Analysis of the prognostic value of mitochondria-related genes in patients with acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) is a leading cause of death worldwide. Mitochondrial dysfunction is a key determinant of cell death post-AMI. Preventing mitochondrial dysfunction is thus a key therapeutic strategy. This study aimed to explore key genes and target compounds related to mitochondrial dysfunction in AMI patients and their association with major adverse cardiovascular events (MACE). METHODS: Differentially expressed genes in AMI were identified from the Gene Expression Omnibus (GEO) datasets (GSE166780 and GSE24519), and mitochondria-related genes were obtained from MitoCarta3.0 database. By intersection of the two gene groups, mitochondria-related genes in AMI were identified. Next, the identified genes related to mitochondria were subject to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Protein-protein interaction (PPI) network was constructed, and key genes were screened. Then, targeted drug screening and molecular docking were performed. Blood samples from AMI patients and healthy volunteers were analyzed for the key genes expressions using quantitative real time polymerase chain reaction (qRT-PCR). Later, receiver operating characteristic (ROC) curves assessed the diagnostic value of key genes, and univariate and multivariate COX analyses identified risk factors and protective factors for MACE in AMI patients. RESULTS: After screening and identification, 138 mitochondria-related genes were identified, mainly enriched in the processes and pathways of cellular respiration, redox, mitochondrial metabolism, apoptosis, amino acid and fatty acid metabolism. According to the PPI network, 5 key mitochondria-related genes in AMI were obtained: translational activator of cytochrome c oxidase I (TACO1), cytochrome c oxidase subunit Va (COX5A), PTEN-induced putative kinase 1 (PINK1), SURF1, and NDUFA11. Molecular docking showed that Cholic Acid, N-Formylmethionine interacted with COX5A, nicotinamide adenine dinucleotide + hydrogen (NADH) and NDUFA11. Subsequent basic experiments revealed that COX5A and NDUFA11 expressions were significantly lower in the blood of patients with AMI than those in the corresponding healthy volunteers; also, AMI patients with MACE had lower COX5A and NDUFA11 expressions in the blood than those without MACE (P < 0.01). ROC analysis also showed high diagnostic value for COX5A and NDUFA11 [area under the curve (AUC) > 0.85]. In terms of COX results, COX5A, NDUFA11 and left ventricular ejection fraction (LVEF) were protective factors for MACE in AMI, while C-reactive protein (CRP) was a risk factor. CONCLUSION: COX5A and NDUFA11, key mitochondria-related genes in AMI, may be used as biomarkers to diagnose AMI and predict MACE.

Hypoxia-Inducible Factor-1α-Activated Protein Switch Based on Allosteric Self-Splicing Reduces Nonspecific Cytotoxicity of Pharmaceutical Drugs.

Protein-based therapeutic agents currently used for targeted tumor therapy exhibit limited penetrability, nonspecific toxicity, and a short circulation half-life. Although targeting cell surface receptors improves cancer selectivity, the receptors are also slightly expressed in normal cells; consequently, the nonspecific toxicity of recombinant protein-based therapeutic agents has not been eliminated. In this study, an allosteric-regulated protein switch was designed that achieved cytoplasmic reorganization of engineered immunotoxins in tumor cells via interactions between allosteric self-splicing elements and cancer markers. It can target the accumulated HIF-1α in hypoxic cancer cells and undergo allosteric activation, and the splicing products were present in hypoxic cancer cells but were absent in normoxic cells, selectively killing tumor cells and reducing nonspecific toxicity to normal cells. The engineered pro-protein provides a platform for targeted therapy of tumors while offering a novel universal strategy for combining the activation of therapeutic functions with specific cancer markers. The allosteric self-splicing element is a powerful tool that significantly reduces the nonspecific cytotoxicity of therapeutic proteins.

GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation.

Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world, particularly with respect to data privacy. Existing approaches predominantly focus on Euclidean data, such as images and videos, while the exploration of non-Euclidean graph data remains scarce. Recent graph neural network (GNN) approaches could suffer from serious performance decline due to domain shift and label scarcity in source-free adaptation scenarios. In this study, we propose a novel method named Graph Diffusion-based Alignment with Jigsaw (GALA) tailored for source-free graph domain adaptation. To achieve domain alignment, GALA employs a graph diffusion model to reconstruct source-style graphs from target data. Specifically, a score-based graph diffusion model is trained using source graphs to learn the generative source styles. Then, we introduce perturbations to target graphs via a stochastic differential equation instead of sampling from a prior, followed by the reverse process to reconstruct source-style graphs. We feed them into an off-the-shelf GNN and introduce class-specific thresholds with curriculum learning, which can generate accurate and unbiased pseudo-labels for target graphs. Moreover, we develop a simple yet effective graph mixing strategy named graph jigsaw to combine confident graphs and unconfident graphs, which can enhance generalization capabilities and robustness via consistency learning. Extensive experiments on benchmark datasets validate the effectiveness of GALA. The source code is available at https://github.com/luo-junyu/GALA.

A Comprehensive Survey on Deep Graph Representation Learning.

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future.

Unsupervised graph-level representation learning with hierarchical contrasts.

Unsupervised graph-level representation learning has recently shown great potential in a variety of domains, ranging from bioinformatics to social networks. Plenty of graph contrastive learning methods have been proposed to generate discriminative graph-level representations recently. They typically design multiple types of graph augmentations and enforce a graph to have consistent representations under different views. However, these techniques mostly neglect the intrinsic hierarchical structure of the graph, resulting in a limited exploration of semantic information for graph representation. Moreover, they often rely on a large number of negative samples to prevent collapsing into trivial solutions, while a great need for negative samples may lead to memory issues during optimization in graph domains. To address the two issues, this paper develops an unsupervised graph-level representation learning framework named Hierarchical Graph Contrastive Learning (HGCL), which investigates the hierarchical structural semantics of a graph at both node and graph levels. Specifically, our HGCL consists of three parts, i.e., node-level contrastive learning, graph-level contrastive learning, and mutual contrastive learning to capture graph semantics hierarchically. Furthermore, the Siamese network and momentum update are further involved to release the demand for excessive negative samples. Finally, the experimental results on both benchmark datasets for graph classification and large-scale OGB datasets for transfer learning demonstrate that our proposed HGCL significantly outperforms a broad range of state-of-the-art baselines.

Whole-Genome Identification and Analysis of Multiple Gene Families Reveal Candidate Genes for Theasaponin Biosynthesis in Camellia oleifera.

Camellia oleifera is an economically important oilseed tree. Seed meals of C. oleifera have a long history of use as biocontrol agents in shrimp farming and as cleaning agents in peoples' daily lives due to the presence of theasaponins, the triterpene saponins from the genus Camellia. To characterize the biosynthetic pathway of theasaponins in C. oleifera, members of gene families involved in triterpenoid biosynthetic pathways were identified and subjected to phylogenetic analysis with corresponding members in Arabidopsis thaliana, Camellia sinensis, Actinidia chinensis, Panax ginseng, and Medicago truncatula. In total, 143 triterpenoid backbone biosynthetic genes, 1169 CYP450s, and 1019 UGTs were identified in C. oleifera. The expression profiles of triterpenoid backbone biosynthetic genes were analyzed in different tissue and seed developmental stages of C. oleifera. The results suggested that MVA is the main pathway for triterpenoid backbone biosynthesis. Moreover, the candidate genes for theasaponin biosynthesis were identified by WGCNA and qRT-PCR analysis; these included 11 CYP450s, 14 UGTs, and eight transcription factors. Our results provide valuable information for further research investigating the biosynthetic and regulatory network of theasaponins.

CLEAR: Cluster-Enhanced Contrast for Self-Supervised Graph Representation Learning.

This article studies self-supervised graph representation learning, which is critical to various tasks, such as protein property prediction. Existing methods typically aggregate representations of each individual node as graph representations, but fail to comprehensively explore local substructures (i.e., motifs and subgraphs), which also play important roles in many graph mining tasks. In this article, we propose a self-supervised graph representation learning framework named cluster-enhanced Contrast (CLEAR) that models the structural semantics of a graph from graph-level and substructure-level granularities, i.e., global semantics and local semantics, respectively. Specifically, we use graph-level augmentation strategies followed by a graph neural network-based encoder to explore global semantics. As for local semantics, we first use graph clustering techniques to partition each whole graph into several subgraphs while preserving as much semantic information as possible. We further employ a self-attention interaction module to aggregate the semantics of all subgraphs into a local-view graph representation. Moreover, we integrate both global semantics and local semantics into a multiview graph contrastive learning framework, enhancing the semantic-discriminative ability of graph representations. Extensive experiments on various real-world benchmarks demonstrate the efficacy of the proposed over current graph self-supervised representation learning approaches on both graph classification and transfer learning tasks.

Comparative study on fruit development and oil synthesis in two cultivars of Camellia oleifera.

BACKGROUND: The oil-tea tree (Camellia oleifera Abel.) is a woody tree species that produces edible oil in the seed. C. oleifera oil has high nutritional value and is also an important raw material for medicine and cosmetics. In China, due to the uncertainty on maturity period and oil synthesis mechanism of many C. oleifera cultivars, growers may harvest fruits prematurely, which could not maximize fruit and oil yields. In this study, our objective was to explore the mechanism and differences of oil synthesis between two Camellia oleifera cultivars for a precise definition of the fruit ripening period and the selection of appropriate cultivars. RESULTS: The results showed that 'Huashuo' had smaller fruits and seeds, lower dry seed weight and lower expression levels of fatty acid biosynthesis genes in July. We could not detect the presence of oil and oil bodies in 'Huashuo' seeds until August, and oil and oil bodies were detected in 'Huajin' seeds in July. Moreover, 'Huashuo' seeds were not completely blackened in October with up to 60.38% of water and approximately 37.98% of oil in seed kernels whose oil content was much lower than normal mature seed kernels. The oil bodies in seed endosperm cells of 'Huajin' were always higher than those of 'Huashuo' from July to October. CONCLUSION: Our results confirmed that C. oleifera 'Huashuo' fruits matured at a lower rate compared to 'Huajin' fruits and that 'Huajin' seeds entered the oil synthesis period earlier than 'Huashuo' seeds. Moreover, 'Huashuo' fruits did not mature during the Frost's Descent period (October 23-24 each year).

Preparation of a Cross-Linked Sulfonated Poly(arylene ether ketone) Proton Exchange Membrane with Enhanced Proton Conductivity and Methanol Resistance by Introducing an Ionic Liquid-Impregnated Metal Organic Framework.

A novel ionic liquid-impregnated metal-organic-framework (IL@NH2-MIL-101) was prepared and introduced into sulfonated poly(arylene ether ketone) with pendent carboxyl groups (SPAEK) as the nanofiller for achieving hybrid proton exchange membranes. The nanofiller was anchored in the polymeric matrix by the formation of amido linkage between the pendent carboxyl group attached to the molecule chain of SPAEK and amino group belonging to the inorganic framework, thus leading to the enhancement in mechanical properties and dimensional stability. Besides, the hybrid membrane (IL@MOF-1) exhibits an enhanced proton conductivity up to 0.184 S·cm-1 because of the incorporation of ionic liquid in the nanocages of NH2-MIL-101. Moreover, the special structure of NH2-MIL-101 contributes to a low leakage of ionic liquid so as to retain the stable proton conductivity of hybrid membranes under fully hydrated conditions. Furthermore, as a result of a cross-linked structure formed by inorganic nanofiller, the IL@MOF-1 hybrid membrane shows a lower methanol permeability (7.53 × 10-7 cm2 s-1) and superior selectivity (2.44 × 105 S s cm-3) than the pristine SPAEK membrane. Especially, IL@MOF-1 performs high single-cell efficiency with a peak power density of 37.5 mW cm-2, almost 2.3-fold to SPAEK. Electrochemical impedance spectroscopy and scanning electron microscopy indicated that the nanofiller not only contributed to faster proton transfer but also resulted in a tighter bond between the membrane and catalyst. Therefore, the incorporation of IL@NH2-MIL-101 to prepare the hybrid membrane is proven to be suitable for application in direct methanol fuel cells.

Association between CYP2E1 and GOT2 gene polymorphisms and susceptibility and low-dose N,N-dimethylformamide occupational exposure-induced liver injury.

OBJECTIVE: To investigate the effects of the interactions between the CYP2E1 and GOT2 gene polymorphisms and N,N-dimethylformamide (DMF) on liver injury. METHODS: A total of 672 DMF-exposed workers were randomly selected from two synthetic leather enterprises in Suzhou, China, for follow-up in a cohort study. Information on exposure to DMF in the air was collected through a fixed-point air sampler in the worker's breathing zone. The subjects were assessed every year during the period of 2010-2015, they underwent occupational health examinations. Alanine aminotransferase and aspartate aminotransferase levels were measured. Peripheral blood was collected and DNA was extracted. The genotypes rs2031920, rs3813867 and rs6413432 of the CYP2E1 gene and rs7204324 of the GOT2 gene were detected by PCR, and analyzed using the Chi-square test and logistic regression analysis. RESULTS: Workers exposed to a high cumulative dose of DMF were significantly more likely than low-exposed workers to develop liver injury. No association was observed between rs2031920, rs3813867 and rs6413432 of the CYP2E1 gene and DMF-induced liver damage. However, the A allele of rs7204324 on the GOT2 gene may be a risk factor for susceptibility to DMF-induced liver injury. CONCLUSION: Polymorphisms of rs7204324 on GOT2 may play an important role in susceptibility to liver injury following exposure to DMF.

Relationship between N,N-dimethylformamide exposure, PNPLA3, GCKR, COL13A1 and TM6SF2 genes, and liver injury.

BACKGROUND: Current researches show that N,N-dimethylformamide (DMF) exposure is associated with liver injury, but it is debatable whether PNPLA3, GCKR, COL13A1 and TM6SF2 gene polymorphisms are associated with liver injury. Our objective was to examine the relationship among DMF exposure, PNPLA3 rs738409, GCKR rs780094, COL13A1 rs1227756, TM6SF2 rs58542926 and liver injury. METHODS: The cohort consisted of 461 workers exposed above the DMF threshold limit value (TLV) and 211 exposed below the DMF TLV in China, who were followed for 5 years. The relationship between the measured dose of DMF and the relative risk (RR) of liver injury was also investigated by Poisson analysis. Logistic regression models were used to examine the association between measured dose of DMF, gene locus, and RR for liver injury. All workers had a annual physical examinations were conducted at certified physical examination centers in Taicang CDC, including liver serum transaminase assessment and abdominal ultrasound. Genomic DNA was extracted from peripheral blood leukocytes using a genomic DNA extraction kit. RESULTS: The incidence of liver injury in the above DMF TLV group was significantly higher than in the below DMF TLV group. GCKR rs780094 was associated with liver injury. The interaction among the GCKR rs780094, DMF exposure and liver injury showed no significant association. CONCLUSIONS: Our data indicated that in DMF exposure, GCKR rs780094 may contribute to the risk of liver injury. Our results suggest that GCKR rs780094 is a useful genetic marker to help identify liver injury.

Effects of Low-Temperature Stress and Brassinolide Application on the Photosynthesis and Leaf Structure of Tung Tree Seedlings.

The tung tree is an important woody oil tree species. Tung oil extracted from the tung fruit seeds is used in the manufacture of environmentally friendly paint. This study investigated the effects of the application of brassinolide (BR) under different temperature conditions on the chlorophyll content, photosynthesis, chlorophyll fluorescence, leaf structure, and chloroplast ultrastructure in Vernicia fordii and Vernicia montana. The conditions used were 8°C-Control (low temperature and no BR), 8°C-BR (low temperature and BR application), 28°C-Control (normal temperature and no BR), and 28°C-BR (normal temperature and BR application), and effects were monitored from 5 to 15 days after the treatments (DAT). The results showed that the low temperature treatment (8°C-Control) significantly reduced the net photosynthetic rate (Pn ), stomatal conductance (Gs ), maximum fluorescence (Fm ), maximum photochemical efficiency (F v/F m), and actual photochemical and quantum efficiency (Φ PSII ) compared to the control condition (28°C-Control). However, the external application of BR alleviated the negative effects of low-temperature stress to some degree for all the above parameters for both species tested, except for P n and G s at 15 DAT. There were no significant differences in most of the parameters in either species between the 28°C-Control and 28°C-BR treatments. At 10 and 15 DAT of low-temperature stress, the 8°C-Control treatment significantly reduced leaf cell tense ratio (CTR) and increased spongy ratio (SR) compared to the 28°C-Control, whereas BR application alleviated the adverse effects. Moreover, the 8°C-Control treatment significantly destroyed the chloroplast structure, loosening the thylakoids until they disintegrated, while exogenous spraying of BR protected the chloroplast structure and enabled it to function properly in both species. Our results suggested that long-term low temperatures significantly reduced the photosynthetic efficiency of tung tree seedlings, affecting the formation of the internal structure of plant leaves and destroying the integrity and function of the chloroplast. To prevent this, external application of BR to tung tree seedlings could enhance the photosynthetic potential of tung trees by maintaining the stability of the leaf structure, morphology, and function, and alleviating the damage caused by cold injury. The results also showed that V. fordii seedlings are more resistant to low temperatures than V. montana seedlings.

Low-Dose N,N-Dimethylformamide Exposure and Liver Injuries in a Cohort of Chinese Leather Industry Workers.

OBJECTIVE: We assessed the risk of liver injuries following low doses of N,N-dimethylformamide (DMF) below threshold limit values (20 mg/m) among leather industry workers and comparison groups. METHODS: A cohort of 429 workers from a leather factory and 466 non-exposed subjects in China were followed for 4 years. Poisson regression and piece-wise linear regression were used to examine the relationship between DMF and liver injury. RESULTS: Workers exposed to a cumulative dose of DMF were significantly more likely than non-exposed workers to develop liver injury. A nonlinear relationship between DMF and liver injury was observed, and a threshold of the cumulative DMF dose for liver injury was 7.30 (mg/m) year. CONCLUSIONS: The findings indicate the importance of taking action to reduce DMF occupational exposure limits for promoting worker health.

ROS-mediated apoptosis of HAPI microglia through p53 signaling following PFOS exposure.

Perfluorooctane sulfonate (PFOS), the most extensively studied member of perfluoroalkyl and polyfluoroalkyl substances (PFASs), has been thought to be toxic to the central nervous system (CNS) of mammals. However, the neurotoxic effects of PFOS remain largely unknown. In this study, the effect of PFOS on microglial apoptosis was examined. The results showed that PFOS could significantly reduce the cell viability and mediate cell apoptosis in HAPI microglia, which was closely accompanied with ROS production and p53 overexpression. Moreover, p53 interference significantly ameliorated PFOS-triggered cytotoxic effects in HAPI microglia, including the downregulation of cleaved PARP and cleaved caspase 3. Interestingly, NAC, a ROS inhibitor, inhibited p53 expression, and decreased the apoptosis of HAPI microglia. Taken together, these findings suggest that upregulated production of ROS plays a vital role in PFOS-mediated apoptosis in HAPI microglia via the modulation of p53 signaling.

Effect of Changing Work Stressors and Coping Resources on Psychological Distress.

OBJECTIVES: We examined whether or not changing work stressors and coping resources affect the risk of psychological distress. METHOD: A baseline evaluation of work stressors and coping resources and mental health was assessed for 4362 petroleum industry workers after 12 years. RESULTS: Increased task and organizational stressors were associated with an elevated risk of psychological distress. Decreased task stressors, increased job control, and increased coping resources were associated with a reduced risk of psychological distress. Increased coping also had a buffering effect on increased work stressors and psychological distress. Gender-specific differences were observed in the factors influencing mental health. CONCLUSIONS: The findings indicated that reducing gender-specific task and organizational stressors, and promoting coping resources at work may help prevent the onset of psychological distress.

Reactive oxygen species mediate nitric oxide production through ERK/JNK MAPK signaling in HAPI microglia after PFOS exposure.

Perfluorooctane sulfonate (PFOS), an emerging persistent contaminant that is commonly encountered during daily life, has been shown to exert toxic effects on the central nervous system (CNS). However, the molecular mechanisms underlying the neurotoxicity of PFOS remain largely unknown. It has been widely acknowledged that the inflammatory mediators released by hyper-activated microglia play vital roles in the pathogenesis of various neurological diseases. In the present study, we examined the impact of PFOS exposure on microglial activation and the release of proinflammatory mediators, including nitric oxide (NO) and reactive oxidative species (ROS). We found that PFOS exposure led to concentration-dependent NO and ROS production by rat HAPI microglia. We also discovered that there was rapid activation of the ERK/JNK MAPK signaling pathway in the HAPI microglia following PFOS treatment. Moreover, the PFOS-induced iNOS expression and NO production were attenuated after the inhibition of ERK or JNK MAPK by their corresponding inhibitors, PD98059 and SP600125. Interestingly, NAC, a ROS inhibitor, blocked iNOS expression, NO production, and activation of ERK and JNK MAPKs, which suggested that PFOS-mediated microglial NO production occurs via a ROS/ERK/JNK MAPK signaling pathway. Finally, by exposing SH-SY5Y cells to PFOS-treated microglia-conditioned medium, we demonstrated that NO was responsible for PFOS-mediated neuronal apoptosis.