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Optical three-dimensional (3D) molecular imaging is highly desirable for providing precise distribution of the target-of-interest in disease models. However, such 3D imaging is still far from wide applications in biomedical research; 3D brain optical molecular imaging, in particular, has rarely been reported. In this report, we designed chemiluminescence probes with high quantum yields, relatively long emission wavelengths, and high signal-to-noise ratios to fulfill the requirements for 3D brain imaging in vivo. With assistance from density-function theory (DFT) computation, we designed ADLumin-Xs by locking up the rotation of the double bond via fusing the furan ring to the phenyl ring. Our results showed that ADLumin-5 had a high quantum yield of chemiluminescence and could bind to amyloid beta (Aß). Remarkably, ADLumin-5's radiance intensity in brain areas could reach 4 × 107 photon/s/cm2/sr, which is probably 100-fold higher than most chemiluminescence probes for in vivo imaging. Because of its strong emission, we demonstrated that ADLumin-5 could be used for in vivo 3D brain imaging in transgenic mouse models of Alzheimer's disease.
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Enfermedad de Alzheimer , Ratones , Animales , Enfermedad de Alzheimer/diagnóstico por imagen , Enfermedad de Alzheimer/metabolismo , Péptidos beta-Amiloides/metabolismo , Luminiscencia , Encéfalo/diagnóstico por imagen , Encéfalo/metabolismo , Ratones Transgénicos , Neuroimagen/métodos , Placa Amiloide/metabolismo , Modelos Animales de EnfermedadRESUMEN
MOTIVATION: Understanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to learn to predict enhancer-promoter (EP) relationships in a data-driven manner. RESULTS: We applied machine learning to one of the largest enhancer perturbation studies integrated with transcription factor (TF) and histone modification ChIP-seq. The results uncovered a discrepancy in the prediction of genome-wide data compared to data from targeted experiments. Relative strength of contact was important for prediction, confirming the basic principle of EP regulation. Novel features such as the density of the enhancers/promoters in the genomic region was found to be important, highlighting our lack of understanding on how other elements in the region contribute to the regulation. Several TF peaks were identified that improved the prediction by identifying the negatives and reducing False Positives. In summary, integrating genomic assays with enhancer perturbation studies increased the accuracy of the model, and provided novel insights into the understanding of enhancer-driven transcription. AVAILABILITY AND IMPLEMENTATION: The trained models, data, and the source code are available at http://doi.org/10.5281/zenodo.11290386 and https://github.com/HanLabUNLV/sleps.
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Elementos de Facilitación Genéticos , Regiones Promotoras Genéticas , Aprendizaje Automático Supervisado , Humanos , Factores de Transcripción/metabolismo , Factores de Transcripción/genética , Genómica/métodos , Secuenciación de Inmunoprecipitación de Cromatina/métodosRESUMEN
BACKGROUND: RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins. RESULTS: We aimed to investigate the impact of data preprocessing steps-focusing on normalization, batch effect correction, and data scaling-through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer. CONCLUSION: By using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate.
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Aprendizaje Automático , Neoplasias , RNA-Seq , Humanos , RNA-Seq/métodos , Neoplasias/genética , Transcriptoma/genética , Análisis de Secuencia de ARN/métodos , Perfilación de la Expresión Génica/métodos , Biología Computacional/métodosRESUMEN
In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.
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RASopathies are a group of genetic disorders that are caused by genes that affect the canonical Ras/mitogen-activated protein kinase (MAPK) signaling pathway. Despite tremendous progress in understanding the molecular consequences of these genetic anomalies, little movement has been made in translating these findings to the clinic. This year, the seventh International RASopathies Symposium focused on expanding the research knowledge that we have gained over the years to enhance new discoveries in the field, ones that we hope can lead to effective therapeutic treatments. Indeed, for the first time, research efforts are finally being translated to the clinic, with compassionate use of Ras/MAPK pathway inhibitors for the treatment of RASopathies. This biannual meeting, organized by the RASopathies Network, brought together basic scientists, clinicians, clinician scientists, patients, advocates, and their families, as well as representatives from pharmaceutical companies and the National Institutes of Health. A history of RASopathy gene discovery, identification of new disease genes, and the latest research, both at the bench and in the clinic, were discussed.
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Síndrome de Costello , Síndrome de Noonan , Síndrome de Costello/genética , Humanos , Proteínas Quinasas Activadas por Mitógenos/metabolismo , Síndrome de Noonan/genética , Transducción de Señal , Proteínas ras/genética , Proteínas ras/metabolismoRESUMEN
Monoacylglycerol lipase (MAGL) constitutes a serine hydrolase that orchestrates endocannabinoid homeostasis and exerts its function by catalyzing the degradation of 2-arachidonoylglycerol (2-AG) to arachidonic acid (AA). As such, selective inhibition of MAGL represents a potential therapeutic and diagnostic approach to various pathologies including neurodegenerative disorders, metabolic diseases and cancers. Based on a unique 4-piperidinyl azetidine diamide scaffold, we developed a reversible and peripheral-specific radiofluorinated MAGL PET ligand [18F]FEPAD. Pharmacokinetics and binding studies on [18F]FEPAD revealed its outstanding specificity and selectivity towards MAGL in brown adipose tissue (BAT) - a tissue that is known to be metabolically active. We employed [18F]FEPAD in PET studies to assess the abundancy of MAGL in BAT deposits of mice and found a remarkable degree of specific tracer binding in the BAT, which was confirmed by post-mortem tissue analysis. Given the negative regulation of endocannabinoids on the metabolic BAT activity, our study supports the concept that dysregulation of MAGL is likely linked to metabolic disorders. Further, we now provide a suitable imaging tool that allows non-invasive assessment of MAGL in BAT deposits, thereby paving the way for detailed mechanistic studies on the role of BAT in endocannabinoid system (ECS)-related pathologies.
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Endocannabinoides , Monoacilglicerol Lipasas , Endocannabinoides/metabolismo , Tejido Adiposo Pardo/diagnóstico por imagen , Tejido Adiposo Pardo/metabolismo , Tomografía de Emisión de Positrones/métodos , Ligandos , Inhibidores Enzimáticos/farmacologíaRESUMEN
OBJECTIVES: To enable non-invasive real-time quantification of vasopressin 1A (V1A) receptors in peripheral organs, we sought to develop a suitable PET probe that would allow specific and selective V1A receptor imaging in vitro and in vivo. METHODS: We synthesized a high-affinity and -selectivity ligand, designated compound 17. The target structure was labeled with carbon-11 and tested for its utility as a V1A-targeted PET tracer by cell uptake studies, autoradiography, in vivo PET imaging and ex vivo biodistribution experiments. RESULTS: Compound 17 (PF-184563) and the respective precursor for radiolabeling were synthesized in an overall yield of 49% (over 7 steps) and 40% (over 8 steps), respectively. An inhibitory constant of 0.9 nM towards the V1A receptors was measured, while excellent selectivity over the related V1B, V2 and OT receptor (IC50 >10,000 nM) were obtained. Cell uptake studies revealed considerable V1A binding, which was significantly reduced in the presence of V1A antagonists. Conversely, there was no significant blockade in the presence of V1B and V2 antagonists. In vitro autoradiography and PET imaging studies in rodents indicated specific tracer binding mainly in the liver. Further, the pancreas, spleen and the heart exhibited specific binding of [11C]17 ([11C]PF-184563) by ex vivo biodistribution experiments. CONCLUSION: We have developed the first V1A-targeted PET ligand that is suitable for subtype-selective receptor imaging in peripheral organs including the liver, heart, pancreas and spleen. Our findings suggest that [11C]PF-184563 can be a valuable tool to study the role of V1A receptors in liver diseases, as well as in cardiovascular pathologies.
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Benzodiazepinas/farmacología , Radiofármacos/farmacología , Receptores de Vasopresinas/metabolismo , Triazoles/farmacología , Animales , Autorradiografía , Benzodiazepinas/farmacocinética , Células CHO , Radioisótopos de Carbono , Cricetulus , Femenino , Ligandos , Hígado/metabolismo , Masculino , Ratones , Miocardio/metabolismo , Páncreas/metabolismo , Tomografía de Emisión de Positrones , Radiofármacos/farmacocinética , Ratas Wistar , Bazo/metabolismo , Triazoles/farmacocinéticaRESUMEN
RIT1 is a member of the Ras family of GTPases that direct broad cellular physiological responses through tightly controlled signaling networks. The canonical Ras GTPases are well-defined regulators of the RAF/MEK/ERK pathway and mutations in these are pathogenic in cancer and a class of developmental disorders termed RASopathies. Emerging clinical evidences have now demonstrated a role for RIT1 in RASopathies, namely Noonan syndrome, and various cancers including lung adenocarcinoma and myeloid malignancies. While RIT1 has been mostly described in the context of neuronal differentiation and survival, the mechanisms underlying aberrant RIT1-mediated signaling remain elusive. Here, we will review efforts undertaken to characterize the biochemical and functional properties of the RIT1 GTPase at the molecular, cellular, and organismal level, as well as provide a phenotypic overview of different human conditions caused by RIT1 mutations. Deeper understanding of RIT1 biological function and insight to its pathogenic mechanisms are imperative to developing effective therapeutic interventions for patients with RIT1-mutant Noonan syndrome and cancer.
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Neoplasias/genética , Síndrome de Noonan/genética , Proteínas ras/genética , Proteínas ras/metabolismo , Animales , Modelos Animales de Enfermedad , Humanos , Mutación , Síndrome de Noonan/etiología , Proteínas ras/químicaRESUMEN
Selective DAAO inhibitors have demonstrated promising therapeutic effects in clinical studies, including clinically alleviating symptoms of schizophrenic patients and ameliorating cognitive function in Alzheimer's patients with early phase. Herein we report the synthesis and preliminary evaluation of a 11C-labeled positron emission tomography ligand based on a DAAO inhibitor, DAO-1903 (8). 11C-Isotopologue of 8 was prepared in high radiochemical yield with high radiochemical purity (>99%) and high molar activity (>37 GBq/µmol). In vitro autoradiography studies indicated that the ligand possessed high in vitro specific binding to DAAO, while in vivo dynamic PET studies demonstrated that [11C]8 failed to cross the blood-brain barrier possibly due to moderate brain efflux mechanism. Further chemical scaffold optimization is necessary to overcome limited brain permeability and improve specific binding.
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Encéfalo/diagnóstico por imagen , Tomografía de Emisión de Positrones , Radiofármacos/química , Animales , D-Aminoácido Oxidasa/antagonistas & inhibidores , D-Aminoácido Oxidasa/metabolismo , Relación Dosis-Respuesta a Droga , Humanos , Ratones , Simulación del Acoplamiento Molecular , Estructura Molecular , Radiofármacos/farmacología , Ratas , Ratas Sprague-Dawley , Relación Estructura-ActividadRESUMEN
To ensure safe implementation of robot-assisted minimally invasive esophagectomy (RAMIE), the learning process should be optimized. This study aimed to report the results of a surgeon who implemented RAMIE in a German high-volume center by following a tailored and structured training pathway that involved proctoring. Consecutive patients who underwent RAMIE during the course of the program were included from a prospective database. A single surgeon, who had prior experience in conventional MIE, performed all RAMIE procedures. Cumulative sum (CUSUM) learning curves were plotted for the thoracic operating time and intraoperative blood loss. Perioperative outcomes were compared between patients who underwent surgery before and after a learning curve plateau occurred. Between 2017 and 2018, the adopting center adhered to the structured training pathway, and a total of 70 patients were included in the analysis. The CUSUM learning curves showed plateaus after 22 cases. In consecutive cases 23 to 70, the operating time was shorter for both the thoracic phase (median 215 vs. 249 minutes, P = 0.001) and overall procedure (median 394 vs. 440 minutes, P = 0.005), intraoperative blood loss was less (median 210 vs. 400 milliliters, P = 0.029), and lymph node yield was higher (median 32 vs. 23 nodes, P = 0.001) when compared to cases 1 to 22. No significant differences were found in terms of conversion rates, postoperative complications, length of stay, completeness of resection, or mortality. In conclusion, the structured training pathway resulted in a short and safe learning curve for RAMIE in this single center's experience. As the pathway seems effective in implementing RAMIE without compromising the early oncological outcomes and complication rates, it is advised for surgeons who are wanting to adopt this technique.
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Neoplasias Esofágicas , Procedimientos Quirúrgicos Robotizados , Robótica , Neoplasias Esofágicas/cirugía , Esofagectomía , Humanos , Curva de Aprendizaje , Procedimientos Quirúrgicos Mínimamente Invasivos , Procedimientos Quirúrgicos Robotizados/efectos adversos , Resultado del TratamientoRESUMEN
Optoacoustic imaging has grown in clinical relevance due to inherent advantages in sensitivity, resolution, and imaging depth, but the development of contrast agents is lacking. This study assesses the influence of structural features of squaraine dyes on optoacoustic activity through computational models, in vitro testing, and in vivo experimentation. The squaraine scaffold was decorated with halogens and side-chain extensions. Extension of side chains and heavy halogenation of squaraines both increased optoacoustic signals individually, although they had a more significant effect in tandem. Density functional theory models suggest that the origin of the increased optoacoustic signal is the increase in transition dipole moment and vibrational entropy, which manifested as increased absorbance in near-infrared region (NIR) wavelengths and decreased fluorescence quantum yield. This study provides insight into the structure-function relationships that will lead guiding principles for optimizing optoacoustic contrast agents. Further developments of squaraines and other agents will further increase the relevance of optoacoustic imaging in a clinical setting.
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[This corrects the article PMC11087056.].
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[This corrects the article PMC11087056.].
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The metabotropic glutamate receptor 2 (mGluR2) has emerged as a potential therapeutic target for the treatment of various neurological diseases, prompting substantial interest in the development of mGluR2-targeted drug candidates. As part of our medicinal chemistry program, we synthesized a series of isoindolone derivatives and assessed their potential as mGluR2 positive allosteric modulators (PAMs). Notably, AZ12559322 exhibited high affinity (K i mGluR2 = 1.31 nM) and an excellent in vitro binding specificity of 89% while demonstrating selectivity over other mGluR subtypes (>4000-fold). Autoradiography with the radiolabeled counterpart, [3H]AZ12559322, revealed a heterogeneous accumulation with the highest binding in mGluR2-rich brain regions. Radioligand binding was significantly reduced by pretreatment with nonradioactive mGluR2 PAMs in brains of rats and nonhuman primates. Although positron emission tomography imaging of [11C]AZ12559322 (6a) revealed low brain uptake in a nonhuman primate, this study provides valuable guidance to further design novel isoindolone-based mGluR2 PAMs with improved brain exposure.
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Parkinson's disease (PD) is one of the most highly debilitating neurodegenerative disorders, which affects millions of people worldwide, and leucine-rich repeat kinase 2 (LRRK2) mutations have been involved in the pathogenesis of PD. Developing a potent LRRK2 positron emission tomography (PET) tracer would allow for in vivo visualization of LRRK2 distribution and expression in PD patients. In this work, we present the facile synthesis of two potent and selective LRRK2 radioligands [11C]3 ([11C]PF-06447475) and [18F]4 ([18F]PF-06455943). Both radioligands exhibited favorable brain uptake and specific bindings in rodent autoradiography and PET imaging studies. More importantly, [18F]4 demonstrated significantly higher brain uptake in the transgenic LRRK2-G2019S mutant and lipopolysaccharide (LPS)-injected mouse models. This work may serve as a roadmap for the future design of potent LRRK2 PET tracers.
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Morfolinas , Nitrilos , Enfermedad de Parkinson , Pirimidinas , Ratones , Animales , Humanos , Proteína 2 Quinasa Serina-Treonina Rica en Repeticiones de Leucina/genética , Proteína 2 Quinasa Serina-Treonina Rica en Repeticiones de Leucina/metabolismo , Leucina , Tomografía de Emisión de Positrones/métodos , Enfermedad de Parkinson/metabolismo , MutaciónRESUMEN
In enzyme mechanistic studies and mutant design, it is highly desirable to know the individual residue contributions to the reaction free energy and barrier. In this work, we show that such free energy contributions from each residue can be readily obtained by postprocessing ab initio quantum mechanical molecular mechanical (ai-QM/MM) free energy simulation trajectories. Specifically, through a mean force integration along the minimum free energy pathway, one can obtain the electrostatic, polarization, and van der Waals contributions from each residue to the free energy barrier. Separately, a similar analysis procedure allows us to assess the contribution from different collective variables along the reaction coordinate. The chorismate mutase reaction is used to demonstrate the utilization of these two trajectory analysis tools.
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Teoría Cuántica , Simulación por ComputadorRESUMEN
Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, 155, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol-1 Å-1 from the reference values, and the MLP-based free energy surface differed from that obtained from explicit solvent MD simulations by an RMSD of less than 0.9 kcal mol-1. Our MLP training protocol could also accurately reproduce combined quantum mechanical molecular mechanical (QM/MM) forces on the quantum mechanical (QM) solute in ASEC environment, thus enabling the development of accurate ML-based implicit solvent models for ab initio-QM MD simulations. Such ML-based implicit solvent models for QM calculations are cost-effective in both the training stage, where the use of ASEC reduces the number of data points to be labelled, and the inference stage, where the MLP can be evaluated at a relatively small additional cost on top of the QM calculation of the solute.
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Optical three-dimensional (3D) molecular imaging is highly desirable for providing precise distribution of the target-of-interest in disease models. However, such 3D imaging is still far from wide applications in biomedical research; 3D brain optical molecular imaging, in particular, has rarely been reported. In this report, we designed chemiluminescence probes with high quantum yields (QY), relatively long emission wavelengths, and high signal-to-noise ratios (SNRs) to fulfill the requirements for 3D brain imaging in vivo. With assistance from density-function theory (DFT) computation, we designed ADLumin-Xs by locking up the rotation of the double-bond via fusing the furan ring to the phenyl ring. Our results showed that ADLumin-5 had a high quantum yield of chemiluminescence and could bind to amyloid beta (Aß). Remarkably, ADLumin-5's radiance intensity in brain areas could reach 4×107 photon/s/cm2/sr, which is probably 100-fold higher than most chemiluminescence probes for in vivo imaging. Because of its strong emission, we demonstrated that ADLumin-5 could be used for in vivo 3D brain imaging in transgenic mouse models of Alzheimer's disease (AD).
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RIT1 is a RAS guanosine triphosphatase (GTPase) that regulates different aspects of signal transduction and is mutated in lung cancer, leukemia, and in the germline of individuals with Noonan syndrome. Pathogenic RIT1 proteins promote mitogen-activated protein kinase (MAPK) hyperactivation; however, this mechanism remains poorly understood. Here, we show that RAF kinases are direct effectors of membrane-bound mutant RIT1 necessary for MAPK activation. We identify critical residues in RIT1 that facilitate interaction with membrane lipids and show that these are necessary for association with RAF kinases and MAPK activation. Although mutant RIT1 binds to RAF kinases directly, it fails to activate MAPK signaling in the absence of classical RAS proteins. Consistent with aberrant RAF/MAPK activation as a driver of disease, we show that pathway inhibition alleviates cardiac hypertrophy in a mouse model of RIT1 mutant Noonan syndrome. These data shed light on the function of pathogenic RIT1 and identify avenues for therapeutic intervention.
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Neoplasias Pulmonares , Síndrome de Noonan , Animales , Ratones , Síndrome de Noonan/genética , Síndrome de Noonan/metabolismo , Síndrome de Noonan/patología , Proteínas Quinasas Activadas por Mitógenos/metabolismo , Cardiomegalia/genética , Transducción de SeñalRESUMEN
Molecular dynamics (MD) simulations employing ab initio quantum mechanical and molecular mechanical (ai-QM/MM) potentials are considered to be the state of the art, but the high computational cost associated with the ai-QM calculations remains a theoretical challenge for their routine application. Here, we present a modified protocol of the multiple time step (MTS) method for accelerating ai-QM/MM MD simulations of condensed-phase reactions. Within a previous MTS protocol [Nam J. Chem. Theory Comput. 2014, 10, 4175], reference forces are evaluated using a low-level (semiempirical QM/MM) Hamiltonian and employed at inner time steps to propagate the nuclear motions. Correction forces, which arise from the force differences between high-level (ai-QM/MM) and low-level Hamiltonians, are applied at outer time steps, where the MTS algorithm allows the time-reversible integration of the correction forces. To increase the outer step size, which is bound by the highest-frequency component in the correction forces, the semiempirical QM Hamiltonian is recalibrated in this work to minimize the magnitude of the correction forces. The remaining high-frequency modes, which are mainly bond stretches involving hydrogen atoms, are then removed from the correction forces. When combined with a Langevin or SIN(R) thermostat, the modified MTS-QM/MM scheme remains robust with an up to 8 (with Langevin) or 10 fs (with SIN(R)) outer time step (with 1 fs inner time steps) for the chorismate mutase system. This leads to an over 5-fold speedup over standard ai-QM/MM simulations, without sacrificing the accuracy in the predicted free energy profile of the reaction.