Disease-related concept mining by knowledge-based two-dimensional gene mapping

There is a strong need to systematically organize and comprehend the rapidly expanding stores of biomedical knowledge to formulate hypotheses on disease mechanisms. However, no method is available that automatically structuralizes fragmentary knowledge along with domain-specific expressions for a large-scale integration. A method presented here, cross-subspace analysis (CSA), produces a holistic view of over 3,000 human genes with a two-dimensional (2D) arrangement. The genes are plotted in relation to functions determined by machine learning from the occurrence patterns of various biomedical terms in MEDLINE abstracts. By focusing on the 2D distributions of gene plots that share the same biomedical concepts, as defined by databases such as Gene Ontology, relevant biomedical concepts can be computationally extracted. In an analysis where myocardial infarction and ischemic stroke were taken as examples, we found valid relations with lifestyle, diet-related metabolism, and host immune responses, all of which are known risk factors for the diseases. These results demonstrate that systematizing accumulated gene knowledge can lead to hypothesis generation and knowledge discovery, regardless of the area of inquiry or discipline.     

Identification of cellular pathways of "type 1," Th17 T cells, and TNF- and inducible nitric oxide synthase-producing dendritic cells in autoimmune inflammation through pharmacogenomic study of cyclosporine A in psoriasis

Therapeutic modulation of psoriasis with targeted immunosuppressive agents defines inflammatory genes associated with disease activity and may be extrapolated to a wide range of autoimmune diseases. Cyclosporine A (CSA) is considered a "gold standard" therapy for moderate-to-severe psoriasis. We conducted a clinical trial with CSA and analyzed the treatment outcome in blood and skin of 11 responding patients. In the skin, as expected, CSA modulated genes from activated T cells and the "type 1" pathway (p40, IFN-gamma, and STAT-1-regulated genes). However, CSA also modulated genes from the newly described Th17 pathway (IL-17, IL-22, and downstream genes S100A12, DEFB-2, IL-1beta, SEPRINB3, LCN2, and CCL20). CSA also affected dendritic cells, reducing TNF and inducible NO synthase (products of inflammatory TNF- and inducible NO synthase-producing dendritic cells), CD83, and IL-23p19. We detected 220 early response genes (day 14 posttreatment) that were down-regulated by CSA. We classified >95% into proinflammatory or skin resident cells. More myeloid-derived than activated T cell genes were modulated by CSA (54 myeloid genes compared with 11 lymphocyte genes), supporting the hypothesis that myeloid derived genes contribute to pathogenic inflammation in psoriasis. In circulating mononuclear leukocytes, in stark contrast, no inflammatory gene activity was detected. Thus, we have constructed a genomic signature of successful treatment of psoriasis which may serve as a reference to guide development of other new therapies. In addition, these data also identify new gene targets for therapeutic modulation and may be applied to wide range of autoimmune diseases.     

Cyclosporin A-mediated inhibition of mitogen-induced gene transcription is specific for the mitogenic stimulus and cell type

We have examined the effect of cyclosporin A (CSA) on the mitogen-induced expression of 11 genes previously cloned from mitogen-activated T lymphocytes. Levels of induced gene expression in the human T cell line Jurkat were determined by mRNA blotting and nuclear run-on assay, after stimulation with one or combinations of the mitogens PMA, PHA, and the ionophore A23187. In the presence of CSA, gene expression induced with PMA alone was not inhibited, whereas PHA-induced increases in gene expression were inhibited by CSA. For one group of genes, including IL-2 and two novel genes with sequences suggestive of lymphokines, A23187 plus PMA-induced gene expression was inhibited by CSA. In contrast, another group of induced genes was unaffected by CSA after A23187 and PMA induction. This finding implies that A23187 and PMA stimulate gene induction by more than one mechanism, and that not all activation signals mediated through calcium fluxes are sensitive to CSA. In addition, 8 of the 11 genes were expressed in the fibroblast cell line Mrc 5 after stimulation with PMA, A23187, or serum; CSA had no effect on genes induced with these agents in Mrc 5 cells in both mRNA blotting and run-on experiments, although 5 of these genes were markedly inhibited by CSA in Jurkat after PMA/PHA induction. These data indicate that separate pathways for induction of identical genes exist, and that the inciting stimulus and cell type are determining factors in the ability of CSA to inhibit gene expression.     

ANDVisio: a new tool for graphic visualization and analysis of literature mined associative gene networks in the ANDSystem

The ANDVisio tool is designed to reconstruct and analyze associative gene networks in the earlier developed Associative Network Discovery System (ANDSystem) software package. The ANDSystem incorporates utilities for automated extraction of knowledge from Pubmed published scientific texts, analysis of factographic databases, also the ANDCell database containing information on molecular-genetic events retrieved from texts and databases. ANDVisio is a new user's interface to the ANDCell database stored in a remote server. ANDVisio provides graphic visualization, editing, search, also saving of associative gene networks in different formats resulting from user's request. The associative gene networks describe semantic relationships between molecular-genetic objects (proteins, genes, metabolites and others), biological processes, and diseases. ANDVisio is provided with various tools to support filtering by object types, relationships between objects and information sources; graph layout; search of the shortest pathway; cycles in graphs.     

Characterization of a gene encoding two isoforms of a mitochondrial protein up-regulated by cyclosporin A in activated T cells

Cyclosporin A (CSA) is an immunosuppressor used in organ transplantation. A recent proteomic analysis has revealed that activation of T cells in the presence of CSA induces the synthesis of hundreds of new proteins. Here we used representational difference analysis to characterize some of the corresponding induced genes. After cDNA bank screening we focused on one of these genes, which we named CSA-conditional, T cell activation-dependent (CSTAD) gene. This gene produces two mRNAs resulting from alternative splicing events. They encode two proteins of 104 and 141 amino acids, CSTADp-S and CSTADp-L, for the short and long forms, respectively. FK506 had the same effect as CSA, whereas rapamycin did not affect the level of CSTAD gene expression, demonstrating that inhibition of the calcineurin activation pathway is involved in CSTAD gene up-regulation. CSA also led to overexpression of CSTAD in mice immunized in the presence of CSA, confirming the in vitro analysis. Microscopic and cytofluorimetric analysis of cells expressing green fluorescent protein-tagged CSTADp-L and CSTADp-S showed that both proteins colocalize with mitochondrial markers and depolarize the mitochondrial transmembrane potential without causing release of cytochrome c, apoptosis, or necrosis. Both CSTADp isoforms are sensitive to proteinase K, implying that they are located in the mitochondrial outer membrane. These data reveal a new mechanism of action for CSA, which involves up-regulation of a gene whose products are sorted to mitochondria and depolarize the mitochondrial membrane.     

Gene Expression in Chicken Reveals Correlation with Structural Genomic Features and Conserved Patterns of Transcription in the Terrestrial Vertebrates

Background

The chicken is an important agricultural and avian-model species. A survey of gene expression in a range of different tissues will provide a benchmark for understanding expression levels under normal physiological conditions in birds. With expression data for birds being very scant, this benchmark is of particular interest for comparative expression analysis among various terrestrial vertebrates.

Methodology/Principal Findings

We carried out a gene expression survey in eight major chicken tissues using whole genome microarrays. A global picture of gene expression is presented for the eight tissues, and tissue specific as well as common gene expression were identified. A Gene Ontology (GO) term enrichment analysis showed that tissue-specific genes are enriched with GO terms reflecting the physiological functions of the specific tissue, and housekeeping genes are enriched with GO terms related to essential biological functions. Comparisons of structural genomic features between tissue-specific genes and housekeeping genes show that housekeeping genes are more compact. Specifically, coding sequence and particularly introns are shorter than genes that display more variation in expression between tissues, and in addition intergenic space was also shorter. Meanwhile, housekeeping genes are more likely to co-localize with other abundantly or highly expressed genes on the same chromosomal regions. Furthermore, comparisons of gene expression in a panel of five common tissues between birds, mammals and amphibians showed that the expression patterns across tissues are highly similar for orthologuous genes compared to random gene pairs within each pair-wise comparison, indicating a high degree of functional conservation in gene expression among terrestrial vertebrates.

Conclusions

The housekeeping genes identified in this study have shorter gene length, shorter coding sequence length, shorter introns, and shorter intergenic regions, there seems to be selection pressure on economy in genes with a wide tissue distribution, i.e. these genes are more compact. A comparative analysis showed that the expression patterns of orthologous genes are conserved in the terrestrial vertebrates during evolution.     

Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data

MOTIVATION: Despite the growing literature devoted to finding differentially expressed genes in assays probing different tissues types, little attention has been paid to the combinatorial nature of feature selection inherent to large, high-dimensional gene expression datasets. New flexible data analysis approaches capable of searching relevant subgroups of genes and experiments are needed to understand multivariate associations of gene expression patterns with observed phenotypes. RESULTS: We present in detail a deterministic algorithm to discover patterns of multivariate gene associations in gene expression data. The patterns discovered are differential with respect to a control dataset. The algorithm is exhaustive and efficient, reporting all existent patterns that fit a given input parameter set while avoiding enumeration of the entire pattern space. The value of the pattern discovery approach is demonstrated by finding a set of genes that differentiate between two types of lymphoma. Moreover, these genes are found to behave consistently in an independent dataset produced in a different laboratory using different arrays, thus validating the genes selected using our algorithm. We show that the genes deemed significant in terms of their multivariate statistics will be missed using other methods. AVAILABILITY: Our set of pattern discovery algorithms including a user interface is distributed as a package called Genes@Work. This package is freely available to non-commercial users and can be downloaded from our website (http://www.research.ibm.com/FunGen).     

Co-clustering of biological networks and gene expression data

MOTIVATION: Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. RESULTS: We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.     

Microarray analysis of antimicrobial resistance genes in Salmonella enterica from preharvest poultry environment

Aims:  To detect antimicrobial resistance genes in Salmonella isolates from turkey flocks using the microarray technology.
Methods and Results:  A 775 gene probe oligonucleotide microarray was used to detect antimicrobial resistance genes in 34 isolates. All tetracycline-resistant Salmonella harboured tet(A) , tet(C) or tet(R) , with the exception of one Salmonella serotype Heidelberg isolate. The sul1 gene was detected in 11 of 16 sulfisoxazole-resistant isolates. The aadA , aadA1 , aadA2 , strA or strB genes were found in aminoglycoside-resistant isolates of Salm. Heidelberg, Salmonella serotype Senftenberg and untypeable Salmonella . The prevalence of mobile genetic elements, such as class I integron and transposon genes, in drug-resistant Salmonella isolates suggested that these elements may contribute to the dissemination of antimicrobial resistance genes in the preharvest poultry environment. Hierarchical clustering analysis demonstrated a close relationship between drug-resistant phenotypes and the corresponding antimicrobial resistance gene profiles.
Conclusions:  Salmonella serotypes isolated from the poultry environment carry multiple genes that can render them resistant to several antimicrobials used in poultry and humans.
Significance and Impact of the Study:  Multiple antimicrobial resistance genes in environmental Salmonella isolates could be identified efficiently by microarray analysis. Hierarchical clustering analysis of the data was also found to be a useful tool for analysing emerging patterns of drug resistance.     

Modeling Genome-Wide Dynamic Regulatory Network in Mouse Lungs with Influenza Infection Using High-Dimensional Ordinary Differential Equations

The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.     

Comparison of synonymous codon distribution patterns of bacteriophage and host genomes.

Synonymous codon usage patterns of bacteriophage and host genomes were compared. Two indexes, G + C base composition of a gene (fgc) and fraction of translationally optimal codons of the gene (fop), were used in the comparison. Synonymous codon usage data of all the coding sequences on a genome are represented as a cloud of points in the plane of fop vs. fgc. The Escherichia coli coding sequences appear to exhibit two phases, "rising" and "flat" phases. Genes that are essential for survival and are thought to be native are located in the flat phase, while foreign-type genes from prophages and transposons are found in the rising phase with a slope of nearly unity in the fgc vs. fop plot. Synonymous codon distribution patterns of genes from temperate phages P4, P2, N15 and lambda are similar to the pattern of E. coli rising phase genes. In contrast, genes from the virulent phage T7 or T4, for which a phage-encoded DNA polymerase is identified, fall in a linear curve with a slope of nearly zero in the fop vs. fgc plane. These results may suggest that the G + C contents for T7, T4 and E. coli flat phase genes are subject to the directional mutation pressure and are determined by the DNA polymerase used in the replication. There is significant variation in the fop values of the phage genes, suggesting an adjustment to gene expression level. Similar analyses of codon distribution patterns were carried out for Haemophilus influenzae, Bacillus subtilis, Mycobacterium tuberculosis and their phages with complete genomic sequences available.     

Gene and protein nomenclature in public databases

Background  

Frequently, several alternative names are in use for biological objects such as genes and proteins. Applications like manual literature search, automated text-mining, named entity identification, gene/protein annotation, and linking of knowledge from different information sources require the knowledge of all used names referring to a given gene or protein. Various organism-specific or general public databases aim at organizing knowledge about genes and proteins. These databases can be used for deriving gene and protein name dictionaries. So far, little is known about the differences between databases in terms of size, ambiguities and overlap.     

Statistical estimation of gene expression using multiple laser scans of microarrays

We propose a statistical model for estimating gene expression using data from multiple laser scans at different settings of hybridized microarrays. A functional regression model is used, based on a non-linear relationship with both additive and multiplicative error terms. The function is derived as the expected value of a pixel, given that values are censored at 65 535, the maximum detectable intensity for double precision scanning software. Maximum likelihood estimation based on a Cauchy distribution is used to fit the model, which is able to estimate gene expressions taking account of outliers and the systematic bias caused by signal censoring of highly expressed genes. We have applied the method to experimental data. Simulation studies suggest that the model can estimate the true gene expression with negligible bias. AVAILABILITY: FORTRAN 90 code for implementing the method can be obtained from the authors.     

Mining gene expression data based on template theory

MOTIVATION: It is understood that clustering genes are useful for exploring scientific knowledge from DNA microarray gene expression data. The explored knowledge can be finally used for annotating biological function for novel genes. Representing the explored knowledge in an efficient manner is then closely related to the classification accuracy. However, this issue has not yet been paid the attention it deserves. RESULT: A novel method based on template theory in cognitive psychology and pattern recognition is developed in this study for representing knowledge extracted from cluster analysis effectively. The basic principle is to represent knowledge according to the relationship between genes and a found cluster structure. Based on this novel knowledge representation method, a pattern recognition algorithm (the decision tree algorithm C4.5) is then used to construct a classifier for annotating biological functions of novel genes. The experiments on five published datasets show that this method has improved the classification performance compared with the conventional method. The statistical tests indicate that this improvement is significant. AVAILABILITY: The software package can be obtained upon request from the author.     

In silico gene function prediction using ontology-based pattern identification

MOTIVATION: With the emergence of genome-wide expression profiling data sets, the guilt by association (GBA) principle has been a cornerstone for deriving gene functional interpretations in silico. Given the limited success of traditional methods for producing clusters of genes with great amounts of functional similarity, new data-mining algorithms are required to fully exploit the potential of high-throughput genomic approaches. RESULTS: Ontology-based pattern identification (OPI) is a novel data-mining algorithm that systematically identifies expression patterns that best represent existing knowledge of gene function. Instead of relying on a universal threshold of expression similarity to define functionally related groups of genes, OPI finds the optimal analysis settings that yield gene expression patterns and gene lists that best predict gene function using the principle of GBA. We applied OPI to a publicly available gene expression data set on the life cycle of the malarial parasite Plasmodium falciparum and systematically annotated genes for 320 functional categories based on current Gene Ontology annotations. An ontology-based hierarchical tree of the 320 categories provided a systems-wide biological view of this important malarial parasite.