Antimicrobial-Resistant Bacteria Carriage in Rodents According to Habitat Anthropization.

It is increasingly suggested that the dynamics of antimicrobial-resistant bacteria in the wild are mostly anthropogenically driven, but the spatial and temporal scales at which these phenomena occur in landscapes are only partially understood. Here, we explore this topic by studying antimicrobial resistance in the commensal bacteria from micromammals sampled at 12 sites from a large heterogenous landscape (the Carmargue area, Rhone Delta) along a gradient of anthropization: natural reserves, rural areas, towns, and sewage-water treatment plants. There was a positive relationship between the frequency of antimicrobial-resistant bacteria and the level of habitat anthropization. Although low, antimicrobial resistance was also present in natural reserves, even in the oldest one, founded in 1954. This study is one of the first to support the idea that rodents in human-altered habitats are important components of the environmental pool of resistance to clinically relevant antimicrobials and also that a "One Health" approach is required to assess issues related to antimicrobial resistance dynamics in anthropized landscapes.

DeepRank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces.

MOTIVATION: Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein-protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein-protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations. RESULTS: We have developed DeepRank-GNN, a framework that converts protein-protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN's performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank. AVAILABILITY AND IMPLEMENTATION: DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multiresistant Enterobacteriaceae in yellow-legged gull chicks in their first weeks of life.

Wild animal species living in anthropogenic areas are commonly carriers of antimicrobial-resistant bacteria (AMRB), but their role in the epidemiology of these bacteria is unclear. Several studies on AMRB in wildlife have been cross-sectional in design and sampled individual animals at only one point in time. To further understand the role of wildlife in maintaining and potentially transmitting these bacteria to humans and livestock, longitudinal studies are needed in which samples are collected from individual animals over multiple time periods. In Europe, free-ranging yellow-legged gulls (Larus michahellis) commonly live in industrialized areas, forage in landfills, and have been found to carry AMRB in their feces. Using bacterial metagenomics and antimicrobial resistance characterization, we investigated the spatial and temporal patterns of AMRB in a nesting colony of yellow-legged gulls from an industrialized area in southern France. We collected 54 cloacal swabs from 31 yellow-legged gull chicks in 20 nests on three dates in 2016. We found that AMRB in chicks increased over time and was not spatially structured within the gull colony. This study highlights the complex occurrence of AMRB in a free-ranging wildlife species and contributes to our understanding of the public health risks and implications associated with ARMB-carrying gulls living in anthropogenic areas.

PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes.

Deeper understanding of T-cell-mediated adaptive immune responses is important for the design of cancer immunotherapies and antiviral vaccines against pandemic outbreaks. T-cells are activated when they recognize foreign peptides that are presented on the cell surface by Major Histocompatibility Complexes (MHC), forming peptide:MHC (pMHC) complexes. 3D structures of pMHC complexes provide fundamental insight into T-cell recognition mechanism and aids immunotherapy design. High MHC and peptide diversities necessitate efficient computational modelling to enable whole proteome structural analysis. We developed PANDORA, a generic modelling pipeline for pMHC class I and II (pMHC-I and pMHC-II), and present its performance on pMHC-I here. Given a query, PANDORA searches for structural templates in its extensive database and then applies anchor restraints to the modelling process. This restrained energy minimization ensures one of the fastest pMHC modelling pipelines so far. On a set of 835 pMHC-I complexes over 78 MHC types, PANDORA generated models with a median RMSD of 0.70 Å and achieved a 93% success rate in top 10 models. PANDORA performs competitively with three pMHC-I modelling state-of-the-art approaches and outperforms AlphaFold2 in terms of accuracy while being superior to it in speed. PANDORA is a modularized and user-configurable python package with easy installation. We envision PANDORA to fuel deep learning algorithms with large-scale high-quality 3D models to tackle long-standing immunology challenges.

DeepRank: a deep learning framework for data mining 3D protein-protein interfaces.

Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.

Embolus Retriever with Interlinked Cages (ERIC) versus conventional stent retrievers for thrombectomy: a propensity score-based analysis.

BACKGROUND: The Embolus Retriever with Interlinked Cages (ERIC) is one of the latest devices for thrombectomies. It has several architectural features that are supposed to enhance its ability to remove clots and prevent distal emboli. We aimed to compare ERIC with standard stent retrievers (SRs) using propensity score (PS) matching. METHODS: The clinical and radiological data of all consecutive patients treated with ERIC or standard FDA-approved stent retrievers were collected from a prospective multicenter registry. We compared procedural outcomes (recanalization rates according to the modified Thrombolysis In Cerebral Infarction (mTICI) score and procedural complications) and clinical outcomes (modified Rankin Scale (mRS) and mortality at 3 months). Matching of the populations with PS was performed to account for differences in baseline characteristics. RESULTS: A total of 1230 patients were included. In both the PS-matched cohort (195 ERIC patients, 630 SR patients) and the inverse probability of treatment weighting PS-adjusted cohort (206 ERIC patients, 1024 SR patients) there was no difference in terms of successful recanalization (modified TICI score ≥2b), good clinical outcome (mRS=0-2 or equal to pre-stroke mRS), or mortality at 3 months. Patients treated with first-line ERIC had a higher rate of complete recanalization (mTICI 3); however, they also required more passes and more frequent rescue therapy than the SR patient group. CONCLUSION: In a large multicenter registry with PS matching, the ERIC device provided equivalent angiographic and clinical results to conventional SRs. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov Unique identifier: NCT03776877.

iScore: An MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machines.

Computational docking is a promising tool to model three-dimensional (3D) structures of protein-protein complexes, which provides fundamental insights of protein functions in the cellular life. Singling out near-native models from the huge pool of generated docking models (referred to as the scoring problem) remains as a major challenge in computational docking. We recently published iScore, a novel graph kernel based scoring function. iScore ranks docking models based on their interface graph similarities to the training interface graph set. iScore uses a support vector machine approach with random-walk graph kernels to classify and rank protein-protein interfaces. Here, we present the software for iScore. The software provides executable scripts that fully automate the computational workflow. In addition, the creation and analysis of the interface graph can be distributed across different processes using Message Passing interface (MPI) and can be offloaded to GPUs thanks to dedicated CUDA kernels.

iScore: a novel graph kernel-based function for scoring protein-protein docking models.

MOTIVATION: Protein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge. RESULTS: Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein-protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to, that of state-of-the-art scoring functions on two independent datasets: (i) Docking software-specific models and (ii) the CAPRI score set generated by a wide variety of docking approaches (i.e. docking software-non-specific). iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary, topological and energetic information for scoring docked conformations. This work represents the first successful demonstration of graph kernels to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. AVAILABILITY AND IMPLEMENTATION: The iScore code is freely available from Github: https://github.com/DeepRank/iScore (DOI: 10.5281/zenodo.2630567). And the docking models used are available from SBGrid: https://data.sbgrid.org/dataset/684). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The effect of the magnitude and direction of the dipoles of organic cations on the electronic structure of hybrid halide perovskites.

We present ab initio calculations (DFT and SOC-G0W0) of the optoelectronic properties of different hybrid-halide perovskites, namely X-PbI3 (X = methylamonimum, formamidinium, guanidinium, hydrazinium, and hydroxylammonium). These calculations shed new light on how the substitution of different organic cations in the material influences its optoelectronic properties. Our simulations show a significant modification of the lattice parameter and band gap of the material upon cation substitution. These modifications are not only due to steric effects but also due to electrostatic interactions between the organic and inorganic parts of the material. In addition to this, we demonstrate how the relative orientations of neighboring cations in the material modify the local electrostatic potential of the system and its fundamental band gap. This change in the band gap is accompanied by the formation of localized and spatially separated electronic states. These localized states modify the carrier mobility in the materials and can be a reason for the formation and recombination of the charge carriers in these very promising materials.

Computational Design of Two-Dimensional Perovskites with Functional Organic Cations.

Two-dimensional (2D) halide perovskites are a class of materials in which 2D layers of perovskite are separated by large organic cations. Conventionally, the 2D perovskites incorporate organic cations as spacers, but these organic cations also offer a route to introduce specific functionality in the material. In this work, we demonstrate, by density functional theory calculations, that the introduction of electron withdrawing and electron donating molecules leads to the formation of localized states, either in the organic or the inorganic part. Furthermore, we show that the energy of the bands located in the organic and inorganic parts can be tuned independently. The organic cation levels can be tuned by changing the electron withdrawing/donating character, whereas the energy levels in the inorganic part can be modified by varying the number of inorganic perovskite layers. This opens a new window for the design of 2D perovskites with properties tuned for specific applications.

Hot-electron transfer in quantum-dot heterojunction films.

Thermalization losses limit the photon-to-power conversion of solar cells at the high-energy side of the solar spectrum, as electrons quickly lose their energy relaxing to the band edge. Hot-electron transfer could reduce these losses. Here, we demonstrate fast and efficient hot-electron transfer between lead selenide and cadmium selenide quantum dots assembled in a quantum-dot heterojunction solid. In this system, the energy structure of the absorber material and of the electron extracting material can be easily tuned via a variation of quantum-dot size, allowing us to tailor the energetics of the transfer process for device applications. The efficiency of the transfer process increases with excitation energy as a result of the more favorable competition between hot-electron transfer and electron cooling. The experimental picture is supported by time-domain density functional theory calculations, showing that electron density is transferred from lead selenide to cadmium selenide quantum dots on the sub-picosecond timescale.

Signatures of Conformational Dynamics and Electrode-Molecule Interactions in the Conductance Profile During Pulling of Single-Molecule Junctions.

We demonstrate that conductance can act as a sensitive probe of conformational dynamics and electrode-molecule interactions during the equilibrium and nonequilibrium pulling of molecular junctions. To do so, we use a combination of classical molecular dynamics simulations and Landauer electron transport computations to investigate the conductance of a family of Au-alkanedithiol-Au junctions as they are mechanically elongated. The simulations show an overall decay of the conductance during pulling that is due to a decrease in the through-space electrode-molecule interactions, and that sensitivity depends on the electrode geometry. In addition, characteristic kinks induced by level alignment shifts (and to a lesser extent by quantum destructive interference) were also observed superimposed to the overall decay during pulling simulations. The latter effect depends on the variation of the molecular dihedral angles during pulling and therefore offers an efficient solution to experimentally monitor conformational dynamics at the single-molecule limit.

Interconversion between Free Charges and Bound Excitons in 2D Hybrid Lead Halide Perovskites.

The optoelectronic properties of hybrid perovskites can be easily tailored by varying their components. Specifically, mixing the common short organic cation (methylammonium (MA)) with a larger one (e.g., butyl ammonium (BA)) results in 2-dimensional perovskites with varying thicknesses of inorganic layers separated by the large organic cation. In both of these applications, a detailed understanding of the dissociation and recombination of electron-hole pairs is of prime importance. In this work, we give a clear experimental demonstration of the interconversion between bound excitons and free charges as a function of temperature by combining microwave conductivity techniques with photoluminescence measurements. We demonstrate that the exciton binding energy varies strongly (between 80 and 370 meV) with the thickness of the inorganic layers. Additionally, we show that the mobility of charges increases with the layer thickness, in agreement with calculated effective masses from electronic structure calculations.

Temperature Dependent Charge Carrier Dynamics in Formamidinium Lead Iodide Perovskite.

The fundamental opto-electronic properties of organic-inorganic hybrid perovskites are strongly affected by their structural parameters. These parameters are particularly critical in formamidinium lead iodide (FAPbI3), in which its large structural disorder leads to a non-perovskite yellow phase that hinders its photovoltaic performance. A clear understanding of how the structural parameters affect the opto-electronic properties is currently lacking. We have studied the opto-electronic properties of FAPbI3 using microwave conductivity measurements. We find that the mobility of FAPbI3 increases at low temperature following a phonon scattering behavior. Unlike methylammonium lead iodide (MAPbI3), there are no abrupt changes after the low-temperature ß/γ phase transition and the lifetime is remarkably long. This absence of abrupt changes can be understood in terms of the reduced rotational freedom and smaller dipole moment of the formamidinium, as compared to methylammonium.

Evolution in fecal bacterial/viral composition in infants of two central African countries (Gabon and Republic of the Congo) during their first month of life.

Few studies have analyzed the gut microbiota of child in unindustrialized countries, but none during the first month of life. Stool samples were collected from healthy newborns in hospitals of Gabon (n = 6) and Republic of the Congo (n = 9) at different time points during the first month of life: meconium, day 2 (D02), day 7 (D07) and day 28 (D28). In addition, one fecal sample was collected from each mother after delivery. Metagenomic sequencing was performed to determine the bacterial communities, and multiplex real-time PCR was used to detect the presence of seven enteric viruses (rotavirus a, adenovirus, norovirus I and II, sapovirus, astrovirus, enterovirus) in these samples. Bacterial diversity was high in the first days of life, and was dominated by the genus Prevotella. Then, it rapidly decreased and remained low up to D28 when the gut flora was composed almost exclusively of strictly anaerobic bacteria. Each infant's fecal bacterial microbiota composition was significantly closer to that of their mother than to that of any other woman in the mothers' group, suggesting an intrauterine, placental or amniotic fluid origin of such bacteria. Moreover, bacterial communities differed according to the delivery mode. Overall, the bacterial microbiota communities displayed a similar diversification and expansion in newborns within and between countries during the first four weeks of life. Moreover, six of the fifteen infants of this study harbored enteric viruses (rotavirus, enterovirus and adenovirus) in fecal samples, but never in the meconium. A maternal source for the viruses detected at D02 and D07 can be excluded because none of them was found also in the child's mother. These findings improve our knowledge on the gut bacterial and viral communities of infants from two Sub-Saharan countries during their first month of life.

Design and Characterization of an Electrically Powered Single Molecule on Gold.

The surface diffusion of individual molecules is of paramount importance in self-assembly processes and catalytic processes. However, the fundamental understanding of molecule diffusion peculiarities considering conformations and adsorption sites remain poorly known at the atomic scale. Here, we probe the 4'-(4-tolyl)-2,2':6',2″-terpyridine adsorbed on the Au(111) herringbone structure combining scanning tunneling microscopy and atomic force microscopy. Molecules are controllably translated by electrons excitations over the reconstruction, except at elbows acting as pinning centers. Experimental data supported by theoretical calculations show the formation of coordination bonds between the molecule and Au atoms of the surface. Using force spectroscopy, we quantify local variation of the surface potential and the lateral force required to move the molecule. We found an elevation of the diffusion barrier at elbows of the reconstruction of ∼100 meV compared to the rest of the surface.

Ga for Zn Cation Exchange Allows for Highly Luminescent and Photostable InZnP-Based Quantum Dots.

In this work, we demonstrate that a preferential Ga-for-Zn cation exchange is responsible for the increase in photoluminescence that is observed when gallium oleate is added to InZnP alloy QDs. By exposing InZnP QDs with varying Zn/In ratios to gallium oleate and monitoring their optical properties, composition, and size, we conclude that Ga3+ preferentially replaces Zn2+, leading to the formation of InZnP/InGaP core/graded-shell QDs. This cation exchange reaction results in a large increase of the QD photoluminescence, but only for InZnP QDs with Zn/In ≥ 0.5. For InP QDs that do not contain zinc, Ga is most likely incorporated only on the quantum dot surface, and a PL enhancement is not observed. After further growth of a GaP shell and a lattice-matched ZnSeS outer shell, the cation-exchanged InZnP/InGaP QDs continue to exhibit superior PL QY (over 70%) and stability under long-term illumination (840 h, 5 weeks) compared to InZnP cores with the same shells. These results provide important mechanistic insights into recent improvements in InP-based QDs for luminescent applications.

VIM-1 carbapenemase-producing Escherichia coli in gulls from southern France.

Acquired carbapenemases currently pose one of the most worrying public health threats related to antimicrobial resistance. A NDM-1-producing Salmonella Corvallis was reported in 2013 in a wild raptor. Further research was needed to understand the role of wild birds in the transmission of bacteria resistant to carbapenems. Our aim was to investigate the presence of carbapenem-resistant Escherichia coli in gulls from southern France. In 2012, we collected 158 cloacal swabs samples from two gull species: yellow-legged gulls (Larus michahellis) that live in close contact with humans and slender-billed gulls (Chroicocephalus genei) that feed at sea. We molecularly compared the carbapenem-resistant bacteria we isolated through culture on selective media with the carbapenem-susceptible strains sampled from both gull species and from stool samples of humans hospitalized in the study area. The genes coding for carbapenemases were tested by multiplex PCR. We isolated 22 carbapenem-resistant E. coli strains from yellow-legged gulls while none were isolated from slender-billed gulls. All carbapenem-resistant isolates were positive for blaVIM-1 gene. VIM-1-producing E. coli were closely related to carbapenem-susceptible strains isolated from the two gull species but also to human strains. Our results are alarming enough to make it urgently necessary to determine the contamination source of the bacteria we identified. More generally, our work highlights the need to develop more bridges between studies focusing on wildlife and humans in order to improve our knowledge of resistant bacteria transmission routes.

High Electronic Conductance through Double-Helix DNA Molecules with Fullerene Anchoring Groups.

Determining the mechanism of charge transport through native DNA remains a challenge as different factors such as measuring conditions, molecule conformations, and choice of technique can significantly affect the final results. In this contribution, we have used a new approach to measure current flowing through isolated double-stranded DNA molecules, using fullerene groups to anchor the DNA to a gold substrate. Measurements were performed at room temperature in an inert environment using a conductive AFM technique. It is shown that the π-stacked B-DNA structure is conserved on depositing the DNA. As a result, currents in the nanoampere range were obtained for voltages ranging between ±1 V. These experimental results are supported by a theoretical model that suggests that a multistep hopping mechanism between delocalized domains is responsible for the long-range current flow through this specific type of DNA.

Mechanically controlled quantum interference in individual π-stacked dimers.

Recent observations of destructive quantum interference in single-molecule junctions confirm the role of quantum effects in the electronic conductance properties of molecular systems. These effects are central to a broad range of chemical and biological processes and may be beneficial for the design of single-molecule electronic components to exploit the intrinsic quantum effects that occur at the molecular scale. Here we show that destructive interference can be turned on or off within the same molecular system by mechanically controlling its conformation. Using a combination of ab initio calculations and single-molecule conductance measurements, we demonstrate the existence of a quasiperiodic destructive quantum-interference pattern along the breaking traces of π-stacked molecular dimers. The results demonstrate that it is possible to control the molecular conductance over more than one order of magnitude and with a sub-ångström resolution by exploiting the subtle structure-property relationship of π-stacked dimers.