Mapping cumulative compound hydrometeorological and marine-induced risks on the NW Mediterranean coast.

Coastal risks in the Mediterranean are a result of the complex interplay between hydrometeorological and marine hazards. The region encompasses areas with varying degrees of vulnerability to these hazards, as well as spatial variations in exposure values, making it essential to adopt a comprehensive and nuanced approach to risk assessment and management. It is worth noting that hydrometeorological hazards, such as flash floods, can often have a greater impact than strictly coastal hazards, highlighting the need to consider the full range of potential risks. Therefore, coastal managers must adopt a multi-hazard approach to make sound risk management decisions. This study addresses this need using an index-based framework that assesses the integrated risk in time and space (hereafter referred to as cumulative compound risk) in coastal zones by aggregating the main hydrometeorological and marine hazards, the vulnerability of the territory to both types of hazards, and values at exposure. The framework is designed for use at large spatial scales (applied to a 1100 km coastline in this study), with the basic spatial unit being relevant for management (here set as the municipality in this study). Its application enables the assessment of spatial variations in integrated risk as well as individual hydrometeorological and marine contributions. The combined use of the indices and cluster analysis helps identify similarities and differences in the risk profile of spatial units, and thus, define homogeneous areas from a risk management perspective. In this study, the framework was applied to the Spanish Mediterranean coastline, an area representative of the climatic, geomorphological, and socioeconomic conditions of the Mediterranean coast.

In Vitro Antibacterial Activity of Silver Nanoparticles Conjugated with Amikacin and Combined with Hyperthermia against Drug-Resistant and Biofilm-Producing Strains.

In view of the current increase and spread of antimicrobial resistance (AMR), there is an urgent need to find new strategies to combat it. This study had two aims. First, we synthesized highly monodispersed silver nanoparticles (AgNPs) of approximately 17 nm, and we functionalized them with mercaptopoly(ethylene glycol) carboxylic acid (mPEG-COOH) and amikacin (AK). Second, we evaluated the antibacterial activity of this treatment (AgNPs_mPEG_AK) alone and in combination with hyperthermia against planktonic and biofilm-growing strains. AgNPs, AgNPs_mPEG, and AgNPs_mPEG_AK were characterized using a suite of spectroscopy and microscopy methods. Susceptibility to these treatments and AK was determined after 24 h and over time against 12 clinical multidrug-resistant (MDR)/extensively drug-resistant (XDR) isolates of Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The efficacy of the treatments alone and in combination with hyperthermia (1, 2, and 3 pulses at 41°C to 42°C for 15 min) was tested against the same planktonic strains using quantitative culture and against one P. aeruginosa strain growing on silicone disks using confocal laser scanning microscopy. The susceptibility studies showed that AgNPs_mPEG_AK was 10-fold more effective than AK alone, and bactericidal efficacy after 4, 8, 24, or 48 h was observed against 100% of the tested strains. The combination of AgNPs_mPEG_AK and hyperthermia eradicated 75% of the planktonic strains and exhibited significant reductions in biofilm formation by P. aeruginosa in comparison with the other treatments tested, except for AgNPs_mPEG_AK without hyperthermia. In conclusion, the combination of AgNPs_mPEG_AK and hyperthermia may be a promising therapy against MDR/XDR and biofilm-producing strains. IMPORTANCE Antimicrobial resistance (AMR) is one of the greatest public health challenges, accounting for 1.27 million deaths worldwide in 2019. Biofilms, a complex microbial community, directly contribute to increased AMR. Therefore, new strategies are urgently required to combat infections caused by AMR and biofilm-producing strains. Silver nanoparticles (AgNPs) exhibit antimicrobial activity and can be functionalized with antibiotics. Although AgNPs are very promising, their effectiveness in complex biological environments still falls below the concentrations at which AgNPs are stable in terms of aggregation. Thus, improving the antibacterial effectiveness of AgNPs by functionalizing them with antibiotics may be a significant change to consolidate AgNPs as an alternative to antibiotics. It has been reported that hyperthermia has a large effect on the growth of planktonic and biofilm-producing strains. Therefore, we propose a new strategy based on AgNPs functionalized with amikacin and combined with hyperthermia (41°C to 42°C) to treat AMR and biofilm-related infections.

Structural basis for substrate specificity of heteromeric transporters of neutral amino acids.

Despite having similar structures, each member of the heteromeric amino acid transporter (HAT) family shows exquisite preference for the exchange of certain amino acids. Substrate specificity determines the physiological function of each HAT and their role in human diseases. However, HAT transport preference for some amino acids over others is not yet fully understood. Using cryo-electron microscopy of apo human LAT2/CD98hc and a multidisciplinary approach, we elucidate key molecular determinants governing neutral amino acid specificity in HATs. A few residues in the substrate-binding pocket determine substrate preference. Here, we describe mutations that interconvert the substrate profiles of LAT2/CD98hc, LAT1/CD98hc, and Asc1/CD98hc. In addition, a region far from the substrate-binding pocket critically influences the conformation of the substrate-binding site and substrate preference. This region accumulates mutations that alter substrate specificity and cause hearing loss and cataracts. Here, we uncover molecular mechanisms governing substrate specificity within the HAT family of neutral amino acid transporters and provide the structural bases for mutations in LAT2/CD98hc that alter substrate specificity and that are associated with several pathologies.

Mutations Suppressing the Lack of Prepilin Peptidase Provide Insights Into the Maturation of the Major Pilin Protein in Cyanobacteria.

Type IV pili are bacterial surface-exposed filaments that are built up by small monomers called pilin proteins. Pilins are synthesized as longer precursors (prepilins), the N-terminal signal peptide of which must be removed by the processing protease PilD. A mutant of the cyanobacterium Synechocystis sp. PCC 6803 lacking the PilD protease is not capable of photoautotrophic growth because of the impaired function of Sec translocons. Here, we isolated phototrophic suppressor strains of the original ΔpilD mutant and, by sequencing their genomes, identified secondary mutations in the SigF sigma factor, the γ subunit of RNA polymerase, the signal peptide of major pilin PilA1, and in the pilA1-pilA2 intergenic region. Characterization of suppressor strains suggests that, rather than the total prepilin level in the cell, the presence of non-glycosylated PilA1 prepilin is specifically harmful. We propose that the restricted lateral mobility of the non-glycosylated PilA1 prepilin causes its accumulation in the translocon-rich membrane domains, which attenuates the synthesis of membrane proteins.

Combining Monte Carlo and Molecular Dynamics Simulations for Enhanced Binding Free Energy Estimation through Markov State Models.

We present a multistep protocol, combining Monte Carlo and molecular dynamics simulations, for the estimation of absolute binding free energies, one of the most significant challenges in computer-aided drug design. The protocol is based on an initial short enhanced Monte Carlo simulation, followed by clustering of the ligand positions, which serve to identify the most relevant states of the unbinding process. From these states, extensive molecular dynamics simulations are run to estimate an equilibrium probability distribution obtained with Markov State Models, which is subsequently used to estimate the binding free energy. We tested the procedure on two different protein systems, the Plasminogen kringle domain 1 and Urokinase, each with multiple ligands, for an aggregated molecular dynamics length of 760 µs. Our results indicate that the initial sampling of the unbinding events largely facilitates the convergence of the subsequent molecular dynamics exploration. Moreover, the protocol is capable to properly rank the set of ligands examined, albeit with a significant computational cost for the, more realistic, Urokinase complexes. Overall, this work demonstrates the usefulness of combining enhanced sampling methods with regular simulation techniques as a way to obtain more reliable binding affinity estimates.

Spatial Variability of Heat-Related Mortality in Barcelona from 1992-2015: A Case Crossover Study Design.

Numerous studies have demonstrated the relationship between summer temperatures and increased heat-related deaths. Epidemiological analyses of the health effects of climate exposures usually rely on observations from the nearest weather station to assess exposure-response associations for geographically diverse populations. Urban climate models provide high-resolution spatial data that may potentially improve exposure estimates, but to date, they have not been extensively applied in epidemiological research. We investigated temperature-mortality relationships in the city of Barcelona, and whether estimates vary among districts. We considered georeferenced individual (natural) mortality data during the summer months (June-September) for the period 1992-2015. We extracted daily summer mean temperatures from a 100-m resolution simulation of the urban climate model (UrbClim). Summer hot days (above percentile 70) and reference (below percentile 30) temperatures were compared by using a conditional logistic regression model in a case crossover study design applied to all districts of Barcelona. Relative Risks (RR), and 95% Confidence Intervals (CI), of all-cause (natural) mortality and summer temperature were calculated for several population subgroups (age, sex and education level by districts). Hot days were associated with an increased risk of death (RR = 1.13; 95% CI = 1.10-1.16) and were significant in all population subgroups compared to the non-hot days. The risk ratio was higher among women (RR = 1.16; 95% CI= 1.12-1.21) and the elderly (RR = 1.18; 95% CI = 1.13-1.22). Individuals with primary education had similar risk (RR = 1.13; 95% CI = 1.08-1.18) than those without education (RR = 1.10; 95% CI= 1.05-1.15). Moreover, 6 out of 10 districts showed statistically significant associations, varying the risk ratio between 1.12 (95% CI = 1.03-1.21) in Sants-Montjuïc and 1.25 (95% CI = 1.14-1.38) in Sant Andreu. Findings identified vulnerable districts and suggested new insights to public health policy makers on how to develop district-specific strategies to reduce risks.

PELE-MSM: A Monte Carlo Based Protocol for the Estimation of Absolute Binding Free Energies.

In this study, we present a fully automatic platform based on our Monte Carlo algorithm, the Protein Energy Landscape Exploration method (PELE), for the estimation of absolute protein-ligand binding free energies, one of the most significant challenges in computer aided drug design. Based on a ligand pathway approach, an initial short enhanced sampling simulation is performed to identify reasonable starting positions for more extended sampling. This stepwise approach allows for a significant faster convergence of the free energy estimation using the Markov State Model (MSM) technique. PELE-MSM was applied on four diverse protein and ligand systems, successfully ranking compounds for two systems. Based on the results, current limitations and challenges with physics-based methods in computational structural biology are discussed. Overall, PELE-MSM constitutes a promising step toward computing absolute binding free energies and in their application into drug discovery projects.

Adaptive simulations, towards interactive protein-ligand modeling.

Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.