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BACKGROUND AND AIMS: The assembly and secretion of VLDL from the liver, a pathway that affects hepatic and plasma lipids, remains incompletely understood. We set out to identify players in the VLDL biogenesis pathway by identifying genes that are co-expressed with the MTTP gene that encodes for microsomal triglyceride transfer protein, key to the lipidation of apolipoprotein B, the core protein of VLDL. Using human and murine transcriptomic data sets, we identified small leucine-rich protein 1 ( SMLR1 ), encoding for small leucine-rich protein 1, a protein of unknown function that is exclusively expressed in liver and small intestine. APPROACH AND RESULTS: To assess the role of SMLR1 in the liver, we used somatic CRISPR/CRISPR-associated protein 9 gene editing to silence murine Smlr1 in hepatocytes ( Smlr1 -LKO). When fed a chow diet, male and female mice show hepatic steatosis, reduced plasma apolipoprotein B and triglycerides, and reduced VLDL secretion without affecting microsomal triglyceride transfer protein activity. Immunofluorescence studies show that SMLR1 is in the endoplasmic reticulum and Cis-Golgi complex. The loss of hepatic SMLR1 in female mice protects against diet-induced hyperlipidemia and atherosclerosis but causes NASH. On a high-fat, high-cholesterol diet, insulin and glucose tolerance tests did not reveal differences in male Smlr1 -LKO mice versus controls. CONCLUSIONS: We propose a role for SMLR1 in the trafficking of VLDL from the endoplasmic reticulum to the Cis-Golgi complex. While this study uncovers SMLR1 as a player in the VLDL assembly, trafficking, and secretion pathway, it also shows that NASH can occur with undisturbed glucose homeostasis and atheroprotection.
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Aterosclerosis , Lipoproteínas VLDL , Enfermedad del Hígado Graso no Alcohólico , Proteoglicanos Pequeños Ricos en Leucina , Animales , Femenino , Humanos , Masculino , Ratones , Apolipoproteínas B/sangre , Aterosclerosis/sangre , Aterosclerosis/genética , Aterosclerosis/metabolismo , Aterosclerosis/prevención & control , Leucina , Lipoproteínas VLDL/biosíntesis , Lipoproteínas VLDL/sangre , Lipoproteínas VLDL/metabolismo , Hígado/metabolismo , Enfermedad del Hígado Graso no Alcohólico/sangre , Enfermedad del Hígado Graso no Alcohólico/genética , Enfermedad del Hígado Graso no Alcohólico/metabolismo , Proteoglicanos Pequeños Ricos en Leucina/genética , Proteoglicanos Pequeños Ricos en Leucina/metabolismo , Triglicéridos/sangreRESUMEN
The human equilibrative nucleoside transporter 1 (SLC29A1, hENT1) is a solute carrier that modulates the passive transport of nucleosides and nucleobases, such as adenosine. This nucleoside regulates various physiological processes, such as vasodilation and -constriction, neurotransmission and immune defense. Marketed drugs such as dilazep and dipyridamole have proven useful in cardiovascular afflictions, but the application of hENT1 inhibitors can be beneficial in a number of other diseases. In this study, 39 derivatives of dilazep's close analogue ST7092 were designed, synthesized and subsequently assessed using [3H]NBTI displacement assays and molecular docking. Different substitution patterns of the trimethoxy benzoates of ST7092 reduced interactions within the binding pocket, resulting in diminished hENT1 affinity. Conversely, [3H]NBTI displacement by potentially covalent compounds 14b, 14c, and 14d resulted in high affinities (Ki values between 1.1 and 17.5 nM) for the transporter, primarily by the ability of accommodating the inhibitors in various ways in the binding pocket. However, any indication of covalent binding with amino acid residue C439 remained absent, conceivably as a result of decreased nucleophilic residue reactivity. In conclusion, this research introduces novel dilazep derivatives that are active as hENT1 inhibitors, along with the first high affinity dilazep derivatives equipped with an electrophilic warhead. These findings will aid the rational and structure-based development of novel hENT1 inhibitors and pharmacological tools to study hENT1's function, binding mechanisms, and its relevance in (patho)physiological conditions.
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In a cluster randomized trial clusters of persons, for instance, schools or health centers, are assigned to treatments, and all persons in the same cluster get the same treatment. Although less powerful than individual randomization, cluster randomization is a good alternative if individual randomization is impossible or leads to severe treatment contamination (carry-over). Focusing on cluster randomized trials with a pretest and post-test of a quantitative outcome, this paper shows the equivalence of four methods of analysis: a three-level mixed (multilevel) regression for repeated measures with as levels cluster, person, and time, and allowing for unstructured between-cluster and within-cluster covariance matrices; a two-level mixed regression with as levels cluster and person, using change from baseline as outcome; a two-level mixed regression with as levels cluster and time, using cluster means as data; a one-level analysis of cluster means of change from baseline. Subsequently, similar equivalences are shown between a constrained mixed model and methods using the pretest as covariate. All methods are also compared on a cluster randomized trial on mental health in children. From these equivalences follows a simple method to calculate the sample size for a cluster randomized trial with baseline measurement, which is demonstrated step-by-step.
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Proyectos de Investigación , Niño , Humanos , Tamaño de la Muestra , Análisis por Conglomerados , Ensayos Clínicos Controlados Aleatorios como AsuntoRESUMEN
Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.
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Proteínas de la Membrana , Neoplasias , Humanos , Reacciones Cruzadas , Descubrimiento de Drogas , Aprendizaje Automático , Neoplasias/tratamiento farmacológicoRESUMEN
PURPOSE: Fluorescence-guided surgery (FGS) can play a key role in improving radical resection rates by assisting surgeons to gain adequate visualization of malignant tissue intraoperatively. Designed ankyrin repeat proteins (DARPins) possess optimal pharmacokinetic and other properties for in vivo imaging. This study aims to evaluate the preclinical potential of epithelial cell adhesion molecule (EpCAM)-binding DARPins as targeting moieties for near-infrared fluorescence (NIRF) and photoacoustic (PA) imaging of cancer. METHODS: EpCAM-binding DARPins Ac2, Ec4.1, and non-binding control DARPin Off7 were conjugated to IRDye 800CW and their binding efficacy was evaluated on EpCAM-positive HT-29 and EpCAM-negative COLO-320 human colon cancer cell lines. Thereafter, NIRF and PA imaging of all three conjugates were performed in HT-29_luc2 tumor-bearing mice. At 24 h post-injection, tumors and organs were resected and tracer biodistributions were analyzed. RESULTS: Ac2-800CW and Ec4.1-800CW specifically bound to HT-29 cells, but not to COLO-320 cells. Next, 6 nmol and 24 h were established as the optimal in vivo dose and imaging time point for both DARPin tracers. At 24 h post-injection, mean tumor-to-background ratios of 2.60 ± 0.3 and 3.1 ± 0.3 were observed for Ac2-800CW and Ec4.1-800CW, respectively, allowing clear tumor delineation using the clinical Artemis NIRF imager. Biodistribution analyses in non-neoplastic tissue solely showed high fluorescence signal in the liver and kidney, which reflects the clearance of the DARPin tracers. CONCLUSION: Our encouraging results show that EpCAM-binding DARPins are a promising class of targeting moieties for pan-carcinoma targeting, providing clear tumor delineation at 24 h post-injection. The work described provides the preclinical foundation for DARPin-based bimodal NIRF/PA imaging of cancer.
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G protein-coupled receptors (GPCRs) are known to be involved in tumor progression and metastasis. The adenosine A1 receptor (A1 AR) has been detected to be over-expressed in various cancer cell lines. However, the role of A1 AR in tumor development is not yet well characterized. A series of A1 AR mutations were identified in the Cancer Genome Atlas from cancer patient samples. In this study, we have investigated the pharmacology of mutations located outside of the 7-transmembrane domain by using a "single-GPCR-one-G protein" yeast system. Concentration-growth curves were obtained with the full agonist CPA for 12 mutant receptors and compared to the wild-type hA1 AR. Most mutations located at the extracellular loops (EL) reduced the levels of constitutive activity of the receptor and agonist potency. For mutants at the intracellular loops (ILs) of the receptor, an increased constitutive activity was found for mutant receptor L211R5.69 , while a decreased constitutive activity and agonist response were found for mutant receptor L113F34.51 . Lastly, mutations identified on the C-terminus did not significantly influence the pharmacological function of the receptor. A selection of mutations was also investigated in a mammalian system. Overall, similar effects on receptor activation compared to the yeast system were found with mutations located at the EL, but some contradictory effects were observed for mutations located at the IL. Taken together, this study will enrich the insight of A1 AR structure and function, enlightening the consequences of these mutations in cancer. Ultimately, this may provide potential precision medicine in cancer treatment.
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Neoplasias , Adenosina/farmacología , Animales , Línea Celular , Humanos , Mamíferos/metabolismo , Mutación , Neoplasias/tratamiento farmacológico , Neoplasias/genética , Receptor de Adenosina A1/genética , Receptor de Adenosina A1/metabolismo , Saccharomyces cerevisiae/genéticaRESUMEN
Solute carriers (SLCs) are relatively underexplored compared to other prominent protein families such as kinases and G protein-coupled receptors. However, proteins from the SLC family play an essential role in various diseases. One such SLC is the high-affinity norepinephrine transporter (NET/SLC6A2). In contrast to most other SLCs, the NET has been relatively well studied. However, the chemical space of known ligands has a low chemical diversity, making it challenging to identify chemically novel ligands. Here, a computational screening pipeline was developed to find new NET inhibitors. The approach increases the chemical space to model for NETs using the chemical space of related proteins that were selected utilizing similarity networks. Prior proteochemometric models added data from related proteins, but here we use a data-driven approach to select the optimal proteins to add to the modeled data set. After optimizing the data set, the proteochemometric model was optimized using stepwise feature selection. The final model was created using a two-step approach combining several proteochemometric machine learning models through stacking. This model was applied to the extensive virtual compound database of Enamine, from which the top predicted 22,000 of the 600 million virtual compounds were clustered to end up with 46 chemically diverse candidates. A subselection of 32 candidates was synthesized and subsequently tested using an impedance-based assay. There were five hit compounds identified (hit rate 16%) with sub-micromolar inhibitory potencies toward NET, which are promising for follow-up experimental research. This study demonstrates a data-driven approach to diversify known chemical space to identify novel ligands and is to our knowledge the first to select this set based on the sequence similarity of related targets.
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Proteínas de Transporte de Noradrenalina a través de la Membrana Plasmática , Proteínas de Transporte de Noradrenalina a través de la Membrana Plasmática/antagonistas & inhibidores , Proteínas de Transporte de Noradrenalina a través de la Membrana Plasmática/genética , Ligandos , Filogenia , Humanos , Línea Celular , Conjuntos de Datos como Asunto , Unión Proteica , Modelos BiológicosRESUMEN
Protein kinases are a protein family that plays an important role in several complex diseases such as cancer and cardiovascular and immunological diseases. Protein kinases have conserved ATP binding sites, which when targeted can lead to similar activities of inhibitors against different kinases. This can be exploited to create multitarget drugs. On the other hand, selectivity (lack of similar activities) is desirable in order to avoid toxicity issues. There is a vast amount of protein kinase activity data in the public domain, which can be used in many different ways. Multitask machine learning models are expected to excel for these kinds of data sets because they can learn from implicit correlations between tasks (in this case activities against a variety of kinases). However, multitask modeling of sparse data poses two major challenges: (i) creating a balanced train-test split without data leakage and (ii) handling missing data. In this work, we construct a protein kinase benchmark set composed of two balanced splits without data leakage, using random and dissimilarity-driven cluster-based mechanisms, respectively. This data set can be used for benchmarking and developing protein kinase activity prediction models. Overall, the performance on the dissimilarity-driven cluster-based split is lower than on random split-based sets for all models, indicating poor generalizability of models. Nevertheless, we show that multitask deep learning models, on this very sparse data set, outperform single-task deep learning and tree-based models. Finally, we demonstrate that data imputation does not improve the performance of (multitask) models on this benchmark set.
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Aprendizaje Automático , Proteínas , Proteínas Quinasas , Fosforilación , Procesamiento Proteico-PostraduccionalRESUMEN
The discovery of novel molecules with desirable properties is a classic challenge in medicinal chemistry. With the recent advancements of machine learning, there has been a surge of de novo drug design tools. However, few resources exist that are user-friendly as well as easily customizable. In this application note, we present the new versatile open-source software package DrugEx for multiobjective reinforcement learning. This package contains the consolidated and redesigned scripts from the prior DrugEx papers including multiple generator architectures, a variety of scoring tools, and multiobjective optimization methods. It has a flexible application programming interface and can readily be used via the command line interface or the graphical user interface GenUI. The DrugEx package is publicly available at https://github.com/CDDLeiden/DrugEx.
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Aprendizaje Profundo , Programas Informáticos , Diseño de Fármacos , Aprendizaje AutomáticoRESUMEN
Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation.
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Estrés Oxidativo , Xenobióticos , Humanos , Especies Reactivas de Oxígeno , Células Hep G2 , Estudios Prospectivos , Relación Estructura-ActividadRESUMEN
Transmembranal G Protein-Coupled Receptors (GPCRs) transduce extracellular chemical signals to the cell, via conformational change from a resting (inactive) to an active (canonically bound to a G-protein) conformation. Receptor activation is normally modulated by extracellular ligand binding, but mutations in the receptor can also shift this equilibrium by stabilizing different conformational states. In this work, we built structure-energetic relationships of receptor activation based on original thermodynamic cycles that represent the conformational equilibrium of the prototypical A2A adenosine receptor (AR). These cycles were solved with efficient free energy perturbation (FEP) protocols, allowing to distinguish the pharmacological profile of different series of A2AAR agonists with different efficacies. The modulatory effects of point mutations on the basal activity of the receptor or on ligand efficacies could also be detected. This methodology can guide GPCR ligand design with tailored pharmacological properties, or allow the identification of mutations that modulate receptor activation with potential clinical implications.
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Receptor de Adenosina A2A/química , Agonistas del Receptor de Adenosina A2/química , Agonistas del Receptor de Adenosina A2/farmacología , Antagonistas del Receptor de Adenosina A2/química , Antagonistas del Receptor de Adenosina A2/farmacología , Sustitución de Aminoácidos , Biología Computacional , Humanos , Ligandos , Modelos Moleculares , Simulación de Dinámica Molecular , Mutación Puntual , Conformación Proteica/efectos de los fármacos , Receptor de Adenosina A2A/genética , Receptor de Adenosina A2A/metabolismo , TermodinámicaRESUMEN
AIMS: During phase I study conduct, blinded data are reviewed to predict the safety of increasing the dose level. The aim of the present study was to describe the probability that effects are observed in blinded evaluations of data in a simulated phase I study design. METHODS: An application was created to simulate blinded pharmacological response curves over time for 6 common safety/efficacy measurements in phase I studies for 1 or 2 cohorts (6 active, 2 placebo per cohort). Effect sizes between 0 and 3 between-measurement standard deviations (SDs) were simulated. Each set of simulated graphs contained the individual response and mean ± SD over time. Reviewers (n = 34) reviewed a median of 100 simulated datasets and indicated whether an effect was present. RESULTS: Increasing effect sizes resulted in a higher chance of the effect being identified by the blinded reviewer. On average, 6% of effect sizes of 0.5 between-measurement SD were correctly identified, increasing to 72% in 3.0 between-measurement SD effect sizes. In contrast, on average 92-95% of simulations with no effect were correctly identified, with little effect of between-measurement variability in single cohort simulations. Adding a dataset of a second cohort at half the simulated dose did not appear to improve the interpretation. CONCLUSION: Our analysis showed that effect sizes <2× the between-measurement SD of the investigated outcome frequently go unnoticed by blinded reviewers, indicating that the weight given to these blinded analyses in current phase I practice is inappropriate and should be re-evaluated.
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Ensayos Clínicos Fase I como Asunto , Humanos , Estudios de Factibilidad , Interpretación Estadística de DatosRESUMEN
Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 µM), three OATP1B1 inhibitors (2.69 to 10 µM), and five OATP1B3 inhibitors (1.53 to 10 µM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.
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Transportadores de Anión Orgánico , Transportadores de Anión Orgánico/metabolismo , Transportador 1 de Anión Orgánico Específico del Hígado/metabolismo , Simulación del Acoplamiento Molecular , Ligandos , Miembro 1B3 de la Familia de los Transportadores de Solutos de Aniones Orgánicos/metabolismo , Transporte Biológico/fisiología , Hígado/metabolismo , Proteínas de Transporte de Membrana/metabolismo , Péptidos/metabolismo , Interacciones FarmacológicasRESUMEN
OBJECTIVES: Tinnitus is the perception of sound without an external source, affecting quality of life that can cause severe distress in approximately 1 to 3% of the population of people with tinnitus. Randomized controlled trials of cognitive behavioral therapy for tinnitus have demonstrated its effectiveness in improving quality of life, but the effects of their implementation on a large scale in routine practice remains unknown. Therefore, the main purpose of this study was to examine the effects of stepped-care cognitive behavioral therapy for tinnitus delivered in a tertiary audiological center of a regional hospital. Second, we wished to examine predictors of favorable outcome. DESIGN: Four hundred three adults with chronic tinnitus were enrolled in this prospective observational study (at 3 months, N=334, 8 months, N=261; 12 months, N=214). The primary outcome was health-related quality of life as measured by the Health Utilities Index III (HUI-III) at 12 months. Secondary outcomes were self-reported levels of tinnitus-related distress, disability, affective distress and tinnitus-related negative beliefs and fear. Measures were completed pre-intervention at 3 months, 8 months, and 12 months. Multilevel modeling was used to examine effects and their predictors. RESULTS: Younger participants with lower levels of tinnitus distress were more likely to dropout while those with higher tinnitus distress at baseline and quality of life were more likely to receive step 2 of treatment. MLM analyses revealed, with one exception, no relation between any baseline variable and outcome change over time. Most participants' improvement exceeded minimally clinical important difference criteria for quality of life, tinnitus-related handicap, and tinnitus distress. CONCLUSIONS: Results from this large pragmatic study complements those from randomized controlled trials of cognitive behavioral therapy for chronic tinnitus distress and supports its implementation under "real-world" conditions.
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Terapia Cognitivo-Conductual , Acúfeno , Adulto , Humanos , Terapia Cognitivo-Conductual/métodos , Estudios Prospectivos , Calidad de Vida , Acúfeno/terapia , Acúfeno/psicología , Enfermedad Crónica , Resultado del TratamientoRESUMEN
The literature on dealing with missing covariates in nonrandomized studies advocates the use of sophisticated methods like multiple imputation (MI) and maximum likelihood (ML)-based approaches over simple methods. However, these methods are not necessarily optimal in terms of bias and efficiency of treatment effect estimation in randomized studies, where the covariate of interest (treatment group) is independent of all baseline (pre-randomization) covariates due to randomization. This has been shown in the literature, but only for missingness on a single baseline covariate. Here, we extend the situation to multiple baseline covariates with missingness and evaluate the performance of MI and ML compared with simple alternative methods under various missingness scenarios in RCTs with a quantitative outcome. We first derive asymptotic relative efficiencies of the simple methods under the missing completely at random (MCAR) scenario and then perform a simulation study for non-MCAR scenarios. Finally, a trial on chronic low back pain is used to illustrate the implementation of the methods. The results show that all simple methods give unbiased treatment effect estimation but with increased mean squared residual. It also turns out that mean imputation and the missing-indicator method are most efficient under all covariate missingness scenarios and perform at least as well as MI and LM in each scenario.
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Proyectos de Investigación , Sesgo , Simulación por Computador , Interpretación Estadística de Datos , Humanos , Ensayos Clínicos Controlados Aleatorios como AsuntoRESUMEN
OBJECTIVE: The aim of the present study was to develop a neural network to characterize the effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG features on aging was evaluated. METHODS & RESULTS: A total of 6228 healthy subjects without structural heart disease were included in this study. A neural network regression model was created to predict age of the subjects based on their ECG; 577 parameters derived from a 12lead ECG of each subject were used to develop and validate the neural network; A tenfold cross-validation was performed, using 118 subjects for validation each fold. Using SHapley Additive exPlanations values the impact of the individual features on the prediction of age was determined. Of 6228 subjects tested, 1808 (29%) were females and mean age was 34 years, range 18-75 years. Physiologic age was estimated as a continuous variable with an average error of 6.9 ± 5.6 years (R2 = 0.72 ± 0.04). The correlation was slightly stronger for men (R2 = 0.74) than for women (R2 = 0.66). The most important features on the prediction of physiologic age were T wave morphology indices in leads V4 and V5, and P wave amplitude in leads AVR and II. CONCLUSION: The application of machine learning to the ECG using a neural network regression model, allows accurate estimation of physiologic cardiac age. This technique could be used to pick up subtle age-related cardiac changes, but also estimate the reversing of these age-associated effects by administered treatments.
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Benchmarking , Electrocardiografía , Adolescente , Adulto , Anciano , Niño , Preescolar , Electrocardiografía/métodos , Femenino , Voluntarios Sanos , Humanos , Lactante , Aprendizaje Automático , Masculino , Persona de Mediana Edad , Redes Neurales de la Computación , Adulto JovenRESUMEN
Overexpression of the adenosine A1 receptor (A1AR) has been detected in various cancer cell lines. However, the role of A1AR in tumor development is still unclear. Thirteen A1AR mutations were identified in the Cancer Genome Atlas from cancer patient samples. We have investigated the pharmacology of the mutations located at the 7-transmembrane domain using a yeast system. Concentration-growth curves were obtained with the full agonist CPA and compared to the wild type hA1AR. H78L3.23 and S246T6.47 showed increased constitutive activity, while only the constitutive activity of S246T6.47 could be reduced to wild type levels by the inverse agonist DPCPX. Decreased constitutive activity was observed on five mutant receptors, among which A52V2.47 and W188C5.46 showed a diminished potency for CPA. Lastly, a complete loss of activation was observed in five mutant receptors. A selection of mutations was also investigated in a mammalian system, showing comparable effects on receptor activation as in the yeast system, except for residues pointing toward the membrane. Taken together, this study will enrich the view of the receptor structure and function of A1AR, enlightening the consequences of these mutations in cancer. Ultimately, this may provide an opportunity for precision medicine for cancer patients with pathological phenotypes involving these mutations.
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Neoplasias , Receptor de Adenosina A1 , Adenosina/metabolismo , Adenosina/farmacología , Animales , Humanos , Mamíferos , Mutación , Neoplasias/tratamiento farmacológico , Neoplasias/genética , Estructura Secundaria de Proteína , Receptor de Adenosina A1/metabolismo , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismoRESUMEN
The adenosine A2A receptor (A2AAR) is a class A G-protein-coupled receptor (GPCR). It is an immune checkpoint in the tumor micro-environment and has become an emerging target for cancer treatment. In this study, we aimed to explore the effects of cancer-patient-derived A2AAR mutations on ligand binding and receptor functions. The wild-type A2AAR and 15 mutants identified by Genomic Data Commons (GDC) in human cancers were expressed in HEK293T cells. Firstly, we found that the binding affinity for agonist NECA was decreased in six mutants but increased for the V275A mutant. Mutations A165V and A265V decreased the binding affinity for antagonist ZM241385. Secondly, we found that the potency of NECA (EC50) in an impedance-based cell-morphology assay was mostly correlated with the binding affinity for the different mutants. Moreover, S132L and H278N were found to shift the A2AAR towards the inactive state. Importantly, we found that ZM241385 could not inhibit the activation of V275A and P285L stimulated by NECA. Taken together, the cancer-associated mutations of A2AAR modulated ligand binding and receptor functions. This study provides fundamental insights into the structure-activity relationship of the A2AAR and provides insights for A2AAR-related personalized treatment in cancer.
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Adenosina , Neoplasias , Adenosina/farmacología , Adenosina-5'-(N-etilcarboxamida) , Células HEK293 , Humanos , Ligandos , Mutación , Neoplasias/tratamiento farmacológico , Neoplasias/genética , Receptor de Adenosina A2A/genética , Receptor de Adenosina A2A/metabolismo , Microambiente TumoralRESUMEN
Chikungunya virus (CHIKV) nonstructural protein 1 (nsP1) harbors the methyltransferase (MTase) and guanylyltransferase (GTase) activities needed for viral RNA capping and represents a promising antiviral drug target. We compared the antiviral efficacies of nsP1 inhibitors belonging to the MADTP, CHVB, and FHNA series (6'-fluoro-homoneplanocin A [FHNA], its 3'-keto form, and 6'-ß-fluoro-homoaristeromycin). Cell-based phenotypic cross-resistance assays revealed that the CHVB and MADTP series had similar modes of action that differed from that of the FHNA series. In biochemical assays with purified Semliki Forest virus and CHIKV nsP1, CHVB compounds strongly inhibited MTase and GTase activities, while MADTP-372 had a moderate inhibitory effect. FHNA did not directly inhibit the enzymatic activity of CHIKV nsP1. The first-of-their-kind molecular-docking studies with the cryo-electron microscopy (cryo-EM) structure of CHIKV nsP1, which is assembled into a dodecameric ring, revealed that the MADTP and CHVB series bind at the S-adenosylmethionine (SAM)-binding site in the capping domain, where they would function as competitive or noncompetitive inhibitors. The FHNA series was predicted to bind at the secondary binding pocket in the ring-aperture membrane-binding and oligomerization (RAMBO) domain, potentially interfering with the membrane binding and oligomerization of nsP1. Our cell-based and enzymatic assays, in combination with molecular docking and mapping of compound resistance mutations to the nsP1 structure, allowed us to group nsP1 inhibitors into functionally distinct classes. This study identified druggable pockets in the nsP1 dodecameric structure and provides a basis for the rational design, optimization, and combination of inhibitors of this unique and promising antiviral drug target.
Asunto(s)
Virus Chikungunya , Proteínas no Estructurales Virales , Adenosina/análogos & derivados , Microscopía por Crioelectrón , Simulación del Acoplamiento Molecular , Proteínas no Estructurales Virales/genética , Replicación ViralRESUMEN
BACKGROUND: Previous studies reported that hypo- and hyperthermia are associated with several atrial and ventricular electrocardiographical parameters, including corrected QT (QTc) interval. Enhanced characterization of variations in QTc interval and normothermic body temperature aids in better understanding the underlying mechanism behind drug induced QTc interval effects. The analysis' objective was to investigate associations between body temperature and electrocardiographical parameters in normothermic healthy volunteers. METHODS: Data from 3023 volunteers collected at our center were retrospectively analyzed. Subjects were considered healthy after review of collected data by a physician, including a normal tympanic body temperature (35.5-37.5°C) and in sinus rhythm. A linear multivariate model with body temperature as a continuous was performed. Another multivariate analysis was performed with only the QT subintervals as independent variables and body temperature as dependent variable. RESULTS: Mean age was 33.8 ± 17.5 years and mean body temperature was 36.6 ± 0.4°C. Body temperature was independently associated with age (standardized coefficient [SC] = -0.255, P < .001), female gender (SC = +0.209, P < .001), heart rate (SC = +0.231, P < .001), P-wave axis (SC = -0.051, P < .001), J-point elevation in lead V4 (SC = -0.121, P < .001), and QTcF duration (SC = -0.061, P = .002). In contrast, other atrial and atrioventricular (AV) nodal parameters were not independently associated with body temperature. QT subinterval analysis revealed that only QRS duration (SC = -0.121, P < .001) was independently associated with body temperature. CONCLUSION: Body temperature in normothermic healthy volunteers was associated with heart rate, P-wave axis, J-point amplitude in lead V4, and ventricular conductivity, the latter primarily through prolongation of the QRS duration.