Genes encoding novel secreted and transmembrane proteins are temporally and spatially regulated during Drosophila melanogaster embryogenesis

Background  

Morphogenetic events that shape the Drosophila melanogaster embryo are tightly controlled by a genetic program in which specific sets of genes are up-regulated. We used a suppressive subtractive hybridization procedure to identify a group of developmentally regulated genes during early stages of D. melanogaster embryogenesis. We studied the spatiotemporal activity of these genes in five different intervals covering 12 stages of embryogenesis.     

A non-tree-based comprehensive study of metazoan Hox and ParaHox genes prompts new insights into their origin and evolution

Background  

Hox and the closely-related ParaHox genes, which emerged prior to the divergence between cnidarians and bilaterians, are the most well-known members of the ancient genetic toolkit that controls embryonic development across all metazoans. Fundamental questions relative to their origin and evolutionary relationships remain however unresolved. We investigate here the evolution of metazoan Hox and ParaHox genes using the HoxPred program that allows the identification of Hox genes without the need of phylogenetic tree reconstructions.     

Transcriptional programs activated by exposure of human prostate cancer cells to androgen

Background  

Androgens are required for both normal prostate development and prostate carcinogenesis. We used DNA microarrays, representing approximately 18,000 genes, to examine the temporal program of gene expression following treatment of the human prostate cancer cell line LNCaP with a synthetic androgen.     

The histone deacetylase Rpd3p is required for transient changes in genomic expression in response to stress

Background  

Yeast responding to stress activate a large gene expression program called the Environmental Stress Response that consists of approximately 600 repressed genes and approximately 300 induced genes. Numerous factors are implicated in regulating subsets of Environmental Stress Response genes; however, a complete picture of Environmental Stress Response regulation remains unclear. We investigated the role of the histone deacetylase Rpd3p, previously linked to the upstream regions of many Environmental Stress Response genes, in producing Environmental Stress Response gene expression changes in response to stress.     

A statistical analysis of the three-fold evolution of genomic compression through frame overlaps in prokaryotes

Background  

Among microbial genomes, genetic information is frequently compressed, exploiting redundancies in the genetic code in order to store information in overlapping genes. We investigate the length, phase and orientation properties of overlap in 58 prokaryotic species evaluating neutral and selective mechanisms of evolution.     

Atlas – a data warehouse for integrative bioinformatics

Background  

We present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development.     

A human functional protein interaction network and its application to cancer data analysis

Background  

One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system.     

Prediction of human disease genes by human-mouse conserved coexpression analysis

Background

Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates.

Methodology/Principal Findings

We show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.

Conclusion

Our results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes.     

Transcriptome analysis of <Emphasis Type="Italic">Sinorhizobium meliloti</Emphasis>during symbiosis

Background  

Rhizobia induce the formation on specific legumes of new organs, the root nodules, as a result of an elaborated developmental program involving the two partners. In order to contribute to a more global view of the genetics underlying this plant-microbe symbiosis, we have mined the recently determined Sinorhizobium meliloti genome sequence for genes potentially relevant to symbiosis. We describe here the construction and use of dedicated nylon macroarrays to study simultaneously the expression of 200 of these genes in a variety of environmental conditions, pertinent to symbiosis.     

Detecting variants with Metabolic Design,a new software tool to design probes for explorative functional DNA microarray development

Background  

Microorganisms display vast diversity, and each one has its own set of genes, cell components and metabolic reactions. To assess their huge unexploited metabolic potential in different ecosystems, we need high throughput tools, such as functional microarrays, that allow the simultaneous analysis of thousands of genes. However, most classical functional microarrays use specific probes that monitor only known sequences, and so fail to cover the full microbial gene diversity present in complex environments. We have thus developed an algorithm, implemented in the user-friendly program Metabolic Design, to design efficient explorative probes.     

Retrieval with gene queries

Background  

Accuracy of document retrieval from MEDLINE for gene queries is crucially important for many applications in bioinformatics. We explore five information retrieval-based methods to rank documents retrieved by PubMed gene queries for the human genome. The aim is to rank relevant documents higher in the retrieved list. We address the special challenges faced due to ambiguity in gene nomenclature: gene terms that refer to multiple genes, gene terms that are also English words, and gene terms that have other biological meanings.     

Joint mapping of genes and conditions via multidimensional unfolding analysis

Background  

Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding.     

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

Background  

The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes.     

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.     

Relationship between gene co-expression and probe localization on microarray slides

Background  

Microarray technology allows simultaneous measurement of thousands of genes in a single experiment. This is a potentially useful tool for evaluating co-expression of genes and extraction of useful functional and chromosomal structural information about genes.     

Save the tree of life or get lost in the woods

Background  

The wealth of prokaryotic genomic data available has revealed that the histories of many genes are inconsistent, leading some to question the value of the tree of life hypothesis. It has been argued that a tree-like representation requires suppressing too much information, and that a more pluralistic approach is necessary for understanding prokaryotic evolution. We argue that trees may still be a useful representation for evolutionary histories in light of new data.     

Linking enzyme sequence to function using conserved property difference locator to identify and annotate positions likely to control specific functionality

Background  

Families of homologous enzymes evolved from common progenitors. The availability of multiple sequences representing each activity presents an opportunity for extracting information specifying the functionality of individual homologs. We present a straightforward method for the identification of residues likely to determine class specific functionality in which multiple sequence alignments are converted to an annotated graphical form by the Conserved Property Difference Locator (CPDL) program.     

Quantitative comparison of EST libraries requires compensation for systematic biases in cDNA generation

Background  

Publicly accessible EST libraries contain valuable information that can be utilized for studies of tissue-specific gene expression and processing of individual genes. This information is, however, confounded by multiple systematic effects arising from the procedures used to generate these libraries.     

Insights into the pathogenesis of axial spondyloarthropathy from network and pathway analysis

Background

Complex chronic diseases are usually not caused by changes in a single causal gene but by an unbalanced regulating network resulting from the dysfunctions of multiple genes or their products. Therefore, network based systems approach can be helpful for the identification of candidate genes related to complex diseases and their relationships. Axial spondyloarthropathy (SpA) is a group of chronic inflammatory joint diseases that mainly affect the spine and the sacroiliac joints. The pathogenesis of SpA remains largely unknown.

Results

In this paper, we conducted a network study of the pathogenesis of SpA. We integrated data related to SpA, from the OMIM database, proteomics and microarray experiments of SpA, to prioritize SpA candidate disease genes in the context of human protein interactome. Based on the top ranked SpA related genes, we constructed a SpA specific PPI network, identified potential pathways associated with SpA, and finally sketched an overview of biological processes involved in the development of SpA.

Conclusions

The protein-protein interaction (PPI) network and pathways reflect the link between the two pathological processes of SpA, i.e., immune mediated inflammation, as well as imbalanced bone modelling caused new boneformation and bone loss. We found that some known disease causative genes, such as TNFand ILs, play pivotal roles in this interaction.