Fungal colonization in Cystic Fibrosis (CF): Epidemiology and antifungal resistance in a French cohort of CF patients – Focused on Aspergillus fumigatus colonization

Introduction: Cystic fibrosis (CF) is the major genetic inherited disease in the European Caucasian population, with an average of 1 in 3000 living births in France. Prognostic depend essentially on the lung impairments. While considerable attention therefore has been paid over recent decades to prevent and treat bacterial respiratory infections, we observed emergence of fungi colonization in CF respiratory tract. In particular, Aspergillus fumigatus represents the most common causative agent colonizing the airways of CF patients; it can be responsible for Allergic Bronchopulmonary Aspergillosis (ABPA). Since oral corticosteroids and itraconazole represent the mainstay of ABPA treatment, long-term therapy may increase the risk of acquired resistance to azoles that is mainly associated with amino acid substitutions in the CYP51A gene of A. fumigatus. Objective: First, we managed to have exhaustive epidemiological data on species of filamentous fungi able to colonize the airway tract of 300 CF patients followed-up in our national prospective study ("MucoFong" study – PHRC1902). Second, CF patients being chronically exposed to azole (especially to itraconazole), our study aimed to evaluate the prevalence of azole resistance in isolates prospectively collected from CF patients followed-up in seven French hospitals involved in our national prospective study. Third, we focused on the most prevalent species: Aspergillus fumigatus, studying the azole resistance at molecular level. To our knowledge, it is the first multicenter study focused on azole resistance of A. fumigatus in CF. Methods: A total of 243 sputa were analyzed using the same protocol in each centre. The MICs of antifungal drugs were evaluated for each isolate using the E-test ® strips. Focusing on A. fumigatus, a total of 87 isolates was collected in 85 patients. These isolates were characterized at the molecular level by targeting ITS, ß-tubulin and MAT-A/α genes. The CYP51A gene as well as its promoter was sequenced; a 3D Cyp51A protein homology model was built. Results and discussion: 300 patients were enrolled in this study. At inclusion time, most of them were adults colonized with A. fumigatus (about 35% of the patients). Scedosporium was isolated in 5%, and Exophiala in about 2%. Regarding antifungal susceptibility, isolates of Scedosporium and Exophiala exhibited antifungal resistance comparable with published data. Regarding A. fumigatus, a majority of isolates (88.1%) were found sensitive to itraconazole (MIC≤ 2μg/ml), and 2 new mutations were identified and localized within 3-dimensional Cyp51A protein model. To obtain insight into azole resistance of A. fumigatus, the results are analyzed taking into account clinical data, itraconazole exposition, and the potential correlation between the identified CYP5IA mutations and azole resistance is discussed based on the Cyp51A protein homology model

Integrated monitoring of nature restoration along ecotones, the example of the Yser Estuary

Within the framework of LIFE, one of the larger nature restoration projects in Flanders was realized on the right bank of the estuarine part of the Yser. General aim of the initiative was to restore or create beach-dune-salt marsh ecotones with salt-fresh, dynamic-stable, wet-dry and mud-sand ecotones. In order to reach this goal, several large buildings and roads were broken down, an entire tidal dock was restructured and some 500,000m³ of dredging material was removed to restore or create intertidal and coastal dune habitats and their connecting ecotones. Measures were taken to avoid abrupt topographical transitions along potential ecological gradients. It was decided to begin monitoring (2001-2004) from the very start of the restoration process (1999-2003). Monitoring was multidisciplinary and realized in a partnership between several scientific institutes (Ghent University, Catholic University of Louvain, Royal Belgian Institute of Natural Sciences and Institute of Nature Conservation with facility support of VLIZ). Monitoring included the most relevant abiotic conditions such as sedimentation and erosion, topography and ground water fluctuations, and biological response variables, i.e. flora and vegetation, terrestrial arthropods, benthic macrofauna and birds. It was decided to include two monitoring levels, an area-covering monitoring of the entire nature reserve (ca. 128ha) and a detailed monitoring of changes along transects perpendicular to the main ecological gradients. In this paper we present some results of the first three years of monitoring

Selenoprotein gene nomenclature

The human genome contains 25 genes coding for selenocysteine-containing proteins (selenoproteins). These proteins are involved in a variety of functions, most notably redox homeostasis. Selenoprotein enzymes with known functions are designated according to these functions: TXNRD1, TXNRD2, and TXNRD3 (thioredoxin reductases), GPX1, GPX2, GPX3, GPX4 and GPX6 (glutathione peroxidases), DIO1, DIO2, and DIO3 (iodothyronine deiodinases), MSRB1 (methionine-R-sulfoxide reductase 1) and SEPHS2 (selenophosphate synthetase 2). Selenoproteins without known functions have traditionally been denoted by SEL or SEP symbols. However, these symbols are sometimes ambiguous and conflict with the approved nomenclature for several other genes. Therefore, there is a need to implement a rational and coherent nomenclature system for selenoprotein-encoding genes. Our solution is to use the root symbol SELENO followed by a letter. This nomenclature applies to SELENOF (selenoprotein F, the 15 kDa selenoprotein, SEP15), SELENOH (selenoprotein H, SELH, C11orf31), SELENOI (selenoprotein I, SELI, EPT1), SELENOK (selenoprotein K, SELK), SELENOM (selenoprotein M, SELM), SELENON (selenoprotein N, SEPN1, SELN), SELENOO (selenoprotein O, SELO), SELENOP (selenoprotein P, SeP, SEPP1, SELP), SELENOS (selenoprotein S, SELS, SEPS1, VIMP), SELENOT (selenoprotein T, SELT), SELENOV (selenoprotein V, SELV) and SELENOW (selenoprotein W, SELW, SEPW1). This system, approved by the HUGO Gene Nomenclature Committee, also resolves conflicting, missing and ambiguous designations for selenoprotein genes and is applicable to selenoproteins across vertebrates

Size matters: a view of selenocysteine incorporation from the ribosome

This review focuses on the known factors required for selenocysteine (Sec) incorporation in eukaryotes and highlights recent findings that have compelled us to propose a new model for the mechanism of Sec incorporation. In light of this data we also review the controversial aspects of the previous modelspecifically regarding the proposed interaction between SBP2 and eEFSec. In addition, the relevance of two recently discovered factors in the recoding of Sec are reviewed. The role of the ribosome in this process is emphasized along with a detailed analysis of kinkturn structures present in the ribosome and the L7Ae RNA-binding motif present in SBP2 and other proteins

A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem

<p>Abstract</p> <p>Background</p> <p>The inverse-QSAR problem seeks to find a new molecular descriptor from which one can recover the structure of a molecule that possess a desired activity or property. Surprisingly, there are very few papers providing solutions to this problem. It is a difficult problem because the molecular descriptors involved with the inverse-QSAR algorithm must adequately address the forward QSAR problem for a given biological activity if the subsequent recovery phase is to be meaningful. In addition, one should be able to construct a feasible molecule from such a descriptor. The difficulty of recovering the molecule from its descriptor is the major limitation of most inverse-QSAR methods.</p> <p>Results</p> <p>In this paper, we describe the reversibility of our previously reported descriptor, the vector space model molecular descriptor (VSMMD) based on a vector space model that is suitable for kernel studies in QSAR modeling. Our inverse-QSAR approach can be described using five steps: (1) generate the VSMMD for the compounds in the training set; (2) map the VSMMD in the input space to the kernel feature space using an appropriate kernel function; (3) design or generate a new point in the kernel feature space using a kernel feature space algorithm; (4) map the feature space point back to the input space of descriptors using a pre-image approximation algorithm; (5) build the molecular structure template using our VSMMD molecule recovery algorithm.</p> <p>Conclusion</p> <p>The empirical results reported in this paper show that our strategy of using kernel methodology for an inverse-Quantitative Structure-Activity Relationship is sufficiently powerful to find a meaningful solution for practical problems.</p

Adenine and guanine recognition of stop codon is mediated by different N domain conformations of translation termination factor eRF1

Positioning of release factor eRF1 toward adenines and the ribose-phosphate backbone of the UAAA stop signal in the ribosomal decoding site was studied using messenger RNA (mRNA) analogs containing stop signal UAA/UAAA and a photoactivatable cross-linker at definite locations. The human eRF1 peptides cross-linked to these analogs were identified. Cross-linkers on the adenines at the 2nd, 3rd or 4th position modified eRF1 near the conserved YxCxxxF loop (positions 125–131 in the N domain), but cross-linker at the 4th position mainly modified the tripeptide 26-AAR-28. This tripeptide cross-linked also with derivatized 3′-phosphate of UAA, while the same cross-linker at the 3′-phosphate of UAAA modified both the 26–28 and 67–73 fragments. A comparison of the results with those obtained earlier with mRNA analogs bearing a similar cross-linker at the guanines indicates that positioning of eRF1 toward adenines and guanines of stop signals in the 80S termination complex is different. Molecular modeling of eRF1 in the 80S termination complex showed that eRF1 fragments neighboring guanines and adenines of stop signals are compatible with different N domain conformations of eRF1. These conformations vary by positioning of stop signal purines toward the universally conserved dipeptide 31-GT-32, which neighbors guanines but is oriented more distantly from adenines

Evaluation of short‐term safety of ultrasound‐guided foetal fluid sampling in the dog (Canis lupus familiaris)

Background: In humans, analysis of amniotic fluid is widely used for diagnostic and prognostic purposes. Amniocentesis has scarcely been used in veterinary medicine to date, despite a tremendous potential for clinical and research applications in dogs. Our study aimed to establish a safe method for foetal fluid sampling in female dogs. Methods: Two transabdominal ultrasound-guided methods were assessed: the "free hand" and the needle-guided bracket sampling. In addition, through a subsequent routinely scheduled ovariohysterectomy, fluid was directly collected. Samples from 98 conceptuses were collected at day 46.7 +/- 7.5 of pregnancy. Results: The amount of fluid retrieved varied between 0.5 and 5.0 ml per collection. Macroscopic examination of the uterus and conceptuses identified 53% of the puncture sites. Neither fluid leakage nor foetal injury was detected, and six hematomas (5.8%) were visible. Ultrasound-guided foetal fluid collection was found to be potentially safe, and it can be performed by using either transabdominal method. Conclusion: Foetal fluid collection is possible with relative ease and low short-term risk, and may open paths for diagnostic, therapeutic and research purposes in dogs. The procedure can provide new insights into prenatal clinical medicine, including diagnostics of foetal deaths, early identification of heritable diseases and so on

A Role for Immune Responses against Non-CS Components in the Cross-Species Protection Induced by Immunization with Irradiated Malaria Sporozoites

Immunization with irradiated Plasmodium sporozoites induces sterile immunity in rodents, monkeys and humans. The major surface component of the sporozoite the circumsporozoite protein (CS) long considered as the antigen predominantly responsible for this immunity, thus remains the leading candidate antigen for vaccines targeting the parasite's pre-erythrocytic (PE) stages. However, this role for CS was questioned when we recently showed that immunization with irradiated sporozoites (IrrSpz) of a P. berghei line whose endogenous CS was replaced by that of P. falciparum still conferred sterile protection against challenge with wild type P. berghei sporozoites. In order to investigate the involvement of CS in the cross-species protection recently observed between the two rodent parasites P. berghei and P. yoelii, we adopted our gene replacement approach for the P. yoelii CS and exploited the ability to conduct reciprocal challenges. Overall, we found that immunization led to sterile immunity irrespective of the origin of the CS in the immunizing or challenge sporozoites. However, for some combinations, immune responses to CS contributed to the acquisition of protective immunity and were dependent on the immunizing IrrSpz dose. Nonetheless, when data from all the cross-species immunization/challenges were considered, the immune responses directed against non-CS parasite antigens shared by the two parasite species played a major role in the sterile protection induced by immunization with IrrSpz. This opens the perspective to develop a single vaccine formulation that could protect against multiple parasite species

DPRESS: Localizing estimates of predictive uncertainty

<p>Abstract</p> <p>Background</p> <p>The need to have a quantitative estimate of the uncertainty of prediction for QSAR models is steadily increasing, in part because such predictions are being widely distributed as tabulated values disconnected from the models used to generate them. Classical statistical theory assumes that the error in the population being modeled is independent and identically distributed (IID), but this is often not actually the case. Such inhomogeneous error (heteroskedasticity) can be addressed by providing an individualized estimate of predictive uncertainty for each particular new object <it>u</it>: the standard error of prediction <it>s</it>_ucan be estimated as the non-cross-validated error <it>s</it>_t*for the closest object <it>t</it>* in the training set adjusted for its separation <it>d </it>from <it>u </it>in the descriptor space relative to the size of the training set.</p> <p><display-formula><graphic file="1758-2946-1-11-i1.gif"/></display-formula></p> <p>The predictive uncertainty factor <it>γ</it>_t*is obtained by distributing the internal predictive error sum of squares across objects in the training set based on the distances between them, hence the acronym: <it>D</it>istributed <it>PR</it>edictive <it>E</it>rror <it>S</it>um of <it>S</it>quares (DPRESS). Note that <it>s</it>_t*and <it>γ</it>_t*are characteristic of each training set compound contributing to the model of interest.</p> <p>Results</p> <p>The method was applied to partial least-squares models built using 2D (molecular hologram) or 3D (molecular field) descriptors applied to mid-sized training sets (<it>N </it>= 75) drawn from a large (<it>N </it>= 304), well-characterized pool of cyclooxygenase inhibitors. The observed variation in predictive error for the external 229 compound test sets was compared with the uncertainty estimates from DPRESS. Good qualitative and quantitative agreement was seen between the distributions of predictive error observed and those predicted using DPRESS. Inclusion of the distance-dependent term was essential to getting good agreement between the estimated uncertainties and the observed distributions of predictive error. The uncertainty estimates derived by DPRESS were conservative even when the training set was biased, but not excessively so.</p> <p>Conclusion</p> <p>DPRESS is a straightforward and powerful way to reliably estimate individual predictive uncertainties for compounds outside the training set based on their distance to the training set and the internal predictive uncertainty associated with its nearest neighbor in that set. It represents a sample-based, <it>a posteriori </it>approach to defining applicability domains in terms of localized uncertainty.</p