A domain-label-guided translation model for molecular optimization.

Molecular optimization, which aims to improve molecular properties by modifying complex molecular structures, is a crucial and challenging task in drug discovery. In recent years, translation models provide a promising way to transform low-property molecules to high-property molecules, which enables molecular optimization to achieve remarkable progress. However, most existing models require matched molecular pairs, which are prone to be limited by the datasets. Although some models do not require matched molecular pairs, their performance is usually sacrificed due to the lack of useful supervising information. To address this issue, a domain-label-guided translation model is proposed in this paper, namely DLTM. In the model, the domain label information of molecules is exploited as a control condition to obtain different embedding representations, enabling the model to generate diverse molecules. Besides, the model adopts a classifier network to identify the property categories of transformed molecules, guiding the model to generate molecules with desired properties. The performance of DLTM is verified on two optimization tasks, namely the quantitative estimation of drug-likeness and penalized logP. Experimental results show that the proposed DLTM is superior to the compared baseline models.

Tandem duplication and sub-functionalization of clerodane diterpene synthase originate the blooming of clerodane diterpenoids in Scutellaria barbata.

Scutellaria barbata is a traditional Chinese herb medicine and a major source of bioactive clerodane diterpenoids. However, barely clerodanes have been isolated from the closely related S. baicalensis. Here we assembled a chromosome-level genome of S. barbata and identified three class II clerodane diterpene synthases (SbarKPS1, SbarKPS2 and SbaiKPS1) from these two organisms. Using in vitro and in vivo assays, SbarKPS1 was characterized as a monofunctional (-)-kolavenyl diphosphate synthases ((-)-KPS), while SbarKPS2 and SbaiKPS1 produced major neo-cleroda-4(18),13E-dienyl diphosphate with small amount of (-)-KPP. SbarKPS1 and SbarKPS2 shared a high protein sequence identity and formed a tandem gene pair, indicating tandem duplication and sub-functionalization probably led to the evolution of monofunctional (-)-KPS in S. barbata. Additionally, SbarKPS1 and SbarKPS2 were primarily expressed in the leaves and flowers of S. barbata, which was consistent with the distribution of major clerodane diterpenoids scutebarbatine A and B. In contrast, SbaiKPS1 was barely expressed in any tissue of S. baicalensis. We further explored the downstream class I diTPS by functional characterizing of SbarKSL3 and SbarKSL4. Unfortunately, no dephosphorylated product was detected in the coupled assays with SbarKSL3/KSL4 and four class II diTPSs (SbarKPS1, SbarKPS2, SbarCPS2 and SbarCPS4) when a phosphatase inhibitor cocktail was included. Co-expression of SbarKSL3/KSL4 with class II diTPSs in yeast cells did not increase the yield of the corresponding dephosphorylated products, either. Together, these findings elucidated the involvement of two class II diTPSs in clerodane biosynthesis in S. barbata, while the class I diTPS is likely not responsible for the subsequent dephosphorylation step.

Evolutionary Multiobjective Molecule Optimization in an Implicit Chemical Space.

Optimization techniques play a pivotal role in advancing drug development, serving as the foundation of numerous generative methods tailored to efficiently design optimized molecules derived from existing lead compounds. However, existing methods often encounter difficulties in generating diverse, novel, and high-property molecules that simultaneously optimize multiple drug properties. To overcome this bottleneck, we propose a multiobjective molecule optimization framework (MOMO). MOMO employs a specially designed Pareto-based multiproperty evaluation strategy at the molecular sequence level to guide the evolutionary search in an implicit chemical space. A comparative analysis of MOMO with five state-of-the-art methods across two benchmark multiproperty molecule optimization tasks reveals that MOMO markedly outperforms them in terms of diversity, novelty, and optimized properties. The practical applicability of MOMO in drug discovery has also been validated on four challenging tasks in the real-world discovery problem. These results suggest that MOMO can provide a useful tool to facilitate molecule optimization problems with multiple properties.

Steroid hormone ecdysone deficiency stimulates preparation for photoperiodic reproductive diapause.

Diapause, a programmed developmental arrest primarily induced by seasonal environmental changes, is very common in the animal kingdom, and found in vertebrates and invertebrates alike. Diapause provides an adaptive advantage to animals, as it increases the odds of surviving adverse conditions. In insects, individuals perceive photoperiodic cues and modify endocrine signaling to direct reproductive diapause traits, such as ovary arrest and increased fat accumulation. However, it remains unclear as to which endocrine factors are involved in this process and how they regulate the onset of reproductive diapause. Here, we found that the long day-mediated drop in the concentration of the steroid hormone ecdysone is essential for the preparation of photoperiodic reproductive diapause in Colaphellus bowringi, an economically important cabbage beetle. The diapause-inducing long-day condition reduced the expression of ecdysone biosynthetic genes, explaining the drop in the titer of 20-hydroxyecdysone (20E, the active form of ecdysone) in female adults. Application of exogenous 20E induced vitellogenesis and ovarian development but reduced fat accumulation in the diapause-destined females. Knocking down the ecdysone receptor (EcR) in females destined for reproduction blocked reproductive development and induced diapause traits. RNA-seq and hormone measurements indicated that 20E stimulates the production of juvenile hormone (JH), a key endocrine factor in reproductive diapause. To verify this, we depleted three ecdysone biosynthetic enzymes via RNAi, which confirmed that 20E is critical for JH biosynthesis and reproductive diapause. Importantly, impairing Met function, a component of the JH intracellular receptor, partially blocked the 20E-regulated reproductive diapause preparation, indicating that 20E regulates reproductive diapause in both JH-dependent and -independent manners. Finally, we found that 20E deficiency decreased ecdysis-triggering hormone signaling and reduced JH production, thereby inducing diapause. Together, these results suggest that 20E signaling is a pivotal regulator that coordinates reproductive plasticity in response to environmental inputs.

Adaptive Fuzzy Integral Sliding Mode Cooperative Control Based on Time-Delay Estimation for Free-Floating Close-Chain Manipulators.

Space manipulators are expected to perform more challenging missions in on-orbit service (OOS) systems, but there are some unique characteristics that are not found on ground-based robots, such as dynamic coupling between space bases and manipulators, limited fuel supply, and working with unfixed bases. This paper focuses on trajectory-tracking control and internal force control for free-floating close-chain manipulators. First, the kinematics and dynamics of free-floating close-chain manipulators are given using the momentum conservation and spatial operator algebra (SOA) methodologies, respectively. Furthermore, an adaptive fuzzy integral sliding mode controller (AFISMC) based on time delay estimation (TDE) was designed for trajectory-tracking control, and a proportional-integral (PI) control strategy was adopted for internal force control. The global asymptotic stability of the proposed controller was proven by using the Lyapunov methodology. Three cases were conducted to verify the efficiency of the controller by using numerical simulations on two six-link manipulators with a free-floating base. The controller presents the desired tracking capability.

Genome-wide identification and functional characterization of borneol dehydrogenases in Wurfbainia villosa.

MAIN CONCLUSION: Genome-wide screening of short-chain dehydrogenases/reductases (SDR) family reveals functional diversification of borneol dehydrogenase (BDH) in Wurfbainia villosa. Wurfbainia villosa is an important medicinal plant, the fruits of which accumulate abundant terpenoids, especially bornane-type including borneol and camphor. The borneol dehydrogenase (BDH) responsible for the conversion of borneol to camphor in W. villosa remains unknown. BDH is one member of short-chain dehydrogenases/reductases (SDR) family. Here, a total of 115 classical WvSDR genes were identified through genome-wide screening. These WvSDRs were unevenly distributed on different chromosomes. Seven candidate WvBDHs based on phylogenetic analysis and expression levels were selected for cloning. Of them, four BDHs can catalyze different configurations of borneol and other monoterpene alcohol substrates to generate the corresponding oxidized products. WvBDH1 and WvBDH2, preferred (+)-borneol to (-)-borneol, producing the predominant ( +)-camphor. WvBDH3 yielded approximate equivalent amount of (+)-camphor and (-)-camphor, in contrast, WvBDH4 generated exclusively (+)-camphor. The metabolic profiles of the seeds showed that the borneol and camphor present were in the dextrorotatory configuration. Enzyme kinetics and expression pattern in different tissues suggested WvBDH2 might be involved in the biosynthesis of camphor in W. villosa. All results will increase the understanding of functional diversity of BDHs.

GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest.

Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. In this paper, we proposed a machine learning techniques-based classification approach to identify disease-related lncRNAs by graph auto-encoder (GAE) and random forest (RF) (GAERF). First, we combined the relationship of lncRNA, miRNA and disease into a heterogeneous network. Then, low-dimensional representation vectors of nodes were learned from the network by GAE, which reduce the dimension and heterogeneity of biological data. Taking these feature vectors as input, we trained a RF classifier to predict new lncRNA-disease associations (LDAs). Related experiment results show that the proposed method for the representation of lncRNA-disease characterizes them accurately. GAERF achieves superior performance owing to the ensemble learning method, outperforming other methods significantly. Moreover, case studies further demonstrated that GAERF is an effective method to predict LDAs.

Rhamnosyltransferases involved in the biosynthesis of flavone rutinosides in Chrysanthemum species.

Linarin (acacetin-7-O-rutinoside), isorhoifolin (apigenin-7-O-rutinoside), and diosmin (diosmetin-7-O-rutinoside) are chemically and structurally similar flavone rutinoside (FR) compounds found in Chrysanthemum L. (Anthemideae, Asteraceae) plants. However, their biosynthetic pathways remain largely unknown. In this study, we cloned and compared FRs and genes encoding rhamnosyltransferases (RhaTs) among eight accessions of Chrysanthemum polyploids. We also biochemically characterized RhaTs of Chrysanthemum plants and Citrus (Citrus sinensis and Citrus maxima). RhaTs from these two genera are substrate-promiscuous enzymes catalyzing the rhamnosylation of flavones, flavanones, and flavonols. Substrate specificity analysis revealed that Chrysanthemum 1,6RhaTs preferred flavone glucosides (e.g. acacetin-7-O-glucoside), whereas Cs1,6RhaT preferred flavanone glucosides. The nonsynonymous substitutions of RhaTs found in some cytotypes of diploids resulted in the loss of catalytic function. Phylogenetic analysis and specialized pathways responsible for the biosynthesis of major flavonoids in Chrysanthemum and Citrus revealed that rhamnosylation activity might share a common evolutionary origin. Overexpression of RhaT in hairy roots resulted in 13-, 2-, and 5-fold increases in linarin, isorhoifolin, and diosmin contents, respectively, indicating that RhaT is mainly involved in the biosynthesis of linarin. Our findings not only suggest that the substrate promiscuity of RhaTs contributes to the diversity of FRs in Chrysanthemum species but also shed light on the evolution of flavone and flavanone rutinosides in distant taxa.

Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning.

Complete coverage path planning requires that the mobile robot traverse all reachable positions in the environmental map. Aiming at the problems of local optimal path and high path coverage ratio in the complete coverage path planning of the traditional biologically inspired neural network algorithm, a complete coverage path planning algorithm based on Q-learning is proposed. The global environment information is introduced by the reinforcement learning method in the proposed algorithm. In addition, the Q-learning method is used for path planning at the positions where the accessible path points are changed, which optimizes the path planning strategy of the original algorithm near these obstacles. Simulation results show that the algorithm can automatically generate an orderly path in the environmental map, and achieve 100% coverage with a lower path repetition ratio.

Interval Type-2 Fuzzy PID Controller Using Disassembled Gradational Optimization.

This paper presents an interval type-2 fuzzy proportional-integral-derivative (IT2F-PID) controller that is designed using a new disassembled gradational optimization (D-GO) method. A PID controller is first optimized using the D-GO method and then connected to a type-1 fuzzy logic system (T1-FLS). The parameters of the T1-FLS are optimized, and the T1-FLS is blurred into the interval type-2 fuzzy logic system (IT2-FLS). Finally, the IT2F-PID controller is formed. The proposed method is compared with the concurrent and general optimization methods. The simulation results show that the D-GO method reduces the optimization time by over 90% compared with the general method, and decreases the integral-of-time-absolute-error (ITAE) by 30%. Beyond that, compared with the concurrent optimization method, the D-GO method reduces time by over 25%, and the ITAE value by about 95%. In the normal case, model uncertainty, target uncertainty, and external disturbance, the control ability of the IT2F-PID controller designed using the D-GO method is verified via simulations using a nonlinear forced closed-loop system. The results show that the overshoot is reduced by 80% and the fluctuation is reduced by 67% compared with a traditional PID controller and an IT2F-PID controller built using the general method.

Identification and functional characterization of three iridoid synthases in Gardenia jasminoides.

MAIN CONCLUSION: The discovery of three iridoid synthases (GjISY, GjISY2 and GjISY4) from Gardenia jasminoides and their functional characterization increase the understanding of iridoid scaffold/iridoid glycoside biosynthesis in iridoid-producing plants. Iridoids are a class of noncanonical monoterpenes that are found naturally in the plant kingdom mostly as glycosides. Over 40 iridoid glycosides (e.g., geniposide, gardenoside and shanzhiside) have been isolated from Gardenia jasminoides. They have multiple pharmacological properties and health-promoting effects. However, their biosynthetic pathway is poorly understood, and the iridoid synthase (ISY) responsible for the cyclization of the core scaffold remains unclear. In this study, three homologs of ISYs from G. jasminoides (GjISY, GjISY2 and GjISY4) were identified on the basis of transcriptomic data and functionally characterized. The genomic structure and intron-exon arrangement revealed that all three ISYs contained an intron. Biochemical assays indicated that all three recombinant enzymes reduced 8-oxogeranial to nepetalactol and its open forms (iridodials) as the products of the classical CrISY (Catharanthus roseus). In addition, all three enzymes reduced progesterone to 5-ß-prognane-3,20-dione. However, only GjISY2 and GjISY4 reduced 2-cyclohexen-1-one to cyclohexanone. Overall, the GjISY2 expression levels in the flowers and fruits were similar to the GjISY and GjISY4 expression levels. By contrast, the GjISY2 expression levels in the upper and lower leaves were substantially higher than the GjISY and GjISY4 expression levels. Among the three, GjISY2 exhibited the highest catalytic efficiency for 8-oxogeranial. GjISY2 might be the major contributor to iridoid biosynthesis in G. jasminoides. Collectively, our results advance the understanding of iridoid scaffold/iridoid glycoside biosynthesis in G. jasminoides and provide a potential target for metabolic engineering and breeding.

AEMDA: inferring miRNA-disease associations based on deep autoencoder.

MOTIVATION: MicroRNAs (miRNAs) are a class of non-coding RNAs that play critical roles in various biological processes. Many studies have shown that miRNAs are closely related to the occurrence, development and diagnosis of human diseases. Traditional biological experiments are costly and time consuming. As a result, effective computational models have become increasingly popular for predicting associations between miRNAs and diseases, which could effectively boost human disease diagnosis and prevention. RESULTS: We propose a novel computational framework, called AEMDA, to identify associations between miRNAs and diseases. AEMDA applies a learning-based method to extract dense and high-dimensional representations of diseases and miRNAs from integrated disease semantic similarity, miRNA functional similarity and heterogeneous related interaction data. In addition, AEMDA adopts a deep autoencoder that does not need negative samples to retrieve the underlying associations between miRNAs and diseases. Furthermore, the reconstruction error is used as a measurement to predict disease-associated miRNAs. Our experimental results indicate that AEMDA can effectively predict disease-related miRNAs and outperforms state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/CunmeiJi/AEMDA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.

BACKGROUND: MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time-consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. METHODS: This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. RESULT: Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. CONCLUSION: MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA-disease association predication.

Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction.

BACKGROUND: The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers. RESULTS: Here, we present a computational framework based on graph Laplacian regularized L2, 1-nonnegative matrix factorization (GRL2, 1-NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL2,1-NMF framework was used to predict links between microRNAs and diseases. CONCLUSIONS: The new method (GRL2, 1-NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL2, 1-NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.

A compact and lightweight two-dimensional gimbal for inter-satellite laser communication applications.

To achieve precise alignment, tracking and pointing, inter-satellite laser communication generally requires a two-dimensional gimbal. To satisfy the requirements of space applications, the gimbal needs to be high-precision and as small as possible. To meet this need, we designed a small and lightweight gimbal assembly. We adopted piezoelectric ceramic motors instead of a torque motor to ensure driving precision and effectively reduce the mass of the drive unit. The yoke was made from low-volume fraction SiCp/Al with excellent specific stiffness, which ensured rigidity and reduced the mass of the structure. This allowed us to reduce the mass of the gimbal to just 5.8 kg. With a laser communication head mass of 5.55 kg, this gave a load-to-gimbal mass ratio of 95.7%. We carried out experiments on our small and lightweight gimbal, followed by a finite element analysis. The results of vibration test and shaking test showed that the structure has adequate stiffness and high precision.

Poly( N-isopropylacrylamide- co-1-vinyl-3-alkylimidazolium bromide) Microgels with Internal Nanophase-Separated Structures.

Microgels with internal nanophase-separated structures were fabricated by surfactant-free emulsion copolymerization of N-isopropylacrylamide (NIPAm) and ionic liquid comonomers, namely, 1-vinyl-3-alkylimidazolium bromide (VIM nBr) with various lengths n of long alkyl side chain, in an aqueous solution at 70 °C using N, N'-methylenebisacrylamide as the cross-linker. Combined techniques of transmission electron microscopy, dynamic and static light-scattering, differential scanning calorimetry (DSC), wide-angle X-ray diffraction (WAXD), small-angle X-ray scattering (SAXS), and polarized optical microscopy were employed to systematically investigate the sizes, morphologies, and properties of the obtained microgels as well as the microstructures and phase transition of nanophases inside the microgels. The obtained P(NIPAm/VIM nBr) microgels are spherical with narrow size distributions, and the nanophases have a radius of about 8-12 nm and are randomly distributed inside the microgels. The cooperative competition of the hydrophilic quaternary vinylimidazole moieties and hydrophobic long alkyl side chains determines the thermal sensitive behavior of the P(NIPAm/VIM nBr) microgels. DSC and WAXD results reveal that the nanophases consist of the ordered alkyl side chains with a layered crystalline structure at low temperature, which exhibit a low melting temperature and a broad melting transition. SAXS results further show that the nanophases form a layered liquid crystalline structure at high temperature for the microgel suspensions and freeze-dried microgels.

Airborne ultraviolet imaging system for oil slick surveillance: oil-seawater contrast, imaging concept, signal-to-noise ratio, optical design, and optomechanical model.

The airborne ultraviolet imaging system, which assesses oil slick areas better than visible and infrared optical systems, was designed to monitor and track oil slicks in coastal regions. A model was built to achieve the upwelling radiance distribution of oil-covered sea and clean seawater, based on the radiance transfer software. With this model, the oil-seawater contrast, which affects the detection of oil-covered coastal areas, was obtained. The oil-seawater contrast, fundamental imaging concept, analog calculation of SNR, optical design, and optomechanical configuration of the airborne ultraviolet imaging system are illustrated in this paper. The study of an airborne ultraviolet imaging system with F-number 3.4 and a 40° field of view (FOV) in near ultraviolet channel (0.32-0.38 µm) was illustrated and better imaging quality was achieved. The ground sample distance (GSD) is from 0.35 to 0.7 m with flight height ranges from 0.5 to 1 km. Comparisons of detailed characteristics of the airborne ultraviolet imaging system with the corresponding characteristics of previous ultraviolet systems were tabulated, and these comparisons showed that this system can achieve a wide FOV and a relative high SNR. A virtual mechanical prototype and tolerances analysis are illustrated in this paper to verify the performance of fabrication and assembly of the ultraviolet system.

AMPFLDAP: Adaptive Message Passing and Feature Fusion on Heterogeneous Network for LncRNA-Disease Associations Prediction.

Exploration of the intricate connections between long noncoding RNA (lncRNA) and diseases, referred to as lncRNA-disease associations (LDAs), plays a pivotal and indispensable role in unraveling the underlying molecular mechanisms of diseases and devising practical treatment approaches. It is imperative to employ computational methods for predicting lncRNA-disease associations to circumvent the need for superfluous experimental endeavors. Graph-based learning models have gained substantial popularity in predicting these associations, primarily because of their capacity to leverage node attributes and relationships within the network. Nevertheless, there remains much room for enhancing the performance of these techniques by incorporating and harmonizing the node attributes more effectively. In this context, we introduce a novel model, i.e., Adaptive Message Passing and Feature Fusion (AMPFLDAP), for forecasting lncRNA-disease associations within a heterogeneous network. Firstly, we constructed a heterogeneous network involving lncRNA, microRNA (miRNA), and diseases based on established associations and employing Gaussian interaction profile kernel similarity as a measure. Then, an adaptive topological message passing mechanism is suggested to address the information aggregation for heterogeneous networks. The topological features of nodes in the heterogeneous network were extracted based on the adaptive topological message passing mechanism. Moreover, an attention mechanism is applied to integrate both topological and semantic information to achieve the multimodal features of biomolecules, which are further used to predict potential LDAs. The experimental results demonstrated that the performance of the proposed AMPFLDAP is superior to seven state-of-the-art methods. Furthermore, to validate its efficacy in practical scenarios, we conducted detailed case studies involving three distinct diseases, which conclusively demonstrated AMPFLDAP's effectiveness in the prediction of LDAs.

Genomic insights into the evolution of flavonoid biosynthesis and O-methyltransferase and glucosyltransferase in Chrysanthemum indicum.

Flavonoids are a class of secondary metabolites widely distributed in plants. Regiospecific modification by methylation and glycosylation determines flavonoid diversity. A rare flavone glycoside, diosmin (luteolin-4'-methoxyl-7-O-glucosyl-rhamnoside), occurs in Chrysanthemum indicum. How Chrysanthemum plants evolve new biosynthetic capacities remains elusive. Here, we assemble a 3.11-Gb high-quality C. indicum genome with a contig N50 value of 4.39 Mb and annotate 50,606 protein-coding genes. One (CiCOMT10) of the tandemly repeated O-methyltransferase genes undergoes neofunctionalization, preferentially transferring the methyl group to the 4'-hydroxyl group of luteolin with ortho-substituents to form diosmetin. In addition, CiUGT11 (UGT88B3) specifically glucosylates 7-OH group of diosmetin. Next, we construct a one-pot cascade biocatalyst system by combining CiCOMT10, CiUGT11, and our previously identified rhamnosyltransferase, effectively producing diosmin with over 80% conversion from luteolin. This study clarifies the role of transferases in flavonoid diversity and provides important gene elements essential for producing rare flavone.

Curcumin rescues aging-related loss of hippocampal synapse input specificity of long term potentiation in mice.

Curcumin has neuroprotective effect and could enhance memory. However, the mechanisms underlying the protection of curcumin on aging-related memory decline are not well understood. In this study, high frequency stimulation (HFS)-induced long term potentiation (LTP) was evaluated by a cellular model of memory formation. A two-input stimulation paradigm was used to record the potentiation as well as synapse input specificity. The data suggested that an N-Methyl-D-aspartate receptors (NMDAR) -dependent LTP was inducible in adult hippocampal slices with a characteristic of synapse input specificity. It also indicated that aging resulted in a reduction in LTP but more importantly a loss of synaptic input specificity. The reason behind the above conclusions is that LTP induction is more dependent on the calcium channel. This is due to a switch of the dependence of LTP induction to voltage-dependent calcium channel (VDCC) compared to NMDA receptors. Curcumin administration recovers input specificity by re-establishing NMDA receptor dependence of induction. In addition, curcumin administration ameliorated aging-related increase of brain thiobarbituric acid-reactive substances and elevated aging-related decrease of glutathione in hippocampus. It is then concluded that curcumin modulates hippocampal redox status and restores aging-related loss of synapse input specificity of HFS-induced LTP by switching VDCC calcium source into NMDA receptor-dependent one.