Microsporidian EnP1 alters host cell H2B monoubiquitination and prevents ferroptosis facilitating microsporidia survival.

Microsporidia are intracellular eukaryotic pathogens that pose a substantial threat to immunocompromised hosts. The way these pathogens manipulate host cells during infection remains poorly understood. Using a proximity biotinylation strategy we established that microsporidian EnP1 is a nucleus-targeted effector that modifies the host cell environment. EnP1's translocation to the host nucleus is meditated by nuclear localization signals (NLSs). In the nucleus, EnP1 interacts with host histone H2B. This interaction disrupts H2B monoubiquitination (H2Bub), subsequently impacting p53 expression. Crucially, this inhibition of p53 weakens its control over the downstream target gene SLC7A11, enhancing the host cell's resilience against ferroptosis during microsporidian infection. This favorable condition promotes the proliferation of microsporidia within the host cell. These findings shed light on the molecular mechanisms by which microsporidia modify their host cells to facilitate their survival.

A guide to the BRAIN Initiative Cell Census Network data ecosystem.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

EphB2-dependent prefrontal cortex activation promotes long-range social approach and partner responsiveness.

Social behavior starts with dynamic approach prior to the final consummation. The flexible processes ensure mutual feedback across social brains to transmit signals. However, how the brain responds to the initial social stimuli precisely to elicit timed behaviors remains elusive. Here, by using real-time calcium recording, we identify the abnormalities of EphB2 mutant with autism-associated Q858X mutation in processing long-range approach and accurate activity of prefrontal cortex (dmPFC). The EphB2-dependent dmPFC activation precedes the behavioral onset and is actively associated with subsequent social action with the partner. Furthermore, we find that partner dmPFC activity is responsive coordinately to the approaching WT mouse rather than Q858X mutant mouse, and the social defects caused by the mutation are rescued by synchro-optogenetic activation in dmPFC of paired social partners. These results thus reveal that EphB2 sustains neuronal activation in the dmPFC that is essential for the proactive modulation of social approach to initial social interaction.

Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer's disease.

At present, the study on the pathogenesis of Alzheimer's disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.

SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs.

piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. In this study, a novel computational model based on the structural perturbation method is proposed to predict potential disease-associated piRNAs, called SPRDA. Notably, SPRDA belongs to positive-unlabeled learning, which is unaffected by negative examples in contrast to previous approaches. In the 5-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.

Phillygenin Alleviates Arthritis through the Inhibition of the NLRP3 Inflammasome and Ferroptosis by AMPK.

We investigated the potential arthritis-inducing effects of Phillygenin and its underlying mechanisms. RAW264.7 cells were stimulated with lipopolysaccharide to induce inflammation. Phillygenin was found to reduce arthritis score, histopathological changes, paw edema, spleen index, and ALP levels in a dose-dependent manner in a model of arthritis. Additionally, Phillygenin was able to decrease levels of inflammation markers in serum samples of mice with arthritis and also inhibited inflammation markers in the cell supernatant of an in vitro model of arthritis. Phillygenin increased cell viability and JC-1 disaggregation, enhanced calcien-AM/CoCl2, reduced LDH activity levels and IL-1a levels, and inhibited Calcein/PI levels and iron concentration in an in vitro model. Phillygenin was also found to reduce ROS-induced oxidative stress and Ferroptosis, and suppress the NLRP3 inflammasome in both in vivo and in vitro models through AMPK. In the in vivo model, Phillygenin was observed to interact with AMPK protein. These findings suggest that Phillygenin may be a potential therapeutic target for preventing arthritis by inhibiting NLRP3 inflammasome and Ferroptosis through AMPK. This indicates that Phillygenin could have disease-modifying effects on arthritis.

Nitrogen Doping-Roused Synergistic Active Sites in Perovskite Enabling Highly Selective CO2 Photoreduction into CH4.

The intricate protonation process in carbon dioxide reduction usually makes the product unpredictable. Thus, it is significant to control the reactive intermediates to manipulate the reaction steps. Here, we propose that the synergistic La-Ti active sites in the N-La2Ti2O7 nanosheets enable the highly selective carbon dioxide photoreduction into methane. In the photoreduction of CO2 over N-La2Ti2O7 nanosheets, in situ Fourier transform infrared spectra are utilized to monitor the *CH3O intermediate, pivotal for methane production, whereas such monitoring is not conducted for La2Ti2O7 nanosheets. Also, theoretical calculations testify to the increased charge densities on the Ti and La atoms and the regulated formation energy barrier of *CO and *CH3O intermediates by the constructed synergistic active sites. Accordingly, the methane formation rate of 7.97 µL h-1 exhibited by the N-La2Ti2O7 nanosheets, along with an electron selectivity of 96.6%, exceeds that of most previously reported catalysts under similar conditions.

High-Rate and Selective C2H6-to-C2H4 Photodehydrogenation Enabled by Partially Oxidized Pdδ+ Species Anchored on ZnO Nanosheets under Mild Conditions.

Photocatalytic C2H6-to-C2H4 conversion is very promising, yet it remains a long-lasting challenge due to the high C-H bond dissociation energy of 420 kJ mol-1. Herein, partially oxidized Pdδ+ species anchored on ZnO nanosheets are designed to weaken the C-H bond by the electron interaction between Pdδ+ species and H atoms, with efforts to achieve high-rate and selective C2H6-to-C2H4 conversion. X-ray photoelectron spectra, Bader charge calculations, and electronic localization function demonstrate the presence of partially oxidized Pdδ+ sites, while quasi-in situ X-ray photoelectron spectra disclose the Pdδ+ sites initially adopt and then donate the photoexcited electrons for C2H6 dehydrogenation. In situ electron paramagnetic resonance spectra, in situ Fourier transform infrared spectra, and trapping agent experiments verify C2H6 initially converts to CH3CH2OH via ·OH radicals, then dehydroxylates to CH3CH2· and finally to C2H4, accompanied by H2 production. Density-functional theory calculations elucidate that loading Pd site can lengthen the C-H bond of C2H6 from 1.10 to 1.12 Å, which favors the C-H bond breakage, affirmed by a lowered energy barrier of 0.04 eV. As a result, the optimized 5.87% Pd-ZnO nanosheets achieve a high C2H4 yield of 16.32 mmol g-1 with a 94.83% selectivity as well as a H2 yield of 14.49 mmol g-1 from C2H6 dehydrogenation in 4 h, outperforming all the previously reported photocatalysts under similar conditions.

Breaking the Activity-Selectivity Trade-off for CH4-to-C2H6 Photoconversion.

Photocatalytic conversion of methane (CH4) to ethane (C2H6) has attracted extensive attention from academia and industry. Typically, the traditional oxidative coupling of CH4 (OCM) reaches a high C2H6 productivity, yet the inevitable overoxidation limits the target product selectivity. Although the traditional nonoxidative coupling of CH4 (NOCM) can improve the product selectivity, it still encounters unsatisfied activity, arising from being thermodynamically unfavorable. To break the activity-selectivity trade-off, we propose a conceptually new mechanism of H2O2-triggered CH4 coupling, where the H2O2-derived ·OH radicals are rapidly consumed for activating CH4 into ·CH3 radicals exothermically, which bypasses the endothermic steps of the direct CH4 activation by photoholes and the interaction between ·CH3 and ·OH radicals, affirmed by in situ characterization techniques, femtosecond transient absorption spectroscopy, and density-functional theory calculation. By this pathway, the designed Au-WO3 nanosheets achieve unprecedented C2H6 productivity of 76.3 mol molAu-1 h-1 with 95.2% selectivity, and TON of 1542.7 (TOF = 77.1 h-1) in a self-designed flow reactor, outperforming previously reported photocatalysts regardless of OCM and NOCM pathways. Also, under outdoor natural sunlight irradiation, the Au-WO3 nanosheets exhibit similar activity and selectivity toward C2H6 production, showing the possibility for practical applications. Interestingly, this strategy can be applied to other various photocatalysts (Au-WO3, Au-TiO2, Au-CeO2, Pd-WO3, and Ag-WO3), showing a certain universality. It is expected that the proposed mechanism adds another layer to our understanding of CH4-to-C2H6 conversion.

Metabolic Heterogeneity and Potential Immunotherapeutic Responses Revealed by Single-Cell Transcriptomics of Breast Cancer.

BACKGROUND: Breast cancer (BC) exhibits remarkable heterogeneity. However, the transcriptomic heterogeneity of BC at the single-cell level has not been fully elucidated. METHODS: We acquired BC samples from 14 patients. Single-cell RNA sequencing (scRNA-seq), bioinformatic analyses, along with immunohistochemistry (IHC) and immunofluorescence (IF) assays were carried out. RESULTS: According to the scRNA-seq results, 10 different cell types were identified. We found that Cancer-Associated Fibroblasts (CAFs) exhibited distinct biological functions and may promote resistance to therapy. Metabolic analysis of tumor cells revealed heterogeneity in glycolysis, gluconeogenesis, and fatty acid synthetase reprogramming, which led to chemotherapy resistance. Furthermore, patients with multiple metastases and progression were predicted to benefit from immunotherapy based on a heterogeneity analysis of T cells and tumor cells. CONCLUSIONS: Our findings provide a comprehensive understanding of the heterogeneity of BC, provide comprehensive insight into the correlation between cancer metabolism and chemotherapy resistance, and enable the prediction of immunotherapy responses based on T-cell heterogeneity.

Hyperfractionation compared with standard fractionation in intensity-modulated radiotherapy for patients with locally advanced recurrent nasopharyngeal carcinoma: a multicentre, randomised, open-label, phase 3 trial.

BACKGROUND: Reirradiation in standard fractionation for locally advanced recurrent nasopharyngeal carcinoma after a previous course of high-dose radiotherapy is often associated with substantial late toxicity, negating its overall benefit. We therefore aimed to investigate the efficacy and safety of hyperfractionation compared with standard fractionation in intensity-modulated radiotherapy. METHODS: This multicentre, randomised, open-label, phase 3 trial was done in three centres in Guangzhou, China. Eligible patients were aged 18-65 years with histopathologically confirmed undifferentiated or differentiated, non-keratinising, advanced locally recurrent nasopharyngeal carcinoma. Participants were randomly assigned (1:1) to either receive hyperfractionation (65 Gy in 54 fractions, given twice daily with an interfractional time interval of at least 6 h) or standard fractionation (60 Gy in 27 fractions, given once a day). Intensity-modulated radiotherapy was used in both groups. A computer program generated the assignment sequence and randomisation was stratified by treatment centre, recurrent tumour stage (T2-T3 vs T4), and recurrent nodal stage (N0 vs N1-N2), determined at the time of randomisation. The two primary endpoints were the incidence of severe late complications defined as the incidence of grade 3 or worse late radiation-induced complications occurring 3 months after the completion of radiotherapy until the latest follow-up in the safety population, and overall survival defined as the time interval from randomisation to death due to any cause in the intention-to-treat population. This trial is registered with ClinicalTrials.gov, NCT02456506. FINDINGS: Between July 10, 2015, and Dec 23, 2019, 178 patients were screened for eligibility, 144 of whom were enrolled and randomly assigned to hyperfractionation or standard fractionation (n=72 in each group). 35 (24%) participants were women and 109 (76%) were men. After a median follow-up of 45·0 months (IQR 37·3-53·3), there was a significantly lower incidence of grade 3 or worse late radiation-induced toxicity in the hyperfractionation group (23 [34%] of 68 patients) versus the standard fractionation group (39 [57%] of 68 patients; between-group difference -23% [95% CI -39 to -7]; p=0·023). Patients in the hyperfractionation group had better 3-year overall survival than those in the standard fractionation group (74·6% [95% CI 64·4 to 84·8] vs 55·0% [43·4 to 66·6]; hazard ratio for death 0·54 [95% CI 0·33 to 0·88]; p=0·014). There were fewer grade 5 late complications in the hyperfractionation group (five [7%] nasal haemorrhage) than in the standard fractionation group (16 [24%], including two [3%] nasopharyngeal necrosis, 11 [16%] nasal haemorrhage, and three [4%] temporal lobe necrosis). INTERPRETATION: Hyperfractionated intensity-modulated radiotherapy could significantly decrease the rate of severe late complications and improve overall survival among patients with locally advanced recurrent nasopharyngeal carcinoma. Our findings suggest that hyperfractionated intensity-modulated radiotherapy could be used as the standard of care for these patients. FUNDING: Key-Area Research and Development of Guangdong Province, the National Natural Science Foundation of China, the Special Support Program for High-level Talents in Sun Yat-sen University Cancer Center, the Guangzhou Science and Technology Plan Project, and the National Ten Thousand Talents Program Science and Technology Innovation Leading Talents, Sun Yat-Sen University Clinical Research 5010 Program.

NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks.

As a frontier field of individualized therapy, microRNA (miRNA) pharmacogenomics facilitates the understanding of different individual responses to certain drugs and provides a reasonable reference for clinical treatment. However, the known drug resistance-associated miRNAs are not yet sufficient to support precision medicine. Although existing methods are effective, they all focus on modelling miRNA-drug resistance interaction graphs, making their performance bounded by the interaction density. In this study, we propose a framework for miRNA-drug resistance prediction through efficient neural architecture search and graph isomorphism networks (NASMDR). NASMDR uses attribute information instead of the commonly used interactive graph information. In the cross-validation experiment, the proposed framework can achieve an AUC of 0.9468 on the ncDR dataset, which is 2.29% higher than the state-of-the-art method. In addition, we propose a novel sequence characterization approach, k-mer Sparse Nonnegative Matrix Factorization (KSNMF). The results show that NASMDR provides novel insights for integrating efficient neural architecture search and graph isomorphic networks into a unified framework to predict drug resistance-related miRNAs. The codes for NASMDR are available at https://github.com/kaizheng-academic/NASMDR.

A similarity-based deep learning approach for determining the frequencies of drug side effects.

The side effects of drugs present growing concern attention in the healthcare system. Accurately identifying the side effects of drugs is very important for drug development and risk assessment. Some computational models have been developed to predict the potential side effects of drugs and provided satisfactory performance. However, most existing methods can only predict whether side effects will occur and cannot determine the frequency of side effects. Although a few existing methods can predict the frequency of drug side effects, they strongly depend on the known drug-side effect relationships. Therefore, they cannot be applied to new drugs without known side effect frequency information. In this paper, we develop a novel similarity-based deep learning method, named SDPred, for determining the frequencies of drug side effects. Compared with the existing state-of-the-art models, SDPred integrates rich features and can be applied to predict the side effect frequencies of new drugs without any known drug-side effect association or frequency information. To our knowledge, this is the first work that can predict the side effect frequencies of new drugs in the population. The comparison results indicate that SDPred is much superior to all previously reported models. In addition, some case studies also demonstrate the effectiveness of our proposed method in practical applications. The SDPred software and data are freely available at https://github.com/zhc940702/SDPred, https://zenodo.org/record/5112573 and https://hub.docker.com/r/zhc940702/sdpred.

Line graph attention networks for predicting disease-associated Piwi-interacting RNAs.

PIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlate with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. Line graphs can extend many heterogeneous networks to replace dichotomous networks. In this study, we present a new graph neural network framework, line graph attention networks (LGAT). And we apply it to predict PiRNA disease association (GAPDA). In the experiment, GAPDA performs excellently in 5-fold cross-validation with an AUC of 0.9038. Not only that, it still has superior performance compared with methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.

IL-17 signaling pathway: A potential therapeutic target for reducing skeletal muscle inflammation.

BACKGROUND: The interleukin-17 (IL-17) signaling pathway is intricately linked with immunity and inflammation; however, the association between the IL-17 signaling pathway and skeletal muscle inflammation remains poorly understood. The study aims to investigate the role of the IL-17 signaling pathway in skeletal muscle inflammation and to evaluate the therapeutic potential of anti-IL-17 antibodies in reducing muscle inflammation. METHODS: A skeletal muscle inflammation model was induced by cardiotoxin (CTX) injection in C57BL6/J mice. Following treatment with an anti-IL-17 antibody, we conducted a comprehensive analysis integrating single-cell RNA sequencing (scRNA-seq), bioinformatics, enzyme-linked immunosorbent assay (ELISA), immunofluorescence, and Western blot techniques to elucidate underlying mechanisms. RESULTS: scRNA-seq analysis revealed a significant increase in neutrophil numbers and activity in inflamed skeletal muscle compared to other cell types, including macrophages, T cells, B cells, endothelial cells, fast muscle cells, fibroblasts, and skeletal muscle satellite cells. The top 30 differentially expressed genes within neutrophils, along with 55 chemokines, were predominantly enriched in the IL-17 signaling pathway. Moreover, the IL-17 signaling pathway exhibited heightened expression in inflamed skeletal muscle, particularly within neutrophils. Treatment with anti-IL-17 antibody resulted in the suppression of IL-17 signaling pathway expression, accompanied by reduced levels of pro-inflammatory cytokines IL-1ß, IL-6, and TNF-α, as well as decreased numbers and activity of Ly6g+/Mpo+ neutrophils compared to CTX-induced skeletal muscle inflammation. CONCLUSION: Our findings suggest that the IL-17 signaling pathway plays a crucial role in promoting inflammation within skeletal muscle. Targeting this pathway may hold promise as a therapeutic strategy for ameliorating the inflammatory micro-environment and reducing cytokine production.

Studying the role of Bombyx mori molybdenum cofactor sulfurase in Bombyx mori nucleopolyhedrovirus infection.

Molybdenum cofactor sulfurase (MoCoS) is a key gene involved in the uric acid metabolic pathway that activates xanthine dehydrogenase to synthesise uric acid. Uric acid is harmful to mammals but plays crucial roles in insects, one of which is the immune responses. However, the function of Bombyx mori MoCoS in response to BmNPV remains unclear. In this study, BmMoCoS was found to be relatively highly expressed in embryonic development, gonads and the Malpighian tubules. In addition, the expression levels of BmMoCoS were significantly upregulated in three silkworm strains with different levels of resistance after virus infection, suggesting a close link between them. Furthermore, RNAi and overexpression studies showed that BmMoCoS was involved in resistance to BmNPV infection, and its antivirus effects were found to be related to the regulation of uric acid metabolism, which was uncovered by inosine- and febuxostat-coupled RNAi and overexpression. Finally, the BmMoCoS-mediated uric acid pathway was preliminarily confirmed to be a potential target to protect silkworms from BmNPV infection. Overall, this study provides new evidence for elucidating the molecular mechanism of silkworms in response to BmNPV infection and new strategies for the prevention of viral infections in sericulture.

Uridine diphosphate glucosyltransferase is vital for fenpropathrin resistance in Bombyx mori (Lepidoptera).

The silkworm (Bombyx mori) is an important model lepidopteran insect and can be used to identify pesticide resistance-related genes of great significance for biological control of pests. Uridine diphosphate glucosyltransferases (UGTs), found in all organisms, are the main secondary enzymes involved in the metabolism of heterologous substances. However, it remains uncertain if silkworm resistance to fenpropathrin involves UGT. This study observes significant variations in BmUGT expression among B. mori strains with variable fenpropathrin resistance post-feeding, indicating BmUGT's role in fenpropathrin detoxification. Knockdown of BmUGT with RNA interference and overexpression of BmUGT significantly decreased and increased BmN cell activity, respectively, indicating that BmUGT plays an important role in the resistance of silkworms to fenpropathrin. In addition, fenpropathrin residues were significantly reduced after incubation for 12 h with different concentrations of a recombinant BmUGT fusion protein. Finally, we verified the conservation of UGT to detoxify fenpropathrin in Spodoptera exigua: Its resistance to fenpropathrin decreased significantly after knocking down SeUGT. In a word, UGT plays an important role in silkworm resistance to fenpropathrin by directly degrading the compound, a function seen across other insects. The results of this study are of great significance for breeding silkworm varieties with high resistance and for biological control of pests.

The composition and function of bacterial communities in Bombyx mori (Lepidoptera: Bombycidae) changed dramatically with infected fungi: A new potential to culture Cordyceps cicadae.

Cordyceps cicadae (Hypocreales: Cordycipitaceae) is a renowned entomopathogenic fungus used as herbal medicine in China. However, wild C. cicadae resources have been threatened by heavy harvesting. We hypothesised that Bombyx mori L. (Lepidoptera: Bombycidae) could be a new alternative to cultivate C. cicadae due to the low cost of rearing. Bacterial communities are crucial for the formation of Cordyceps and for promoting the production of metabolites. To better understand the bacterial community structure associated with Cordyceps, three Claviciptaceae fungi were used to explore the pathogenicity of the silkworms. Here, fifth-instar silkworms were infected with C. cicadae, Cordyceps cateniannulata (Hypocreales: Cordycipitaceae) and Beauveria bassiana (Hypocreales: Cordycipitaceae). Subsequently, we applied high-throughput sequencing to explore the composition of bacterial communities in silkworms. Our results showed that all three fungi were highly pathogenic to silkworms, which suggests that silkworms have the potential to cultivate Cordyceps. After fungal infection, the diversity of bacterial communities in silkworms decreased significantly, and the abundance of Staphylococcus increased in mummified larvae, which may play a role in the death process when the host suffers infection by entomopathogenic fungi. Furthermore, there were high similarities in the bacterial community composition and function in the C. cicadae and C. cateniannulata infected samples, and the phylogenetic analysis suggested that these similarities may be related to the fungal phylogenetic relationship. Our findings reveal that infection with different entomopathogenic fungi affects the composition and function of bacterial communities in silkworms and that the bacterial species associated with Cordyceps are primarily host dependent, while fungal infection affects bacterial abundance.

Identification of POU4F1 as a novel prognostic biomarker and therapeutic target in esophageal squamous cell carcinoma.

BACKGROUND: Esophageal cancer is a significant global health concern, ranking seventh in incidence and sixth in mortality. It encompasses two pathological types: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma, with ESCC being more prevalent globally and associated with higher mortality rates. The POU (Pit-Oct-Unc) domain family transcription factors, comprising 15 members, play important roles in embryonic development and organ formation. Aberrant expression of POUs has been observed in several human cancers, influencing cell proliferation, tumor invasion, and drug resistance. However, their specific role in ESCC remains unknown. METHODS: We analyzed TCGA and GEO databases to assess POUs expression in ESCC tissues. Kaplan-Meier and ROC analyses were used to evaluate the prognostic value of POUs. Gene Set Enrichment Analysis and Protein-Protein interaction network were used to explore the potential pathway. Functional assays (Cell Counting Kit-8, EdU Staining assay, and cloning formation assay) and mechanism analyses (RNA-seq, flow cytometry, and Western blot) were conducted to determine the effects of POU4F1 knockdown on ESCC cell phenotypes and signaling pathways. RESULTS: POU4F1 and POU6F2 were upregulated in various cancer tissues, including ESCC, compared to normal tissues. POU4F1 expression was significantly correlated with patient survival and superior to previous models (AUC = 0.776). Knockdown of POU4F1 inhibited ESCC cell proliferation and affected cell cycle, autophagy, and DNA damage pathways in ESCC cells. CONCLUSION: POU4F1 is a novel and promising prognostic and therapeutic target for ESCC patients, providing insights into potential treatment strategies.

Resolving neuroinflammatory and social deficits in ASD model mice: Dexmedetomidine downregulates NF-κB/IL-6 pathway via α2AR.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that severely affects individuals' daily life and social development. Unfortunately, there are currently no effective treatments for ASD. Dexmedetomidine (DEX) is a selective agonist of α2 adrenergic receptor (α2AR) and is widely used as a first-line medication for sedation and hypnosis in clinical practice. In recent years, there have been reports suggesting its potential positive effects on improving emotional and cognitive functions. However, whether dexmedetomidine has therapeutic effects on the core symptoms of ASD, namely social deficits and repetitive behaviors, remains to be investigated. In the present study, we employed various behavioral tests to assess the phenotypes of animals, including the three-chamber, self-grooming, marble burying, open field, and elevated plus maze. Additionally, electrophysiological recordings, western blotting, qPCR were mainly used to investigate and validate the potential mechanisms underlying the role of dexmedetomidine. We found that intraperitoneal injection of dexmedetomidine in ASD model mice-BTBR T+ Itpr3tf/J (BTBR) mice could adaptively improve their social deficits. Further, we observed a significant reduction in c-Fos positive signals and interleukin-6 (IL-6) expression level in the prelimbic cortex (PrL) of the BTBR mice treated with dexmedetomidine. Enhancing or inhibiting the action of IL-6 directly affects the social behavior of BTBR mice. Mechanistically, we have found that NF-κB p65 is a key pathway regulating IL-6 expression in the PrL region. In addition, we have confirmed that the α2AR acts as a receptor switch mediating the beneficial effects of dexmedetomidine in improving social deficits. This study provides the first evidence of the beneficial effects of dexmedetomidine on core symptoms of ASD and offers a theoretical basis and potential therapeutic approach for the clinical treatment of ASD.