Biomedical subjects

Pawel Herzyk

Publications and source records attributed to Pawel Herzyk.

9 recordsLinked to original sources

PubMed2006

Phenotypic responses to mechanical stress in fibroblasts from tendon, cornea and skin.

Primary fibroblasts isolated from foetal mouse cornea, skin and tendon were subjected to linear shear stress and analysed for morphological parameters and by microarray, as compared with unstimulated controls. Approx. 350 genes were either up- or down-regulated by a significant amount, with 51 of these being common to all three cell types. Approx. 50% of altered genes in tendon and cornea fibroblasts were changed in common with one of the other cell types, with the remaining approx. 50% being specific to tendon or cornea. In skin fibroblasts, however, less than 25% of genes whose transcription was altered were specific only to skin. The functional spectrum of genes that were up- or down-regulated was diverse, with apparent house-keeping genes forming the major category of up-regulated genes. However, a significant number of genes associated with cell adhesion, extracellular matrix and matrix remodelling, as well as cytokines and other signalling factors, were also affected. Somewhat surprisingly, in these latter categories the trend was towards a reduction in mRNA levels. Verification of the mRNA quantity of a subset of these genes was performed by reverse transcriptase PCR and was found to be in agreement with the microarray analysis. These findings provide the first in-depth analysis of phenotypic differences between fibroblast cells from different tissue sources and reveal the responses of these cells to mechanical stress.

Animals↗

PubMed2005

A UV-B-specific signaling component orchestrates plant UV protection.

UV-B radiation in sunlight has diverse effects on humans, animals, plants, and microorganisms. UV-B can cause damage to molecules and cells, and consequently organisms need to protect against and repair UV damage to survive in sunlight. In plants, low nondamaging levels of UV-B stimulate transcription of genes involved in UV-protective responses. However, remarkably little is known about the underlying mechanisms of UV-B perception and signal transduction. Here we report that Arabidopsis UV RESISTANCE LOCUS 8 (UVR8) is a UV-B-specific signaling component that orchestrates expression of a range of genes with vital UV-protective functions. Moreover, we show that UVR8 regulates expression of the transcription factor HY5 specifically when the plant is exposed to UV-B. We demonstrate that HY5 is a key effector of the UVR8 pathway, and that it is required for survival under UV-B radiation. UVR8 has sequence similarity to the eukaryotic guanine nucleotide exchange factor RCC1, but we found that it has little exchange activity. However, UVR8, like RCC1, is located principally in the nucleus and associates with chromatin via histones. Chromatin immunoprecipitation showed that UVR8 associates with chromatin in the HY5 promoter region, providing a mechanistic basis for its involvement in regulating transcription. We conclude that UVR8 defines a UV-B-specific signaling pathway in plants that orchestrates the protective gene expression responses to UV-B required for plant survival in sunlight.

Arabidopsis↗

PubMed2005

Biological master games: using biologists' reasoning to guide algorithm development for integrated functional genomics.

We review some powerful new algorithms that build on the intuitive biological interpretation techniques for statistical analysis of functional genomics experiments. Although they were originally designed for transcriptomics, we argue that these algorithms are applicable to any type of -omics study (transcriptomics, proteomics, metabolomics). Rank Products (RP), a strictly non-parametric test statistic to detect differentially regulated elements (genes, proteins, metabolites) in genome-wide screens. RP is particularly powerful for noisy data and low numbers of replicates and makes full use of the availability of a large number of parallel measurements that is typical of modern large-scale experiments. Iterative Group Analysis (iGA), a statistical method that makes the transition from regulated single elements to significant classes of elements, and thus provides an automatic functional annotation of an experiment. Graph-based iGA (GiGA), an extension of iGA that combines experimental data with a broad variety of biological annotations to highlight physiologically relevant regions in a given "evidence graph" (e.g., metabolic networks, signaling pathway diagrams, protein interaction maps). The sequential application of these techniques yields an increasingly abstract interpretation of experimental data that is at the same time quantitative, statistically rigorous, and biologically significant. The results can be used either as helpful tools to guide data visualization and exploration, or as the input for downstream computational applications in a systems biology framework.

Algorithms↗

PubMed2005

Rank-based methods as a non-parametric alternative of the T-statistic for the analysis of biological microarray data.

We have recently introduced a rank-based test statistic, RankProducts (RP), as a new non-parametric method for detecting differentially expressed genes in microarray experiments. It has been shown to generate surprisingly good results with biological datasets. The basis for this performance and the limits of the method are, however, little understood. Here we explore the performance of such rank-based approaches under a variety of conditions using simulated microarray data, and compare it with classical Wilcoxon rank sums and t-statistics, which form the basis of most alternative differential gene expression detection techniques. We show that for realistic simulated microarray datasets, RP is more powerful and accurate for sorting genes by differential expression than t-statistics or Wilcoxon rank sums - in particular for replicate numbers below 10, which are most commonly used in biological experiments. Its relative performance is particularly strong when the data are contaminated by non-normal random noise or when the samples are very inhomogenous, e.g. because they come from different time points or contain a mixture of affected and unaffected cells. However, RP assumes equal measurement variance for all genes and tends to give overly optimistic p-values when this assumption is violated. It is therefore essential that proper variance stabilizing normalization is performed on the data before calculating the RP values. Where this is impossible, another rank-based variant of RP (average ranks) provides a useful alternative with very similar overall performance. The Perl scripts implementing the simulation and evaluation are available upon request. Implementations of the RP method are available for download from the authors website (http://www.brc.dcs.gla.ac.uk/glama).

Algorithms↗

PubMed2004

Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments.

One of the main objectives in the analysis of microarray experiments is the identification of genes that are differentially expressed under two experimental conditions. This task is complicated by the noisiness of the data and the large number of genes that are examined simultaneously. Here, we present a novel technique for identifying differentially expressed genes that does not originate from a sophisticated statistical model but rather from an analysis of biological reasoning. The new technique, which is based on calculating rank products (RP) from replicate experiments, is fast and simple. At the same time, it provides a straightforward and statistically stringent way to determine the significance level for each gene and allows for the flexible control of the false-detection rate and familywise error rate in the multiple testing situation of a microarray experiment. We use the RP technique on three biological data sets and show that in each case it performs more reliably and consistently than the non-parametric t-test variant implemented in Tusher et al.'s significance analysis of microarrays (SAM). We also show that the RP results are reliable in highly noisy data. An analysis of the physiological function of the identified genes indicates that the RP approach is powerful for identifying biologically relevant expression changes. In addition, using RP can lead to a sharp reduction in the number of replicate experiments needed to obtain reproducible results.

Acute Disease↗

PubMed2004

Function-informed transcriptome analysis of Drosophila renal tubule.

BACKGROUND: Comprehensive, tissue-specific, microarray analysis is a potent tool for the identification of tightly defined expression patterns that might be missed in whole-organism scans. We applied such an analysis to Drosophila melanogaster Malpighian (renal) tubule, a defined differentiated tissue. RESULTS: The transcriptome of the D. melanogaster Malpighian tubule is highly reproducible and significantly different from that obtained from whole-organism arrays. More than 200 genes are more than 10-fold enriched and over 1,000 are significantly enriched. Of the top 200 genes, only 18 have previously been named, and only 45% have even estimates of function. In addition, 30 transcription factors, not previously implicated in tubule development, are shown to be enriched in adult tubule, and their expression patterns respect precisely the domains and cell types previously identified by enhancer trapping. Of Drosophila genes with close human disease homologs, 50 are enriched threefold or more, and eight enriched 10-fold or more, in tubule. Intriguingly, several of these diseases have human renal phenotypes, implying close conservation of renal function across 400 million years of divergent evolution. CONCLUSIONS: From those genes that are identifiable, a radically new view of the function of the tubule, emphasizing solute transport rather than fluid secretion, can be obtained. The results illustrate the phenotype gap: historically, the effort expended on a model organism has tended to concentrate on a relatively small set of processes, rather than on the spread of genes in the genome.

Animals↗

PubMed2004

Graph-based iterative Group Analysis enhances microarray interpretation.

BACKGROUND: One of the most time-consuming tasks after performing a gene expression experiment is the biological interpretation of the results by identifying physiologically important associations between the differentially expressed genes. A large part of the relevant functional evidence can be represented in the form of graphs, e.g. metabolic and signaling pathways, protein interaction maps, shared GeneOntology annotations, or literature co-citation relations. Such graphs are easily constructed from available genome annotation data. The problem of biological interpretation can then be described as identifying the subgraphs showing the most significant patterns of gene expression. We applied a graph-based extension of our iterative Group Analysis (iGA) approach to obtain a statistically rigorous identification of the subgraphs of interest in any evidence graph. RESULTS: We validated the Graph-based iterative Group Analysis (GiGA) by applying it to the classic yeast diauxic shift experiment of DeRisi et al., using GeneOntology and metabolic network information. GiGA reliably identified and summarized all the biological processes discussed in the original publication. Visualization of the detected subgraphs allowed the convenient exploration of the results. The method also identified several processes that were not presented in the original paper but are of obvious relevance to the yeast starvation response. CONCLUSIONS: GiGA provides a fast and flexible delimitation of the most interesting areas in a microarray experiment, and leads to a considerable speed-up and improvement of the interpretation process.

Algorithms↗

PubMed2004

Iterative Group Analysis (iGA): a simple tool to enhance sensitivity and facilitate interpretation of microarray experiments.

BACKGROUND: The biological interpretation of even a simple microarray experiment can be a challenging and highly complex task. Here we present a new method (Iterative Group Analysis) to facilitate, improve, and accelerate this process. RESULTS: Our Iterative Group Analysis approach (iGA) uses elementary statistics to identify those functional classes of genes that are significantly changed in an experiment and at the same time determines which of the class members are most likely to be differentially expressed. iGA does not require that all members of a class change and is therefore robust against imperfect class assignments, which can be derived from public sources (e.g. GeneOntologies) or automated processes (e.g. key word extraction from gene names). In contrast to previous non-iterative approaches, iGA does not depend on the availability of fixed lists of differentially expressed genes, and thus can be used to increase the sensitivity of gene detection especially in very noisy or small data sets. In the extreme, iGA can even produce statistically meaningful results without any experimental replication. The automated functional annotation provided by iGA greatly reduces the complexity of microarray results and facilitates the interpretation process. In addition, iGA can be used as a fast and efficient tool for the platform-independent comparison of a microarray experiment to the vast number of published results, automatically highlighting shared genes of potential interest. CONCLUSIONS: By applying iGA to a wide variety of data from diverse organisms and platforms we show that this approach enhances and accelerates the interpretation of microarray experiments.

Animals↗

PubMed2003

Transcriptome analysis of root transporters reveals participation of multiple gene families in the response to cation stress.

Plant nutrition critically depends on the activity of membrane transporters that translocate minerals from the soil into the plant and are responsible for their intra- and intercellular distribution. Most plant membrane transporters are encoded by multigene families whose members often exhibit overlapping expression patterns and a high degree of sequence homology. Furthermore, many inorganic nutrients are transported by more than one transporter family. These considerations, coupled with a large number of so-far non-annotated putative transporter genes, hamper our progress in understanding how the activity of specific transporters is integrated into a response to fluctuating conditions. We designed an oligonucleotide microarray representing 1096 Arabidopsis transporter genes and analysed the root transporter transcriptome over a 96-h period with respect to 80 mM NaCl, K+ starvation and Ca2+ starvation. Our data show that cation stress led to changes in transcript level of many genes across most transporter gene families. Analysis of transcriptionally modulated genes across all functional groups of transporters revealed families such as V-type ATPases and aquaporins that responded to all treatments, and families - which included putative non-selective cation channels for the NaCl treatment and metal transporters for Ca2+ starvation conditions - that responded to specific ionic environments. Several gene families including primary pumps, antiporters and aquaporins were analysed in detail with respect to the mRNA levels of different isoforms during ion stress. Cluster analysis allowed identification of distinct expression profiles, and several novel putative regulatory motifs were discovered within sets of co-expressed genes.

Arabidopsis↗