Transcript analysis of the extended hyp-operon in the cyanobacteria Nostoc sp. strain PCC 7120 and Nostoc punctiforme ATCC 29133

<p>Abstract</p> <p>Background</p> <p>Cyanobacteria harbor two [NiFe]-type hydrogenases consisting of a large and a small subunit, the Hup- and Hox-hydrogenase, respectively. Insertion of ligands and correct folding of nickel-iron hydrogenases require assistance of accessory maturation proteins (encoded by the <it>hyp</it>-genes). The intergenic region between the structural genes encoding the uptake hydrogenase (<it>hupSL</it>) and the accessory maturation proteins (<it>hyp </it>genes) in the cyanobacteria <it>Nostoc </it>PCC 7120 and <it>N. punctiforme </it>were analysed using molecular methods.</p> <p>Findings</p> <p>The five ORFs, located in between the uptake hydrogenase structural genes and the <it>hyp</it>-genes, can form a transcript with the <it>hyp</it>-genes. An identical genomic localization of these ORFs are found in other filamentous, N₂-fixing cyanobacterial strains. In <it>N. punctiforme </it>and <it>Nostoc </it>PCC 7120 the ORFs upstream of the <it>hyp</it>-genes showed similar transcript level profiles as <it>hupS </it>(hydrogenase structural gene), <it>nifD </it>(nitrogenase structural gene), <it>hypC </it>and <it>hypF </it>(accessory hydrogenase maturation genes) after nitrogen depletion. <it>In silico </it>analyzes showed that these ORFs in <it>N. punctiform</it>e harbor the same conserved regions as their homologues in <it>Nostoc </it>PCC 7120 and that they, like their homologues in <it>Nostoc </it>PCC 7120, can be transcribed together with the <it>hyp</it>-genes forming a larger extended <it>hyp-</it>operon. DNA binding studies showed interactions of the transcriptional regulators CalA and CalB to the promoter regions of the extended <it>hyp</it>-operon in <it>N. punctiforme </it>and <it>Nostoc </it>PCC 7120.</p> <p>Conclusions</p> <p>The five ORFs upstream of the <it>hyp</it>-genes in several filamentous N₂-fixing cyanobacteria have an identical genomic localization, in between the genes encoding the uptake hydrogenase and the maturation protein genes. In <it>N. punctiforme </it>and <it>Nostoc </it>PCC 7120 they are transcribed as one operon and may form transcripts together with the <it>hyp</it>-genes. The expression pattern of the five ORFs within the extended <it>hyp</it>-operon in both <it>Nostoc punctiforme </it>and <it>Nostoc </it>PCC 7120 is similar to the expression patterns of <it>hupS</it>, <it>nifD</it>, <it>hypF </it>and <it>hypC</it>. CalA, a known transcription factor, interacts with the promoter region between <it>hupSL </it>and the five ORFs in the extended <it>hyp</it>-operon in both <it>Nostoc </it>strains.</p

Genome fluctuations in cyanobacteria reflect evolutionary, developmental and adaptive traits

<p>Abstract</p> <p>Background</p> <p>Cyanobacteria belong to an ancient group of photosynthetic prokaryotes with pronounced variations in their cellular differentiation strategies, physiological capacities and choice of habitat. Sequencing efforts have shown that genomes within this phylum are equally diverse in terms of size and protein-coding capacity. To increase our understanding of genomic changes in the lineage, the genomes of 58 contemporary cyanobacteria were analysed for shared and unique orthologs.</p> <p>Results</p> <p>A total of 404 protein families, present in all cyanobacterial genomes, were identified. Two of these are unique to the phylum, corresponding to an AbrB family transcriptional regulator and a gene that escapes functional annotation although its genomic neighbourhood is conserved among the organisms examined. The evolution of cyanobacterial genome sizes involves a mix of gains and losses in the clade encompassing complex cyanobacteria, while a single event of reduction is evident in a clade dominated by unicellular cyanobacteria. Genome sizes and gene family copy numbers evolve at a higher rate in the former clade, and multi-copy genes were predominant in large genomes. Orthologs unique to cyanobacteria exhibiting specific characteristics, such as filament formation, heterocyst differentiation, diazotrophy and symbiotic competence, were also identified. An ancestral character reconstruction suggests that the most recent common ancestor of cyanobacteria had a genome size of approx. 4.5 Mbp and 1678 to 3291 protein-coding genes, 4%-6% of which are unique to cyanobacteria today.</p> <p>Conclusions</p> <p>The different rates of genome-size evolution and multi-copy gene abundance suggest two routes of genome development in the history of cyanobacteria. The expansion strategy is driven by gene-family enlargment and generates a broad adaptive potential; while the genome streamlining strategy imposes adaptations to highly specific niches, also reflected in their different functional capacities. A few genomes display extreme proliferation of non-coding nucleotides which is likely to be the result of initial expansion of genomes/gene copy number to gain adaptive potential, followed by a shift to a life-style in a highly specific niche (e.g. symbiosis). This transition results in redundancy of genes and gene families, leading to an increase in junk DNA and eventually to gene loss. A few orthologs can be correlated with specific phenotypes in cyanobacteria, such as filament formation and symbiotic competence; these constitute exciting exploratory targets.</p

Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed

Cytokines are signaling molecules secreted and sensed by immune and other cell types, enabling dynamic intercellular communication. Although a vast amount of data on these interactions exists, this information is not compiled, integrated or easily searchable. Here we report immuneXpresso, a text-mining engine that structures and standardizes knowledge of immune intercellular communication. We applied immuneXpresso to PubMed to identify relationships between 340 cell types and 140 cytokines across thousands of diseases. The method is able to distinguish between incoming and outgoing interactions, and it includes the effect of the interaction and the cellular function involved. These factors are assigned a confidence score and linked to the disease. By leveraging the breadth of this network, we predicted and experimentally verified previously unappreciated cell-cytokine interactions. We also built a global immune-centric view of diseases and used it to predict cytokine-disease associations. This standardized knowledgebase (http://www.immunexpresso.org) opens up new directions for interpretation of immune data and model-driven systems immunology