Pancreatic STAT5 activation promotes KrasG12D-induced and inflammation-induced acinar-to-ductal metaplasia and pancreatic cancer.

OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy because it is often diagnosed at a late-stage. Signal transducer and activator of transcription 5 (STAT5) is a transcription factor implicated in the progression of various cancer types. However, its role in KRAS-driven pancreatic tumourigenesis remains unclear. DESIGN: We performed studies with LSL-Kras G12D; Ptf1a-Cre ERT (KCERT) mice or LSL-KrasG12D; LSL-Trp53R172H ; Pdx1-Cre (KPC) mice crossed with conditional disruption of STAT5 or completed deficiency interleukin (IL)-22. Pancreatitis was induced in mice by administration of cerulein. Pharmacological inhibition of STAT5 on PDAC prevention was studied in the orthotopic transplantation and patient-derived xenografts PDAC model, and KPC mice. RESULTS: The expression and phosphorylation of STAT5 were higher in human PDAC samples than control samples and high levels of STAT5 in tumour cells were associated with a poorer prognosis. The loss of STAT5 in pancreatic cells substantially reduces the KRAS mutation and pancreatitis-derived acinar-to-ductal metaplasia (ADM) and PDAC lesions. Mechanistically, we discovered that STAT5 binds directly to the promoters of ADM mediators, hepatocyte nuclear factor (HNF) 1ß and HNF4α. Furthermore, STAT5 plays a crucial role in maintaining energy metabolism in tumour cells during PDAC progression. IL-22 signalling induced by chronic inflammation enhances KRAS-mutant-mediated STAT5 phosphorylation. Deficiency of IL-22 signalling slowed the progression of PDAC and ablated STAT5 activation. CONCLUSION: Collectively, our findings identified pancreatic STAT5 activation as a key downstream effector of oncogenic KRAS signalling that is critical for ADM initiation and PDAC progression, highlighting its potential therapeutic vulnerability.

High-throughput microalgae sorting based on the deterministic lateral displacement technique.

Microalgae are a group of photosynthetic organisms that can grow autotrophically, performing photosynthesis to synthesize abundant organic compounds and release oxygen. They are rich in nutritional components and chemical precursors, presenting wide-ranging application prospects. However, potential contamination by foreign strains or bacteria can compromise their analytical applications. Therefore, the obtaining of pure algal strains is crucial for the subsequent analysis and application of microalgae. This study designed a deterministic lateral displacement (DLD) chip with dual input and dual outlet of equal width for the separation of Haematococcus pluvialis and Chlorella vulgaris. Optimal separation parameters were determined through a series of experiments, resulting in a purity of 99.80 % for Chlorella vulgaris and 94.58 % for Haematococcus pluvialis, with recovery rates maintained above 90 %, demonstrating high efficiency. This study provides a reliable foundation for future research and applications of microalgae, which holds considerable significance for the subsequent analysis and utilization of microalgae.

Heterogeneous graph convolutional network for multi-view semi-supervised classification.

This paper proposes a novel approach to semantic representation learning from multi-view datasets, distinct from most existing methodologies which typically handle single-view data individually, maintaining a shared semantic link across the multi-view data via a unified optimization process. Notably, even recent advancements, such as Co-GCN, continue to treat each view as an independent graph, subsequently aggregating the respective GCN representations to form output representations, which ignores the complex semantic interactions among heterogeneous data. To address the issue, we design a unified framework to connect multi-view data with heterogeneous graphs. Specifically, our study envisions multi-view data as a heterogeneous graph composed of shared isomorphic nodes and multi-type edges, wherein the same nodes are shared across different views, but each specific view possesses its own unique edge type. This perspective motivates us to utilize the heterogeneous graph convolutional network (HGCN) to extract semantic representations from multi-view data for semi-supervised classification tasks. To the best of our knowledge, this is an early attempt to transfigure multi-view data into a heterogeneous graph within the realm of multi-view semi-supervised learning. In our approach, the original input of the HGCN is composed of concatenated multi-view matrices, and its convolutional operator (the graph Laplacian matrix) is adaptively learned from multi-type edges in a data-driven fashion. After rigorous experimentation on eight public datasets, our proposed method, hereafter referred to as HGCN-MVSC, demonstrated encouraging superiority over several state-of-the-art competitors for semi-supervised classification tasks.

Misclassification in Weakly Supervised Object Detection.

Weakly supervised object detection (WSOD) aims to train detectors using only image-category labels. Current methods typically first generate dense class-agnostic proposals and then select objects based on the classification scores of these proposals. These methods mainly focus on selecting the proposal having high Intersection-over-Union with the true object location, while ignoring the problem of misclassification, which occurs when some proposals exhibit semantic similarities with objects from other categories due to viewing perspective and background interference. We observe that the positive class that is misclassified typically has the following two characteristics: 1) It is usually misclassified as one or a few specific negative classes, and the scores of these negative classes are high; 2) Compared to other negative classes, the score of the positive class is relatively high. Based on these two characteristics, we propose misclassification correction (MCC) and misclassification tolerance (MCT) respectively. In MCC, we establish a misclassification memory bank to record and summarize the class-pairs with high frequencies of potential misclassifications in the early stage of training, that is, cases where the score of a negative class is significantly higher than that of the positive class. In the later stage of training, when such cases occur and correspond to the summarized class-pairs, we select the top-scoring negative class proposal as the positive training example. In MCT, we decrease the loss weights of misclassified classes in the later stage of training to avoid them dominating training and causing misclassification of objects from other classes that are semantically similar to them during inference. Extensive experiments on the PASCAL VOC and MS COCO demonstrate our method can alleviate the problem of misclassification and achieve the state-of-the-art results.

UMCGL: Universal Multi-View Consensus Graph Learning With Consistency and Diversity.

Existing multi-view graph learning methods often rely on consistent information for similar nodes within and across views, however they may lack adaptability when facing diversity challenges from noise, varied views, and complex data distributions. These challenges can be mainly categorized into: 1) View-specific diversity within intra-view from noise and incomplete information; 2) Cross-view diversity within inter-view caused by various latent semantics; 3) Cross-group diversity within inter-group due to data distribution differences. To this end, we propose a universal multi-view consensus graph learning framework that considers both original and generative graphs to balance consistency and diversity. Specifically, the proposed framework can be divided into the following four modules: i) Multi-channel graph module to extract principal node information, ensuring view-specific and cross-view consistency while mitigating view-specific and cross-view diversity within original graphs; ii) Generative module to produce cleaner and more realistic graphs, enriching graph structure while maintaining view-specific consistency and suppressing view-specific diversity; iii) Contrastive module to collaborate on generative semantics to facilitate cross-view consistency and reducing cross-view diversity within generative graphs; iv) Consensus graph module to consolidate learning a consensual graph, pursuing cross-group consistency and cross-group diversity. Extensive experimental results on real-world datasets demonstrate its effectiveness and superiority.

Multilayer regulatory landscape and new regulators identification for bud dormancy release and bud break in Populus.

For trees originating from boreal and temperate regions, the dormancy-to-active transition, also known as bud dormancy release and bud break, are crucial processes that allow trees to reactive growth in the spring. The molecular mechanisms underlying these two processes remain poorly understood. Here, through integrative multiomics analysis of the transcriptome, DNA methylome, and proteome, we gained insights into the reprogrammed cellular processes associated with bud dormancy release and bud break. Our findings revealed multilayer regulatory landscapes governing bud dormancy release and bud break regulation, providing a valuable reference framework for future functional studies. Based on the multiomics analysis, we have determined a novel long intergenic noncoding RNA named Phenology Responsive Intergenic lncRNA 1 (PRIR1) plays a role in the activation of bud break. that the molecular mechanism of PRIR1 has been preliminary explored, and it may partially promote bud break by activating its neighbouring gene, EXORDIUM LIKE 5 (PtEXL5), which has also been genetically confirmed as an activator for bud break. This study has revealed a lncRNA-mediated regulatory mechanism for the control of bud break in Populus, operating independently of known regulatory pathways.

Identification of ClpB, a molecular chaperone involved in the stress tolerance and virulence of Streptococcus agalactiae.

Bacterial ClpB is an ATP-dependent disaggregate that belongs to the Hsp100/Clp family and facilitates bacterial survival under hostile environmental conditions. Streptococcus agalactiae, which is regarded as the major bacterial pathogen of farmed Nile tilapia (Oreochromis niloticus), is known to cause high mortality and large economic losses. Here, we report a ClpB homologue of S. agalactiae and explore its functionality. S. agalactiae with a clpB deletion mutant (∆clpB) exhibited defective tolerance against heat and acidic stress, without affecting growth or morphology under optimal conditions. Moreover, the ΔclpB mutant exhibited reduced intracellular survival in RAW264.7 cells, diminished adherence to the brain cells of tilapia, increased sensitivity to leukocytes from the head kidney of tilapia and whole blood killing, and reduced mortality and bacterial loads in a tilapia infection assay. Furthermore, the reduced virulence of the ∆clpB mutant was investigated by transcriptome analysis, which revealed that deletion of clpB altered the expression levels of multiple genes that contribute to the stress response as well as certain metabolic pathways. Collectively, our findings demonstrated that ClpB, a molecular chaperone, plays critical roles in heat and acid stress resistance and virulence in S. agalactiae. This finding provides an enhanced understanding of the functionality of this ClpB homologue in gram-positive bacteria and the survival strategy of S. agalactiae against immune clearance during infection.

Genomic evidence for evolutionary history and local adaptation of two endemic apricots: Prunus hongpingensis and P. zhengheensis.

Apricot, belonging to the Armeniaca section of Rosaceae, is one of the economically important crop fruits that has been extensively cultivated. The natural wild apricots offer valuable genetic resources for crop improvement. However, some of them are endemic, with small populations, and are even at risk of extinction. In this study we unveil chromosome-level genome assemblies for two southern China endemic apricots, Prunus hongpingensis (PHP) and P. zhengheensis (PZH). We also characterize their evolutionary history and the genomic basis of their local adaptation using whole-genome resequencing data. Our findings reveal that PHP and PZH are closely related to Prunus armeniaca and form a distinct lineage. Both species experienced a decline in effective population size following the Last Glacial Maximum (LGM), which likely contributed to their current small population sizes. Despite the observed decrease in genetic diversity and heterozygosity, we do not observe an increased accumulation of deleterious mutations in these two endemic apricots. This is likely due to the combined effects of a low inbreeding coefficient and strong purifying selection. Furthermore, we identify a set of genes that have undergone positive selection and are associated with local environmental adaptation in PHP and PZH, respectively. These candidate genes can serve as valuable genetic resources for targeted breeding and improvement of cultivated apricots. Overall, our study not only enriches our comprehension of the evolutionary history of apricot species but also offers crucial insights for the conservation and future breeding of other endemic species amidst rapid climate changes.

The mitochondrial stress-induced protein carboxyl-terminal alanine and threonine tailing (msiCAT-tailing) promotes glioblastoma tumorigenesis by modulating mitochondrial functions.

The rapid and sustained proliferation in cancer cells requires accelerated protein synthesis. Accelerated protein synthesis and disordered cell metabolism in cancer cells greatly increase the risk of translation errors. ribosome-associated quality control (RQC) is a recently discovered mechanism for resolving ribosome collisions caused by frequent translation stalls. The role of the RQC pathway in cancer initiation and progression remains controversial and confusing. In this study, we investigated the pathogenic role of mitochondrial stress-induced protein carboxyl-terminal terminal alanine and threonine tailing (msiCAT-tailing) in glioblastoma (GBM), which is a specific RQC response to translational arrest on the outer mitochondrial membrane. We found that msiCAT-tailed mitochondrial proteins frequently exist in glioblastoma stem cells (GSCs). Ectopically expressed msiCAT-tailed mitochondrial ATP synthase F1 subunit alpha (ATP5α) protein increases the mitochondrial membrane potential and blocks mitochondrial permeability transition pore (MPTP) formation/opening. These changes in mitochondrial properties confer resistance to staurosporine (STS)-induced apoptosis in GBM cells. Therefore, msiCAT-tailing can promote cell survival and migration, while genetic and pharmacological inhibition of msiCAT-tailing can prevent the overgrowth of GBM cells. Highlights: The RQC pathway is disturbed in glioblastoma (GBM) cellsmsiCAT-tailing on ATP5α elevates mitochondrial membrane potential and inhibits MPTP openingmsiCAT-tailing on ATP5α inhibits drug-induced apoptosis in GBM cellsInhibition of msiCAT-tailing impedes overall growth of GBM cells.

Upregulation of neuronal ER-phagy improves organismal fitness and alleviates APP toxicity.

ER-phagy, a selective autophagy targeting the endoplasmic reticulum (ER) for lysosomal degradation through cargo receptors, plays a critical role in ER quality control and is linked to various diseases. However, its physiological and pathological roles remain largely unclear due to a lack of animal model studies. This study establishes Drosophila as an in vivo ER-phagy model. Starvation triggers ER-phagy across multiple fly tissues. Disturbing ER-phagy by either globally upregulating or downregulating ER-phagy receptors, Atl or Rtnl1, harms the fly. Notably, moderate upregulation of ER-phagy in fly brains by overexpressing Atl or Rtnl1 significantly attenuates age-associated neurodegenerations. Furthermore, in a Drosophila model of Alzheimer's disease expressing human amyloid precursor protein (APP), impaired ER-phagy is observed. Enhancing ER-phagy in the APP-expressing fly brain facilitates APP degradation, significantly alleviating disease symptoms. Therefore, our findings suggest that modulating ER-phagy may offer a therapeutic strategy to treat aging and diseases associated with ER protein aggregation.

A WOX homolog disrupted by a transposon led to the loss of spines and contributed to the domestication of lettuce.

The loss of spines is one of the most important domestication traits for lettuce (Lactuca sativa). However, the genetics and regulation of spine development in lettuce remain unclear. We examined the genetics of spines in lettuce using a segregating population derived from a cross between cultivated and wild lettuce (Lactuca serriola). A gene encoding WUSCHEL-related homeobox transcription factor, named as WOX-SPINE1 (WS1), was identified as the candidate gene controlling the spine development in lettuce, and its function on spines was verified. A CACTA transposon was found to be inserted into the first exon of the ws1 allele, knocking out its function and leading to the lack of spines in cultivated lettuce. All lettuce cultivars investigated have the nonfunctional ws1 gene, and a selection sweep was found at the WS1 locus, suggesting its important role in lettuce domestication. The expression levels of WS1 were associated with the density of spines among different accessions of wild lettuce. At least two independent loss-of-function mutations in the ws1 gene caused the loss of spines in wild lettuce. These findings provide new insights into the development of spines and facilitate the exploitation of wild genetic resources in future lettuce breeding programs.

Fe-CGS Effectively Inhibits the Dynamic Migration and Transformation of Cadmium and Arsenic in Soil.

The iron-modified coal gasification slag (Fe-CGS) material has excellent performance in purifying heavy-metal-contaminated water due to its good surface properties and adsorption capacities. However, it is unclear whether it can provide long-term simultaneous stabilization of Cd and As in composite-contaminated soils in extreme environments. This study investigated the long-term stabilization of Cd and As in acidic (JLG) and alkaline (QD) soils by simulating prolonged heavy rainfall with the addition of Fe-CGS. Multiple extraction methods were used to analyze the immobilization mechanisms of Cd and As in soil and their effects on bioavailability. The results indicate that the stabilization efficiency was related to the dosage of Fe-CGS. The concentrations of Cd and As in the JLG soil leachate were reduced by 77.6% (2.0 wt%) and 87.8% (1.0 wt%), respectively. Additionally, the availability of Cd and As decreased by 46.7% (2.0 wt%) and 53.0% (1.0 wt%), respectively. In the QD soil leachate, the concentration of Cd did not significantly change, while the concentration of As decreased by 92.3% (2.0 wt%). Furthermore, the availability of Cd and As decreased by 22.1% (2.0 wt%) and 40.2% (1.0 wt%), respectively. Continuous extraction revealed that Fe-CGS facilitated the conversion of unstable, acid-soluble Cd into oxidizable Cd and acid-soluble Cd. Additionally, it promoted the transformation of both non-specifically and specifically adsorbed As into amorphous iron oxide-bound and residual As. Fe-CGS effectively improved the soil pH, reduced the bioavailability of Cd and As, and blocked the migration of Cd and As under extreme rainfall leaching conditions. It also promoted the transformation of Cd and As into more stable forms, exhibiting satisfactory long-term stabilization performance for Cd and As.

Unveiling unique alternative splicing responses to low temperature in Zoysia japonica through ZjRTD1.0, a high-quality reference transcript dataset.

Inadequate reference databases in RNA-seq analysis can hinder data utilization and interpretation. In this study, we have successfully constructed a high-quality reference transcript dataset, ZjRTD1.0, for Zoysia japonica, a widely-used turfgrass with exceptional tolerance to various abiotic stress, including low temperatures and salinity. This dataset comprises 113,089 transcripts from 57,143 genes. BUSCO analysis demonstrates exceptional completeness (92.4%) in ZjRTD1.0, with reduced proportions of fragmented (3.3%) and missing (4.3%) orthologs compared to prior datasets. ZjRTD1.0 enables more precise analyses, including transcript quantification and alternative splicing assessments using public datasets, which identified a substantial number of differentially expressed transcripts (DETs) and differential alternative splicing (DAS) events, leading to several novel findings on Z. japonica's responses to abiotic stresses. First, spliceosome gene expression influenced alternative splicing significantly under abiotic stress, with a greater impact observed during low-temperature stress. Then, a significant positive correlation was found between the number of differentially expressed genes (DEGs) encoding protein kinases and the frequency of DAS events, suggesting the role of protein phosphorylation in regulating alternative splicing. Additionally, our results suggest possible involvement of serine/arginine-rich (SR) proteins and heterogeneous nuclear ribonucleoproteins (hnRNPs) in generating inclusion/exclusion isoforms under low-temperature stress. Furthermore, our investigation revealed a significantly enhanced overlap between DEGs and differentially alternatively spliced genes (DASGs) in response to low-temperature stress, suggesting a unique co-regulatory mechanism governing transcription and splicing in the context of low-temperature response. In conclusion, we have proven that ZjRTD1.0 will serve as a reliable and useful resource for future transcriptomic analyses in Z. japonica.

Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs.

Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi-relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta-paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically explores meta-paths that involve multi-hop neighbors by aggregating multi-order adjacency matrices. The proposed model first constructs different orders of adjacency matrices from manually designed node connections. Next, AMOGCN fuses these various orders of adjacency matrices to create an intact multi-order adjacency matrix. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we employ a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN achieves superior semi-supervised classification performance compared with state-of-the-art competitors.

Lactate drives CD38 signaling to promote Epithelial-Mesenchymal Transition through Snail induction in non-small cell lung cancer cells.

CD38 is the main NADase in mammalian cells. It regulates the homeostasis of nicotinamide adenine dinucleotide (NAD+) and extracellular nucleotides. Its function plays an important role in infection and aging. However, its potential functions in tumor cells have not been fully elucidated. In the present study, we demonstrated that lactate, which is derived from tumor metabolism remodeling, upregulates the expression of CD38 through OXPHOS-driven Hippo-TAZ pathway. The highly expressed CD38 converts NAD + to adenosine through the CD203a/CD73 complex and adenosine binds and activates its receptor A2AR, inducing the expression of Snail and promoting the invasion and metastasis of lung cancer cells. This finding elucidates a new perspective on the interplay between NAD + metabolism and glycolysis in tumor development.

Spatiotemporal Controllable Sono-Nanovaccines Driven by Free-Field Based Whole-Body Ultrasound for Personalized Cancer Therapy.

Therapeutic cancer vaccines fail to produce satisfactory outcomes against solid tumors since vaccine-induced anti-tumor immunity is significantly hampered by immunosuppression. Generating an in situ cancer vaccine targeting immunological cold tumor microenvironment (TME) appears attractive. Here, a type of free-field based whole-body ultrasound (US)-driven nanovaccines are constructed, named G5-CHC-R, by conjugating the sonosensitizer, Chenghai chlorin (CHC) and the immunomodulator, resiquimod (R848) on top of a super small-sized dendrimeric nanoscaffold. Once entering tumors, R848 can be cleaved from a hypoxia-sensitive linker, thus modifying the TME via converting macrophage phenotypes. The animals bearing orthotopic pancreatic cancer with intestinal metastasis and breast cancer with lung metastasis are treated with G5-CHC-R under a free-field based whole-body US system. Benefit from the deep penetration capacity and highly spatiotemporal selectiveness, G5-CHC-R triggered by US represented a superior alternative for noninvasive irradiation of deep-seated tumors and magnification of local immune responses via driving mass release of tumor antigens and "cold-warm-hot" three-state transformation of TME. In addition to irradiating primary tumors, a robust adaptive anti-tumor immunity is potentiated, leading to successful induction of systemic tumor suppression. The sono-nanovaccines with good biocompatibility posed wide applicability to a broad spectrum of tumors, revealing immeasurable potential for translational research in oncology.

Stalled translation by mitochondrial stress upregulates a CNOT4-ZNF598 ribosomal quality control pathway important for tissue homeostasis.

Translational control exerts immediate effect on the composition, abundance, and integrity of the proteome. Ribosome-associated quality control (RQC) handles ribosomes stalled at the elongation and termination steps of translation, with ZNF598 in mammals and Hel2 in yeast serving as key sensors of translation stalling and coordinators of downstream resolution of collided ribosomes, termination of stalled translation, and removal of faulty translation products. The physiological regulation of RQC in general and ZNF598 in particular in multicellular settings is underexplored. Here we show that ZNF598 undergoes regulatory K63-linked ubiquitination in a CNOT4-dependent manner and is upregulated upon mitochondrial stresses in mammalian cells and Drosophila. ZNF598 promotes resolution of stalled ribosomes and protects against mitochondrial stress in a ubiquitination-dependent fashion. In Drosophila models of neurodegenerative diseases and patient cells, ZNF598 overexpression aborts stalled translation of mitochondrial outer membrane-associated mRNAs, removes faulty translation products causal of disease, and improves mitochondrial and tissue health. These results shed lights on the regulation of ZNF598 and its functional role in mitochondrial and tissue homeostasis.

New mechanistic insight into catalytic decomposition of dioxins over MnOx-CeO2/TiO2 catalysts: A combined experimental and density functional theory study.

Elucidation of the catalytic decomposition mechanism of dioxins is pivotal in developing highly efficient dioxin degradation catalysts. In order to accurately simulate the whole molecular structure of dioxins, two model compounds, o-dichlorobenzene (o-DCB) and furan, were employed to represent the chlorinated benzene ring and oxygenated central ring within a dioxin molecule, respectively. Experiments and Density Functional Theory (DFT) calculations were combined to investigate the adsorption as well as oxidation of o-DCB and furan over MnOx-CeO2/TiO2 catalyst (denoted as MnCe/Ti). The results indicate that competitive adsorption exists between furan and o-DCB. The former exhibits superior adsorption capacity on MnCe/Ti catalyst at 100 °C - 150 °C, for it can adsorb on both surface metal atom and surface oxygen vacancies (Ov) via its O-terminal; while the latter adsorbs primarily by anchoring its Cl atom to surface Ov. Regarding oxidation, furan can be completely oxidized at 150 °C - 300 °C with a high CO2 selectivity (above 80 %). However, o-DCB cannot be totally oxidized and the resulting intermediates cause the deactivation of catalyst. Interestingly, the pre-adsorption of furan on catalyst surface can facilitate the catalytic oxidation of o-DCB below 200 °C, possibly because the dissociated adsorption of furan may form additional reactive oxygen species on catalyst surface. Therefore, this work provides new insights into the catalytic decomposition mechanism of dioxins as well as the optimization strategies for developing dioxin-degradation catalysts with high efficiency at low temperature.

Transcriptome and Metabolome Analyses Reflect the Molecular Mechanism of Drought Tolerance in Sweet Potato.

Sweet potato (Ipomoea batatas (L.) Lam.) is one of the most widely cultivated crops in the world, with outstanding stress tolerance, but drought stress can lead to a significant decrease in its yield. To reveal the response mechanism of sweet potato to drought stress, an integrated physiological, transcriptome and metabolome investigations were conducted in the leaves of two sweet potato varieties, drought-tolerant zhenghong23 (Z23) and a more sensitive variety, jinong432 (J432). The results for the physiological indexes of drought showed that the peroxidase (POD) and superoxide dismutase (SOD) activities of Z23 were 3.68 and 1.21 times higher than those of J432 under severe drought, while Z23 had a higher antioxidant capacity. Transcriptome and metabolome analysis showed the importance of the amino acid metabolism, respiratory metabolism, and antioxidant systems in drought tolerance. In Z23, amino acids such as asparagine participated in energy production during drought by providing substrates for the citrate cycle (TCA cycle) and glycolysis (EMP). A stronger respiratory metabolism ability could better maintain the energy supply level under drought stress. Drought stress also activated the expression of the genes encoding to antioxidant enzymes and the biosynthesis of flavonoids such as rutin, resulting in improved tolerance to drought. This study provides new insights into the molecular mechanisms of drought tolerance in sweet potato.

Microfluidics-Prepared Ultra-small Biomimetic Nanovesicles for Brain Tumor Targeting.

Blood-brain-barrier (BBB) serves as a fatal guard of the central nervous system as well as a formidable obstacle for the treatment of brain diseases such as brain tumors. Cell membrane-derived nanomedicines are promising drug carriers to achieve BBB-penetrating and brain lesion targeting. However, the challenge of precise size control of such nanomedicines has severely limited their therapeutic effect and clinical application in brain diseases. To address this problem, this work develops a microfluidic mixing platform that enables the fabrication of cell membrane-derived nanovesicles with precise controllability and tunability in particle size and component. Sub-100 nm macrophage plasma membrane-derived vesicles as small as 51 nm (nanoscale macrophage vesicles, NMVs), with a narrow size distribution (polydispersity index, PDI: 0.27) and a high drug loading rate (up to 89% for indocyanine green-loaded NMVs, NMVs@ICG (ICG is indocyanine green)), are achieved through a one-step process. Compared to beyond-100 nm macrophage cell membrane vesicles (general macrophage vesicles, GMVs) prepared via the traditional methods, the new NMVs exhibits rapid (within 1 h post-injection) and enhanced orthotopic glioma targeting (up to 78% enhancement), with no extra surface modification. This work demonstrates the great potential of such real-nanoscale cell membrane-derived nanomedicines in targeted brain tumor theranostics.