Common Biological Functions Research Articles

Abstract B1-28: GEO2Enrichr: A Google Chrome extension to extract gene sets from the Gene Expression Omnibus and analyze these lists for common biological functions

Abstract Background: Identification of differentially expressed genes from gene expression profiling studies is a necessary and important step in the analysis of microarray and RNA-seq data. Such data is deposited into the Gene Expression Omnibus (GEO). However, currently, to extract and analyze differentially expressed genes from GEO requires significant computational expertise. Methodology: GEO2Enrichr has a front-end browser extension and back-end server application. The back-end is written in Python and uses the Flask framework for the web application server. The server exposes three API endpoints to: (1) query GEO; (2) identify the differentially expressed genes; and (3) query Enrichr. The API endpoints are chained into a useful data pipeline. The front-end is a Google Chrome Extension written in JavaScript and CSS and programmatically inserts HTML elements into the native GEO interface. The Chrome web browser loads the extension whenever the user navigates to an NCBI GEO webpage. GEO2Enrichr unobtrusively inserts itself onto the page as a single button and checkboxes for easy sample selection. The button opens a modal box that allows users to edit their selected data, choose settings for differential expression identification, insert metadata about the experiments, download their resultant gene lists, and submit their gene lists to Enrichr for further analysis. Differential expression analysis has five options: Characteristic Direction1, T-test, limma, SAM, and fold-change. Other options are available to control for the size of the resultant differentially expressed gene lists by setting p-value and FDR cutoffs. Results: GEO2Enrichr is a Google Chrome Extension and Python-based API that adds functionality to GEO by allowing users to pipe Simple Omnibus Format in Text (SOFT) files to a differential expression analysis tool and then pipe the differentially expressed genes for analysis to Enrichr2, a popular gene list enrichment analysis web application. GEO2Enrichr addresses a need for improved functionality of GEO by embedding new features into the existing GEO pages, allowing researchers to easily select samples and process them for differential expression by various methods and perform enrichment analyses. GEO2Enrichr is free and available for installation at the Chrome Web Store. Conclusions: GEO2Enrichr can facilitate the more broad reusability of the GEO resource by lowering the point of entry to biologists without computational expertise. The systematic use of GEO2Enrichr can generate a new useful searchable resource. Overall, GEO2Enrichr can lead to improved extraction of knowledge from data3. [1] http://maayanlab.net/CD [2] http://amp.pharm.mssm.edu/Enrichr [3] http://bd2k.nih.gov Citation Format: Gregory W. Gundersen, Matthew R. Jones, Avi Ma'ayan. GEO2Enrichr: A Google Chrome extension to extract gene sets from the Gene Expression Omnibus and analyze these lists for common biological functions. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-28.

Effect of 90Sr internal emitter on gene expression in mouse blood.

BackgroundThe radioactive isotope Strontium-90 (90Sr) may be released as a component of fallout from nuclear accidents, or in the event of a radiological incident such as detonation of an improvised nuclear device, and if ingested poses a significant health risk to exposed individuals. In order to better understand the response to 90Sr, using an easily attainable and standard biodosimetry sample fluid, we analyzed the global transcriptomic response of blood cells in an in vivo model system.ResultsWe injected C57BL/6 mice with a solution of 90SrCl2 and followed them over a 30-day period. At days 4, 7, 9, 25 and 30, we collected blood and isolated RNA for microarray analyses. These days corresponded to target doses in a range from 1–5 Gy. We investigated changes in mRNA levels using microarrays, and changes in specific microRNA (miRNA) predicted to be involved in the response using qRT-PCR. We identified 8082 differentially expressed genes in the blood of mice exposed to 90Sr compared with controls. Common biological functions were affected throughout the study, including apoptosis of B and T lymphocytes, and atrophy of lymphoid organs. Cellular functions such as RNA degradation and lipid metabolism were also affected during the study. The broad down regulation of genes observed in our study suggested a potential role for miRNA in gene regulation. We tested candidate miRNAs, mmu-miR-16, mmu-miR-124, mmu-miR-125 and mmu-mir-21; and found that all were induced at the earliest time point, day 4.ConclusionsOur study is the first to report the transcriptomic response of blood cells to the internal emitter 90Sr in mouse and a possible role for microRNA in gene regulation after 90Sr exposure. The most dramatic effect was observed on gene expression related to B-cell development and RNA maintenance. These functions were affected by genes that were down regulated throughout the study, suggesting severely compromised antigen response, which may be a result of the deposition of the radioisotope proximal to the hematopoietic compartment in bone.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1774-z) contains supplementary material, which is available to authorized users.

Biomarkers: Non-destructive Method for Predicting Meat Tenderization.

Meat tenderness is the primary and most important quality attribute for the consumers worldwide. Tenderness is the process of breakdown of collagen tissue in meat to make it palatable. The earlier methods of tenderness evaluation like taste panels and shear force methods are destructive, time consuming and ill suited as they requires removing a piece of steak from the carcass for performing the test. Therefore, a non-destructive method for predicting the tenderness would be more desirable. The development of a meat quality grading and guarantee system through muscle profiling research can help to meet this demand. Biomarkers have the ability to identify if an exposure has occurred. Biomarkers of the meat quality are of prime importance for meat industry, which has ability to satisfy consumers' expectations. The biomarkers so far identified have been then sorted and grouped according to their common biological functions. All of them refer to a series of biological pathways including glycolytic and oxidative energy production, cell detoxification, protease inhibition and production of Heat Shock Proteins. On this basis, a detailed analysis of these metabolic pathways helps in identifying tenderization of meat having some domains of interest. It was, therefore, stressed forward that biomarkers can be used to determine meat tenderness. This review article summarizes the uses of several biomarkers for predicting the meat tenderness.

Systematic computation with functional gene-sets among leukemic and hematopoietic stem cells reveals a favorable prognostic signature for acute myeloid leukemia

BackgroundGenes that regulate stem cell function are suspected to exert adverse effects on prognosis in malignancy. However, diverse cancer stem cell signatures are difficult for physicians to interpret and apply clinically. To connect the transcriptome and stem cell biology, with potential clinical applications, we propose a novel computational “gene-to-function, snapshot-to-dynamics, and biology-to-clinic” framework to uncover core functional gene-sets signatures. This framework incorporates three function-centric gene-set analysis strategies: a meta-analysis of both microarray and RNA-seq data, novel dynamic network mechanism (DNM) identification, and a personalized prognostic indicator analysis. This work uses complex disease acute myeloid leukemia (AML) as a research platform.ResultsWe introduced an adjustable “soft threshold” to a functional gene-set algorithm and found that two different analysis methods identified distinct gene-set signatures from the same samples. We identified a 30-gene cluster that characterizes leukemic stem cell (LSC)-depleted cells and a 25-gene cluster that characterizes LSC-enriched cells in parallel; both mark favorable-prognosis in AML. Genes within each signature significantly share common biological processes and/or molecular functions (empirical p = 6e-5 and 0.03 respectively). The 25-gene signature reflects the abnormal development of stem cells in AML, such as AURKA over-expression. We subsequently determined that the clinical relevance of both signatures is independent of known clinical risk classifications in 214 patients with cytogenetically normal AML. We successfully validated the prognosis of both signatures in two independent cohorts of 91 and 242 patients respectively (log-rank p < 0.0015 and 0.05; empirical p < 0.015 and 0.08).ConclusionThe proposed algorithms and computational framework will harness systems biology research because they efficiently translate gene-sets (rather than single genes) into biological discoveries about AML and other complex diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0510-7) contains supplementary material, which is available to authorized users.

Extensive microRNA-mediated crosstalk between lncRNAs and mRNAs in mouse embryonic stem cells.

Recently, a handful of intergenic long noncoding RNAs (lncRNAs) have been shown to compete with mRNAs for binding to miRNAs and to contribute to development and disease. Beyond these reports, little is yet known of the extent and functional consequences of miRNA-mediated regulation of mRNA levels by lncRNAs. To gain further insight into lncRNA-mRNA miRNA-mediated crosstalk, we reanalyzed transcriptome-wide changes induced by the targeted knockdown of over 100 lncRNA transcripts in mouse embryonic stem cells (mESCs). We predicted that, on average, almost one-fifth of the transcript level changes induced by lncRNAs are dependent on miRNAs that are highly abundant in mESCs. We validated these findings experimentally by temporally profiling transcriptome-wide changes in gene expression following the loss of miRNA biogenesis in mESCs. Following the depletion of miRNAs, we found that >50% of lncRNAs and their miRNA-dependent mRNA targets were up-regulated coordinately, consistent with their interaction being miRNA-mediated. These lncRNAs are preferentially located in the cytoplasm, and the response elements for miRNAs they share with their targets have been preserved in mammals by purifying selection. Lastly, miRNA-dependent mRNA targets of each lncRNA tended to share common biological functions. Post-transcriptional miRNA-mediated crosstalk between lncRNAs and mRNA, in mESCs, is thus surprisingly prevalent, conserved in mammals, and likely to contribute to critical developmental processes.

The relative vertex clustering value--a new criterion for the fast discovery of functional modules in protein interaction networks.

BackgroundCellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many community detection methods have been devised for their discovery from protein interaction networks (PINs) data. In current agglomerative clustering approaches, vertices with just a very few neighbors are often classified as separate clusters, which does not make sense biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large PINs. Finally, PIN data obtained from large scale experiments generally contain many false positives, and this makes it hard for agglomerative clustering methods to find the correct clusters, since they are known to be sensitive to noisy data.ResultsWe propose a local similarity premetric, the relative vertex clustering value, as a new criterion allowing to decide when a node can be added to a given node's cluster and which addresses the above three issues. Based on this criterion, we introduce a novel and very fast agglomerative clustering technique, FAC-PIN, for discovering functional modules and protein complexes from a PIN data.ConclusionsOur proposed FAC-PIN algorithm is applied to nine PIN data from eight different species including the yeast PIN, and the identified functional modules are validated using Gene Ontology (GO) annotations from DAVID Bioinformatics Resources. Identified protein complexes are also validated using experimentally verified complexes. Computational results show that FAC-PIN can discover functional modules or protein complexes from PINs more accurately and more efficiently than HC-PIN and CNM, the current state-of-the-art approaches for clustering PINs in an agglomerative manner.

Bridging topological and functional information in protein interaction networks by short loops profiling.

Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.

A Parallel Algorithm for &lt;formula&gt;&lt;tex&gt;$N$&lt;/tex&gt;&lt;/formula&gt;-Way Interval Set Intersection

The comparison of sets of genome intervals (e.g., genes, repeats, ChIP-seq peaks) is essential to genome research, especially as modern sequencing technologies enable ever larger and more complex experiments. Relationships between genomic features are commonly identified by their intersection: that is, if feature sets contain overlapping intervals then it is inferred that they share a common biological function or origin. Using this technique, researchers identify genomic regions that are common among multiple (or unique to individual) datasets. While there have been recent advances in algorithms for pairwise intersections between two sets of genomic intervals, few advances have been made to the intersection of many sets of genomic intervals. Identifying intersections among many interval sets is particularly important when attempting to distill biological insights from the massive, multi-dimensional datasets that are common to modern genome research. For such analyses, speed and efficiency are crucial given the size and sheer number of datasets involved. To solve this problem, we present a novel "slice-then-sweep" algorithm that, given N interval sets, efficiently reveals the subset of intervals that are common to all N sets. We demonstrate that our algorithm is more efficient in the sequential case and has a vastly higher capacity for parallelization with a 19x speedup over the existing algorithm.

Differential regulation enrichment analysis via the integration of transcriptional regulatory network and gene expression data.

Although many gene set analysis methods have been proposed to explore associations between a phenotype and a group of genes sharing common biological functions or involved in the same biological process, the underlying biological mechanisms of identified gene sets are typically unexplained. We propose a method called Differential Regulation-based enrichment Analysis for GENe sets (DRAGEN) to identify gene sets in which a significant proportion of genes have their transcriptional regulatory patterns changed in a perturbed phenotype. We conduct comprehensive simulation studies to demonstrate the capability of our method in identifying differentially regulated gene sets. We further apply our method to three human microarray expression datasets, two with hormone treated and control samples and one concerning different cell cycle phases. Results indicate that the capability of DRAGEN in identifying phenotype-associated gene sets is significantly superior to those of four existing methods for analyzing differentially expressed gene sets. We conclude that the proposed differential regulation enrichment analysis method, though exploratory in nature, complements the existing gene set analysis methods and provides a promising new direction for the interpretation of gene expression data. The program of DRAGEN is freely available at http://bioinfo.au.tsinghua.edu.cn/dragen/. ruijiang@tsinghua.edu.cn or jiang@cs.ucr.edu Supplementary data are available at Bioinformatics online.

An evolutionary restricted neighborhood search clustering approach for PPI networks

Protein–protein interaction networks have been broadly studied in the last few years, in order to understand the behavior of proteins inside the cell. Proteins interacting with each other often share common biological functions or they participate in the same biological process. Thus, discovering protein complexes made of a group of proteins strictly related can be useful to predict protein functions. Clustering techniques have been widely employed to detect significant biological complexes. In this paper, we integrate one of the most popular network clustering techniques, namely the Restricted Neighborhood Search Clustering (RNSC), with evolutionary computation. The two cost functions introduced by RNSC, besides a new one that combines them, are used by a Genetic Algorithm as fitness functions to be optimized. Experimental evaluations performed on two different groups of interactions of the budding yeast Saccharomyces cerevisiae show that the clusters obtained by the genetic approach are a larger number of those found by RNSC, though this method predicts more true complexes.

Gene Set Enrichment Analysis (GSEA) of Toxoplasma gondii expression datasets links cell cycle progression and the bradyzoite developmental program

BackgroundLarge amounts of microarray expression data have been generated for the Apicomplexan parasite Toxoplasma gondii in an effort to identify genes critical for virulence or developmental transitions. However, researchers’ ability to analyze this data is limited by the large number of unannotated genes, including many that appear to be conserved hypothetical proteins restricted to Apicomplexa. Further, differential expression of individual genes is not always informative and often relies on investigators to draw big-picture inferences without the benefit of context. We hypothesized that customization of gene set enrichment analysis (GSEA) to T. gondii would enable us to rigorously test whether groups of genes serving a common biological function are co-regulated during the developmental transition to the latent bradyzoite form.ResultsUsing publicly available T. gondii expression microarray data, we created Toxoplasma gene sets related to bradyzoite differentiation, oocyst sporulation, and the cell cycle. We supplemented these with lists of genes derived from community annotation efforts that identified contents of the parasite-specific organelles, rhoptries, micronemes, dense granules, and the apicoplast. Finally, we created gene sets based on metabolic pathways annotated in the KEGG database and Gene Ontology terms associated with gene annotations available at http://www.toxodb.org. These gene sets were used to perform GSEA analysis using two sets of published T. gondii expression data that characterized T. gondii stress response and differentiation to the latent bradyzoite form.ConclusionsGSEA provides evidence that cell cycle regulation and bradyzoite differentiation are coupled. Δgcn5A mutants unable to induce bradyzoite-associated genes in response to alkaline stress have different patterns of cell cycle and bradyzoite gene expression from stressed wild-type parasites. Extracellular tachyzoites resemble a transitional state that differs in gene expression from both replicating intracellular tachyzoites and in vitro bradyzoites by expressing genes that are enriched in bradyzoites as well as genes that are associated with the G1 phase of the cell cycle. The gene sets we have created are readily modified to reflect ongoing research and will aid researchers’ ability to use a knowledge-based approach to data analysis facilitating the development of new insights into the intricate biology of Toxoplasma gondii.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-515) contains supplementary material, which is available to authorized users.

Integrative approaches for finding modular structure in biological networks

A central goal of systems biology is to elucidate the structural and functional architecture of the cell. To this end, large and complex networks of molecular interactions are being rapidly generated for humans and model organisms. A recent focus of bioinformatics research has been to integrate these networks with each other and with diverse molecular profiles to identify sets of molecules and interactions that participate in a common biological function - that is, 'modules'. Here, we classify such integrative approaches into four broad categories, describe their bioinformatic principles and review their applications.

CORE: Common Region Extension Based Multiple Protein Structure Alignment for Producing Multiple Solution

Over the past several decades, biologists have conducted numerous studies examining both general and specific functions of proteins. Generally, if similarities in either the structure or sequence of amino acids exist for two proteins, then a common biological function is expected. Protein function is determined primarily based on the structure rather than the sequence of amino acids. The algorithm for protein structure alignment is an essential tool for the research. The quality of the algorithm depends on the quality of the similarity measure that is used, and the similarity measure is an objective function used to determine the best alignment. However, none of existing similarity measures became golden standard because of their individual strength and weakness. They require excessive filtering to find a single alignment. In this paper, we introduce a new strategy that finds not a single alignment, but multiple alignments with different lengths. This method has obvious benefits of high quality alignment. However, this novel method leads to a new problem that the running time for this method is considerably longer than that for methods that find only a single alignment. To address this problem, we propose algorithms that can locate a common region (CORE) of multiple alignment candidates, and can then extend the CORE into multiple alignments. Because the CORE can be defined from a final alignment, we introduce CORE* that is similar to CORE and propose an algorithm to identify the CORE*. By adopting CORE* and dynamic programming, our proposed method produces multiple alignments of various lengths with higher accuracy than previous methods. In the experiments, the alignments identified by our algorithm are longer than those obtained by TM-align by 17 % and 15.48 %, on average, when the comparison is conducted at the level of super-family and fold, respectively.

Comparison of gene expression profiles of the jejunum of broilers supplemented with a yeast cell wall-derived mannan oligosaccharide versus bacitractin methylene disalicylate

1. The addition of yeast cell wall (YCW) mannan fractions or low concentrations of antibiotics to the diet of broilers positively affects gut health by improving intestinal cell morphology and improves feed efficiency and performance; however the exact mechanisms are unclear. Based on these production responses, the objective of this study was to compare the effects of supplementing YCW and bacitracin methylene disalicylate (BMD) in the diet on mRNA levels in the jejunum of 6-week-old broilers.2. Dietary treatments were a maize-soya control diet and the control diet with the addition of YCW or BMD. Birds (n = 7) from each dietary treatment were randomly selected and killed at d 42. Whole jejunum (with serosa) samples were collected for RNA isolation. Gene expression analysis was performed using the AffymetrixGeneChip Chicken Genome Array (Santa Clara, CA, USA).3. Supplementation with YCW resulted in 928 genes that were significantly changed (456 down-regulated, 472 up-regulated) and supplementation with BMD resulted in 857 genes that significantly changed (408 down-regulated, 449 up-regulated). In addition, 316 genes were significantly changed by both YCW and BMD (146 down-regulated, 170 up-regulated).4. BMD increased the expression of genes involved in lipid and carbohydrate metabolism and decreased expression of genes associated with T-helper cell pathways. Gene expression profiles from birds fed on diets containing YCW showed changes on a genomic level that correspond to slower gut cell turnover and therefore increased energy preservation for growth.5. In conclusion, supplementation with BMD or YCW had similar influences on the number of differentially expressed genes in the jejunum. Biological functions common to both YCW and BMD with positive activation scores included antiviral response and antimicrobial response. Genes that were affected by BMD or YCW classified into both different and common biological functions and pathways related to improved metabolism and health in the jejunum.

Transcriptome Analysis of Renal Ischemia/Reperfusion Injury and Its Modulation by Ischemic Pre-Conditioning or Hemin Treatment

Ischemia/reperfusion injury (IRI) is a leading cause of acute renal failure. The definition of the molecular mechanisms involved in renal IRI and counter protection promoted by ischemic pre-conditioning (IPC) or Hemin treatment is an important milestone that needs to be accomplished in this research area. We examined, through an oligonucleotide microarray protocol, the renal differential transcriptome profiles of mice submitted to IRI, IPC and Hemin treatment. After identifying the profiles of differentially expressed genes observed for each comparison, we carried out functional enrichment analysis to reveal transcripts putatively involved in potential relevant biological processes and signaling pathways. The most relevant processes found in these comparisons were stress, apoptosis, cell differentiation, angiogenesis, focal adhesion, ECM-receptor interaction, ion transport, angiogenesis, mitosis and cell cycle, inflammatory response, olfactory transduction and regulation of actin cytoskeleton. In addition, the most important overrepresented pathways were MAPK, ErbB, JAK/STAT, Toll and Nod like receptors, Angiotensin II, Arachidonic acid metabolism, Wnt and coagulation cascade. Also, new insights were gained about the underlying protection mechanisms against renal IRI promoted by IPC and Hemin treatment. Venn diagram analysis allowed us to uncover common and exclusively differentially expressed genes between these two protective maneuvers, underscoring potential common and exclusive biological functions regulated in each case. In summary, IPC exclusively regulated the expression of genes belonging to stress, protein modification and apoptosis, highlighting the role of IPC in controlling exacerbated stress response. Treatment with the Hmox1 inducer Hemin, in turn, exclusively regulated the expression of genes associated with cell differentiation, metabolic pathways, cell cycle, mitosis, development, regulation of actin cytoskeleton and arachidonic acid metabolism, suggesting a pleiotropic effect for Hemin. These findings improve the biological understanding of how the kidney behaves after IRI. They also illustrate some possible underlying molecular mechanisms involved in kidney protection observed with IPC or Hemin treatment maneuvers.

Toxicogenomic analysis of chlorine vapor-induced porcine skin injury

Chlorine is an industrial chemical that can cause cutaneous burns. Understanding the molecular mechanisms of tissue damage and wound healing is important for the selection and development of an effective post-exposure treatment. This study investigated the effect of cutaneous chlorine vapor exposure using a weanling swine burn model and microarray analysis. Ventral abdominal sites were exposed to a mean calculated chlorine vapor concentration of 2.9 g/L for 30 min. Skin samples were harvested at 1.5 h, 3 h, 6 h, and 24 h post-exposure and stored in RNAlater® until processing. Total RNA was isolated, processed, and hybridized to Affymetrix GeneChip® Porcine Genome Arrays. Differences in gene expression were observed with respect to sampling time. Ingenuity Pathways Analysis revealed seven common biological functions among the top ten functions of each time point, while canonical pathway analysis revealed 3 genes (IL-6, IL1A, and IL1B) were commonly shared among three significantly altered signaling pathways. The transcripts encoding all three genes were identified as common potential therapeutic targets for Phase II/III clinical trial, or FDA-approved drugs. The present study shows transcriptional profiling of cutaneous wounds induced by chlorine exposure identified potential targets for developing therapeutics against chlorine-induced skin injury.

Preliminary MicroRNA Analysis in Lung Tissue to Identify Potential Therapeutic Targets Against H5N1 Infection

Within the past decade, human infections with the highly pathogenic avian influenza H5N1 have resulted in approximately 60% mortality and increased the need for vaccines and therapeutics. Understanding the molecular events associated with pathology can aid this effort; therefore, this study was conducted to assess microRNA (miRNA) expression in mouse lungs infected with H5N1 A/Vietnam/1203/04. Intranasal administration of 1500 median tissue culture infectious dose of H5N1 promoted differences in the number and expression pattern of miRNA from lung tissue collected at 2, 4, 6, 24, and 96 h post-exposure that mapped to common biological functions. Informatics analysis identified miRNA-specific predicted genes known to be therapeutic drug targets in which Furin was common to all time periods. This study provides insight into the differential miRNA expression with respect to the host-pathogen relationship and identification of potential therapeutic drug targets.

Transcriptomics Data Integration Reveals Jak-STAT as a Common Pathway Affected by Pathogenic Intracellular Bacteria in Natural Reservoir Hosts

The study of the host-pathogen interface in natural reservoir hosts is essential to identify host-cell mechanisms affected by bacterial infection and persistence. Herein we used the Database for Annotation, Visualization and Integrated Discovery (DAVID) to integrate transcriptomics data and find common host-cell biological processes, molecular functions and pathways affected by pathogenic intracellular bacteria of the genera Anaplasma, Brucella and Mycobacterium during infection and persistence in two natural reservoir hosts, wild boar and sheep. The results showed that the upregulation of host innate immune pro-inflammatory genes and signaling pathways constitutes a general antibacterial mechanism in response to intracellular bacteria. Pathway focused analysis revealed a role for the Jak-STAT pathway during bacterial intracellular infection, a fact reported before in Mycobacterium infected cells but not during Brucella spp. and A. phagocytophilum infection. A clear activation of the Jak-STAT pathway was observed in A. phagocytophilum infected wild boar and sheep when compared to uninfected controls. Brucella spp. infection resulted in a balanced regulation of the Jak-STAT signaling and M. bovis infection of wild boar clearly produced a downregulation of some of the Jak-STAT effectors such as IL5 and TKY2. These results suggested that mycobacteria and brucellae induce host innate immune responses while manipulating adaptive immunity to circumvent host-cell defenses and establish infection. In contrast, A. phagocytophilum infection induces both innate and adaptive immunity, those suggesting that this pathogen uses other mechanisms to circumvent host-cell defenses by downregulating other adaptive immune genes and delaying the apoptotic death of neutrophils through activation of the Jak-STAT pathway among other mechanisms.

Functional Analysis of the Ceramide Synthase Gene ALT7, A Homolog of the Disease Resistance Gene Asc1, in the Plant Pathogen Alternaria alternata

The tomato pathotype of Alternaria alternata produces a host-specific AAL-toxin and causes Alternaria stem canker on susceptible tomato cultivars. AAL-toxin is a sphinganine-analog mycotoxin which induces apoptotic cell death in tomato cells and mammalian cells by inhibiting ceramide biosynthesis. Insensitivity to the AAL-toxin in resistant tomatoes and other plants is conferred by the Asc1 gene, a homolog of the yeast ceramide synthase gene Lag1 . The ALT7 gene, a putative acyl-CoA-dependent ceramide synthase, was found to be located in the AAL-toxin biosynthetic ( ALT ) gene cluster of the tomato pathotype of A. alternata . ALT7 and Asc1 have the TLC (TRAM/Lag1/ CLN8) domain characteristic of proteins involved in ceramide biosynthesis and are members of the LASS/Lag family. To test the hypothesis that ALT7 and Asc1 , both of which are Lag1 ceramide synthase gene homologs, might share a common biological function as toxin tolerance genes, we have cloned and characterized ALT7. ALT7 -deleted mutants were generated to investigate the effects of the deletion on vegetative growth, sporulation, toxin-sensitivity, toxin-production and pathogenicity. The deletion of ALT7 has no deleterious effect on the toxin-producing pathogen, indicating that the gene does not act as a resistance/self-tolerance factor against the toxin in the toxin biosynthetic gene cluster.

SAFE Software and FED Database to Uncover Protein-Protein Interactions using Gene Fusion Analysis

Domain Fusion Analysis takes advantage of the fact that certain proteins in a given proteome A, are found to have statistically significant similarity with two separate proteins in another proteome B. In other words, the result of a fusion event between two separate proteins in proteome B is a specific full-length protein in proteome A. In such a case, it can be safely concluded that the protein pair has a common biological function or even interacts physically. In this paper, we present the Fusion Events Database (FED), a database for the maintenance and retrieval of fusion data both in prokaryotic and eukaryotic organisms and the Software for the Analysis of Fusion Events (SAFE), a computational platform implemented for the automated detection, filtering and visualization of fusion events (both available at: http://www.bioacademy.gr/bioinformatics/projects/ProteinFusion/index.htm). Finally, we analyze the proteomes of three microorganisms using these tools in order to demonstrate their functionality.