An Integrated In Silico Approach to Design Specific Inhibitors Targeting Human Poly(A)-Specific Ribonuclease

Poly(A)-specific ribonuclease (PARN) is an exoribonuclease/deadenylase that degrades 3′-end poly(A) tails in almost all eukaryotic organisms. Much of the biochemical and structural information on PARN comes from the human enzyme. However, the existence of PARN all along the eukaryotic evolutionary ladder requires further and thorough investigation. Although the complete structure of the full-length human PARN, as well as several aspects of the catalytic mechanism still remain elusive, many previous studies indicate that PARN can be used as potent and promising anti-cancer target. In the present study, we attempt to complement the existing structural information on PARN with in-depth bioinformatics analyses, in order to get a hologram of the molecular evolution of PARNs active site. In an effort to draw an outline, which allows specific drug design targeting PARN, an unequivocally specific platform was designed for the development of selective modulators focusing on the unique structural and catalytic features of the enzyme. Extensive phylogenetic analysis based on all the publicly available genomes indicated a broad distribution for PARN across eukaryotic species and revealed structurally important amino acids which could be assigned as potentially strong contributors to the regulation of the catalytic mechanism of PARN. Based on the above, we propose a comprehensive in silico model for the PARN’s catalytic mechanism and moreover, we developed a 3D pharmacophore model, which was subsequently used for the introduction of DNP-poly(A) amphipathic substrate analog as a potential inhibitor of PARN. Indeed, biochemical analysis revealed that DNP-poly(A) inhibits PARN competitively. Our approach provides an efficient integrated platform for the rational design of pharmacophore models as well as novel modulators of PARN with therapeutic potential.     

Characterization of microsatellite loci in the European pond turtle Emys orbicularis

A set of eight highly polymorphic microsatellite markers was isolated and characterized from a genomic library enriched for dinucleotide repeats in the European pond turtle, Emys orbicularis. The markers were tested for polymorphism in a total of 33 turtles sampled in two natural ponds in the nature reserve of Kerkini, northern Greece. Number of alleles varied from 10 to 18, and expected heterozygosity ranged between 0.738 and 0.921. This novel set of loci will be particularly useful to assess fine-scale population structure and for parentage analysis in E. orbicularis.     

PKC zeta participates in activation of inflammatory response induced by enteropathogenic E. coli

We showed previously that enteropathogenic Escherichia coli (EPEC) infection of intestinal epithelial cells induces inflammation by activating NF-B and upregulating IL-8 expression. We also reported that extracellular signal-regulated kinases (ERKs) participate in EPEC-induced NF-B activation but that other signaling molecules such as PKC may be involved. The aim of this study was to determine whether PKC is activated by EPEC and to investigate whether it also plays a role in EPEC-associated inflammation. EPEC infection induced the translocation of PKC from the cytosol to the membrane and its activation as determined by kinase activity assays. Inhibition of PKC by the pharmacological inhibitor rottlerin, the inhibitory myristoylated PKC pseudosubstrate (MYR-PKC-PS), or transient expression of a nonfunctional PKC significantly suppressed EPEC-induced IB phosphorylation. Although PKC can activate ERK, MYR-PKC-PS had no effect on EPEC-induced stimulation of this pathway, suggesting that they are independent events. PKC can regulate NF-B activation by interacting with and activating IB kinase (IKK). Coimmunoprecipitation studies showed that the association of PKC and IKK increased threefold 60 min after infection. Kinase activity assays using immunoprecipitated PKC-IKK complexes from infected intestinal epithelial cells and recombinant IB as a substrate showed a 2.5-fold increase in IB phosphorylation. PKC can also regulate NF-B by serine phosphorylation of the p65 subunit. Serine phosphorylation of p65 was increased after EPEC infection but could not be consistently attenuated by MYR-PKC-PS, suggesting that other signaling events may be involved in this particular arm of NF-B regulation. We speculate that EPEC infection of intestinal epithelial cells activates several signaling pathways including PKC and ERK that lead to NF-B activation, thus ensuring the proinflammatory response. inflammation; enteropathogenic Escherichia coli; nuclear factor-B; protein kinase C; IB kinase; extracellular signal-regulated kinase     

IL-10-independent STAT3 activation by Toxoplasma gondii mediates suppression of IL-12 and TNF-alpha in host macrophages

Infection of mouse macrophages by Toxoplasma gondii renders the cells resistant to proinflammatory effects of LPS triggering. In this study, we show that cell invasion is accompanied by rapid and sustained activation of host STAT3. Activation of STAT3 did not occur with soluble T. gondii extracts or heat-killed tachyzoites, demonstrating a requirement for live parasites. Parasite-induced STAT3 phosphorylation and suppression of LPS-triggered TNF-alpha and IL-12 was intact in IL-10-deficient macrophages, ruling out a role for this anti-inflammatory cytokine in the suppressive effects of T. gondii. Most importantly, Toxoplasma could not effectively suppress LPS-triggered TNF-alpha and IL-12 synthesis in STAT3-deficient macrophages. These results demonstrate that T. gondii exploits host STAT3 to prevent LPS-triggered IL-12 and TNF-alpha production, revealing for the first time a molecular mechanism underlying the parasite's suppressive effect on macrophage proinflammatory cytokine production.     

Evidence for male dispersal along the coasts but no migration in pelagic waters in dusky dolphins (Lagenorhynchus obscurus)

Using nine nuclear species-specific microsatellite loci and two mitochondrial gene fragments (cytochrome b and control region), we investigated the processes that have shaped the geographical distribution of genetic diversity exhibited by contemporary dusky dolphin (Lagenorhynchus obscurus) populations. A total of 221 individuals from four locations (Peru, Argentina, southern Africa, and New Zealand) were assayed, covering most of the species’ distribution range. Although our analyses identify a general demographic decline in the Peruvian dusky dolphin stock (recently affected by high natural and human-induced mortality levels), comparison between the different molecular markers hint at an ancient bottleneck that predates recent El Niño oscillations and human exploitation. Moreover, we find evidence of a difference in dispersal behaviour of dusky dolphins along the South American coast and across the Atlantic. While data in Peruvian and Argentine waters are best explained by male-specific gene flow between these two populations, our analyses suggest that dusky dolphins from Argentina and southern Africa recently separated from an ancestral Atlantic population and, since then, diverged without considerable gene flow. The inclusion of a few New Zealand samples further confirms the low levels of genetic differentiation among most dusky dolphin populations. Only the Peruvian dusky dolphin stock is highly differentiated, especially at mitochondrial loci, suggesting that major fluctuations in its population size have led to an increased rate of genetic drift.     

Cytokeratin 18 interacts with the enteropathogenic Escherichia coli secreted protein F (EspF) and is redistributed after infection

Enteropathogenic Escherichia coli (EPEC) pathogenesis requires the delivery of effector proteins into host cytosol by a type III secretion system. The effector protein EspF, while critical for disruption of epithelial barrier function through alteration of tight junctions, is not required for bacterial viability or attachment. Yeast two-hybrid analyses revealed host intermediate filament (IF) protein cytokeratin 18 (CK18) as an interacting partner of EspF. This was confirmed by co-immunoprecipitation of extracts from EPEC-infected epithelial cells. EPEC infection increased detergent-soluble CK18 amounts without significantly altering CK18 expression. The adaptor protein 14-3-3 binds to CK18 and modulates its solubility. EPEC infection promoted CK18/14-3-3 interactions, corresponding to the increase of CK18 in the soluble fractions. 14-3-3 also co-immunoprecipitated with EspF, suggesting that CK18, 14-3-3 and EspF may form a complex in infected cells. The 14-3-3zeta isoform was co-immunoprecipitated with CK18, suggesting the involvement of specific signalling pathways. Immunofluorescence studies revealed a dramatic alteration in the architecture of the IF network in EPEC-infected epithelial cells. IF fragmentation, evident at 2 h post infection, progressed to a collapse of this network at later time points. The secretion mutant (DeltaescN) failed to alter CK18 solubility and IF morphology, while deletion of espF partially impaired the ability of EPEC to induce CK18 modifications. These results suggest that modifications in 14-3-3 interactions and IF network, modulated by type III secreted proteins, may be an important step in EPEC pathogenesis.     

Identification of gene expression profiles in myocardial infarction: a systematic review and meta-analysis

Background

Myocardial infarction (MI) is a multifactorial disease with complex pathogenesis, mainly the result of the interplay of genetic and environmental risk factors. The regulation of thrombosis, inflammation and cholesterol and lipid metabolism are the main factors that have been proposed thus far to be involved in the pathogenesis of MI. Traditional risk-estimation tools depend largely on conventional risk factors but there is a need for identification of novel biochemical and genetic markers. The aim of the study is to identify differentially expressed genes that are consistently associated with the incidence myocardial infarction (MI), which could be potentially incorporated into the traditional cardiovascular diseases risk factors models.

Methods

The biomedical literature and gene expression databases, PubMed and GEO, respectively, were searched following the PRISMA guidelines. The key inclusion criteria were gene expression data derived from case-control studies on MI patients from blood samples. Gene expression datasets regarding the effect of medicinal drugs on MI were excluded. The t-test was applied to gene expression data from case-control studies in MI patients.

Results

A total of 162 articles and 174 gene expression datasets were retrieved. Of those a total of 4 gene expression datasets met the inclusion criteria, which contained data on 31,180 loci in 93 MI patients and 89 healthy individuals. Collectively, 626 differentially expressed genes were detected in MI patients as compared to non-affected individuals at an FDR q-value?=?0.01. Of those, 88 genes/gene products were interconnected in an interaction network. Totally, 15 genes were identified as hubs of the network.

Conclusions

Functional enrichment analyses revealed that the DEGs and that they are mainly involved in inflammatory/wound healing, RNA processing/transport mechanisms and a yet not fully characterized pathway implicated in RNA transport and nuclear pore proteins. The overlap between the DEGs identified in this study and the genes identified through genetic-association studies is minimal. These data could be useful in future studies on the molecular mechanisms of MI as well as diagnostic and prognostic markers.

     

Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication)

Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set.