Functional characterization and comparison of lycopene epsilon-cyclase genes in Nicotiana tabacum.

BACKGROUND: Lycopene epsilon-cyclase (ε-LCY) is a key enzyme in the carotenoid biosynthetic pathway (CBP) of higher plants. In previous work, we cloned two Ntε-LCY genes from allotetraploid tobacco (Nicotiana tabacum), Ntε-LCY2 and Ntε-LCY1, and demonstrated the overall effect of Ntε-LCY genes on carotenoid biosynthesis and stress resistance. However, their genetic and functional characteristics require further research in polyploid plants. RESULTS: Here, we used CRISPR/Cas9 to obtain Ntε-LCY2 and Ntε-LCY1 mutants in allotetraploid N.tabacum K326. Ntε-LCY2 and Ntε-LCY1 had similar promoter cis-acting elements, including light-responsive elements. The Ntε-LCY genes were expressed in roots, stems, leaves, flowers, and young fruit, and their highest expression levels were found in leaves. Ntε-LCY2 and Ntε-LCY1 genes responded differently to normal light and high light stress. Both the Ntε-LCY2 and the Ntε-LCY1 mutants had a more rapid leaf growth rate, especially ntε-lcy2-1. The expression levels of CBP genes were increased in the ntε-lcy mutants, and their total carotenoid content was higher. Under both normal light and high light stress, the ntε-lcy mutants had higher photosynthetic capacities and heat dissipation levels than the wild type, and this was especially true of ntε-lcy2-1. The reactive oxygen species content was lower in leaves of the ntε-lcy mutants. CONCLUSION: In summary, the expression patterns and biological functions of the Ntε-LCY genes Ntε-LCY1 and Ntε-LCY2 differed in several respects. The mutation of Ntε-LCY2 was associated with a greater increase in the content of chlorophyll and various carotenoid components, and it enhanced the stress resistance of tobacco plants under high light.

Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity.

The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In this study, we developed a spatiotemporal graph convolutional network (STGCN) framework to learn discriminative features from functional connectivity for automatic diagnosis and treatment response prediction of MDD. Briefly, dynamic functional networks were first obtained from the resting-state fMRI with the sliding temporal window method. Secondly, a novel STGCN approach was proposed by introducing the modules of spatial graph attention convolution (SGAC) and temporal fusion. A novel SGAC was proposed to improve the feature learning ability and special anatomy prior guided pooling was developed to enable the feature dimension reduction. A temporal fusion module was proposed to capture the dynamic features of functional connectivity between adjacent sliding windows. Finally, the STGCN proposed approach was utilized to the tasks of diagnosis and antidepressant treatment response prediction for MDD. Performances of the framework were comprehensively examined with large cohorts of clinical data, which demonstrated its effectiveness in classifying MDD patients and predicting the treatment response. The sound performance suggests the potential of the STGCN for the clinical use in diagnosis and treatment prediction.

Nanotechnology-driven strategies to enhance the treatment of drug-resistant bacterial infections.

The misuse of antibiotics has led to increased bacterial resistance, posing a global public health crisis and seriously endangering lives. Currently, antibiotic therapy remains the most common approach for treating bacterial infections, but its effectiveness against multidrug-resistant bacteria is diminishing due to the slow development of new antibiotics and the increase of bacterial drug resistance. Consequently, developing new a\ntimicrobial strategies and improving antibiotic efficacy to combat bacterial infection has become an urgent priority. The emergence of nanotechnology has revolutionized the traditional antibiotic treatment, presenting new opportunities for refractory bacterial infection. Here we comprehensively review the research progress in nanotechnology-based antimicrobial drug delivery and highlight diverse platforms designed to target different bacterial resistance mechanisms. We also outline the use of nanotechnology in combining antibiotic therapy with other therapeutic modalities to enhance the therapeutic effectiveness of drug-resistant bacterial infections. These innovative therapeutic strategies have the potential to enhance bacterial susceptibility and overcome bacterial resistance. Finally, the challenges and prospects for the application of nanomaterial-based antimicrobial strategies in combating bacterial resistance are discussed. This article is categorized under: Biology-Inspired Nanomaterials > Nucleic Acid-Based Structures Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease.

Multi-Connectivity Representation Learning Network for Major Depressive Disorder Diagnosis.

The pathophysiology of major depressive disorder (MDD) has been demonstrated to be highly associated with the dysfunctional integration of brain activity. Existing studies only fuse multi-connectivity information in a one-shot approach and ignore the temporal property of functional connectivity. A desired model should utilize the rich information in multiple connectivities to help improve the performance. In this study, we develop a multi-connectivity representation learning framework to integrate multi-connectivity topological representation from structural connectivity, functional connectivity and dynamic functional connectivities for automatic diagnosis of MDD. Briefly, structural graph, static functional graph and dynamic functional graphs are first computed from the diffusion magnetic resonance imaging (dMRI) and resting state functional magnetic resonance imaging (rsfMRI). Secondly, a novel Multi-Connectivity Representation Learning Network (MCRLN) approach is developed to integrate the multiple graphs with modules of structural-functional fusion and static-dynamic fusion. We innovatively design a Structural-Functional Fusion (SFF) module, which decouples graph convolution to capture modality-specific features and modality-shared features separately for an accurate brain region representation. To further integrate the static graphs and dynamic functional graphs, a novel Static-Dynamic Fusion (SDF) module is developed to pass the important connections from static graphs to dynamic graphs via attention values. Finally, the performance of the proposed approach is comprehensively examined with large cohorts of clinical data, which demonstrates its effectiveness in classifying MDD patients. The sound performance suggests the potential of the MCRLN approach for the clinical use in diagnosis. The code is available at https://github.com/LIST-KONG/MultiConnectivity-master.

Rational design of geranylgeranyl diphosphate synthase enhances carotenoid production and improves photosynthetic efficiency in Nicotiana tabacum.

Restricted genetic diversity can supply only a limited number of elite genes for modern plant cultivation and transgenesis. In this study, we demonstrate that rational design enables the engineering of geranylgeranyl diphosphate synthase (NtGGPPS), an enzyme of the methylerythritol phosphate pathway (MEP) in the model plant Nicotiana tabacum. As the crucial bottleneck in carotenoid biosynthesis, NtGGPPS1 interacts with phytoene synthase (NtPSY1) to channel GGPP into the production of carotenoids. Loss of this enzyme in the ntggpps1 mutant leads to decreased carotenoid accumulation. With the aim of enhancing NtGGPPS1 activity, we undertook structure-guided rational redesign of its substrate binding pocket in combination with sequence alignment. The activity of the designed NtGGPPS1 (a pentuple mutant of five sites V154A/I161L/F218Y/I209S/V233E, d-NtGGPPS1) was measured by a high-throughput colorimetric assay. d-NtGGPPS1 exhibited significantly higher conversion of IPP and each co-substrate (DMAPP ~1995.5-fold, GPP ~25.9-fold, and FPP ~16.7-fold) for GGPP synthesis compared with wild-type NtGGPPS1. Importantly, the transient and stable expression of d-NtGGPPS1 in the ntggpps1 mutant increased carotenoid levels in leaves, improved photosynthetic efficiency, and increased biomass relative to NtGGPPS1. These findings provide a firm basis for the engineering of GGPPS and will facilitate the development of quality and yield traits. Our results open the door for the structure-guided rational design of elite genes in higher plants.

The T-Type Calcium Channel Cav3.2 in Somatostatin Interneurons in Spinal Dorsal Horn Participates in Mechanosensation and Mechanical Allodynia in Mice.

Somatostatin-positive (SOM+) neurons have been proposed as one of the key populations of excitatory interneurons in the spinal dorsal horn involved in mechanical pain. However, the molecular mechanism for their role in pain modulation remains unknown. Here, we showed that the T-type calcium channel Cav3.2 was highly expressed in spinal SOM+ interneurons. Colocalization of Cacna1h (which codes for Cav3.2) and SOM tdTomato was observed in the in situ hybridization studies. Fluorescence-activated cell sorting of SOM tdTomato cells in spinal dorsal horn also proved a high expression of Cacna1h in SOM+ neurons. Behaviorally, virus-mediated knockdown of Cacna1h in spinal SOM+ neurons reduced the sensitivity to light touch and responsiveness to noxious mechanical stimuli in naïve mice. Furthermore, knockdown of Cacna1h in spinal SOM+ neurons attenuated thermal hyperalgesia and dynamic allodynia in the complete Freund's adjuvant-induced inflammatory pain model, and reduced both dynamic and static allodynia in a neuropathic pain model of spared nerve injury. Mechanistically, a decrease in the percentage of neurons with Aß-eEPSCs and Aß-eAPs in superficial dorsal horn was observed after Cacna1h knockdown in spinal SOM+ neurons. Altogether, our results proved a crucial role of Cav3.2 in spinal SOM+ neurons in mechanosensation under basal conditions and in mechanical allodynia under pathological pain conditions. This work reveals a molecular basis for SOM+ neurons in transmitting mechanical pain and shows a functional role of Cav3.2 in tactile and pain processing at the level of spinal cord in addition to its well-established peripheral role.

Global topology alteration of the brain functional network affects the 8-week antidepressant response in major depressive disorder.

PURPOSE: Previous studies have indicated that the global topology of the brain functional network in patients with major depressive disorder (MDD) differs from that of those with normal controls (NCs). However, the relationship between an altered global topology and the response to antidepressants remains unclear. Here, we investigated whether differences in global topology affect the efficacy of antidepressants in MDD patients. METHODS: 108 MDD patients and 61 NCs were recruited. A magnetic resonance imaging (MRI) scan was performed at the baseline, and the Hamilton Depression Scale-24 (HAMD-24) was assessed at baseline and after 2 and 8 weeks of antidepressant treatment. Seven global topological parameters of the brain functional network were measured and compared between groups. A correlation analysis was performed to identify the relationships between global topological parameters and antidepressant efficacy. RESULTS: The brain networks of MDD patients and NCs were both small-world networks. The clustering coefficient (Cp) and local efficiency (Eloc) were significantly smaller in MDD patients compared with those in NCs. The characteristic path length (Lp) were negatively correlated with the 8-week reductive rate of HAMD-24 in the MDD group. CONCLUSION: The present research found that the brain functional network of MDD patients still had a small-world organization but with a lower Cp and Eloc than the NCs. In addition, the brain network global topology might have an impact on the antidepressant response and thus had the potential to become a treatment predictor of MDD.

Dandelion-like nickel/cobalt metal-organic framework based electrode materials for high performance supercapacitors.

Metal-organic frameworks (MOFs), serving as a promising electrode material in the supercapacitors, have attracted tremendous interests in recent years. Here, through modifying the molar ratio of the Ni2+ and Co2+, we have successfully fabricated Ni-MOF and Ni/Co-MOF by a facile hydrothermal method. The Ni/Co-MOF with a dandelion-like hollow structure shows an excellent specific capacitance of 758 F g-1 at 1 A g-1 in the three-electrode system. Comparing with Ni-MOF, the obtained Ni/Co-MOF has a better rate capacitance (89% retention at 10 A g-1) and cycling life (75% retention after 5000 circulations). Besides, the assembled asymmetric supercapacitor based on Ni/Co-MOF and active carbon exhibits a high specific energy density of 20.9 W h kg-1 at the power density of 800 W kg-1. All these results demonstrate that the mixed-metal strategy is an effective way to optimize the morphology and improve the electrochemical property of the MOFs.