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Protein-protein interaction changes in an archaeal light-signal transduction.

ABSTRACT: Negative phototaxis in Natronomonas pharaonis is initiated by transient interaction changes between photoreceptor and transducer. pharaonis phoborhodopsin (ppR; also called pharaonis sensory rhodopsin II, psR-II) and the cognate transducer protein, pHtrII, form a tight 2 : 2 complex in the unphotolyzed state, and the interaction is somehow altered during the photocycle of ppR. We have studied the signal transduction mechanism in the ppR/pHtrII system by means of low-temperature Fourier-transform infrared (FTIR) spectroscopy. In the paper, spectral comparison in the absence and presence of pHtrII provided fruitful information in atomic details, where vibrational bands were identified by the use of isotope-labeling and site-directed mutagenesis. From these studies, we established the two pathways of light-signal conversion from the receptor to the transducer; (i) from Lys205 (retinal) of ppR to Asn74 of pHtrII through Thr204 and Tyr199, and (ii) from Lys205 of ppR to the cytoplasmic loop region of pHtrII that links Gly83.

SUBMITTER: Kandori H

PROVIDER: S-EPMC2910557 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Protein-protein interaction changes in an archaeal light-signal transduction.

Kandori Hideki H Sudo Yuki Y Furutani Yuji Y

Journal of biomedicine & biotechnology 20100629

Negative phototaxis in Natronomonas pharaonis is initiated by transient interaction changes between photoreceptor and transducer. pharaonis phoborhodopsin (ppR; also called pharaonis sensory rhodopsin II, psR-II) and the cognate transducer protein, pHtrII, form a tight 2 : 2 complex in the unphotolyzed state, and the interaction is somehow altered during the photocycle of ppR. We have studied the signal transduction mechanism in the ppR/pHtrII system by means of low-temperature Fourier-transform ...[more]

PMID: 20671933

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Project description:BackgroundUnderstanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases (Cancer CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID), PANTHER, Reactome, I2D, and STRING). We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data.ResultsWe have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a "bow tie" architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NFκB, and apoptotic signaling. Individual pathways exhibit "fuzzy" modularity that is statistically significant but still involving a majority of "cross-talk" interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples (epidermal growth factor, transforming growth factor-beta, tumor necrosis factor, and wingless), we find a multiplicity of network topologies in which receptors couple to downstream components through myriad alternate paths. Many of these paths are inconsistent with well-established mechanistic features of signalling networks, such as a requirement for a transmembrane receptor in sensing extracellular ligands.ConclusionsWide inconsistencies among interaction databases, pathway annotations, and the numbers and identities of nodes associated with a given pathway pose a major challenge for deriving causal and mechanistic insight from network graphs. We speculate that these inconsistencies are at least partially attributable to cell, and context-specificity of cellular signal transduction, which is largely unaccounted for in available databases, but the absence of standardized vocabularies is an additional confounding factor. As a result of discrepant annotations, it is very difficult to identify biologically meaningful pathways from interactome networks a priori. However, by incorporating prior knowledge, it is possible to successively build out network complexity with high confidence from a simple linear signal transduction scaffold. Such reduced complexity networks appear suitable for use in mechanistic models while being richer and better justified than the simple linear pathways usually depicted in diagrams of signal transduction.

Project description:The two-component signal transduction (TCS) machinery is a key mechanism of sensing environmental changes in the prokaryotic world. TCS systems have been characterized thoroughly in bacteria but to a much lesser extent in archaea. Here, we provide an updated census of more than 2,000 histidine kinases and response regulators encoded in 218 complete archaeal genomes, as well as unfinished genomes available from metagenomic data. We describe the domain architectures of the archaeal TCS components, including several novel output domains, and discuss the evolution of the archaeal TCS machinery. The distribution of TCS systems in archaea is strongly biased, with high levels of abundance in haloarchaea and thaumarchaea but none detected in the sequenced genomes from the phyla Crenarchaeota, Nanoarchaeota, and Korarchaeota The archaeal sensor histidine kinases are generally similar to their well-studied bacterial counterparts but are often located in the cytoplasm and carry multiple PAS and/or GAF domains. In contrast, archaeal response regulators differ dramatically from the bacterial ones. Most archaeal genomes do not encode any of the major classes of bacterial response regulators, such as the DNA-binding transcriptional regulators of the OmpR/PhoB, NarL/FixJ, NtrC, AgrA/LytR, and ActR/PrrA families and the response regulators with GGDEF and/or EAL output domains. Instead, archaea encode multiple copies of response regulators containing either the stand-alone receiver (REC) domain or combinations of REC with PAS and/or GAF domains. Therefore, the prevailing mechanism of archaeal TCS signaling appears to be via a variety of protein-protein interactions, rather than direct transcriptional regulation.IMPORTANCE Although the Archaea represent a separate domain of life, their signaling systems have been assumed to be closely similar to the bacterial ones. A study of the domain architectures of the archaeal two-component signal transduction (TCS) machinery revealed an overall similarity of archaeal and bacterial sensory modules but substantial differences in the signal output modules. The prevailing mechanism of archaeal TCS signaling appears to involve various protein-protein interactions rather than direct transcription regulation. The complete list of histidine kinases and response regulators encoded in the analyzed archaeal genomes is available online at http://www.ncbi.nlm.nih.gov/Complete_Genomes/TCSarchaea.html.

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Protein-protein interaction changes in an archaeal light-signal transduction.

Publications

Protein-protein interaction changes in an archaeal light-signal transduction.

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