Cloning and functional mechanism of the dwarf gene gba affecting stem elongation and cellulose biosynthesis in jute (Corchorus olitorius).

Plant height (PH) is an important factor affecting bast fiber yield in jute. Here, we report the mechanism of dwarfism in the 'Guangbaai' (gba) of jute. The mutant gba had shorter internode length and cell length compared to the standard cultivar 'TaiZi 4' (TZ4). Exogenous GA3 treatment indicated that gba is a GA-insensitive dwarf mutant. Quantitative trait locus (QTL) analysis of three PH-related traits via a high-density genetic linkage map according to re-seq showed that a total of 25 QTLs were identified, including 13 QTLs for PH, with phenotypic variation explained ranging from 2.42 to 74.16%. Notably, the functional mechanism of the candidate gene CoGID1a, the gibberellic acid receptor, of the major locus qPHIL5 was evaluated by transgenic analysis and virus-induced gene silencing. A dwarf phenotype-related single nucleotide mutation in CoGID1a was identified in gba, which was also unique to the dwarf phenotype of gba among 57 cultivars. Cogid1a was unable to interact with the growth-repressor DELLA even in the presence of highly accumulated gibberellins in gba. Differentially expressed genes between transcriptomes of gba and TZ4 after GA3 treatment indicated up-regulation of genes involved in gibberellin and cellulose synthesis in gba. Interestingly, it was found that up-regulation of CoMYB46, a key transcription factor in the secondary cell wall, by the highly accumulated gibberellins in gba promoted the expression of cellulose synthase genes CoCesA4 and CoCesA7. These findings provide valuable insights into fiber development affected by endogenous gibberellin accumulation in plants.

HSV-1 reactivation results in post-herpetic neuralgia by upregulating Prmt6 and inhibiting cGAS-STING.

Chronic varicella zoster virus (VZV) infection induced neuroinflammatory condition is the critical pathology of post-herpetic neuralgia (PHN). The immune escape mechanism of VZV remains elusive. As to mice have no VZV infection receptor, herpes simplex virus type 1 (HSV-1) infection is a well established PHN mice model. Transcriptional expression analysis identified that the protein arginine methyltransferases 6 (Prmt6) was upregulated upon HSV-1 infection, which was further confirmed by immunofluorescence staining in spinal dorsal horn. Prmt6 deficiency decreased HSV-1-induced neuroinflammation and PHN by enhancing antiviral innate immunity and decreasing HSV-1 load in vivo and in vitro. Overexpression of Prmt6 in microglia dampened antiviral innate immunity and increased HSV-1 load. Mechanistically, Prmt6 methylated and inactivated STING, resulting in reduced phosphorylation of TANK binding kinase-1 (TBK1) and interferon regulatory factor 3 (IRF3), diminished production of type I interferon (IFN-I) and antiviral innate immunity. Furthermore, intrathecal or intraperitoneal administration of the Prmt6 inhibitor EPZ020411 decreased HSV-1-induced neuroinflammation and PHN by enhancing antiviral innate immunity and decreasing HSV-1 load. Our findings revealed that HSV-1 escapes antiviral innate immunity and results in PHN by upregulating Prmt6 expression and inhibiting the cGAS-STING pathway, providing novel insights and a potential therapeutic target for PHN.

Plant-derived essential oil contributes to the reduction of multidrug resistance genes in the sludge composting process.

Multidrug-resistant bacteria and multi-resistance genes in sludge have become a serious issue for public health. It is imperative to develop feasible and environmentally friendly methods of sludge composting to alleviate multidrug resistance genes. Plant-derived essential oil is an effective natural and eco-friendly antibacterial, which has great utilization in inhibiting pathogens in the agricultural industry. Nevertheless, the application of plant-derived essential oil to control pathogenic bacteria and antibiotic resistance in composting has not been reported. This study conducted a composting system by adding plant-derived essential oil i.e., oregano essential oil (OEO), to sludge composting. The findings indicated that multidrug resistance genes and priority pathogens (critical, high, and medium categories) were reduced by (17.0 ± 2.2)% and (26.5 ± 3.0)% in the addition of OEO (OH treatment) compared to control. Besides, the OH treatment changed the bacterial community and enhanced the gene sequences related to carbohydrate metabolism in compost microorganisms. Mantel test and variation partitioning analysis revealed that the target virulence factors (VFs), target mobile genetic elements (MGEs), and priority pathogens were the most important factors affecting multidrug resistance in composting. The OH treatment could significantly inhibit the target VFs, target MGEs, and priority pathogens, which were helpful for the suppression and elimination of multidrug resistance genes. These findings provide new insights into the regulation of multidrug resistance genes during sludge composting and a novel way to diminish the environmental risk of antibiotic resistance.

Macrophage ß2-AR activation amplifies inflammation in wound healing by upregulating Trem1 via the cAMP/PKA/CREB pathway.

BACKGROUND: Inflammation is an important part of the wound healing process. The stress hormone epinephrine has been demonstrated to modulate the inflammatory response via its interaction with ß2-adrenergic receptor (ß2-AR). However, the precise molecular mechanism through which ß2-AR exerts its influence on inflammation during the wound healing process remains an unresolved question. METHODS: Transcriptome datasets of wound and macrophages from the GEO database were reanalyzed using bioinformatics. The role of ß2-AR in wound healing was explored by a mouse hind paw plantar wound model, and histological analyses were performed to assess wound healing. In vivo and in vitro assays were performed to elucidate the role of ß2-AR on the inflammatory response. Triggering receptor expressed on myeloid cells 1 (Trem1) was knocked down with siRNA on RAW cells and western blot and qPCR assays were performed. RESULTS: Trem1 was upregulated within 24 h of wounding, and macrophage ß2-AR activation also upregulated Trem1. In vivo experiments demonstrated that ß2-AR agonists impaired wound healing, accompanied by upregulation of Trem1 and activation of cAMP/PKA/CREB pathway, as well as by a high level of pro-inflammatory cytokine production. In vitro experiments showed that macrophage ß2-AR activation amplified LPS-induced inflammation, and knockdown of Trem1 reversed this effect. Using activator and inhibitor of cAMP, macrophage ß2-AR activation was confirmed to upregulate Trem1 via the cAMP/PKA/CREB pathway. CONCLUSION: Our study found that ß2-AR agonists increase Trem1 expression in wounds, accompanied by amplification of the inflammatory response, impairing wound healing. ß2-AR activation in RAW cells induces Trem1 upregulation via the cAMP/PKA/CREB pathway and amplifies LPS-induced inflammatory responses.

Explainable machine learning for predicting neurological outcome in hemorrhagic and ischemic stroke patients in critical care.

Aim: The objective of this study is to develop accurate machine learning (ML) models for predicting the neurological status at hospital discharge of critically ill patients with hemorrhagic and ischemic stroke and identify the risk factors associated with the neurological outcome of stroke, thereby providing healthcare professionals with enhanced clinical decision-making guidance. Materials and methods: Data of stroke patients were extracted from the eICU Collaborative Research Database (eICU-CRD) for training and testing sets and the Medical Information Mart for Intensive Care IV (MIMIC IV) database for external validation. Four machine learning models, namely gradient boosting classifier (GBC), logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF), were used for prediction of neurological outcome. Furthermore, shapley additive explanations (SHAP) algorithm was applied to explain models visually. Results: A total of 1,216 hemorrhagic stroke patients and 954 ischemic stroke patients from eICU-CRD and 921 hemorrhagic stroke patients 902 ischemic stroke patients from MIMIC IV were included in this study. In the hemorrhagic stroke cohort, the LR model achieved the highest area under curve (AUC) of 0.887 in the test cohort, while in the ischemic stroke cohort, the RF model demonstrated the best performance with an AUC of 0.867 in the test cohort. Further analysis of risk factors was conducted using SHAP analysis and the results of this study were converted into an online prediction tool. Conclusion: ML models are reliable tools for predicting hemorrhagic and ischemic stroke neurological outcome and have the potential to improve critical care of stroke patients. The summarized risk factors obtained from SHAP enable a more nuanced understanding of the reasoning behind prediction outcomes and the optimization of the treatment strategy.

Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.

AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes. METHODS: Using data from TBI patients in the multi-center eICU database, we focused on in-hospital mortality, neurological status based on the Glasgow Coma Score (mGCS) motor subscore at discharge, and prolonged ICU stay (PLOS). Three machine learning (ML) models were developed, utilizing EHR features, PTS signals collected 24 h after ICU admission, and their combination. External validation was performed using the MIMIC III dataset, and interpretability was enhanced using the Shapley Additive Explanations (SHAP) algorithm. RESULTS: The analysis included 1085 TBI patients. Compared to individual models and existing scoring systems, the combination of EHR and PTS features demonstrated comparable or even superior performance in predicting in-hospital mortality (AUROC = 0.878), neurological outcomes (AUROC = 0.877), and PLOS (AUROC = 0.835). The model's performance was validated in the MIMIC III dataset, and SHAP algorithms identified six key intervention points for EHR features related to prognostic outcomes. Moreover, the EHR results (All AUROC >0.8) were translated into online tools for clinical use. CONCLUSION: Our study highlights the importance of early-stage PTS signals in predicting TBI patient outcomes. The integration of interpretable algorithms and simplified prediction tools can support treatment decision-making, contributing to the development of accurate prediction models and timely clinical intervention.

Hyperglycemia-induced Sirt3 downregulation increases microglial aerobic glycolysis and inflammation in diabetic neuropathic pain pathogenesis.

BACKGROUND: Hyperglycemia-induced neuroinflammation significantly contributes to diabetic neuropathic pain (DNP), but the underlying mechanisms remain unclear. OBJECTIVE: To investigate the role of Sirt3, a mitochondrial deacetylase, in hyperglycemia-induced neuroinflammation and DNP and to explore potential therapeutic interventions. METHOD AND RESULTS: Here, we found that Sirt3 was downregulated in spinal dorsal horn (SDH) of diabetic mice by RNA-sequencing, which was further confirmed at the mRNA and protein level. Sirt3 deficiency exacerbated hyperglycemia-induced neuroinflammation and DNP by enhancing microglial aerobic glycolysis in vivo and in vitro. Overexpression of Sirt3 in microglia alleviated inflammation by reducing aerobic glycolysis. Mechanistically, high-glucose stimulation activated Akt, which phosphorylates and inactivates FoxO1. The inactivation of FoxO1 diminished the transcription of Sirt3. Besides that, we also found that hyperglycemia induced Sirt3 degradation via the mitophagy-lysosomal pathway. Blocking Akt activation by GSK69093 or metformin rescued the degradation of Sirt3 protein and transcription inhibition of Sirt3 mRNA, which substantially diminished hyperglycemia-induced inflammation. Metformin in vivo treatment alleviated neuroinflammation and diabetic neuropathic pain by rescuing hyperglycemia-induced Sirt3 downregulation. CONCLUSION: Hyperglycemia induces metabolic reprogramming and inflammatory activation in microglia through the regulation of Sirt3 transcription and degradation. This novel mechanism identifies Sirt3 as a potential drug target for treating DNP.