Root branching under high salinity requires auxin-independent modulation of LATERAL ORGAN BOUNDARY DOMAIN 16 function.

Salinity stress constrains lateral root (LR) growth and severely affects plant growth. Auxin signaling regulates LR formation, but the molecular mechanism by which salinity affects root auxin signaling and whether salt induces other pathways that regulate LR development remains unknown. In Arabidopsis thaliana, the auxin-regulated transcription factor LATERAL ORGAN BOUNDARY DOMAIN 16 (LBD16) is an essential player in LR development under control conditions. Here, we show that under high-salt conditions, an alternative pathway regulates LBD16 expression. Salt represses auxin signaling but, in parallel, activates ZINC FINGER OF ARABIDOPSIS THALIANA 6 (ZAT6), a transcriptional activator of LBD16. ZAT6 activates LBD16 expression, thus contributing to downstream cell wall remodeling and promoting LR development under high-salt conditions. Our study thus shows that the integration of auxin-dependent repressive and salt-activated auxin-independent pathways converging on LBD16 modulates root branching under high-salt conditions.

Arabinosylation of cell wall extensin is required for the directional response to salinity in roots.

Soil salinity is a major contributor to crop yield losses. To improve our understanding of root responses to salinity, we developed and exploited a real-time salt-induced tilting assay. This assay follows root growth upon both gravitropic and salt challenges, revealing that root bending upon tilting is modulated by Na+ ions, but not by osmotic stress. Next, we measured this salt-specific response in 345 natural Arabidopsis (Arabidopsis thaliana) accessions and discovered a genetic locus, encoding the cell wall-modifying enzyme EXTENSIN ARABINOSE DEFICIENT TRANSFERASE (ExAD) that is associated with root bending in the presence of NaCl (hereafter salt). Extensins are a class of structural cell wall glycoproteins known as hydroxyproline (Hyp)-rich glycoproteins, which are posttranslationally modified by O-glycosylation, mostly involving Hyp-arabinosylation. We show that salt-induced ExAD-dependent Hyp-arabinosylation influences root bending responses and cell wall thickness. Roots of exad1 mutant seedlings, which lack Hyp-arabinosylation of extensin, displayed increased thickness of root epidermal cell walls and greater cell wall porosity. They also showed altered gravitropic root bending in salt conditions and a reduced salt-avoidance response. Our results suggest that extensin modification via Hyp-arabinosylation is a unique salt-specific cellular process required for the directional response of roots exposed to salinity.

Unsupervised construction of gene regulatory network based on single-cell multi-omics data of colorectal cancer.

Identifying gene regulatory networks (GRNs) at the resolution of single cells has long been a great challenge, and the advent of single-cell multi-omics data provides unprecedented opportunities to construct GRNs. Here, we propose a novel strategy to integrate omics datasets of single-cell ribonucleic acid sequencing and single-cell Assay for Transposase-Accessible Chromatin using sequencing, and using an unsupervised learning neural network to divide the samples with high copy number variation scores, which are used to infer the GRN in each gene block. Accuracy validation of proposed strategy shows that approximately 80% of transcription factors are directly associated with cancer, colorectal cancer, malignancy and disease by TRRUST; and most transcription factors are prone to produce multiple transcript variants and lead to tumorigenesis by RegNetwork database, respectively. The source code access are available at: https://github.com/Cuily-v/Colorectal_cancer.

Upregulation of PECTATE LYASE5 by a NAC transcription factor promotes fruit softening in apple.

Flesh firmness is a critical breeding trait that determines consumer selection, shelf life, and transportation. The genetic basis controlling firmness in apple (Malus × domestica Borkh.) remains to be fully elucidated. We aimed to decipher genetic variance for firmness at harvest and develop potential molecular markers for marker-assisted breeding. Maturity firmness for 439 F1 hybrids from a cross of "Cripps Pink" and "Fuji" was determined in 2016 and 2017. The phenotype segregated extensively, with a Gaussian distribution. In a combined bulked segregant analysis (BSA) and RNA-sequencing analysis, 84 differentially expressed genes were screened from the 10 quantitative trait loci regions. Interestingly, next-generation re-sequencing analysis revealed a Harbinger-like transposon element insertion upstream of the candidate gene PECTATE LYASE5 (MdPL5); the genotype was associated with flesh firmness at harvest. The presence of this transposon repressed MdPL5 expression and was closely linked to the extra-hard phenotype. MdPL5 was demonstrated to promote softening in apples and tomatoes. Subsequently, using the MdPL5 promoter as bait, MdNAC1-L was identified as a transcription activator that positively regulates ripening and softening in the developing fruit. We also demonstrated that MdNAC1-L could induce the up-regulation of MdPL5, MdPG1, and the ethylene-related genes MdACS1 and MdACO1. Our findings provide insight into TE-related genetic variation and the PL-mediated regulatory network for the firmness of apple fruit.

Using TransR to enhance drug repurposing knowledge graph for COVID-19 and its complications.

MOTIVATION: The COVID-19 pandemic has been spreading globally for four years, yet specific drugs that effectively suppress the virus remain elusive. Furthermore, the emergence of complications associated with COVID-19 presents significant challenges, making the development of therapeutics for COVID-19 and its complications an urgent task. However, traditional drug development processes are time-consuming. Drug repurposing, which involves identifying new therapeutic applications for existing drugs, presents a viable alternative. RESULT: In this study, we construct a knowledge graph by retrieving information on genes, drugs, and diseases from databases such as DRUGBANK and GNBR. Next, we employ the TransR knowledge representation learning approach to embed entities and relationships into the knowledge graph. Subsequently, we train the knowledge graph using a graph neural network model based on TransR scoring. This trained knowledge graph is then utilized to predict drugs for the treatment of COVID-19 and its complications. Based on experimental results, we have identified 15 drugs out of the top 30 with the highest success rates associated with treating COVID-19 and its complications. Notably, out of these 15 drugs, 10 specifically aimed at treating COVID-19, such as Torcetrapib and Tocopherol, has not been previously identified in the knowledge graph. This finding highlights the potential of our model in aiding healthcare professionals in drug development and research related to this disease.

DBPboost:A method of classification of DNA-binding proteins based on improved differential evolution algorithm and feature extraction.

DNA-binding proteins are a class of proteins that can interact with DNA molecules through physical and chemical interactions. Their main functions include regulating gene expression, maintaining chromosome structure and stability, and more. DNA-binding proteins play a crucial role in cellular and molecular biology, as they are essential for maintaining normal cellular physiological functions and adapting to environmental changes. The prediction of DNA-binding proteins has been a hot topic in the field of bioinformatics. The key to accurately classifying DNA-binding proteins is to find suitable feature sources and explore the information they contain. Although there are already many models for predicting DNA-binding proteins, there is still room for improvement in mining feature source information and calculation methods. In this study, we created a model called DBPboost to better identify DNA-binding proteins. The innovation of this study lies in the use of eight feature extraction methods, the improvement of the feature selection step, which involves selecting some features first and then performing feature selection again after feature fusion, and the optimization of the differential evolution algorithm in feature fusion, which improves the performance of feature fusion. The experimental results show that the prediction accuracy of the model on the UniSwiss dataset is 89.32%, and the sensitivity is 89.01%, which is better than most existing models.

Ingesting yeast extract causes excitation of neurogenic and myogenic colonic motor patterns in the rat.

Fermented foods play a significant role in the human diet for their natural, highly nutritious and healthy attributes. Our aim was to study the effect of yeast extract, a fermented substance extracted from natural yeast, on colonic motility to better understand its potential therapeutic role. A yeast extract was given to rats by gavage for 3 days, and myogenic and neurogenic components of colonic motility were studied using spatiotemporal maps made from video recordings of the whole colon ex vivo. A control group received saline gavages. The yeast extract caused excitation of the musculature by increasing the propagation length and duration of long-distance contractions, the major propulsive activity of the rat colon. The yeast extract also evoked rhythmic propulsive motor complexes (RPMCs) which were antegrade in the proximal and mid-colon and retrograde in the distal colon. RPMC activity was evoked by distention-induced neural activity, but it was myogenic in nature since we showed it to be generated by bethanechol in the presence of tetrodotoxin. In conclusion, ingestion of yeast extract stimulates rat colon motility by exciting neurogenic and myogenic control mechanisms.

Balancing growth amidst salt stress - lifestyle perspectives from the extremophyte model Schrenkiella parvula.

Schrenkiella parvula, a leading extremophyte model in Brassicaceae, can grow and complete its lifecycle under multiple environmental stresses, including high salinity. Yet, the key physiological and structural traits underlying its stress-adapted lifestyle are unknown along with trade-offs when surviving salt stress at the expense of growth and reproduction. We aimed to identify the influential adaptive trait responses that lead to stress-resilient and uncompromised growth across developmental stages when treated with salt at levels known to inhibit growth in Arabidopsis and most crops. Its resilient growth was promoted by traits that synergistically allowed primary root growth in seedlings, the expansion of xylem vessels across the root-shoot continuum, and a high capacity to maintain tissue water levels by developing thicker succulent leaves while enabling photosynthesis during salt stress. A successful transition from vegetative to reproductive phase was initiated by salt-induced early flowering, resulting in viable seeds. Self-fertilization in salt-induced early flowering was dependent upon filament elongation in flowers otherwise aborted in the absence of salt during comparable plant ages. The maintenance of leaf water status promoting growth, and early flowering to ensure reproductive success in a changing environment, were among the most influential traits that contributed to the extremophytic lifestyle of S. parvula.

Polyvinyl Chloride-Based Luminescent Downshifting Film with High Flame Retardancy and Excellent UV Resistance for Silicon Solar Cells.

Solar power generation, as a clean energy source, has significant potential for development. This work reports the recent efforts to address the challenge of low power conversion efficiency in photovoltaic devices by proposing the fabrication of a luminescence downshifting layer using polyvinyl chloride (PVC) with added fluorescent dots to enhance light utilization. A photoluminescent microsphere (HCPAM) is synthesized by cross-linking hexachlorocyclotriphosphazene, 2-iminobenzimidazoline, and polyethyleneimine. Low addition of HCPAM can improve the fire safety of PVC films, raising the limiting oxygen index of PVC to 32.4% and reducing the total heat release and smoke production rate values by 14.5% and 42.9%, respectively. Additionally, modified PVC film remains a transparency of 88% and shows down-conversion light properties. When the PVC+1%HCPAM film is applied to the solar cell, the short-circuit current density increases from 42.3 to 43.8 mA cm-2, resulting in a 7.0% enhancement in power conversion efficiency. HCPAM also effectively delays the photooxidative aging of PVC, particularly at a 3% content, maintaining the surface morphology and optical properties of PVC samples during ultraviolet aging. This study offers an innovative strategy to enhance the fire and UV-resistant performance of PVC films and expand their applications in protecting and efficiently utilizing photovoltaic devices.

Detection of transcription factors binding to methylated DNA by deep recurrent neural network.

Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confirmed that some TFs have capability to interact with methylated DNA fragments to further regulate gene expression. Although biochemical experiments could recognize TFs binding to methylated DNA sequences, these wet experimental methods are time-consuming and expensive. Machine learning methods provide a good choice for quickly identifying these TFs without experimental materials. Thus, this study aims to design a robust predictor to detect methylated DNA-bound TFs. We firstly proposed using tripeptide word vector feature to formulate protein samples. Subsequently, based on recurrent neural network with long short-term memory, a two-step computational model was designed. The first step predictor was utilized to discriminate transcription factors from non-transcription factors. Once proteins were predicted as TFs, the second step predictor was employed to judge whether the TFs can bind to methylated DNA. Through the independent dataset test, the accuracies of the first step and the second step are 86.63% and 73.59%, respectively. In addition, the statistical analysis of the distribution of tripeptides in training samples showed that the position and number of some tripeptides in the sequence could affect the binding of TFs to methylated DNA. Finally, on the basis of our model, a free web server was established based on the proposed model, which can be available at https://bioinfor.nefu.edu.cn/TFPM/.

CRISPRCasStack: a stacking strategy-based ensemble learning framework for accurate identification of Cas proteins.

CRISPR-Cas system is an adaptive immune system widely found in most bacteria and archaea to defend against exogenous gene invasion. One of the most critical steps in the study of exploring and classifying novel CRISPR-Cas systems and their functional diversity is the identification of Cas proteins in CRISPR-Cas systems. The discovery of novel Cas proteins has also laid the foundation for technologies such as CRISPR-Cas-based gene editing and gene therapy. Currently, accurate and efficient screening of Cas proteins from metagenomic sequences and proteomic sequences remains a challenge. For Cas proteins with low sequence conservation, existing tools for Cas protein identification based on homology cannot guarantee identification accuracy and efficiency. In this paper, we have developed a novel stacking-based ensemble learning framework for Cas protein identification, called CRISPRCasStack. In particular, we applied the SHAP (SHapley Additive exPlanations) method to analyze the features used in CRISPRCasStack. Sufficient experimental validation and independent testing have demonstrated that CRISPRCasStack can address the accuracy deficiencies and inefficiencies of the existing state-of-the-art tools. We also provide a toolkit to accurately identify and analyze potential Cas proteins, Cas operons, CRISPR arrays and CRISPR-Cas locus in prokaryotic sequences. The CRISPRCasStack toolkit is available at https://github.com/yrjia1015/CRISPRCasStack.

Competition-Induced Macroscopic Superlubricity of Ionic Liquid Analogues by Hydroxyl Ligands Revealed by in Situ Raman.

High load-bearing capacity is one of the crucial indicators for liquid superlubricants to move toward practicality. However, some of the current emerging systems not only have low contact pressures but also are highly susceptible to further degradation due to water adsorption and even superlubricity failure. Herein, a novel choline chloride-based ionic liquid analogues (ILAs) of a superlubricant with triethanolamine (TEOA) as the H-bond donor is reported for the first time; it obtains an ultralow coefficient of friction (0.005) and high load-bearing capacity (360 MPa, more than 2 times that of similar systems) due to adsorption of a small amount of water (<5 wt %) from the air. In situ Raman combined with 1H NMR and FTIR techniques reveals that adsorbed water competes with the hydroxyl group of TEOA for coordination with Cl-, leading to the conversion of some strong H-bonds to weak H-bonds in ILAs; the localized strong H-bonds and weak H-bonds endow the ILAs with high load-bearing capacity and the formation of ultralow shear-resistance sliding interfaces, respectively, under the shear motion. This study proposes a strategy to modulate the interactions between liquid species using adsorbed water from air as a competing ligand, which provides new insights into the design of ILA-based macroscopic liquid superlubricants with a high load-bearing capacity.

Melt memory in random ethylene-1-alkene copolymers.

Chain flexibility or stiffness based polymer conformation plays a crucial role in affecting the dynamics and kinetics of polymers, which is related to the hierarchical architecture of chains. A series of random copolymers of ethylene and 1-alkenes including 1-hexene, 1-octene, and 1-dodecene were synthesized with metallocene catalysts. The crystallization behavior and memory effect in random ethylene-1-alkene copolymers with different side groups were investigated via differential scanning calorimetry (DSC) and wide-angle X-ray scattering (WAXS). Rheological tests were performed for understanding their dynamical behavior. The results show that the melting peak and the viscosity decrease but the orthorhombic crystal dimensions increase with co-unit contents increasing in the copolymers. It was found that the scaling relationship between the zero shear viscosity (η0) and molecular weight (Mn) of the copolymers containing ethylene-1-hexene and ethylene-1-octene is 3.6, which is higher than the classical scaling value of 3.4. The memory of crystals in the melt is enhanced with the increase of 1-alkene contents but is independent of the types of 1-alkenes. The enhanced melt memory effect in the copolymers was proposed due to the effect of the 1-alkene based side groups on the dynamics of polymer chains. The present work would be helpful to understand the chain stiffness based polymer dynamics and processing of polyolefins and copolymers prepared with the metallocene catalyst.

Transcriptome analysis reveals tissue-specific responses of Mytilus unguiculatus to Vibrio alginolyticus infection.

Mytilus unguiculatus is an important economic bivalve species with wide consumption and aquaculture value. Disease is one of the primary limiting factors in mussel aquaculture, thus understanding the response of different tissues of M. unguiculatus to pathogens is crucial for disease prevention and control. In this study, we investigated the physiological and transcriptomic responses of the gills, adductor muscle, and mantle of M. unguiculatus infected with Vibrio alginolyticus. The results showed that V. alginolyticus infection caused inflammation and tissue structure changes in the gill, adductor muscle and mantle of M. unguiculatus. Meanwhile, the activities of superoxide dismutase and catalase in the three tissues increased, while the total antioxidant capacity decreased, suggesting that M. unguiculatus have an activated defense mechanism against infection-induced oxidative stress, despite a compromised total antioxidant capacity. Transcriptomic studies reveal that infected M. unguiculatus exhibits upregulation of endocytosis, lysosome activity, cellular apoptosis, and immune-related signaling pathways, indicating that M. unguiculatus responds to pathogen invasion by upregulating efferocytosis. Compared with the gill and adductor muscle, the mantle had a higher level of mytimycin, mytilin and myticin, and the three tissues also increased the expression of mytimycin to cope with the invasion of pathogens. In addition, the analysis of genes related to taste transduction pathways and muscle contraction and relaxation found that after infection with V. alginolyticus, M. unguiculatus may reduce appetite by inhibiting taste transduction in the gill, while improving muscle contraction of the adductor muscle and keeping the shell closed, to resist further invasion of pathogens and reduce the risk of pathogen transmission in the population.

A deep learning based two-layer predictor to identify enhancers and their strength.

The enhancer is a DNA sequence that can increase the activity of promoters and thus speed up the frequency of gene transcription. The enhancer plays an essential role in activating gene expression. Currently, gene sequencing technology has been developed for 30 years from the first generation to the third generation, and a variety of biological sequence data have increased significantly every year. Due to the importance of enhancer functions, it is very expensive to identify enhancers through biochemical experiments. Therefore, we need to study new methods for the identification and classification of enhancers. Based on the K-mer principle this study proposed a feature extraction method that others have not used in convolutional neural networks. Then, we combined it with one-hot encoding to build an efficient one-dimensional convolutional neural network ensemble model for predicting enhancers and their strengths. Finally, we used five commonly used classification problem evaluation indicators to compare with the models proposed by other researchers. The model proposed in this paper has a better performance by using the same independent test dataset as other models.

Identification of adaptor proteins by incorporating deep learning and PSSM profiles.

Adaptor proteins, also known as signal transduction adaptor proteins, are important proteins in signal transduction pathways, and play a role in connecting signal proteins for signal transduction between cells. Studies have shown that adaptor proteins are closely related to some diseases, such as tumors and diabetes. Therefore, it is very meaningful to construct a relevant model to accurately identify adaptor proteins. In recent years, many studies have used a position-specific scoring matrix (PSSM) and neural network methods to identify adaptor proteins. However, ordinary neural network models cannot correlate the contextual information in PSSM profiles well, so these studies usually process 20×N (N > 20) PSSM into 20×20 dimensions, which results in the loss of a large amount of protein information; This research proposes an efficient method that combines one-dimensional convolution (1-D CNN) and a bidirectional long short-term memory network (biLSTM) to identify adaptor proteins. The complete PSSM profiles are the input of the model, and the complete information of the protein is retained during the training process. We perform cross-validation during model training and test the performance of the model on an independent test set; in the data set with 1224 adaptor proteins and 11,078 non-adaptor proteins, five indicators including specificity, sensitivity, accuracy, area under the receiver operating characteristic curve (AUC) metric and Matthews correlation coefficient (MCC), were employed to evaluate model performance. On the independent test set, the specificity, sensitivity, accuracy and MCC were 0.817, 0.865, 0.823 and 0.465, respectively. Those results show that our method is better than the state-of-the art methods. This study is committed to improve the accuracy of adaptor protein identification, and laid a foundation for further research on diseases related to adaptor protein. This research provided a new idea for the application of deep learning related models in bioinformatics and computational biology.

Long-term all-cause death prediction by coronary, aortic, and valvular calcification in patients with acute ST-segment elevation myocardial infarction.

BACKGROUND: To determine the prognostic value of cumulative calcification score of coronary artery calcification (CAC), thoracic aortic calcification (TAC) and aortic valve calcification (AVC) in acute ST segment elevation myocardial infarction (STEMI) patients. METHODS: This was a retrospective, single-center cohort study. A total of 332 STEMI patients who received primary percutaneous coronary intervention (PPCI) were enrolled in this study between January 2010 to October 2018. We assessed the calcification in the left anterior descending branch (LAD), left circumflex branch (LCX), right coronary artery (RCA), thoracic aorta, and aortic valve. Calcification of each part was counted as 1 point, and the cumulative calcification score was calculated as the sum of all points. The primary endpoint was all-cause mortality. Multivariate Cox proportional hazards models were used to determine association of cumulative calcification score with end points. The performance of the score was evaluated by receiver operating characteristic (ROC) curve analysis and absolute net reclassification improvement (NRI), compared with the Global Registry of Acute Coronary Events (GRACE) risk score. RESULTS: The overall population's calcification score was 2.0 ± 1.6. During a mean follow-up time of 69.8 ± 29.3 months, the all-cause mortality rate was 12.1%. Kaplan-Meier curve showed that the score was significantly associated with mortality (log-rank p < 0.001). The multivariable Cox proportional hazard analyses showed that a calcification score of 4-5 was independently associated with all-cause death in STEMI patients [hazard ratio (HR) = 2.32, 95% confidence interval (CI): 1.01-5.31, p = 0.046]. The area under the ROC curve (AUC) of the calcification score was 0.67 (95% CI: 0.61-0.72), and the AUC of the GRACE score was 0.80 (95% CI: 0.75-0.84). There was no statistical difference in the predictive value between both scores for 3-year mortality in STEMI patients after PPCI (p = 0.06). Based on the NRI analysis, the calcification score showed better risk classification compared with the GRACE score (absolute NRI = 6.63%, P = 0.027). CONCLUSION: The cumulative calcification score is independently associated with the long-term prognosis of STEMI patients after PPCI.

Sesquiterpenoids from Carpesium abrotanoides and their anti-inflammatory activity both in vitro and in vivo.

Twenty-nine sesquiterpenoids, including pseudoguaiane-type (1-11), eudesmane-type (12-23), and carabrane-type (24-29), have been identified from the plant Carpesium abrotanoides. Of them, compounds 1-4, 12-15, and 24-27, namely carpabrotins A-L, are twelve previously undescribed ones. Compound 3 possessed a pseudoguaiane backbone with a rearrangement modification at C-11, C-12 and C-13, while compound 4 suffered a carbon bond break between the C-4 and C-5 to form a rare 4,5-seco-pseudoguaiane lactone. Compounds 1-3, 5, 13-16 and 25-27 exhibited anti-inflammatory activity by inhibiting NO production in LPS-induced RAW264.7 macrophages with IC50 values less than 40 µM, while compounds 1, 2, 5, 13, 14, 16, and 25-27 showed significant inhibitory activity comparable to that of dexamethasone. The anti-atopic dermatitis (AD) effects of compounds 5 and 16 were tested according to 2,4-dinitrochlorobenzene (DNCB)-induced AD-like skin lesions in KM mice, and the results revealed that the major products 5 and 16 improved the histological features of AD-like skin lesions and mast cell infiltration in mice. This study suggested that sesquiterpenoids in C. abrotanoides should play a key role in its anti-inflammatory use.

Curcumin alleviates zearalenone-induced liver injury in mice by scavenging reactive oxygen species and inhibiting mitochondrial apoptosis pathway.

Curcumin (CUR) is a compound extracted from turmeric that has a variety of functions including antioxidant and anti-inflammatory. As an estrogen-like mycotoxin, zearalenone (ZEN) not only attacks the reproductive system, but also has toxic effects on the liver. However, whether CUR can alleviate ZEN-induced liver injury remains unclear. This paper aims to investigate the protective effect of CUR against ZEN-induced liver injury in mice and explore the molecular mechanism involved. BALB/c mice were randomly divided into control (CON) group, CUR group (200 mg/kg b. w. CUR), ZEN group (40 mg/kg b. w. ZEN) and CUR+ZEN group (200 mg/kg b. w. CUR+40 mg/kg b. w. ZEN). 28 d after ZEN exposure and CUR treatment, blood and liver samples were collected for subsequent testing. The results showed that CUR reversed ZEN-induced hepatocyte swelling and necrosis in mice. It significantly reduced the serum alkaline phosphatase (ALP), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in mice (p < 0.05). In addition, CUR significantly reduced hepatic ROS, malondialdehyde, hydrogen peroxide and apoptosis levels in mice (p < 0.05). Quantitative RT-PCR and Western blot results showed that CUR significantly reduced the expression of Bax and Caspase3, and reversed the increase of Nrf2, HO-1 and NQO1 expression in the liver of mice induced by ZEN (p < 0.05). In conclusion, CUR alleviated ZEN-induced liver injury in mice by scavenging ROS and inhibiting the mitochondrial apoptotic pathway.

Chronic polystyrene microplastics exposure-induced changes in thick-shell mussel (Mytilus coruscus) metaorganism: A holistic perspective.

Microplastics have emerged as a significant global concern, particularly in marine ecosystems. While extensive research has focused on the toxicological effects of microplastics on marine animals and/or their associated microorganisms as two separate entities, the holistic perspective of the adaptability and fitness of a marine animal metaorganism-comprising the animal host and its microbiome-remains largely unexplored. In this study, mussel metaorganisms subjected chronic PS-MPs exposure experienced acute mortality but rapidly adapted. We investigated the response of innate immunity, digestive enzymes and their associated microbiomes to chronic PS-MPs exposure. We found that PS-MPs directly and indirectly interacted with the host and microbe within the exposure system. The adaptation was a joint effort between the physiological adjustments of mussel host and genetic adaptation of its microbiome. The mussel hosts exhibited increased antioxidant activity, denser gill filaments and increased immune cells, enhancing their innate immunity. Concurrently, the gill microbiome and the digestive gland microbiome respective selectively enriched for plastic-degrading bacteria and particulate organic matter-utilizing bacteria, facilitating the microbiome's adaptation. The microbial adaptation to chronic PS-MPs exposure altered the ecological roles of mussel microbiome, as evidenced by alterations in microbial interactions and nutrient cycling functions. These findings provided new insights into the ecotoxicological impact of microplastics on marine organisms from a metaorganism perspective.