PeFUS3 Drives Lateral Root Growth Via Auxin and ABA Signalling Under Drought Stress in Populus.

Changes in root system architecture are vital for plant adaptation to drought stress, yet the underlying molecular mechanisms of this process remain largely elusive. Here, FUSCA3 (FUS3), a B3 domain transcription factor isolated from Populus euphratica, was found to be an important gene of regulating lateral root (LR) development under drought stress. The expression of PeFUS3 was strongly induced by ABA and dehydration treatments. Overexpressing PeFUS3 in poplar 84 K (P. alba × P. glandulosa) positively regulated LR growth and enhanced drought tolerance, while the knockout lines, generated by the CRISPR/Cas9 system, displayed repressed LR growth and weakened drought tolerance. Further investigation demonstrated that PeFUS3 activated the expression of PIN2, PIN6a and AUX1, which were key genes involved in auxin transport, suggesting PeFUS3 modulated LR development under drought stress through auxin signalling. Moreover, PeFUS3 directly upregulated PePYL3 expression, and overexpressing PePYL3 poplar lines exhibited significantly increased drought resistance. In addition, PeABF2, an ABA responsive transcription factor, interacted with PeFUS3 and activated its transcription, indicating PeFUS3 was involved in ABA signalling pathway. Taken together, PeFUS3 is a key regulator, maintaining root growth of poplar by modulating the crosstalk of auxin and ABA signalling under drought stress.

Inverse design of skull osteoinductive implants with multi-level pore structures through machine learning.

How to accurately design a personalized matching implant that can induce skull regeneration is the focus of current research. However, the design space for the porous structure of implants is extensive, and the mapping relationships between these structures and their mechanical and osteogenic properties are complex. At present, the forward design of skull implants mainly relies on expert experience, leading to high cost and a lengthy process, while the existing inverse design approaches face challenges due to data dependence and manufacturing process errors. This study presents an efficient inverse design method for personalized multilevel structures of skull implants using a machine learning pipeline composed of a finite element method, topological optimization, and neural networks. Based on the mechanical response of the human body falls, this method can tailor multi-level structures for implants in various defect positions. The results show that the proposed method establishes a bidirectional relationship between topological parameters and mechanical properties, enabling the customization of mechanical behavior at low computational cost while accounting for manufacturing errors in the 3D printing process. Additionally, the design results are also mutually consistent with analytical relationships between lattice parameters and the elastic modulus obtained from experiments and finite element simulations. Thus, this study provides a general and practical approach to rapidly design skull osteoinductive implants.

Development and validation of risk prediction model for adverse outcomes in trauma patients.

BACKGROUND: The prognosis of trauma patients is highly dependent on early medical diagnosis. By constructing a nomogram model, the risk of adverse outcomes can be displayed intuitively and individually, which has important clinical implications for medical diagnosis. OBJECTIVE: To develop and evaluate models for predicting patients with adverse outcomes of trauma that can be used in different data availability settings in China. METHODS: This was a retrospective prognostic study using data from 8 public tertiary hospitals in China from 2018. The data were randomly divided into a development set and a validation set. Simple, improved and extended models predicting adverse outcomes were developed, with adverse outcomes defined as in-hospital death or ICU transfer, and patient clinical characteristics, vital signs, diagnoses, and laboratory test values as predictors. The results of the models were presented in the form of nomograms, and performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), precision-recall (PR) curves (PR-AUC), Hosmer-Lemeshow goodness-of-fit test, calibration curve, and decision curve analysis (DCA). RESULTS: Our final dataset consisted of 18,629 patients (40.2% female, mean age of 52.3), 1,089 (5.85%) of whom resulted in adverse outcomes. In the external validation set, three models achieved ROC-AUC of 0.872, 0.881, and 0.903, and a PR-AUC of 0.339, 0.337, and 0.403, respectively. In terms of the calibration curves and DCA, the models also performed well. CONCLUSIONS: This prognostic study found that three prediction models and nomograms including the patient clinical characteristics, vital signs, diagnoses, and laboratory test values can support clinicians in more accurately identifying patients who are at risk of adverse outcomes in different settings based on data availability.

Improving wheat grain composition for human health by constructing a QTL atlas for essential minerals.

Wheat is an important source of minerals for human nutrition and increasing grain mineral content can contribute to reducing mineral deficiencies. Here, we identify QTLs for mineral micronutrients in grain of wheat by determining the contents of six minerals in a total of eleven sample sets of three biparental populations from crosses between A.E. Watkins landraces and cv. Paragon. Twenty-three of the QTLs are mapped in two or more sample sets, with LOD scores above five in at least one set with the increasing alleles for sixteen of the QTLs being present in the landraces and seven in Paragon. Of these QTLs, the number for each mineral varies between three and five and they are located on 14 of the 21 chromosomes, with clusters on chromosomes 5A (four), 6A (three), and 7A (three). The gene content within 5 megabases of DNA on either side of the marker for the QTL with the highest LOD score is determined and the gene responsible for the strongest QTL (chromosome 5A for Ca) identified as an ATPase transporter gene (TraesCS5A02G543300) using mutagenesis. The identification of these QTLs, together with associated SNP markers and candidate genes, will facilitate the improvement of grain nutritional quality.

3D printing calcium phosphate ceramics with high osteoinductivity through pore architecture optimization.

The osteoinductivity of 3D printed calcium phosphate (CaP) ceramics has a large gap compared with those prepared by conventional foaming methods, and improving the osteoinductivity of 3D printing CaP ceramics is crucial for successful application in bone regeneration. Pore architecture plays a critical role in osteoinductivity. In this study, CaP ceramics with a hexagonal close-packed (HCP) spherical pore structure were successfully fabricated using DLP printing technology. Additionally, octahedral (Octahedral), diamond (Diamond), and helical (Gyroid) structures were constructed with similar porosity and macropore diameter. CaP ceramics with the HCP structure exhibited higher compression strength (8.39 ± 1.82 MPa) and lower permeability (6.41 × 10-11 m2) compared to the Octahedral, Diamond, and Gyroid structures. In vitro cellular responses indicated that the macropore architecture strongly influenced the local growth rate of osteoblast-formed cell tissue; cells grew uniformly and formed circular rings in the HCP group. Furthermore, the HCP group promoted the expression of osteogenic genes and proteins more effectively than the other three groups. The outstanding osteoinductivity of the HCP group was confirmed in canine intramuscular implantation studies, where the new bone area reached up to 8.02 ± 1.94 % after a 10-week implantation. Additionally, the HCP group showed effective bone regeneration in repairing femoral condyle defects. Therefore, our findings suggest that 3D printed CaP bioceramics with an HCP structure promote osteoinductivity and can be considered as candidates for personalized precise treatment of bone defects in clinical applications. STATEMENT OF SIGNIFICANCE: 1. 3D printing BCP ceramics with high osteoinductivity were constructed through pore architecture optimization. 2. BCP ceramics with HCP structure exhibited relatively higher mechanical strength and lower permeability than those with Octahedral, Diamond and Gyroid structures. 3. BCP ceramics with HCP structure could promote the osteogenic differentiation of MC3T3-E1, and showed the superior in-vivo osteoinductivity and bone regeneration comparing with the other structures.

Enhanced coalbed methane well production prediction framework utilizing the CNN-BL-MHA approach.

As the mechanization of the CBM extraction process advances and geological conditions continuously evolve, the production data from CBM wells is deviating increasingly from linearity, thereby presenting a significant challenge in accurately predicting future gas production from these wells. When it comes to predicting the production of CBM, a single deep-learning model can face several drawbacks such as overfitting, gradient explosion, and gradient disappearance. These issues can ultimately result in insufficient prediction accuracy, making it important to carefully consider the limitations of any given model. It's impressive to see how advanced technology can enhance the prediction accuracy of CBM. In this paper, the use of a CNN model to extract features from CBM well data and combine it with Bi-LSTM and a Multi-Head Attention mechanism to construct a production prediction model for CBM wells-the CNN-BL-MHA model-is fascinating. It is even more exciting that predictions of gas production for experimental wells can be conducted using production data from Wells W1 and W2 as the model's database. We compared and analyzed the prediction results obtained from the CNN-BL-MHA model we constructed with those from single models like ARIMA, LSTM, MLP, and GRU. The results show that the CNN-BL-MHA model proposed in the study has shown promising results in improving the accuracy of gas production prediction for CBM wells. It's also impressive that this model demonstrated super stability, which is essential for reliable predictions. Compared to the single deep learning model used in this study, its prediction accuracy can be improved up to 35%, and the prediction results match the actual yield data with lower error.

Deep Incomplete Multiview Clustering via Local and Global Pseudo-Label Propagation.

Since the rapid progress in multimedia and sensor technologies, multiview clustering (MVC) has become a prominent research area within machine learning and data mining, experiencing significant advancements over recent decades. MVC is distinguished from single-view clustering by its ability to integrate complementary information from multiple distinct data perspectives and enhance clustering performance. However, the efficacy of MVC methods is predicated on the availability of complete views for all samples-an assumption that frequently fails in practical scenarios where data views are often incomplete. To surmount this challenge, various approaches to incomplete MVC (IMVC) have been proposed, with deep neural networks emerging as a favored technique for their representation learning ability. Despite their promise, previous methods commonly adopt sample-level (e.g., features) or affinity-level (e.g., graphs) guidance, neglecting the discriminative label-level guidance (i.e., pseudo-labels). In this work, we propose a novel deep IMVC method termed pseudo-label propagation for deep IMVC (PLP-IMVC), which integrates high-quality pseudo-labels from the complete subset of incomplete data with deep label propagation networks to obtain improved clustering results. In particular, we first design a local model (PLP-L) that leverages pseudo-labels to their fullest extent. Then, we propose a global model (PLP-G) that exploits manifold regularization to mitigate the label noises, promote view-level information fusion, and learn discriminative unified representations. Experimental results across eight public benchmark datasets and three evaluation metrics prove our method's efficacy, demonstrating superior performance compared to 18 advanced baseline methods.

Systematic single-cell analysis reveals dynamic control of transposable element activity orchestrating the endothelial-to-hematopoietic transition.

BACKGROUND: The endothelial-to-hematopoietic transition (EHT) process during definitive hematopoiesis is highly conserved in vertebrates. Stage-specific expression of transposable elements (TEs) has been detected during zebrafish EHT and may promote hematopoietic stem cell (HSC) formation by activating inflammatory signaling. However, little is known about how TEs contribute to the EHT process in human and mouse. RESULTS: We reconstructed the single-cell EHT trajectories of human and mouse and resolved the dynamic expression patterns of TEs during EHT. Most TEs presented a transient co-upregulation pattern along the conserved EHT trajectories, coinciding with the temporal relaxation of epigenetic silencing systems. TE products can be sensed by multiple pattern recognition receptors, triggering inflammatory signaling to facilitate HSC emergence. Interestingly, we observed that hypoxia-related signals were enriched in cells with higher TE expression. Furthermore, we constructed the hematopoietic cis-regulatory network of accessible TEs and identified potential TE-derived enhancers that may boost the expression of specific EHT marker genes. CONCLUSIONS: Our study provides a systematic vision of how TEs are dynamically controlled to promote the hematopoietic fate decisions through transcriptional and cis-regulatory networks, and pre-train the immunity of nascent HSCs.

Harnessing landrace diversity empowers wheat breeding.

Harnessing genetic diversity in major staple crops through the development of new breeding capabilities is essential to ensure food security1. Here we examined the genetic and phenotypic diversity of the A. E. Watkins landrace collection2 of bread wheat (Triticum aestivum), a major global cereal, by whole-genome re-sequencing of 827 Watkins landraces and 208 modern cultivars and in-depth field evaluation spanning a decade. We found that modern cultivars are derived from two of the seven ancestral groups of wheat and maintain very long-range haplotype integrity. The remaining five groups represent untapped genetic sources, providing access to landrace-specific alleles and haplotypes for breeding. Linkage disequilibrium-based haplotypes and association genetics analyses link Watkins genomes to the thousands of identified high-resolution quantitative trait loci and significant marker-trait associations. Using these structured germplasm, genotyping and informatics resources, we revealed many Watkins-unique beneficial haplotypes that can confer superior traits in modern wheat. Furthermore, we assessed the phenotypic effects of 44,338 Watkins-unique haplotypes, introgressed from 143 prioritized quantitative trait loci in the context of modern cultivars, bridging the gap between landrace diversity and current breeding. This study establishes a framework for systematically utilizing genetic diversity in crop improvement to achieve sustainable food security.

The wheat powdery mildew resistance gene Pm4 also confers resistance to wheat blast.

Wheat blast, caused by the fungus Magnaporthe oryzae, threatens global cereal production since its emergence in Brazil in 1985 and recently spread to Bangladesh and Zambia. Here we demonstrate that the AVR-Rmg8 effector, common in wheat-infecting isolates, is recognized by the gene Pm4, previously shown to confer resistance to specific races of Blumeria graminis f. sp. tritici, the cause of powdery mildew of wheat. We show that Pm4 alleles differ in their recognition of different AVR-Rmg8 alleles, and some confer resistance only in seedling leaves but not spikes, making it important to select for those alleles that function in both tissues. This study has identified a gene recognizing an important virulence factor present in wheat blast isolates in Bangladesh and Zambia and represents an important first step towards developing durably resistant wheat cultivars for these regions.

An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes.

BACKGROUND: Major depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. METHODS: Transcriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the "Enrichr" platform was utilized to identify potential drugs for MDD. RESULTS: Totally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD. CONCLUSION: Our work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.

Development of a next generation SNP genotyping array for wheat.

High-throughput genotyping arrays have provided a cost-effective, reliable and interoperable system for genotyping hexaploid wheat and its relatives. Existing, highly cited arrays including our 35K Wheat Breeder's array and the Illumina 90K array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in wheat sequencing has given us access to a vast pool of SNP diversity, whilst technological improvements have allowed us to fit significantly more probes onto a 384-well format Axiom array than previously possible. Here we describe a novel Axiom genotyping array, the 'Triticum aestivum Next Generation' array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins 'Core Collection'. We used a novel haplotype optimization approach to select SNPs with the highest combined varietal discrimination and a design iteration step to test and replace SNPs which failed to convert to reliable markers. The final design with 43 372 SNPs contains a combination of haplotype-optimized novel SNPs and legacy cross-platform markers. We show that this design has an improved distribution of SNPs compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous array. We also demonstrate the improved performance of TaNGv1.1 for Genome-wide association studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available with supporting marker annotations and initial genotyping results freely available.

The miR6445-NAC029 module regulates drought tolerance by regulating the expression of glutathione S-transferase U23 and reactive oxygen species scavenging in Populus.

MicroRNAs are essential in plant development and stress resistance, but their specific roles in drought stress require further investigation. Here, we have uncovered that a Populus-specific microRNAs (miRNA), miR6445, targeting NAC (NAM, ATAF, and CUC) family genes, is involved in regulating drought tolerance of poplar. The expression level of miR6445 was significantly upregulated under drought stress; concomitantly, seven targeted NAC genes showed significant downregulation. Silencing the expression of miR6445 by short tandem target mimic technology significantly decreased the drought tolerance in poplar. Furthermore, 5' RACE experiments confirmed that miR6445 directly targeted NAC029. The overexpression lines of PtrNAC029 (OE-NAC029) showed increased sensitivity to drought compared with knockout lines (Crispr-NAC029), consistent with the drought-sensitive phenotype observed in miR6445-silenced strains. PtrNAC029 was further verified to directly bind to the promoters of glutathione S-transferase U23 (GSTU23) and inhibit its expression. Both Crispr-NAC029 and PtrGSTU23 overexpressing plants showed higher levels of PtrGSTU23 transcript and GST activity while accumulating less reactive oxygen species (ROS). Moreover, poplars overexpressing GSTU23 demonstrated enhanced drought tolerance. Taken together, our research reveals the crucial role of the miR6445-NAC029-GSTU23 module in enhancing poplar drought tolerance by regulating ROS homeostasis. This finding provides new molecular targets for improving the drought resistance of trees.

Nlrc3 signaling is indispensable for hematopoietic stem cell emergence via Notch signaling in vertebrates.

Hematopoietic stem and progenitor cells generate all the lineages of blood cells throughout the lifespan of vertebrates. The emergence of hematopoietic stem and progenitor cells is finely tuned by a variety of signaling pathways. Previous studies have revealed the roles of pattern-recognition receptors such as Toll-like receptors and RIG-I-like receptors in hematopoiesis. In this study, we find that Nlrc3, a nucleotide-binding domain leucine-rich repeat containing family gene, is highly expressed in hematopoietic differentiation stages in vivo and vitro and is required in hematopoiesis in zebrafish. Mechanistically, nlrc3 activates the Notch pathway and the downstream gene of Notch hey1. Furthermore, NF-kB signaling acts upstream of nlrc3 to enhance its transcriptional activity. Finally, we find that Nlrc3 signaling is conserved in the regulation of murine embryonic hematopoiesis. Taken together, our findings uncover an indispensable role of Nlrc3 signaling in hematopoietic stem and progenitor cell emergence and provide insights into inflammation-related hematopoietic ontogeny and the in vitro expansion of hematopoietic stem and progenitor cells.

Unveiling protein corona composition: predicting with resampling embedding and machine learning.

Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration. When nanoparticles (NPs) enter the living system, they quickly interact with proteins in the body fluid, forming the protein corona (PC). The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs. However, achieving accurate predictions remains a significant challenge. Although several machine learning (ML) models like Random Forest (RF) have been used for PC prediction, they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution. In this study, resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data. Various ML models were evaluated, and RF model was finally used for prediction, and good correlation coefficient (R2) and root-mean-square deviation (RMSE) values were obtained. Our ablation experiments demonstrated that the proposed method achieved an R2 of 0.68, indicating an improvement of approximately 10%, and an RMSE of 0.90, representing a reduction of approximately 10%. Furthermore, through the verification of label-free quantification of four NPs: hydroxyapatite (HA), titanium dioxide (TiO2), silicon dioxide (SiO2) and silver (Ag), and we achieved a prediction performance with an R2 value >0.70 using Random Oversampling. Additionally, the feature analysis revealed that the composition of the PC is most significantly influenced by the incubation plasma concentration, PDI and surface modification.

HALD, a human aging and longevity knowledge graph for precision gerontology and geroscience analyses.

Human aging is a natural and inevitable biological process that leads to an increased risk of aging-related diseases. Developing anti-aging therapies for aging-related diseases requires a comprehensive understanding of the mechanisms and effects of aging and longevity from a multi-modal and multi-faceted perspective. However, most of the relevant knowledge is scattered in the biomedical literature, the volume of which reached 36 million in PubMed. Here, we presented HALD, a text mining-based human aging and longevity dataset of the biomedical knowledge graph from all published literature related to human aging and longevity in PubMed. HALD integrated multiple state-of-the-art natural language processing (NLP) techniques to improve the accuracy and coverage of the knowledge graph for precision gerontology and geroscience analyses. Up to September 2023, HALD had contained 12,227 entities in 10 types (gene, RNA, protein, carbohydrate, lipid, peptide, pharmaceutical preparations, toxin, mutation, and disease), 115,522 relations, 1,855 aging biomarkers, and 525 longevity biomarkers from 339,918 biomedical articles in PubMed. HALD is available at https://bis.zju.edu.cn/hald .

The geological factors affecting gas content and permeability of coal seam and reservoir characteristics in Wenjiaba block, Guizhou province.

The gas content and permeability of coal reservoirs are the main factors affecting the productivity of coalbed methane. To explore the law of gas content and permeability of coal reservoirs in the Zhijin area of Guizhou, taking No.16, No.27 and No.30 coal seams in Wenjiaba mining area of Guizhou as the engineering background, based on the relevant data of coalbed methane exploration in Wenjiaba block, the geological structure, coal seam thickness, coal quality characteristics,coal seam gas content and permeability of the area were studied utilizing geological exploration, analysis of coal components and methane adsorption test. The results show that the average thickness of coal seams in this area is between 1.32 and 1.85 m; the average buried depth of the coal seam is in the range of 301.3-384.2 m; the gas content of No.16 and No.27 coal seams is higher in the syncline core. The gas content of the No.30 coal seam forms a gas-rich center in the south of the mining area. The buried depth and gas content of coal seams in the study area show a strong positive correlation. Under the same pressure conditions, the adsorption capacity of dry ash-free basis is significantly higher than that of air-dried coal. The permeability decreases exponentially with the horizontal maximum principal stress and the horizontal minimum principal stress. The horizontal maximum primary stress and the flat minimum prominent stress increase with the increase of the buried depth of the coal seam. The permeability and coal seam burial depth decrease exponentially. This work can provide engineering reference and theoretical support for selecting high-yield target areas for CBM enrichment in the block.

SP1 transcriptionally upregulates the expression of APOC3 in KGN cells to promote disease progression by regulating TLR2/NF-κB signalling pathway.

INTRODUCTION: Apolipoprotein C3 (APOC3) is known for its important functions in metabolism-related diseases. However, the function and molecular mechanism of APOC3 in polycystic ovarian syndrome (PCOS) have not been reported. MATERIAL AND METHODS: Quantitative polymerase chain reaction and western blot assays were used to detect the expression of APOC3 in KGN cells. Small interference APOC3 (siAPOC3) was applied to reduce APOC3 expression, and the proliferation ability of human granulosa cell line (KGN cells) was measured by cell counting kit-8 and colony formation assays. The protein levels of key genes related to apoptosis were detected by western blot assay. The transcriptional regulator of APOC3 was predicted by the UCSC and PROMO website, and verified by dual luciferase assay. siAPOC3 and pcDNA3.1-specific protein 1 (SP1) vector were co-transfected into KGN cells to detect the function of SP1 and APOC3 in KGN cells. RESULTS: APOC3 was overexpressed in KGN cells, and siAPOC3 transfection significantly reduced the growth ability of KGN cells and increased the apoptosis ability of KGN cells. SP1 directly bound to the promoter of APOC3 and transcriptional regulated APOC3 expression. Overexpression of SP1 increased the growth ability of KGN cells and decreased the apoptosis ability of KGN cells, which were reversed after siAPOC3 transfection. The increased levels of toll-like receptor 2 (TLR2) and p65 phosphorylation (p-P65) nuclear factor kappa B (NF-κB) caused by SP1 overexpression were inhibited by siAPOC3 transfection. APOC3, transcriptionally regulated by SP1, promoted the growth of KGN cells, and inhibited the apoptosis by regulating TLR2/NF-κB signalling pathway.

α-Synuclein induces deficiency in clathrin-mediated endocytosis through inhibiting synaptojanin1 expression.

Parkinson's disease (PD) is an age-related chronic neurological disorder, mainly characterized by the pathological feature of α-synuclein (α-syn) aggregation, with the exact disease pathogenesis unclear. During the onset and progression of PD, synaptic dysfunction, including dysregulation of axonal transport, impaired exocytosis, and endocytosis are identified as crucial events of PD pathogenesis. It has been reported that over-expression of α-syn impairs clathrin-mediated endocytosis (CME) in the synapses. However, the underlying mechanisms still needs to be explored. In this study, we investigated the molecular events underlying the synaptic dysfunction caused by over-expression of wild-type human α-syn and its mutant form, involving series of proteins participating in CME. We found that excessive human α-syn causes impaired fission and uncoating of clathrin-coated vesicles during synaptic vesicle recycling, leading to reduced clustering of synaptic vesicles near the active zone and increased size of plasma membrane and number of endocytic intermediates. Furthermore, over-expressed human α-syn induced changes of CME-associated proteins, among which synaptojanin1 (SYNJ1) showed significant reduction in various brain regions. Over-expression of SYNJ1 in primary hippocampal neurons from α-syn transgenic mice recovered the synaptic vesicle density, clustering and endocytosis. Using fluorescence-conjugated transferrin, we demonstrated that SYNJ1 re-boosted the CME activity by restoring the phosphatidylinositol-4,5-bisphosphate homeostasis. Our data suggested that over-expression of α-syn disrupts synaptic function through interfering with vesicle recycling, which could be alleviated by re-availing of SYNJ1. Our study unrevealed a molecular mechanism of the synaptic dysfunction in PD pathogenesis and provided a potential therapeutic target for treating PD.

A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts.

BACKGROUND: High throughput gene expression profiling is a valuable tool in providing insight into the molecular mechanism of human diseases. Hypoxia- and lactate metabolism-related genes (HLMRGs) are fundamentally dysregulated in sepsis and have great predictive potential. Therefore, we attempted to build an HLMRG signature to predict the prognosis of patients with sepsis. METHODS: Three publicly available transcriptomic profiles of peripheral blood mononuclear cells from patients with sepsis (GSE65682, E-MTAB-4421 and E-MTAB-4451, total n = 850) were included in this study. An HLMRG signature was created by employing Cox regression and least absolute shrinkage and selection operator estimation. The CIBERSORT method was used to analyze the abundances of 22 immune cell subtypes based on transcriptomic data. Metascape was used to investigate pathways related to the HLMRG signature. RESULTS: We developed a prognostic signature based on five HLMRGs (ERO1L, SIAH2, TGFA, TGFBI, and THBS1). This classifier successfully discriminated patients with disparate 28-day mortality in the discovery cohort (GSE65682, n = 479), and consistent results were observed in the validation cohort (E-MTAB-4421 plus E-MTAB-4451, n = 371). Estimation of immune infiltration revealed significant associations between the risk score and a subset of immune cells. Enrichment analysis revealed that pathways related to antimicrobial immune responses, leukocyte activation, and cell adhesion and migration were significantly associated with the HLMRG signature. CONCLUSIONS: Identification of a prognostic signature suggests the critical role of hypoxia and lactate metabolism in the pathophysiology of sepsis. The HLMRG signature can be used as an efficient tool for the risk stratification of patients with sepsis.