Conservation value of the native Hungarian wild grape (Vitis sylvestris Gmel.) evaluated by microsatellite markers

Wild grape (Vitis sylvestris GMEL.) has became a highly threatened species in Europe because of habitat loss, competition with alien grape species and intensive forest exploitation. Twenty-three Vitis spp. samples were analysed at 8 microsatellite loci to estimate the genetic diversity of the natural Vitis sylvestris specimens. In order to analyse the morphological traits and to perform molecular analysis, 11 native individuals were sampled from 6 remnant Hungarian habitats of the wild grape. To compare the genetic relationships among the wild specimens, samples from Turkish habitats, as well as cultivars of Vitis vinifera, Vitis labrusca and Vitis riparia were also included. Genetic diversity was higher within the Hungarian wild grape locations, with a mean of He = 0.74, which was higher that of samples originating from a larger area of Turkey, He = 0.69. Most of the Hungarian samples formed a well-defined, separate branch on the NJ tree. Based on the morphological traits and molecular analysis on the territory of Szentendre Island, formerly considered to be one of the largest native locations of wild grape, interspecific hybrids of Vitis sylvestris and Vitis riparia were identified. It can be concluded from the results that most of the Hungarian habitats studied could be valuable for the conservation. This paper reports on Hungarian Vitis sylvestris habitats, providing the first genetic study on diversity and on the relationship of Vitis sylvestris to other Vitis specimens, wild or cultivated, in the central part of the Carpathian Basin

Helicobacter pylori Type IV Secretion Apparatus Exploits β1 Integrin in a Novel RGD-Independent Manner

Translocation of the Helicobacter pylori (Hp) cytotoxin-associated gene A (CagA) effector protein via the cag-Type IV Secretion System (T4SS) into host cells is a major risk factor for severe gastric diseases, including gastric cancer. However, the mechanism of translocation and the requirements from the host cell for that event are not well understood. The T4SS consists of inner- and outer membrane-spanning Cag protein complexes and a surface-located pilus. Previously an arginine-glycine-aspartate (RGD)-dependent typical integrin/ligand type interaction of CagL with α5β1 integrin was reported to be essential for CagA translocation. Here we report a specific binding of the T4SS-pilus-associated components CagY and the effector protein CagA to the host cell β1 Integrin receptor. Surface plasmon resonance measurements revealed that CagA binding to α5β1 integrin is rather strong (dissociation constant, KD of 0.15 nM), in comparison to the reported RGD-dependent integrin/fibronectin interaction (KD of 15 nM). For CagA translocation the extracellular part of the β1 integrin subunit is necessary, but not its cytoplasmic domain, nor downstream signalling via integrin-linked kinase. A set of β1 integrin-specific monoclonal antibodies directed against various defined β1 integrin epitopes, such as the PSI, the I-like, the EGF or the β-tail domain, were unable to interfere with CagA translocation. However, a specific antibody (9EG7), which stabilises the open active conformation of β1 integrin heterodimers, efficiently blocked CagA translocation. Our data support a novel model in which the cag-T4SS exploits the β1 integrin receptor by an RGD-independent interaction that involves a conformational switch from the open (extended) to the closed (bent) conformation, to initiate effector protein translocation

Conserved and variable correlated mutations in the plant MADS protein network

<p>Abstract</p> <p>Background</p> <p>Plant MADS domain proteins are involved in a variety of developmental processes for which their ability to form various interactions is a key requisite. However, not much is known about the structure of these proteins or their complexes, whereas such knowledge would be valuable for a better understanding of their function. Here, we analyze those proteins and the complexes they form using a correlated mutation approach in combination with available structural, bioinformatics and experimental data.</p> <p>Results</p> <p>Correlated mutations are affected by several types of noise, which is difficult to disentangle from the real signal. In our analysis of the MADS domain proteins, we apply for the first time a correlated mutation analysis to a family of interacting proteins. This provides a unique way to investigate the amount of signal that is present in correlated mutations because it allows direct comparison of mutations in various family members and assessing their conservation. We show that correlated mutations in general are conserved within the various family members, and if not, the variability at the respective positions is less in the proteins in which the correlated mutation does not occur. Also, intermolecular correlated mutation signals for interacting pairs of proteins display clear overlap with other bioinformatics data, which is not the case for non-interacting protein pairs, an observation which validates the intermolecular correlated mutations. Having validated the correlated mutation results, we apply them to infer the structural organization of the MADS domain proteins.</p> <p>Conclusion</p> <p>Our analysis enables understanding of the structural organization of the MADS domain proteins, including support for predicted helices based on correlated mutation patterns, and evidence for a specific interaction site in those proteins.</p

Approximation and inference methods for stochastic biochemical kinetics - a tutorial review

Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important examples include gene expression and enzymatic processes in living cells. Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the Chemical Master Equation. Despite its simple structure, no analytic solutions to the Chemical Master Equation are known for most systems. Moreover, stochastic simulations are computationally expensive, making systematic analysis and statistical inference a challenging task. Consequently, significant effort has been spent in recent decades on the development of efficient approximation and inference methods. This article gives an introduction to basic modelling concepts as well as an overview of state of the art methods. First, we motivate and introduce deterministic and stochastic methods for modelling chemical networks, and give an overview of simulation and exact solution methods. Next, we discuss several approximation methods, including the chemical Langevin equation, the system size expansion, moment closure approximations, time-scale separation approximations and hybrid methods. We discuss their various properties and review recent advances and remaining challenges for these methods. We present a comparison of several of these methods by means of a numerical case study and highlight some of their respective advantages and disadvantages. Finally, we discuss the problem of inference from experimental data in the Bayesian framework and review recent methods developed the literature. In summary, this review gives a self-contained introduction to modelling, approximations and inference methods for stochastic chemical kinetics.Comment: 73 pages, 12 figures in J. Phys. A: Math. Theor. (2016