In silico Prediction of Pranlukast as a Stabilizer of PD-L1 Homodimers.

INTRODUCTION: Tumors can be targeted by modulating the immune response of the patient. Programmed cell death protein 1 (PD-1) and programmed cell death ligand 1 (PD-L1) are critical immune checkpoints in cancer biology. The efficacy of certain cancer immunotherapies has been achieved by targeting these molecules using monoclonal antibodies. METHOD: Small-molecule drugs have also been developed as inhibitors of the PD-1/PD-L1 axis, with a mechanism of action that is distinct from that of antibodies: they induce the formation of PD-L1 homodimers, causing their stabilization, internalization, and subsequent degradation. Drug repurposing is a strategy in which new uses are sought after for approved drugs, expediting their clinical translation based on updated findings. In this study, we generated a pharmacophore model that was based on reported small molecules that targeted PD-L1 and used it to identify potential PD-L1 inhibitors among FDA-approved drugs. RESULTS: We identified 12 pharmacophore-matching compounds, but only 4 reproduced the binding mode of the reference inhibitors in docking experiments. Further characterization by molecular dynamics showed that pranlukast, an antagonist of leukotriene receptors that is used to treat asthma, generated stable and energyfavorable interactions with PD-L1 homodimers and induced homodimerization of recombinant PD-L1. CONCLUSION: Our results suggest that pranlukast inhibits the PD-1/PD-L1 axis, meriting its repurposing as an antitumor drug.

Ursolic acid interaction with transcription factors BRAF, V600E, and V600K: a computational approach towards new potential melanoma treatments.

CONTEXT: Melanoma is one of the cancers with the highest mortality rate for its ability to metastasize. Several targets have undergone investigation for the development of drugs against this pathology. One of the main targets is the kinase BRAF (RAF, rapidly accelerated fibrosarcoma). The most common mutation in melanoma is BRAFV600E and has been reported in 50-90% of patients with melanoma. Due to the relevance of the BRAFV600E mutation, inhibitors to this kinase have been developed, vemurafenib-OMe and dabrafenib. Ursolic acid (UA) is a pentacyclic triterpene with a privileged structure, the pentacycle scaffold, which allows to have a broad variety of biological activity; the most studied is its anticancer capacity. In this work, we reported the interaction profile of vemurafenib-OMe, dabrafenib, and UA, to define whether UA has binding capacity to BRAFWT, BRAFV600E, and BRAFV600K. Homology modeling of BRAFWT, V600E, and V600K; molecular docking; and molecular dynamics simulations were carried out and interactions and residues relevant to the binding of the inhibitors were obtained. We found that UA, like the inhibitors, presents hydrogen bond interactions, and hydrophobic interactions of van der Waals, and π-stacking with I463, Q530, C532, and F583. The ΔG of ursolic acid in complex with BRAFV600K (- 63.31 kcal/mol) is comparable to the ΔG of the selective inhibitor dabrafenib (- 63.32 kcal/mol) in complex to BRAFV600K and presents a ΔG like vemurafenib-OMe with BRAFWT and V600E. With this information, ursolic acid could be considered as a lead compound for design cycles and to optimize the binding profile and the selectivity towards mutations for the development of new selective inhibitors for BRAFV600E and V600K to new potential melanoma treatments. METHODS: The homology modeling calculations were executed on the public servers I-TASSER and ROBETTA, followed by molecular docking calculations using AutoGrid 4.2.6, AutoDockGPU 1.5.3, and AutoDockTools 1.5.6. Molecular dynamics and metadynamics simulations were performed in the Desmond module of the academic version of the Schrödinger-Maestro 2020-4 program, utilizing the OPLS-2005 force field. Ligand-protein interactions were evaluated using Schrödinger-Maestro program, LigPlot + , and PLIP (protein-ligand interaction profiler). Finally, all of the protein figures presented in this article were made in the PyMOL program.

In Silico Analysis of the Molecular Interaction between Anthocyanase, Peroxidase and Polyphenol Oxidase with Anthocyanins Found in Cranberries.

Anthocyanins are bioactive compounds responsible for various physiological processes in plants and provide characteristic colors to fruits and flowers. Their biosynthetic pathway is well understood; however, the enzymatic degradation mechanism is less explored. Anthocyanase (ß-glucosidase (BGL)), peroxidase (POD), and polyphenol oxidase (PPO) are enzymes involved in degrading anthocyanins in plants such as petunias, eggplants, and Sicilian oranges. The aim of this work was to investigate the physicochemical interactions between these enzymes and the identified anthocyanins (via UPLC-MS/MS) in cranberry (Vaccinium macrocarpon) through molecular docking to identify the residues likely involved in anthocyanin degradation. Three-dimensional models were constructed using the AlphaFold2 server based on consensus sequences specific to each enzyme. The models with the highest confidence scores (pLDDT) were selected, with BGL, POD, and PPO achieving scores of 87.6, 94.8, and 84.1, respectively. These models were then refined using molecular dynamics for 100 ns. Additionally, UPLC-MS/MS analysis identified various flavonoids in cranberries, including cyanidin, delphinidin, procyanidin B2 and B4, petunidin, pelargonidin, peonidin, and malvidin, providing important experimental data to support the study. Molecular docking simulations revealed the most stable interactions between anthocyanase and the anthocyanins cyanidin 3-arabinoside and cyanidin 3-glucoside, with a favorable ΔG of interaction between -9.3 and -9.2 kcal/mol. This study contributes to proposing a degradation mechanism and seeking inhibitors to prevent fruit discoloration.

Structural inequality and temporal brain dynamics across diverse samples.

BACKGROUND: Structural income inequality - the uneven income distribution across regions or countries - could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored. METHODS: Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries = 10; healthy individuals = 1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed. FINDINGS: Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions. CONCLUSION: These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.

Species Substitution and Changes in the Structure, Volume, and Biomass of Forest in a Savanna.

Research related to Cerradão vegetation focuses more on the floristic-structural aspect, with rare studies on the quantification of volume and biomass stocks, and even fewer investigating the increments of these attributes. Using a systematic sampling method with subdivided strips and 400 m2 plots, the density found was 1135, 1165, and 1229 trees/ha in 2012, 2020, and 2023, respectively, in Lajeado State Park, Tocantins State, Brazil. Volume was estimated using the equation v=0.000085D2.122270H0.666217, and biomass was estimated using the equation AGB=0.0673ρD2H0.976. Vegetation dynamics were assessed using growth increment, recruitment, mortality, turnover rate, and time. The results indicated that dynamics have increased since the start of monitoring. Typical Cerrado species, in the strict sense, were replaced by those from forest environments. The total production in volume and biomass was 160.91 m3/ha and 118.10 Mg/ha, respectively, in 2023. The species of Emmotum nitens, Mezilaurus itauba, Ocotea canaliculata, and Sacoglottis guianensis showed the highest increment values in volume and biomass. For the community, the average values were 4.04 m3/ha/year and 3.54 Mg/ha/year. The community has not yet reached its carrying capacity and stores a significant amount of biomass. This is influenced by the transition of the study area from an exploited environment to a conservation unit (park) and by its location in a transitional area with the Amazon biome.

The smallest electrochemical bubbles.

Many of the relevant electrochemical processes in the context of catalysis or energy conversion and storage, entail the production of gases. This often implicates the nucleation of bubbles at the interface, with the concomitant blockage of the electroactive area leading to overpotentials and Ohmic drop. Nanoelectrodes have been envisioned as assets to revert this effect, by inhibiting bubble formation. Experiments show, however, that nanobubbles nucleate and attach to nanoscale electrodes, imposing a limit to the current, which turns out to be independent of size and applied potential in a wide range from 3 nm to tenths of microns. Here we investigate the potential-current response for disk electrodes of diameters down to a single-atom, employing molecular simulations including electrochemical generation of gas. Our analysis reveals that nanoelectrodes of 1 nm can offer twice as much current as that delivered by electrodes with areas four orders of magnitude larger at the same bias. This boost in the extracted current is a consequence of the destabilization of the gas phase. The grand potential of surface nanobubbles shows they can not reach a thermodynamically stable state on supports below 2 nm. As a result, the electroactive area becomes accessible to the solution and the current turns out to be sensitive to the electrode radius. In this way, our simulations establish that there is an optimal size for the nanoelectrodes, in between the single-atom and ∼3 nm, that optimizes the gas production.

Unraveling the Spin-to-Charge Current Conversion Mechanism and Charge Transfer Dynamics at the Interface of Graphene/WS2 Heterostructures at Room Temperature.

We report experimental investigations of spin-to-charge current conversion and charge transfer (CT) dynamics at the interface of the graphene/WS2 van der Waals heterostructure. Pure spin current was produced by the spin precession in the microwave-driven ferromagnetic resonance of a permalloy film (Py=Ni81Fe19) and injected into the graphene/WS2 heterostructure through a spin pumping process. The observed spin-to-charge current conversion in the heterostructure is attributed to the inverse Rashba-Edelstein effect (IREE) at the graphene/WS2 interface. Interfacial CT dynamics in this heterostructure was investigated based on the framework of the core-hole clock (CHC) approach. The results obtained from spin pumping and CHC studies show that the spin-to-charge current conversion and charge transfer processes are more efficient in the graphene/WS2 heterostructure compared to isolated WS2 and graphene films. The results show that the presence of WS2 flakes improves the current conversion efficiency. These experimental results are corroborated by density functional theory (DFT) calculations, which reveal (i) Rashba spin-orbit splitting of graphene orbitals and (ii) electronic coupling between graphene and WS2 orbitals. This study provides valuable insights for optimizing the design and performance of spintronic devices.

Insight from atomistic molecular dynamics simulations into the supramolecular assembly of the aldo-keto reductase from Trypanosoma cruzi.

CONTEXT: Currently, Chagas disease represents an important public health problem affecting more than 8 million people worldwide. The vector of this disease is the Trypanosoma cruzi (Tc) parasite. Our research specifically focuses on the structure and aggregation states of the enzyme aldo-keto reductase of Tc (TcAKR) reported in this parasite. TcAKR belongs to the aldo-keto reductase (AKR) superfamily, enzymes that catalyze redox reactions involved in crucial biological processes. While most AKRs are found in monomeric forms, some have been reported to form dimeric and tetrameric structures. This is the case for some TcAKR. To better understand how TcAKR multimers form and remain stable, we conducted a comprehensive computational analysis using molecular dynamics (MD) simulations. Our approach to elucidating the aggregation states of TcAKR involved two strategies. Initially, we explored the dynamic behaviour of pre-assembled TcAKR dimers. Subsequently, we examined the self-aggregation of eight monomers. This investigation led to the identification of crucial residues that contribute to the stabilization of protein-protein interactions. It was also found that TcAKRs can form stable supramolecular assemblies, with each monomer typically surrounded by three first neighbours. These findings align with experimental reports of tetrameric or more complex supramolecular structures. Our computational studies could guide further experimental investigations aiming at drug development and assist in designing strategies to modulate aggregation. METHOD: Atomistic molecular dynamics simulations were carried out. The TcAKR 3D model structure was obtained by homology modelling using the Swiss Model for the TcAKR sequence (GenBank accession no. EU558869). Further, we checked the model with Alphafold2 and found a high degree of similarity between models. Several tools were used to build the dimers including CLUSPRO, GRAMM-Docking, Hdock, and Py-dock. Protein superstructures were built using the PACKMOL package. CHARMM-GUI was used to set up the simulation systems. GROMACS version 2020.5 was used to perform the simulations with the CHARMM36 force field for the protein and ions and the TIP3P model for water. Further analyses were performed using VMD, GROMACS, AMBER tools, MDLovoFit, bio3d, and in-house programs.

Temporal dynamics of Legionella (Proteobacteria, Legionellaceae) in two Pampean shallow lakes from Argentina.

Aquatic systems have traditionally played a key role in the development of human life, providing multiple ecosystem services to society and being a reservoir for a wide biodiversity of organisms. Among them, bacteria belonging to Legionella stand out, mainly because they are of great interest both in the field of microbial ecology and public health, since some of them turn out to be pathogenic for humans. The aim of this work was to study the monthly temporal dynamics of Legionella spp. and its relationship with the environmental variables measured in two Pampean shallow lakes (Gómez and Carpincho, Buenos Aires Province, Argentina). The analysis was carried out using a quantitative approach by real-time polymerase chain reaction (qPCR) and a non-quantitative approach using bacterial diversity data obtained by next-generation sequencing (NGS), using the Illumina MiSeq platform. Our results showed that the overall Legionella abundance was very high in the studied Pampean shallow lakes. Notably, fluctuations in dissolved organic carbon and temperature influenced the dynamics shifts in Legionella abundances. Correlation analyses between Legionella reads from NGS and copy numbers obtained through qPCR revealed positive relationships, unveiling distinctions attributable to the diverse sequence processing algorithms employed in the analysis of NGS data.

The Endocannabinoid Peptide RVD-Hemopressin Is a TRPV1 Channel Blocker.

Neurotransmission is critical for brain function, allowing neurons to communicate through neurotransmitters and neuropeptides. RVD-hemopressin (RVD-Hp), a novel peptide identified in noradrenergic neurons, modulates cannabinoid receptors CB1 and CB2. Unlike hemopressin (Hp), which induces anxiogenic behaviors via transient receptor potential vanilloid 1 (TRPV1) activation, RVD-Hp counteracts these effects, suggesting that it may block TRPV1. This study investigates RVD-Hp's role as a TRPV1 channel blocker using HEK293 cells expressing TRPV1-GFP. Calcium imaging and patch-clamp recordings demonstrated that RVD-Hp reduces TRPV1-mediated calcium influx and TRPV1 ion currents. Molecular docking and dynamics simulations indicated that RVD-Hp interacts with TRPV1's selectivity filter, forming stable hydrogen bonds and van der Waals contacts, thus preventing ion permeation. These findings highlight RVD-Hp's potential as a therapeutic agent for conditions involving TRPV1 activation, such as pain and anxiety.

Synergistic effect of new pyrimidine derivative with oxacilin against methicillin-resistant Staphylococcus aureus.

Aim: This work aimed to synthesize a new pyrimidine PYB01 with potential application against antimicrobial resistance.Materials & methods: PYB01 was synthesized through condensation reaction between 3a and 3b. The antimicrobial evaluation was carried out using the microdilution method in Mueller-Hinton Agar and in silico predictions using different software.Results: PYB01 has moderate antibiotic activity and high capacity to efficiently modulate antibiotic resistance in Staphylococcus aureus. In silico evaluations demonstrated that PYB01 is probably an allosteric inhibitor of Protein Binding Penicilin 2a and modulates the action of oxacillin by decreasing the minimum inhibitory concentration by 64-times. PYB01 demonstrate a good pharmacokinetic profile and toxicological.Conclusion: PYB01 has great potential to go further in investigating its use against antimicrobial resistance.

[Box: see text].

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.

Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based approaches have led to the understanding of specific structural changes observed in MD trajectories, including those induced by mutations. In this study, we model the trajectories resulting from MD simulations of the SARS-CoV-2 spike protein-ACE2, specifically the receptor-binding domain (RBD), as interresidue distance maps, and use deep convolutional neural networks to predict the functional impact of point mutations, related to the virus's infectivity and immunogenicity. Our model was successful in predicting mutant types that increase the affinity of the S protein for human receptors and reduce its immunogenicity, both based on MD trajectories (precision = 0.718; recall = 0.800; [Formula: see text] = 0.757; MCC = 0.488; AUC = 0.800) and their centroids. In an additional analysis, we also obtained a strong positive Pearson's correlation coefficient equal to 0.776, indicating a significant relationship between the average sigmoid probability for the MD trajectories and binding free energy (BFE) changes. Furthermore, we obtained a coefficient of determination of 0.602. Our 2D-RMSD analysis also corroborated predictions for more infectious and immune-evading mutants and revealed fluctuating regions within the receptor-binding motif (RBM), especially in the [Formula: see text] loop. This region presented a significant standard deviation for mutations that enable SARS-CoV-2 to evade the immune response, with RMSD values of 5Å in the simulation. This methodology offers an efficient alternative to identify potential strains of SARS-CoV-2, which may be potentially linked to more infectious and immune-evading mutations. Using clustering and deep learning techniques, our approach leverages information from the ensemble of MD trajectories to recognize a broad spectrum of multiple conformational patterns characteristic of mutant types. This represents a strategic advantage in identifying emerging variants, bypassing the need for long MD simulations. Furthermore, the present work tends to contribute substantially to the field of computational biology and virology, particularly to accelerate the design and optimization of new therapeutic agents and vaccines, offering a proactive stance against the constantly evolving threat of COVID-19 and potential future pandemics.

Mathematical Modeling of Mating Probability and Fertile Egg Production in Helminth Parasites.

In this work, we obtained a general formulation for the mating probability and fertile egg production in helminth parasites, focusing on the reproductive behavior of polygamous parasites and its implications for transmission dynamics. By exploring various reproductive variables in parasites with density-dependent fecundity, such as helminth parasites, we departed from the traditional assumptions of Poisson and negative binomial distributions to adopt an arbitrary distribution model. Our analysis considered critical factors such as mating probability, fertile egg production, and the distribution of female and male parasites among hosts, whether they are distributed together or separately. We show that the distribution of parasites within hosts significantly influences transmission dynamics, with implications for parasite persistence and, therefore, with implications in parasite control. Using statistical models and empirical data from Monte Carlo simulations, we provide insights into the complex interplay of reproductive variables in helminth parasites, enhancing our understanding of parasite dynamics and the transmission of parasitic diseases.

Severe fire regimes decrease resilience of ectothermic populations.

Understanding populations' responses to environmental change is crucial for mitigating human-induced disturbances. Here, we test hypotheses regarding how three essential components of demographic resilience (resistance, compensation and recovery) co-vary along the distinct life histories of three lizard species exposed to variable, prescribed fire regimes. Using a Bayesian hierarchical framework, we estimate vital rates (survival, growth and reproduction) with 14 years of monthly individual-level data and mark-recapture models to parameterize stochastic integral projection models from five sites in Brazilian savannas, each historically subjected to different fire regimes. With these models, we investigate how weather, microclimate and ecophysiological traits of each species influence their vital rates, emergent life history traits and demographic resilience components in varying fire regimes. Overall, weather and microclimate are better predictors of the species' vital rates, rather than their ecophysiological traits. Our findings reveal that severe fire regimes increase populations' resistance but decrease compensation or recovery abilities. Instead, populations have higher compensatory and recovery abilities at intermediate degrees of fire severity. Additionally, we identify generation time and reproductive output as predictors of resilience trends across fire regimes and climate. Our analyses demonstrate that the probability and quantity of monthly reproduction are the proximal drivers of demographic resilience across the three species. Our findings suggest that populations surpass a tipping point in severe fire regimes and achieve an alternative stable state to persist. Thus, higher heterogeneity in fire regimes can increase the reproductive aspects and resilience of different populations and avoid high-severity regimes that homogenize the environment. Despite being more resistant, species with long generation times and low reproductive output take longer to recover and cannot compensate as much as species with faster paces of life. We emphasize how reproductive constraints, such as viviparity and fixed clutch sizes, impact the ability of ectothermic populations to benefit and recover from disturbances, underscoring their relevance in conservation assessments.

Alzheimer's Disease Immunotherapy and Mimetic Peptide Design for Drug Development: Mutation Screening, Molecular Dynamics, and a Quantum Biochemistry Approach Focusing on Aducanumab::Aß2-7 Binding Affinity.

Seven treatments are approved for Alzheimer's disease, but five of them only relieve symptoms and do not alter the course of the disease. Aducanumab (Adu) and lecanemab are novel disease-modifying antiamyloid-ß (Aß) human monoclonal antibodies that specifically target the pathophysiology of Alzheimer's disease (AD) and were recently approved for its treatment. However, their administration is associated with serious side effects, and their use is limited to early stages of the disease. Therefore, drug discovery remains of great importance in AD research. To gain new insights into the development of novel drugs for Alzheimer's disease, a combination of techniques was employed, including mutation screening, molecular dynamics, and quantum biochemistry. These were used to outline the interfacial interactions of the Aducanumab::Aß2-7 complex. Our analysis identified critical stabilizing contacts, revealing up to 40% variation in the affinity of the Adu chains for Aß2-7 depending on the conformation outlined. Remarkably, two complementarity determining regions (CDRs) of the Adu heavy chain (HCDR3 and HCDR2) and one CDR of the Adu light chain (LCDR3) accounted for approximately 77% of the affinity of Adu for Aß2-7, confirming their critical role in epitope recognition. A single mutation, originally reported to have the potential to increase the affinity of Adu for Aß2-7, was shown to decrease its structural stability without increasing the overall binding affinity. Mimetic peptides that have the potential to inhibit Aß aggregation were designed by using computational outcomes. Our results support the use of these peptides as promising drugs with great potential as inhibitors of Aß aggregation.

A contact-based analysis of local energetic frustration dynamics identifies key residues enabling RfaH fold-switch.

Fold-switching enables metamorphic proteins to reversibly interconvert between two highly dissimilar native states to regulate their protein functions. While about 100 proteins have been identified to undergo fold-switching, unveiling the key residues behind this mechanism for each protein remains challenging. Reasoning that fold-switching in proteins is driven by dynamic changes in local energetic frustration, we combined fold-switching simulations generated using simplified structure-based models with frustration analysis to identify key residues involved in this process based on the change in the density of minimally frustrated contacts during refolding. Using this approach to analyze the fold-switch of the bacterial transcription factor RfaH, we identified 20 residues that significantly change their frustration during its fold-switch, some of which have been experimentally and computationally reported in previous works. Our approach, which we developed as an additional module for the FrustratometeR package, highlights the role of local frustration dynamics in protein fold-switching and offers a robust tool to enhance our understanding of other proteins with significant conformational shifts.

In Silico Screening of Therapeutic Targets as a Tool to Optimize the Development of Drugs and Nutraceuticals in the Treatment of Diabetes mellitus: A Systematic Review.

The Target-Based Virtual Screening approach is widely employed in drug development, with docking or molecular dynamics techniques commonly utilized for this purpose. This systematic review (SR) aimed to identify in silico therapeutic targets for treating Diabetes mellitus (DM) and answer the question: What therapeutic targets have been used in in silico analyses for the treatment of DM? The SR was developed following the guidelines of the Preferred Reporting Items Checklist for Systematic Review and Meta-Analysis, in accordance with the protocol registered in PROSPERO (CRD42022353808). Studies that met the PECo strategy (Problem, Exposure, Context) were included using the following databases: Medline (PubMed), Web of Science, Scopus, Embase, ScienceDirect, and Virtual Health Library. A total of 20 articles were included, which not only identified therapeutic targets in silico but also conducted in vivo analyses to validate the obtained results. The therapeutic targets most frequently indicated in in silico studies were GLUT4, DPP-IV, and PPARγ. In conclusion, a diversity of targets for the treatment of DM was verified through both in silico and in vivo reassessment. This contributes to the discovery of potential new allies for the treatment of DM.

Mitochondrial dynamics and sex-specific responses in the developing rat hippocampus: Effect of perinatal asphyxia and mesenchymal stem cell Secretome treatment.

AIMS: Perinatal asphyxia is one of the major causes of neonatal death at birth. Survivors can progress but often suffer from long-term sequelae. We aim to determine the effects of perinatal asphyxia on mitochondrial dynamics and whether mesenchymal stem cell secretome (MSC-S) treatment can alleviate the deleterious effects. MATERIALS AND METHODS: Animals were subjected to 21 min of asphyxia at the time of delivery. MSC-S or vehicle was intranasally administered 2 h post-delivery. Mitochondrial mass (D-loop, qPCR), mitochondrial dynamics proteins (Drp1, Fis1 and OPA1, Western blot), mitochondrial dynamics (TOMM20, Immunofluorescence), as well as mitochondrial membrane potential (ΔΨm) (Safranin O) were evaluated at P1 and P7 in the hippocampus. KEY FINDINGS: Perinatal asphyxia increased levels of mitochondrial dynamics proteins Drp1 and S-OPA1 at P1 and Fis1 at P7. Mitochondrial density and mass were decreased at P1. Perinatal asphyxia induced sex-specific differences, with increased L-OPA1 in females at P7 and increased mitochondria circularity. In males, asphyxia-exposed animals exhibited a reduced ΔΨm at P7. MSC-S treatment normalised levels of mitochondrial dynamics proteins involved in fission. SIGNIFICANCE: This study provides novel insights into the effects of perinatal asphyxia on mitochondrial dynamics in the developing brain and on the therapeutic opportunities provided by mesenchymal stem cell secretome treatment. It also highlights on the relevance of considering sex as a biological variable in perinatal brain injury and therapy development. These findings contribute to the development of targeted, personalised therapies for infants affected by perinatal asphyxia.

In vitro and in silico evaluation of the anti-leishmania activity of synthetic chalcones.

Leishmaniasis is a group of neglected, vector-borne infectious diseases that affect millions of people around the world. The medications available for its treatment, especially in cases of visceral leishmaniasis, are old, outdated and have serious side effects. In this work, 10 chalcones were synthesised and evaluated in vitro against promastigotes and axenic amastigotes of Leishmania infantum. Compounds CP04 and CP06 were the most promising, respectively presenting IC50 values = 13.64 ± 0.25 and 11.19 ± 0.22 µM against promastigotes, and IC50 = 18.92 ± 0.05 and 22.42 ± 0.05 µM against axenic amastigotes. Only compound CP04 did not show cytotoxicity against peripheral blood mononuclear cells (PBMCs). Molecular docking studies conducted with sterol 14-alpha demethylase (CYP-51) (PDB: 3L4D) and trypanothione reductase (PDB: 5EBK) enzymes from L. infantum evidenced the great affinity of compound CP04 for these targets, presenting Moldock score values of -94.0758 and -50.5692 KJ/mol-1.

Discovery of Palindrome Dual PPARγ-GPR40 Agonists for Treating Type 2 Diabetes.

This work describes a first attempt of palindromic design for dual compounds that act simultaneously on peroxisome proliferator-activated receptor gamma (PPARg) and G-protein-coupled receptor 40 (GPR40) for the treatment of type 2 diabetes. The compounds were synthesized by multi-step chemical reactions and the relative mRNA expression levels of PPARg, GPR40, and GLUT-4 were measured in cultured C2C12 muscle cells and RIN-m5f b-pancreatic cells. In addition, insulin secretion and GLUT-4 translocation were measured. Compound 2 displayed a moderate increase in the mRNA expression of PPARg and GPR40. However, the translocation of the GLUT-4 transporter was 400% with a similar effect to pioglitazone. The in vivo effect of compound 2 was determined at 25 mg/kg single dose using a normoglycemic and non-insulin dependent diabetes mellitus (NIDDM) rat models. Compound 2 showed basal plasma glucose in diabetic rats with feed intake, which is associated with the moderate release of insulin measured in cells. Surprisingly, the glucose does not decrease in normoglycemic rats. Compound 2 maintained significant interactions with the GPR40 and PPARg receptors during molecular dynamics. Altogether, the results demonstrate that compound 2, with a palindromic design, simultaneously activates PPARg and GPR40 receptors without inducing hypoglycemia.