Unlocking opioid neuropeptide dynamics with genetically encoded biosensors.

Neuropeptides are ubiquitous in the nervous system. Research into neuropeptides has been limited by a lack of experimental tools that allow for the precise dissection of their complex and diverse dynamics in a circuit-specific manner. Opioid peptides modulate pain, reward and aversion and as such have high clinical relevance. To illuminate the spatiotemporal dynamics of endogenous opioid signaling in the brain, we developed a class of genetically encoded fluorescence sensors based on kappa, delta and mu opioid receptors: κLight, δLight and µLight, respectively. We characterized the pharmacological profiles of these sensors in mammalian cells and in dissociated neurons. We used κLight to identify electrical stimulation parameters that trigger endogenous opioid release and the spatiotemporal scale of dynorphin volume transmission in brain slices. Using in vivo fiber photometry in mice, we demonstrated the utility of these sensors in detecting optogenetically driven opioid release and observed differential opioid release dynamics in response to fearful and rewarding conditions.

Prefrontal cortical dynorphin peptidergic transmission constrains threat-driven behavioral and network states.

Prefrontal cortical (PFC) circuits provide top-down control of threat reactivity. This includes ventromedial PFC (vmPFC) circuitry, which plays a role in suppressing fear-related behavioral states. Dynorphin (Dyn) has been implicated in mediating negative affect and maladaptive behaviors induced by severe threats and is expressed in limbic circuits, including the vmPFC. However, there is a critical knowledge gap in our understanding of how vmPFC Dyn-expressing neurons and Dyn transmission detect threats and regulate expression of defensive behaviors. Here, we demonstrate that Dyn cells are broadly activated by threats and release Dyn locally in the vmPFC to limit passive defensive behaviors. We further demonstrate that vmPFC Dyn-mediated signaling promotes a switch of vmPFC networks to a fear-related state. In conclusion, we reveal a previously unknown role of vmPFC Dyn neurons and Dyn neuropeptidergic transmission in suppressing defensive behaviors in response to threats via state-driven changes in vmPFC networks.

Boosting Gas-Phase TiO2 Photocatalysis with Weak Electric Field Strengths of Volt/Centimeter.

Among semiconductor nanomaterials, titanium dioxide is at the forefront of heterogeneous photocatalysis, but its catalytic activity greatly suffers from the loss of photoexcited charge carriers through deleterious recombination processes. Here, we investigate the impact of an external electric field (EEF) applied to conventional P25 TiO2 nanopowder with or without Au nanoparticles (NPs) to circumvent this issue. The study of two redox reactions in the gas phase, water splitting and toluene degradation, reveals an enhancement of the photocatalytic activity with rather modest electric fields of a few volt/centimeters only. Such an improvement arises from the electric-field-induced quenching of the green emission in anatase, allowing the photoexcited charge carriers to be transferred to the adsorbed reactants instead of pointless radiative recombinations. Applying an EEF across a trap-rich metal oxide material, such as TiO2, which, when impregnated with Au NPs, leads, respectively, to 12- and 6-fold enhancements in the production of hydrogen and the oxidation of toluene for an electric field of 8 V/cm, without any electrolysis, is a simple and elegant strategy to meet higher photocatalytic efficiencies.

Prefrontal cortical dynorphin peptidergic transmission constrains threat-driven behavioral and network states.

Prefrontal cortical (PFC) circuits provide top-down control of threat reactivity. This includes ventromedial PFC (vmPFC) circuitry, which plays a role in suppressing fear-related behavioral states. Dynorphin (Dyn) has been implicated in mediating negative affect and mal-adaptive behaviors induced by severe threats and is expressed in limbic circuits, including the vmPFC. However, there is a critical knowledge gap in our understanding of how vmPFC Dyn-expressing neurons and Dyn transmission detect threats and regulate expression of defensive behaviors. Here, we demonstrate that Dyn cells are broadly activated by threats and release Dyn locally in the vmPFC to limit passive defensive behaviors. We further demonstrate that vmPFC Dyn-mediated signaling promotes a switch of vmPFC networks to a fear-related state. In conclusion, we reveal a previously unknown role of vmPFC Dyn neurons and Dyn neuropeptidergic transmission in suppressing defensive behaviors in response to threats via state-driven changes in vmPFC networks.

Opioidergic signaling contributes to food-mediated suppression of AgRP neurons.

Opioids are generally known to promote hedonic food consumption. Although much of the existing evidence is primarily based on studies of the mesolimbic pathway, endogenous opioids and their receptors are widely expressed in hypothalamic appetite circuits as well; however, their role in homeostatic feeding remains unclear. Using a fluorescent opioid sensor, deltaLight, here we report that mediobasal hypothalamic opioid levels increase by feeding, which directly and indirectly inhibits agouti-related protein (AgRP)-expressing neurons through the µ-opioid receptor (MOR). AgRP-specific MOR expression increases by energy surfeit and contributes to opioid-induced suppression of appetite. Conversely, its antagonists diminish suppression of AgRP neuron activity by food and satiety hormones. Mice with AgRP neuron-specific ablation of MOR expression have increased fat preference without increased motivation. These results suggest that post-ingestion release of endogenous opioids contributes to AgRP neuron inhibition to shape food choice through MOR signaling.

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places.

Background: People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations. Methods: In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models. Results: The final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables. Conclusion: In this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.

Use of symptom diary in primary students: association of nitrogen dioxide with prevalence of symptoms.

Air pollution is a global public health concern, and numerous studies have attempted to identify the health effects of air pollutants, including nitrogen dioxide (NO2). In China, there are few studies investigating the relationship between NO2 exposure and symptoms among children at an individual level. The aim of the study was to evaluate the acute effects of NO2 on prevalence of symptoms of primary students. An environmental and health questionnaire survey was administered to 4240 primary students in seven districts of Shanghai. Daily symptoms, as well as the daily air pollution and meteorological data from each community, were recorded during the corresponding period. A multivariable logistic regression model was utilized to analyze the relationship between the prevalence of symptoms and NO2 exposure in school-age children. A model with interaction items was adopted to estimate the interactive effects of NO2 and confounding factors on symptoms. The average NO2 level in central urban, industrial and rural areas were 62.07 ± 21.66, 54.86 ± 18.32 and 36.62 ± 21.23 µg m-3, respectively. Our findings demonstrate that the occurrence of symptoms was significantly affected by NO2 exposure in the short-term. The largest associations were observed for a 10 µg m-3 increase in 5-day moving average (lag04) NO2 concentration with prevalence of general symptoms (odds ratio [OR] = 1.15, 95% confidence interval [95% CI]: 1.07-1.22), throat symptoms (OR = 1.23, 95% CI: 1.13-1.35) and nasal symptoms (OR = 1.142, 95% CI: 1.02-1.27). Subgroup analysis showed that non-rural areas, boys, nearby environmental pollution source and history of present illness were all susceptible factors to the effects of NO2 exposure. Furthermore, there were interactive effects between NO2 exposure and area types on reported symptoms. NO2 can increase the risk of symptoms in primary students in the short-term, which could be significantly enhanced in central urban and industrial areas.

Psychedelics promote neuroplasticity through the activation of intracellular 5-HT2A receptors.

Decreased dendritic spine density in the cortex is a hallmark of several neuropsychiatric diseases, and the ability to promote cortical neuron growth has been hypothesized to underlie the rapid and sustained therapeutic effects of psychedelics. Activation of 5-hydroxytryptamine (serotonin) 2A receptors (5-HT2ARs) is essential for psychedelic-induced cortical plasticity, but it is currently unclear why some 5-HT2AR agonists promote neuroplasticity, whereas others do not. We used molecular and genetic tools to demonstrate that intracellular 5-HT2ARs mediate the plasticity-promoting properties of psychedelics; these results explain why serotonin does not engage similar plasticity mechanisms. This work emphasizes the role of location bias in 5-HT2AR signaling, identifies intracellular 5-HT2ARs as a therapeutic target, and raises the intriguing possibility that serotonin might not be the endogenous ligand for intracellular 5-HT2ARs in the cortex.

Direct Photocatalytic Synthesis of Acetic Acid from Methane and CO at Ambient Temperature Using Water as Oxidant.

Direct functionalization of methane selectively to value-added chemicals is still one of the main challenges in modern science. Acetic acid is an important industrial chemical produced nowadays by expensive and environmentally unfriendly carbonylation of methanol using homogeneous catalysts. Here, we report a new photocatalytic reaction route to synthesize acetic acid from CH4 and CO at room temperature using water as the sole external oxygen source. The optimized photocatalyst consists of a TiO2 support and ammonium phosphotungstic polyoxometalate (NPW) clusters anchored with isolated Pt single atoms (Pt1). It enables a stable synthesis of 5.7 mmol·L-1 acetic acid solution in 60 h with the selectivity over 90% and 66% to acetic acid on liquid-phase and carbon basis, respectively, with the production of 99 mol of acetic acid per mol of Pt. Combined isotopic and in situ spectroscopy investigation suggests that synthesis of acetic acid proceeds via a photocatalytic oxidative carbonylation of methane over the Pt1 sites, with the methane activation facilitated by water-derived hydroxyl radicals.

CO-tolerant RuNi/TiO2 catalyst for the storage and purification of crude hydrogen.

Hydrogen storage by means of catalytic hydrogenation of suitable organic substrates helps to elevate the volumetric density of hydrogen energy. In this regard, utilizing cheaper industrial crude hydrogen to fulfill the goal of hydrogen storage would show economic attraction. However, because CO impurities in crude hydrogen can easily deactivate metal active sites even in trace amounts such a process has not yet been realized. Here, we develop a robust RuNi/TiO2 catalyst that enables the efficient hydrogenation of toluene to methyl-cyclohexane under simulated crude hydrogen feeds with 1000-5000 ppm CO impurity at around 180 °C under atmospheric pressure. We show that the co-localization of Ru and Ni species during reduction facilitated the formation of tightly coupled metallic Ru-Ni clusters. During the catalytic hydrogenation process, due to the distinct bonding properties, Ru and Ni served as the active sites for CO methanation and toluene hydrogenation respectively. Our work provides fresh insight into the effective utilization and purification of crude hydrogen for the future hydrogen economy.

Fluorescence Imaging of Neural Activity, Neurochemical Dynamics, and Drug-Specific Receptor Conformation with Genetically Encoded Sensors.

Recent advances in fluorescence imaging permit large-scale recording of neural activity and dynamics of neurochemical release with unprecedented resolution in behaving animals. Calcium imaging with highly optimized genetically encoded indicators provides a mesoscopic view of neural activity from genetically defined populations at cellular and subcellular resolutions. Rigorously improved voltage sensors and microscopy allow for robust spike imaging of populational neurons in various brain regions. In addition, recent protein engineering efforts in the past few years have led to the development of sensors for neurotransmitters and neuromodulators. Here, we discuss the development and applications of these genetically encoded fluorescent indicators in reporting neural activity in response to various behaviors in different biological systems as well as in drug discovery. We also report a simple model to guide sensor selection and optimization.

Few-Atom Pt Ensembles Enable Efficient Catalytic Cyclohexane Dehydrogenation for Hydrogen Production.

Identification of catalytic active sites is pivotal in the design of highly effective heterogeneous metal catalysts, especially for structure-sensitive reactions. Downsizing the dimension of the metal species on the catalyst increases the dispersion, which is maximized when the metal exists as single atoms, namely, single-atom catalysts (SACs). SACs have been reported to be efficient for various catalytic reactions. We show here that the Pt SACs, although with the highest metal atom utilization efficiency, are totally inactive in the cyclohexane (C6H12) dehydrogenation reaction, an important reaction that could enable efficient hydrogen transportation. Instead, catalysts enriched with fully exposed few-atom Pt ensembles, with a Pt-Pt coordination number of around 2, achieve the optimal catalytic performance. The superior performance of a fully exposed few-atom ensemble catalyst is attributed to its high d-band center, multiple neighboring metal sites, and weak binding of the product.

Psychedelic-inspired drug discovery using an engineered biosensor.

Ligands can induce G protein-coupled receptors (GPCRs) to adopt a myriad of conformations, many of which play critical roles in determining the activation of specific signaling cascades associated with distinct functional and behavioral consequences. For example, the 5-hydroxytryptamine 2A receptor (5-HT2AR) is the target of classic hallucinogens, atypical antipsychotics, and psychoplastogens. However, currently available methods are inadequate for directly assessing 5-HT2AR conformation both in vitro and in vivo. Here, we developed psychLight, a genetically encoded fluorescent sensor based on the 5-HT2AR structure. PsychLight detects behaviorally relevant serotonin release and correctly predicts the hallucinogenic behavioral effects of structurally similar 5-HT2AR ligands. We further used psychLight to identify a non-hallucinogenic psychedelic analog, which produced rapid-onset and long-lasting antidepressant-like effects after a single administration. The advent of psychLight will enable in vivo detection of serotonin dynamics, early identification of designer drugs of abuse, and the development of 5-HT2AR-dependent non-hallucinogenic therapeutics.

Fully Exposed Cluster Catalyst (FECC): Toward Rich Surface Sites and Full Atom Utilization Efficiency.

Increasing attention has been paid to single-atom catalysts (SACs) in heterogeneous catalysis because of their unique electronic properties, maximized atomic utilization efficiency, and potential to serve as a bridge between the heterogeneous and homogeneous catalysis. However, SACs can have limited advantages or even constrained applications for the reactions that require designated metallic states with multiple atoms or surface sites with metal-metal bonds. As a cross-dimensional extension to the concept of SACs, fully exposed cluster catalysts (FECCs) offer diverse surface sites formed by an ensemble of metal atoms, for the adsorption and transformation of reactants/intermediates. More importantly, FECCs have the advantage of maximized atom utilization efficiency. Thus, FECCs provide a novel platform to design effective and efficient catalysts for certain chemical processes. This outlook summarizes recent advances and proposes prospective research directions in the design of catalysts and characterizations of FECCs, together with potential challenges.

Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning.

Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which has been hampered by our inability to monitor serotonin release and transport with high spatial and temporal resolution. We developed and applied a binding-pocket redesign strategy, guided by machine learning, to create a high-performance, soluble, fluorescent serotonin sensor (iSeroSnFR), enabling optical detection of millisecond-scale serotonin transients. We demonstrate that iSeroSnFR can be used to detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep/wake transitions. We also developed a robust assay of serotonin transporter function and modulation by drugs. We expect that both machine-learning-guided binding-pocket redesign and iSeroSnFR will have broad utility for the development of other sensors and in vitro and in vivo serotonin detection, respectively.

An expanded palette of dopamine sensors for multiplex imaging in vivo.

Genetically encoded dopamine sensors based on green fluorescent protein (GFP) enable high-resolution imaging of dopamine dynamics in behaving animals. However, these GFP-based variants cannot be readily combined with commonly used optical sensors and actuators, due to spectral overlap. We therefore engineered red-shifted variants of dopamine sensors called RdLight1, based on mApple. RdLight1 can be combined with GFP-based sensors with minimal interference and shows high photostability, permitting prolonged continuous imaging. We demonstrate the utility of RdLight1 for receptor-specific pharmacological analysis in cell culture, simultaneous assessment of dopamine release and cell-type-specific neuronal activity and simultaneous subsecond monitoring of multiple neurotransmitters in freely behaving rats. Dual-color photometry revealed that dopamine release in the nucleus accumbens evoked by reward-predictive cues is accompanied by a rapid suppression of glutamate release. By enabling multiplexed imaging of dopamine with other circuit components in vivo, RdLight1 opens avenues for understanding many aspects of dopamine biology.

GC-MS based metabolomics strategy to distinguish three types of acute pancreatitis.

Acute pancreatitis (AP) is a progressive systemic inflammatory response with high morbidity and high mortality, which is mainly caused by alcohol, bulimia, gallstones and hyperlipidemia. The early diagnosis of different types of AP and further explore potential pathophysiological mechanism of each type of AP is beneficial for optimized treatment strategies and better patient's care. In this study, a metabolomics approach based on gas chromatography-mass spectrometry (GC-MS), and random forests algorithm was established to distinguish biliary acute pancreatitis (BAP), Hyperlipidemia acute pancreatitis (HLAP), and alcoholic acute pancreatitis (AAP), from healthy controls. The classification accuracies for BAP, HLAP, and AAP patients compared with healthy control, were 0.886, 0.906 and 0.857, respectively, by using 5-fold cross-validation method. And some special metabolites for each type of AP were discovered, such as l-Lactic acid, (R)-3-Hydroxybutyric acid, Phosphoric acid, Glycine, Erythronic acid, l-Phenylalanine, d-Galactose, l-Tyrosine, Arachidonic acid, Glycerol 1-hexadecanoate. Furthermore, associations between these metabolites with the metabolism of amino acids, fatty acids were identified. Our studies have illuminated the biomarkers and physiological mechanism of disease in a clinical setting, which suggested that metabolomics is a valuable tool for identifying the molecular mechanisms that are involved in the etiology of BAP, AAP, HLAP and thus novel therapeutic targets.

Well-designed Ag/ZnO/3D graphene structure for dye removal: Adsorption, photocatalysis and physical separation capabilities.

In this research, adsorption and photocatalytic degradation process were utilized to remove organic dye from wastewater. To accomplish that, a newly-designed ternary nanostructure based on Ag nanoparticles/ZnO nanorods/three-dimensional graphene network (Ag NPs/ZnO NRs/3DG) was prepared using a combined hydrothermal-photodeposition method. The three-dimensional structure of graphene hydrogel as a support for growth of ZnO nanorods was characterized using field emission scanning electron microscopy (FESEM). In addition, diameter of silver nanoparticles grown on the ZnO nanorods with the average aspect ratio of 5 was determined in the range of 30-80 nm by using transmission electron microscopy (TEM). The X-ray diffraction (XRD) pattern was revealed hexagonal Wurtzite structure of ZnO nanorods and the (1 1 1) lattice plane of the face-centered cubic (FCC) of the silver nanoparticles. The dye adsorption capacity of the synthesized 3DG was evaluated at about 300 mg/g using kinetic study. The photocatalytic dye degradation under both UV and visible light irradiation exhibited an enhanced activity of the prepared ternary Ag/ZnO/3DG sample in comparison to ZnO/3DG and 3DG structures. Different charge-carrier scavengers were utilized to elucidate the synergistic effect of adsorption and visible-light photocatalytic degradation mechanism for dye removal. The facile photocatalyst recovery as well as the high elimination rate of dye is promising for future applications such as efficient removal of organic contaminants from industrial wastewater under solar irradiation.

Personal exposure measurements of school-children to fine particulate matter (PM2.5) in winter of 2013, Shanghai, China.

OBJECTIVE: The aim of this study was to perform an exposure assessment of PM2.5 (particulate matter less than 2.5µm in aerodynamic diameter) among children and to explore the potential sources of exposure from both indoor and outdoor environments. METHODS: In terms of real-time exposure measurements of PM2.5, we collected data from 57 children aged 8-12 years (9.64 ± 0.93 years) in two schools in Shanghai, China. Simultaneously, questionnaire surveys and time-activity diaries were used to estimate the environment at home and daily time-activity patterns in order to estimate the exposure dose of PM2.5 in these children. Principle component regression analysis was used to explore the influence of potential sources of PM2.5 exposure. RESULTS: All the median personal exposure and microenvironment PM2.5 concentrations greatly exceeded the daily 24-h PM2.5 Ambient Air Quality Standards of China, the USA, and the World Health Organization (WHO). The median Etotal (the sum of the PM2.5 exposure levels in different microenvironment and fractional time) of all students was 3014.13 (µg.h)/m3. The concentration of time-weighted average (TWA) exposure of all students was 137.01 µg/m3. The median TWA exposure level during the on-campus period (135.81 µg/m3) was significantly higher than the off-campus period (115.50 µg/m3, P = 0.013 < 0.05). Besides ambient air pollution and meteorological conditions, storey height of the classroom and mode of transportation to school were significantly correlated with children's daily PM2.5 exposure. CONCLUSIONS: Children in the two selected schools were exposed to high concentrations of PM2.5 in winter of 2013 in Shanghai. Their personal PM2.5 exposure was mainly associated with ambient air conditions, storey height of the classroom, and children's transportation mode to school.

Modulation of the Reduction Potential of TiO2- x by Fluorination for Efficient and Selective CH4 Generation from CO2 Photoreduction.

Photocatalytic reduction of CO2 holds great promises for addressing both the environmental and energy issues that are facing the modern society. The major challenge of CO2 photoreduction into fuels such as methane or methanol is the low yield and poor selectivity. Here, we report an effective strategy to enhance the reduction potential of photoexcited electrons by fluorination of mesoporous single crystals of reduced TiO2- x. Density functional theory calculations and photoelectricity tests indicate that the Ti3+ impurity level is upswept by fluorination, owing to the built-in electric field constructed by the substitutional F that replaces surface oxygen vacancies, which leads to the enhanced reduction potential of photoexcited electrons. As a result, the fluorination of the reduced TiO2- x dramatically increases the CH4 production yield by 13 times from 0.125 to 1.63 µmol/g·h under solar light illumination with the CH4 selectivity being improved from 25.7% to 85.8%. Our finding provides a metal-free strategy for the selective CH4 generation from CO2 photoreduction.