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Nucleic Acids Research (Impact Factor: 9.11). 02/2008; 36(Database issue):D25-30. DOI: 10.1093/nar/gkm929
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Available from: Ilene Karsch-Mizrachi, Oct 10, 2015
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    • "A binary interactome was assembled enabling an overview of all physical interactions that can occur between human proteins. Gene association data were downloaded from GeneRIF (Gene References into Function) database at NCBI [110] and the IntAct database [26] at EBI on Febuary 28 2011. The interactions in GeneRIF are sourced from Bind [111, 112], BioGrid [27, 28], EcoCyc [113], and HPRD [114]. "
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    ABSTRACT: Background Kaposi’s sarcoma associated herpes virus (KSHV) is associated with tumors of endothelial and lymphoid origin. During latent infection, KSHV expresses miR-K12-11, an ortholog of the human tumor gene hsa-miR-155. Both gene products are microRNAs (miRNAs), which are important post-transcriptional regulators that contribute to tissue specific gene expression. Advances in target identification technologies and molecular interaction databases have allowed a systems biology approach to unravel the gene regulatory networks (GRNs) triggered by miR-K12-11 in endothelial and lymphoid cells. Understanding the tissue specific function of miR-K12-11 will help to elucidate underlying mechanisms of KSHV pathogenesis. Results Ectopic expression of miR-K12-11 differentially affected gene expression in BJAB cells of lymphoid origin and TIVE cells of endothelial origin. Direct miRNA targeting accounted for a small fraction of the observed transcriptome changes: only 29 genes were identified as putative direct targets of miR-K12-11 in both cell types. However, a number of commonly affected biological pathways, such as carbohydrate metabolism and interferon response related signaling, were revealed by gene ontology analysis. Integration of transcriptome profiling, bioinformatic algorithms, and databases of protein-protein interactome from the ENCODE project identified different nodes of GRNs utilized by miR-K12-11 in a tissue-specific fashion. These effector genes, including cancer associated transcription factors and signaling proteins, amplified the regulatory potential of a single miRNA, from a small set of putative direct targets to a larger set of genes. Conclusions This is the first comparative analysis of miRNA-K12-11’s effects in endothelial and B cells, from tissues infected with KSHV in vivo. MiR-K12-11 was able to broadly modulate gene expression in both cell types. Using a systems biology approach, we inferred that miR-K12-11 establishes its GRN by both repressing master TFs and influencing signaling pathways, to counter the host anti-viral response and to promote proliferation and survival of infected cells. The targeted GRNs are more reproducible and informative than target gene identification, and our approach can be applied to other regulatory factors of interest. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-668) contains supplementary material, which is available to authorized users.
    BMC Genomics 08/2014; 15(1):668. DOI:10.1186/1471-2164-15-668 · 3.99 Impact Factor
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    • "The fasta sequences obtained were filtered for errors using cd-hit, blasted against the NCBI-NR database using the BLASTX default settings (Altschul et al., 1990; Benson et al., 2005) and analyzed using MEGAN4 (Huson et al., 2011). In parallel, metagenomic datasets were analyzed using the Metagenome Rapid Annotation with Subsystem Technology (MG-RAST; Miteva, 2008). "
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    ABSTRACT: The Arctic seasonal snowpack can extend at times over a third of the Earth's land surface. This chemically dynamic environment interacts constantly with different environmental compartments such as atmosphere, soil and meltwater, and thus, strongly influences the entire biosphere. However, the microbial community associated with this habitat remains poorly understood. Our objective was to investigate the functional capacities, diversity and dynamics of the microorganisms in snow and to test the hypothesis that their functional signature reflects the snow environment. We applied a metagenomic approach to nine snow samples taken over 2 months during the spring season. Fungi, Bacteroidetes, and Proteobacteria were predominant in metagenomic datasets and changes in community structure were apparent throughout the field season. Functional data that strongly correlated with chemical parameters like mercury or nitrogen species supported that this variation could be explained by fluctuations in environmental conditions. Through inter-environmental comparisons we examined potential drivers of snowpack microbial community functioning. Known cold adaptations were detected in all compared environments without any apparent differences in their relative abundance, implying that adaptive mechanisms related to environmental factors other than temperature may play a role in defining the snow microbial community. Photochemical reactions and oxidative stress seem to be decisive parameters in structuring microbial communities inside Arctic snowpacks.
    Frontiers in Microbiology 08/2014; 5:413. DOI:10.3389/fmicb.2014.00413 · 3.99 Impact Factor
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    • "[27], Sspace-Basic 2.0 [28], and Consed v23 [29]. ORFs were predicted with Glimmer 3 [30], and annotations were done in Artemis 12.0 graphic display [31] using previous annotations made for R. etli CFN42 [32] and comparing with the non-redundant data base of the Genbank [33], Interpro database [34], and IS database ( "
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    ABSTRACT: Background Symbiosis genes (nod and nif) involved in nodulation and nitrogen fixation in legumes are plasmid-borne in Rhizobium. Rhizobial symbiotic variants (symbiovars) with distinct host specificity would depend on the type of symbiosis plasmid. In Rhizobium etli or in Rhizobium phaseoli, symbiovar phaseoli strains have the capacity to form nodules in Phaseolus vulgaris while symbiovar mimosae confers a broad host range including different mimosa trees. Results We report on the genome of R. etli symbiovar mimosae strain Mim1 and its comparison to that from R. etli symbiovar phaseoli strain CFN42. Differences were found in plasmids especially in the symbiosis plasmid, not only in nod gene sequences but in nod gene content. Differences in Nod factors deduced from the presence of nod genes, in secretion systems or ACC-deaminase could help explain the distinct host specificity. Genes involved in P. vulgaris exudate uptake were not found in symbiovar mimosae but hup genes (involved in hydrogen uptake) were found. Plasmid pRetCFN42a was partially contained in Mim1 and a plasmid (pRetMim1c) was found only in Mim1. Chromids were well conserved. Conclusions The genomic differences between the two symbiovars, mimosae and phaseoli may explain different host specificity. With the genomic analysis presented, the term symbiovar is validated. Furthermore, our data support that the generalist symbiovar mimosae may be older than the specialist symbiovar phaseoli. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-575) contains supplementary material, which is available to authorized users.
    BMC Genomics 07/2014; 15(1):575. DOI:10.1186/1471-2164-15-575 · 3.99 Impact Factor
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