated, solely on the basis of the chemical profile, may in fact cause harmful effects to aquatic organisms. This study highlights the need for biological approaches in the establishment of sediment toxicity levels.

Biological effects of marine contaminated sediments on Sparus aurata juveniles.

Chemical analysis of the compounds present in sediment, although informative, often is not indicative of the downstream biological effects that these contaminants exert on resident aquatic organisms. More direct molecular methods are needed to determine if marine life is affected by exposure to sediments. In this study, we used an aquatic multi-species microarray and q-PCR to investigate the effects on gene expression in juvenile sea bream (Sparus aurata) of two contaminated sediments defined as sediment 1 and 2, respectively, from marine areas in Northern Italy. Both sediments affected gene expression as evidenced by aquatic multi-species microarray analysis and q-PCR. Exposure of S. aurata juveniles to sediment 1 and sediment 2 altered expression of genes that are biomarkers for endocrine disruption. There were differences between the effects of sediment 1 and sediment 2 on gene expression in S. aurata juveniles indicating that the chemicals in the two sediments had different physiological targets. These results suggest that the classification of sediment solely on the basis of specific chemical profiles is inadequate, and not a true indicator of its potential to cause harmful effects. Our data also indicate that integration of physiochemical analysis and bioassays for monitoring the downstream harmful effects on aquatic organisms are required to gain a complete understanding of the effects of sediment on aquatic life.

Microarray truths and consequences.

For many, analysis of a microarray experiment starts with a spreadsheet of expression levels. While great attention is duly paid to RNA extraction, preparation and hybridization, relatively little care is devoted to extraction of expression levels from the fluorescent image. By delegating this step to a click of the mouse the exact extraction process is masked and researchers may be unwittingly compromising their data. In this review, we describe the most common mistakes committed on the path from the image to the spreadsheet and their impact on data quality. Remedies are further proposed for most of the popular microarray platforms in use today.

Statistical analysis of high-density oligonucleotide arrays: a multiplicative noise model.

MOTIVATION: High-density oligonucleotide arrays (GeneChip, Affymetrix, Santa Clara, CA) have become a standard research tool in many areas of biomedical research. They quantitatively monitor the expression of thousands of genes simultaneously by measuring fluorescence from gene-specific targets or probes. The relationship between signal intensities and transcript abundance as well as normalization issues have been the focus of much recent attention (Hill et al., 2001; Chudin et al., 2002; Naef et al., 2002a). It is desirable that a researcher has the best possible analytical tools to make the most of the information that this powerful technology has to offer. At present there are three analytical methods available: the newly released Affymetrix Microarray Suite 5.0 (AMS) software that accompanies the GeneChip product, the method of Li and Wong (LW; Li and Wong, 2001), and the method of Naef et al. (FN; Naef et al., 2001). The AMS method is tailored for analysis of a single microarray, and can therefore be used with any experimental design. The LW method on the other hand depends on a large number of microarrays in an experiment and cannot be used for an isolated microarray, and the FN method is particular to paired microarrays, such as resulting from an experiment in which each 'treatment' sample has a corresponding 'control' sample. Our focus is on analysis of experiments in which there is a series of samples. In this case only the AMS, LW, and the method described in this paper can be used. The present method is model-based, like the LW method, but assumes multiplicative not additive noise, and employs elimination of statistically significant outliers for improved results. Unlike LW and AMS, we do not assume probe-specific background (measured by the so-called mismatch probes). Rather, we assume uniform background, whose level is estimated using both the mismatch and perfect match probe intensities. RESULTS: We present a new method for GeneChip analysis, based on a statistical model with multiplicative noise. We demonstrated that this method yields results superior to those obtained by the Affymetrix Microarray Suite 5.0 software and to those obtained by the model-based method of Li and Wong (Li and Wong, 2001). The present method eliminates the hard-to-interpret negative expression indices, and the binary 'presence' calls (present or absent) are replaced by the statistical significance (p-value) of gene expression. We have found that thresholding the p-values at the (0.1)(16)-level produces about the same number of 'present' calls as the AMS software. By testing our method on a pair of replicate GeneChips (hybridized with the same cRNA), we found that 95.6% of data points lie within the 1.25-fold interval. In other words, our method had a 4.4% type I error rate at the 1.25-fold level. The error rate of the LW method was 15%, and that of the AMS method was 29%. There were no points outside the 2-fold interval with the present method. Analysis of variance (ANOVA) of another experiment with multiple replicates shows that this reduction of variance is not accompanied by a corresponding reduction of signal. On the contrary, the signal-to-noise ratio (as measured by the distribution of F-statistics) of the present method is on average 3.4-times better than that of AMS, and 1.4-times better than that of Li and Wong.

Extracting transcriptional events from temporal gene expression patterns during Dictyostelium development.

MOTIVATION: The DNA microarray technology can generate a large amount of data describing the time-course of gene expression. These data, when properly interpreted, can yield a great deal of information concerning differential gene expression during development. Much current effort in bioinformatics has been devoted to the analysis of gene expression data, usually via some 'clustering analysis' on the raw data in some abstract high dimensional space. Here, we describe a method where we first 'process' the raw time-course data using a simple biologically based kinetic model of gene expression. This allows us to reduce the vast data to a few vital attributes characterizing each expression profile, e.g. the times of the onset and cessation of the expression of the developmentally regulated genes. These vital attributes can then be trivially clustered by visual inspection to reveal biologically significant effects. RESULTS: We have applied this approach to microarray expression data from samples isolated every 2 h throughout the 24 h developmental program of Dictyostelium discoideum. mRNA accumulation patterns for 50 developmental genes were found to fit the kinetic model with a p-value of 0.05 or better. Transcription of these genes appears to be initiated in bursts at well-defined periods during development, in a manner suggestive of a dependent sequence. This approach can be applied to analyses of other temporal gene expression patterns, including those of the cell cycle.

Expression patterns of cell-type-specific genes in Dictyostelium.

Cell-type specific genes were recognized by interrogating microarrays carrying Dictyostelium gene fragments with probes prepared from fractions enriched in prestalk and prespore cells. Cell-type specific accumulation of mRNA from 17 newly identified genes was confirmed by Northern analyses. DNA microarrays carrying 690 targets were used to determine expression profiles during development. The profiles were fit to a biologically based kinetic equation to extract the times of transcription onset and cessation. Although the majority of the genes that were cell-type enriched at the slug stage were first expressed as the prespore and prestalk cells sorted out in aggregates, some were found to be expressed earlier before the cells had even aggregated. These early genes may have been initially expressed in all cells and then preferentially turned over in one or the other cell type. Alternatively, cell type divergence may start soon after the initiation of development.

Temporal gene regulation during HIV-1 infection of human CD4+ T cells.

CD4(+) T-cell depletion is a characteristic of human immunodeficiency virus type 1 (HIV-1) infection. In this study, modulation of mRNA expression of 6800 genes was monitored simultaneously at eight time points in a CD4(+) T-cell line (CEM-GFP) during HIV infection. The responses to infection included: (1) >30% decrease at 72 h after infection in overall host-cell production of monitored mRNA synthesis, with the replacement of host-cell mRNA by viral mRNA, (2) suppression of the expression of selected mitochondrial and DNA repair gene transcripts, (3) increased expression of the proapoptotic gene and its gene p53-induced product Bax, and (4) activation of caspases 2, 3, and 9. The intense HIV-1 transcription resulted in the repression of much cellular RNA expression and was associated with the induction of apoptosis of infected cells but not bystander cells. This choreographed host gene response indicated that the subversion of the cell transcriptional machinery for the purpose of HIV-1 replication is akin to genotoxic stress and represents a major factor leading to HIV-induced apoptosis.

Percolation clustering: a novel approach to the clustering of gene expression patterns in Dictyostelium development.

We present a novel approach to the clustering of gene expression patterns based on the mutual connectivity of the patterns. Unlike certain widely used methods (e.g., self-organizing maps and K-means) which essentially force gene expression data into a fixed number of predetermined clustering structures, our approach aims to reveal the natural tendency of the data to cluster, in analogy to the physical phenomenon of percolation. The approach is probabilistic in nature, and as such accommodates the possibility that one gene participates in multiple clusters. The result is cast in terms of the connectivity of each gene to a certain number of (significant) clusters. A computationally efficient algorithm is developed to implement our approach. Performance of the method is illustrated by clustering both constructed data and gene expression data obtained from Dictyostelium development.

Analytical approach to time lag in binary nucleation.

We present an analytical formula for the time required to establish steady state in a nucleating binary system. To test our solution, we evaluate the time lag for a range of activities of both components at the vapor-liquid transition, and show that our result is in much better agreement with a purely numerical simulation than other available analytical formulas, which overestimate the time lag by factors of from 2 to 200.