Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction.

Interactions between DNA and transcription factors (TFs) play an essential role in understanding transcriptional regulation mechanisms and gene expression. Due to the large accumulation of training data and low expense, deep learning methods have shown huge potential in determining the specificity of TFs-DNA interactions. Convolutional network-based and self-attention network-based methods have been proposed for transcription factor binding sites (TFBSs) prediction. Convolutional operations are efficient to extract local features but easy to ignore global information, while self-attention mechanisms are expert in capturing long-distance dependencies but difficult to pay attention to local feature details. To discover comprehensive features for a given sequence as far as possible, we propose a Dual-branch model combining Self-Attention and Convolution, dubbed as DSAC, which fuses local features and global representations in an interactive way. In terms of features, convolution and self-attention contribute to feature extraction collaboratively, enhancing the representation learning. In terms of structure, a lightweight but efficient architecture of network is designed for the prediction, in particular, the dual-branch structure makes the convolution and the self-attention mechanism can be fully utilized to improve the predictive ability of our model. The experiment results on 165 ChIP-seq datasets show that DSAC obviously outperforms other five deep learning based methods and demonstrate that our model can effectively predict TFBSs based on sequence feature alone. The source code of DSAC is available at https://github.com/YuBinLab-QUST/DSAC/.

DBGRU-SE: predicting drug-drug interactions based on double BiGRU and squeeze-and-excitation attention mechanism.

The prediction of drug-drug interactions (DDIs) is essential for the development and repositioning of new drugs. Meanwhile, they play a vital role in the fields of biopharmaceuticals, disease diagnosis and pharmacological treatment. This article proposes a new method called DBGRU-SE for predicting DDIs. Firstly, FP3 fingerprints, MACCS fingerprints, Pubchem fingerprints and 1D and 2D molecular descriptors are used to extract the feature information of the drugs. Secondly, Group Lasso is used to remove redundant features. Then, SMOTE-ENN is applied to balance the data to obtain the best feature vectors. Finally, the best feature vectors are fed into the classifier combining BiGRU and squeeze-and-excitation (SE) attention mechanisms to predict DDIs. After applying five-fold cross-validation, The ACC values of DBGRU-SE model on the two datasets are 97.51 and 94.98%, and the AUC are 99.60 and 98.85%, respectively. The results showed that DBGRU-SE had good predictive performance for drug-drug interactions.

A universal framework for single-cell multi-omics data integration with graph convolutional networks.

Single-cell omics data are growing at an unprecedented rate, whereas effective integration of them remains challenging due to different sequencing methods, quality, and expression pattern of each omics data. In this study, we propose a universal framework for the integration of single-cell multi-omics data based on graph convolutional network (GCN-SC). Among the multiple single-cell data, GCN-SC usually selects one data with the largest number of cells as the reference and the rest as the query dataset. It utilizes mutual nearest neighbor algorithm to identify cell-pairs, which provide connections between cells both within and across the reference and query datasets. A GCN algorithm further takes the mixed graph constructed from these cell-pairs to adjust count matrices from the query datasets. Finally, dimension reduction is performed by using non-negative matrix factorization before visualization. By applying GCN-SC on six datasets, we show that GCN-SC can effectively integrate sequencing data from multiple single-cell sequencing technologies, species or different omics, which outperforms the state-of-the-art methods, including Seurat, LIGER, GLUER and Pamona.

Drug resistance mechanisms and treatment strategies mediated by Ubiquitin-Specific Proteases (USPs) in cancers: new directions and therapeutic options.

Drug resistance represents a significant obstacle in cancer treatment, underscoring the need for the discovery of novel therapeutic targets. Ubiquitin-specific proteases (USPs), a subclass of deubiquitinating enzymes, play a pivotal role in protein deubiquitination. As scientific research advances, USPs have been recognized as key regulators of drug resistance across a spectrum of treatment modalities, including chemotherapy, targeted therapy, immunotherapy, and radiotherapy. This comprehensive review examines the complex relationship between USPs and drug resistance mechanisms, focusing on specific treatment strategies and highlighting the influence of USPs on DNA damage repair, apoptosis, characteristics of cancer stem cells, immune evasion, and other crucial biological functions. Additionally, the review highlights the potential clinical significance of USP inhibitors as a means to counter drug resistance in cancer treatment. By inhibiting particular USP, cancer cells can become more susceptible to a variety of anti-cancer drugs. The integration of USP inhibitors with current anti-cancer therapies offers a promising strategy to circumvent drug resistance. Therefore, this review emphasizes the importance of USPs as viable therapeutic targets and offers insight into fruitful directions for future research and drug development. Targeting USPs presents an effective method to combat drug resistance across various cancer types, leading to enhanced treatment strategies and better patient outcomes.

A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction.

The monitoring of the lifetime of cutting tools often faces problems such as life data loss, drift, and distortion. The prediction of the lifetime in this situation is greatly compromised with respect to the accuracy. The recent rise of deep learning, such as Gated Recurrent Unit Units (GRUs), Hidden Markov Models (HMMs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention networks, and Transformers, has dramatically improved the data problems in tool lifetime prediction, substantially enhancing the accuracy of tool wear prediction. In this paper, we introduce a novel approach known as PCHIP-Enhanced ConvGRU (PECG), which leverages multiple-feature fusion for tool wear prediction. When compared to traditional models such as CNNs, the CNN Block, and GRUs, our method consistently outperformed them across all key performance metrics, with a primary focus on the accuracy. PECG addresses the challenge of missing tool wear measurement data in relation to sensor data. By employing PCHIP interpolation to fill in the gaps in the wear values, we have developed a model that combines the strengths of both CNNs and GRUs with data augmentation. The experimental results demonstrate that our proposed method achieved an exceptional relative accuracy of 0.8522, while also exhibiting a Pearson's Correlation Coefficient (PCC) exceeding 0.95. This innovative approach not only predicts tool wear with remarkable precision, but also offers enhanced stability.

Defects and Defect Passivation in Perovskite Solar Cells.

Perovskite solar cells have made significant strides in recent years. However, there are still challenges in terms of photoelectric conversion efficiency and long-term stability associated with perovskite solar cells. The presence of defects in perovskite materials is one of the important influencing factors leading to subpar film quality. Adopting additives to passivate defects within perovskite materials is an effective approach. Therefore, we first discuss the types of defects that occur in perovskite materials and the mechanisms of their effect on performance. Then, several types of additives used in perovskite solar cells are discussed, including ionic compounds, organic molecules, polymers, etc. This review provides guidance for the future development of more sustainable and effective additives to improve the performance of solar cells.

CMTM6 overexpression confers trastuzumab resistance in HER2-positive breast cancer.

Human epidermal growth factor receptor 2-positive (HER2+) breast cancer is characterized by invasive growth, rapid metastasis and chemoresistance. Trastuzumab is an effective treatment for HER2+ breast cancer; however, trastuzumab resistance leads to cancer relapse and metastasis. CKLF-like MARVEL transmembrane domain-containing 6 (CMTM6) has been considered as a new immune checkpoint for tumor-induced immunosuppression. The role of CMTM6 in trastuzumab resistance remains unknown. Here, we uncover a role of CMTM6 in trastuzumab-resistant HER2+ breast cancer. CMTM6 expression was upregulated in trastuzumab-resistant HER2+ breast cancer cell. Patients with high CMTM6 expressing HER2+ breast cancer had worse overall and progression-free survival than those with low CMTM6 expression. In vitro, CMTM6 knockdown inhibited the proliferation and migration of HER2+ breast cancer cells, and promoted their apoptosis, while CMTM6 overexpression reversed these effects. CMTM6 and HER2 proteins were co-localized on the surface of breast cancer cells, and CMTM6 silencing reduced HER2 protein levels in breast cancer cells. Co-immunoprecipitation revealed that CMTM6 directly interacted with HER2 in HER2+ breast cancer cells, and CMTM6 overexpression inhibited HER2 ubiquitination. Collectively, these findings highlight that CMTM6 stabilizes HER2 protein, contributing to trastuzumab resistance and implicate CMTM6 as a potential prognostic marker and therapeutic target for overcoming trastuzumab resistance in HER2+ breast cancer.

CMTM6 as a potential therapy target is associated with immunological tumor microenvironment and can promote migration and invasion in pancreatic adenocarcinoma.

CMTM6 has been connected to the development of several malignancies. However, it is still unknown what function CMTM6 serves in pancreatic adenocarcinoma (PAAD). We obtained RNA sequencing information of PAAD from public datasets and predicted statistical significance of CMTM6 survival in accordance with Kaplan-Meier curves. Gene set enrichment assessment (GSEA) was employed to analyze changes in pathways. Then, we systematically investigated the association involving CMTM6 and the immunological traits within the tumor microenvironment (TME) of PAAD, including immune pathways, immunomodulators, immune infiltrating cells, inflammatory activities, and immunotherapy response prediction. To demonstrate the biologically malignant properties of CMTM6 expression, the Cell Counting Kit-8, transwell experiments, colony formation, and wound healing were utilized. Upregulated CMTM6 expression was revealed within PAAD tissues, which was associated with more frequent somatic mutations and worse survival outcomes. Specifically, CMTM6 expression represented stronger immune infiltration, inflammatory activity, and better immunotherapeutic response in TME. Functional studies revealed that CMTM6 promoted the ability to proliferate, migrate, and invade. Additionally, CMTM6 and PD-L1 had a positive relationship, and CMTM6 can co-immunocoprecipitate with PD-L1 protein in pancreatic cell lines. CMTM6 overexpression shapes the inflammatory TME with a strong immune response. These findings support that CMTM6 is an immunotherapeutic target with promising effect to treat PAAD.

Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier.

MOTIVATION: Multi-label (ML) protein subcellular localization (SCL) is an indispensable way to study protein function. It can locate a certain protein (such as the human transmembrane protein that promotes the invasion of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) or expression product at a specific location in a cell, which can provide a reference for clinical treatment of diseases such as coronavirus disease 2019 (COVID-19). RESULTS: The article proposes a novel method named ML-locMLFE. First of all, six feature extraction methods are adopted to obtain protein effective information. These methods include pseudo amino acid composition, encoding based on grouped weight, gene ontology, multi-scale continuous and discontinuous, residue probing transformation and evolutionary distance transformation. In the next part, we utilize the ML information latent semantic index method to avoid the interference of redundant information. In the end, ML learning with feature-induced labeling information enrichment is adopted to predict the ML protein SCL. The Gram-positive bacteria dataset is chosen as a training set, while the Gram-negative bacteria dataset, virus dataset, newPlant dataset and SARS-CoV-2 dataset as the test sets. The overall actual accuracy of the first four datasets are 99.23%, 93.82%, 93.24% and 96.72% by the leave-one-out cross validation. It is worth mentioning that the overall actual accuracy prediction result of our predictor on the SARS-CoV-2 dataset is 72.73%. The results indicate that the ML-locMLFE method has obvious advantages in predicting the SCL of ML protein, which provides new ideas for further research on the SCL of ML protein. AVAILABILITY AND IMPLEMENTATION: The source codes and datasets are publicly available at https://github.com/QUST-AIBBDRC/ML-locMLFE/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Insulin-like growth factor binding protein 2: a core biomarker of left ventricular dysfunction in dilated cardiomyopathy.

BACKGROUND: RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined. METHODS: In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis. RESULTS: A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10-5). Single-cell analysis revealed that this gene was mainly expressed in endothelial cells. CONCLUSION: In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.

First Report of Leaf Tip Blight Caused by Epicoccum sorghinum on Phoebe bournei in Fujian, China.

Phoebe bournei, belonging to the family Lauraceae, is indigenous to China, where it is a protected species. In March 2022, ca. 90% of 20,000 P. bournei saplings suffered from leaf tip blight in a sapling nursery with an area of 200 m2 in Fuzhou, China. Initially, brown discoloration appeared on the tips of young leaves. The symptomatic tissue continued to expand as the leaf grew. To isolate the pathogen, 10 symptomatic leaves were randomly sampled from the nursery, and surface-sterilized in 75% alcohol for 30 s, followed by a 5% NaClO solution for 3 min, and then rinsed 3 times with sterile water. Twenty small pieces (0.3 x 0.3 cm) were excised from the margin of diseased and healthy tissue and transferred to five PDA plates amended with 50 µg/ml ampicillin. The plates were incubated at 25°C for 5 days. Finally, 17 isolates were obtained, and nine isolates with the highest isolation frequency shared the same morphological characteristics. On PDA, these colonies had aerial hyphae, white in the beginning, and became pale brown with the pigment production. Chlamydospores were observed after incubation for 7 days at 25°C, pale brown and nearly spherical, unicellular, or multicellular. Conidia were unicellular or bicellular, hyaline, and ellipsoidal, 5.15 to 9.89× 3.46 to 5.87 µm, n=50. The 9 fungi were identified as Epicoccum sp (Khoo et al. 2022a, b, c). Furthermore, strain MB3-1 was chosen randomly as the representative of the 9 isolates, and ITS, LSU, TUB sequences were amplified using the primers ITS1/ITS4, LR0R/LR5, Bt2a/Bt2b respectively (Raza et al. 2019). The sequences were submitted to NCBI and analyzed using BLAST. Results of BLAST showed that ITS (OP550308), LSU (OP550304), TUB (OP779213) sequences had 99.59% (490bp out of 492bp), 99.89% (870bp out of 871bp), 100% (321bp out of 321bp) identity to Epicoccum sorghinum sequences MH071389, MW800361, MW165323, respectively. ITS, LSU, TUB sequences were concatenated for phylogenetic analysis using the maximum likelihood method with 1000 bootstrap replicates in MEGA 7.0 software. The phylogenetic tree showed that MB3-1 was clustered together with E. sorghinum. Pathogenicity tests were performed on young leaves of healthy P. bournei saplings in vivo by inoculating with fungal conidia suspension. The conidia were eluted from the colony of MB3-1 and adjusted to 1×106 spores/ml. An amount of 20 µl conidia suspension (0.1% tween-80) was evenly sprayed on three leaves of one P. bournei sapling, 20 µl sterile water was sprayed on three other leaves of the same sapling as control, and three saplings were treated. All the treated saplings were kept at 25°C. MB3-1 caused leaf tip blight symptoms similar to those observed in nature at 6 days post inoculation (dpi). The pathogen was reisolated from inoculated leaves and identified as E. sorghinum. The experiment was repeated twice with the same results. Recently, E. sorghinum has been reported in Brazil (Gasparetto et al. 2017), Malaysia (Khoo et al. 2022a, b, c), and the United States (Imran et al. 2022). To our knowledge, this is the first report of E. sorghinum causing leaf tip blight on P. bournei. Wood from P. bournei is used to produce high-quality furniture due to its vertical grain and durability (Chen et al. 2020). And the demand for wood requires numerous saplings for afforestation. But this disease has the risk of causing insufficient saplings, which would affect the development of the P. bournei timber industry.

Beneficial effects of metformin supplementation in hypothalamic paraventricular nucleus and arcuate nucleus of type 2 diabetic rats.

Background Oxidative stress and inflammation play important roles in the development of diabetes. Metformin (MET) is considered as the first-line therapy for patients with type 2 diabetes (T2D). Hypothalamic paraventricular nucleus (PVN) and hypothalamic arcuate nucleus (ARC) are vital in obesity and diabetes. However, there have been few studies on the effects of MET on inflammatory reaction and oxidative stress in the PVN and ARC of T2D diabetic rats. Methods Male Sprague-Dawley (SD) rats were fed with high-fat diet (HFD), and intraperitoneally injected with low-dose streptozotocin (STZ, 30 mg/kg) at 6th week to induce T2D diabetes. After injection of STZ, they were fed with HFD continually. Starting from the 8th week of HFD feeding, T2D rats received intragastrical administration of MET (150 mg/kg/day) in addition to the HFD for another 8 weeks. At the end of the 15th week, the rats were anaesthetized to record the sympathetic nerve activity and collect blood and tissue samples. Results In comparison with control rats, T2D diabetic rats had higher levels of pro-inflammatory cytokines (PICs) and excessive oxidative stress in the PVN and ARC, accompanied with more activated astrocytes. The renal sympathetic nerve activity (RSNA) and the plasma norepinephrine (NE) increased in T2D diabetic rats. The expression of tyrosine hydroxylase (TH) increased and the expression of 67-kDa isoform of glutamate decarboxylase (GAD67) decreased in T2D diabetic rats. Supplementation of MET decreased blood glucose, suppressed RSNA, decreased PICs (TNF-α, IL-1ß and IL-6) in PVN and ARC, attenuated oxidative stress and activation of astrocytes in ARC and PVN of T2D diabetic rats, as well as restored the balance of neurotransmitter synthetase. The number of Fra-LI (chronic neuronal excitation marker) positive neurons in the ARC and PVN of T2D diabetic rats increased. Chronic supplementation of MET also decreased the number of Fra-LI positive neurons in the ARC and PVN of T2D diabetic rats. Conclusion These findings suggest that the PVN and ARC participate in the beneficial effects of MET in T2D diabetic rats, which is possibly mediated via down-regulating of inflammatory molecules, attenuating oxidative stress and restoring the balance of neurotransmitter synthetase by MET in the PVN and ARC.

SIRT1 / NF-κB pathway on neuronal apoptosis in rats with ischemic stroke.

Recent studies have shown that neuronal apoptosis is one of the important mechanisms leading to cerebral ischemic injury. SIRT1/NF-κB is a carrier of protein units in the body, participates in the synthesis of DNA, RNA and important neurotransmitters in the brain, and is an important nutrient necessary to maintain the normal growth and development of the nervous system and the normal function of cells. This article aims to study the effect of the SIRT1/NF-κB pathway on neuronal apoptosis in rats with ischemic stroke. Rats were divided into sham operation group, model group, SIRT1/NF-κB group, each group was given intragastric administration at different doses 3 days before the operation and after the operation, and brain tissue was taken at 24, 48, 72, 96 h after operation HE staining, TTC staining and dynamic changes of SIRT1/NF-κB expression were measured. The results of the experiment showed that the result of Nao Xintong was low, and the SIRT1/NF-κB group could reduce the volume of cerebral infarction in rats (P <0.05), and reduce the expression of SIRT1/NF-κB positive cells. The experimental results show that Naoxintong SIRT1/NF-κB has a protective effect on neuronal apoptosis after focal cerebral ischemia-reperfusion in rats.

A novel signature model based on mitochondrial-related genes for predicting survival of colon adenocarcinoma.

BACKGROUND: Colon cancer is the foremost reason of cancer-related mortality worldwide. Colon adenocarcinoma constitutes 90% of colon cancer, and most patients with colon adenocarcinoma (COAD) are identified until advanced stage. With the emergence of an increasing number of novel pathogenic mechanisms and treatments, the role of mitochondria in the development of cancer, has been studied and reported with increasing frequency. METHODS: We systematically analyzed the effect of mitochondria-related genes in COAD utilizing RNA sequencing dataset from The Cancer Genome Atlas database and 1613 mitochondrial function-related genes from MitoMiner database. Our approach consisted of differentially expressed gene, gene set enrichment analysis, gene ontology terminology, Kyoto Encyclopedia of Genes and Genomes, independent prognostic analysis, univariate and multivariate analysis, Kaplan-Meier survival analysis, immune microenvironment correlation analysis, and Cox regression analysis. RESULTS: Consequently, 8 genes were identified to construct 8 mitochondrial-related gene model by applying Cox regression analysis, CDC25C, KCNJ11, NOL3, P4HA1, QSOX2, Trap1, DNAJC28, and ATCAY. Meanwhile, we assessed the connection between this model and clinical parameters or immune microenvironment. Risk score was an independent predictor for COAD patients' survival with an AUC of 0.687, 0.752 and 0.762 at 1-, 3- and 5-year in nomogram, respectively. The group with the highest risk score had the lowest survival rate and the worst clinical stages. Additionally, its predictive capacity was validated in GSE39582 cohort. CONCLUSION: In summary, we established a prognostic pattern of mitochondrial-related genes, which can predict overall survival in COAD, which may enable a more optimized approach for the clinical treatment and scientific study of COAD. This gene signature model has the potential to improve prognosis and treatment for COAD patients in the future, and to be widely implemented in clinical settings. The utilization of this mitochondrial-related gene signature model may be benefit in the treatments and medical decision-making of COAD.

A New Hypersonic Wind Tunnel Force Measurement System to Reduce Additional Bending Moment and Avoid Time-Varying Stiffness.

In order to improve traditional hypersonic wind tunnel airframe/propulsion integrated aerodynamic testing technology for hypersonic vehicles, a new force measurement system called the aerodynamic force measuring support (AFMS) was designed. The AFMS effectively overcomes the defect that the traditional internal box-balance occupies a large amount of internal space in the aircraft test model, which makes the airframe/propulsion integrated aerodynamic test more difficult. The AFMS also alleviates the interference of the additional bending moment caused by the non-coincidence between the calibration center of traditional external box-balance and the gravity center of the aircraft test model, innovatively designing a convex structure in the joint part of the force measuring system. Furthermore, the AFMS effectively overcomes the time-varying stiffness of joints caused by test model vibration in hypersonic wind tunnel testing, which eventually leads to test errors. Compared with the traditional box-balance, the AFMS proposed in this study has sufficient design space. This ensures more thorough aerodynamic decomposition of the AFMS and less interference between channels, whilst also having the advantages of the large support stiffness of traditional box-balance. Thus, the AFMS provides a new technical path for airframe/propulsion integrated aerodynamic testing of air-breathing hypersonic vehicles in a hypersonic wind tunnel.

Chronic Infusion of Astaxanthin Into Hypothalamic Paraventricular Nucleus Modulates Cytokines and Attenuates the Renin-Angiotensin System in Spontaneously Hypertensive Rats.

ABSTRACT: Oxidative stress, the renin-angiotensin system (RAS), and inflammation are some of the mechanisms involved in the pathogenesis of hypertension. The aim of this study is to examine the protective effect of the chronic administration of astaxanthin, which is extracted from the shell of crabs and shrimps, into hypothalamic paraventricular nucleus (PVN) in spontaneously hypertensive rats. Animals were randomly assigned to 2 groups and treated with bilateral PVN infusion of astaxanthin or vehicle (artificial cerebrospinal fluid) through osmotic minipumps (Alzet Osmotic Pumps, Model 2004, 0.25 µL/h) for 4 weeks. Spontaneously hypertensive rats had higher mean arterial pressure and plasma level of norepinephrine and proinflammatory cytokine; higher PVN levels of reactive oxygen species, NOX2, NOX4, IL-1ß, IL-6, ACE, and AT1-R; and lower PVN levels of IL-10 and Cu/Zn SOD, Mn SOD, ACE2, and Mas receptors than Wistar-Kyoto rats. Our data showed that chronic administration of astaxanthin into PVN attenuated the overexpression of reactive oxygen species, NOX2, NOX4, inflammatory cytokines, and components of RAS within the PVN and suppressed hypertension. The present results revealed that astaxanthin played a role in the brain. Our findings demonstrated that astaxanthin had protective effect on hypertension by improving the balance between inflammatory cytokines and components of RAS.

Implications of gut microbiota dysbiosis and metabolic changes in prion disease.

Evidence of the gut microbiota influencing neurodegenerative diseases has been reported for several neural diseases. However, there is little insight regarding the relationship between the gut microbiota and prion disease. Here, using fecal samples of 12 prion-infected mice and 25 healthy controls, we analyzed the structure of the gut microbiota and metabolic changes by 16S rRNA sequencing and LC-MS-based metabolomics respectively as multi-omic analyses. Additionally, SCFAs and common amino acids were detected by GC-MS and UPLC respectively. Enteric changes induced by prion disease affected both structure and abundances of the gut microbiota. The gut microbiota of infected mice displayed greater numbers of Proteobacteria and less Saccharibacteria at the phylum level and more Lactobacillaceae and Helicobacteraceae and less Prevotellaceae and Ruminococcaceae at the family level. A total of 145 fecal metabolites were found to be significantly different in prion infection, and most (114) of these were lipid metabolites. Using KEGG pathway enrichment analysis, we found that 3 phosphatidylcholine (PC) compounds significantly decreased and 4 hydrophobic bile acids significantly increased. Decreases of 8 types of short-chain acids (SCFAs) and increases of Cys and Tyr and decreases of His, Trp, and Arg were observed in prion infection. Correlation analysis indicated that the gut microbiota changes observed in our study may have been the shared outcome of prion disease. These findings suggest that prion disease can cause significant shifts in the gut microbiota. Certain bacterial taxa can then respond to the resulting change to the enteric environment by causing dramatic shifts in metabolite levels. Our data highlight the health impact of the gut microbiota and related metabolites in prion disease.

Inhibition of Hypothalamic Inhibitor κB Kinase ß/Nuclear Transcription Factor κB Pathway Attenuates Metabolism and Cardiac Dysfunction in Type 2 Diabetic Rats.

BACKGROUND: Inflammation and oxidative stress play important roles in energy imbalance and its complications. Recent research indicates that hypothalamic inflammation may contribute to the pathogenesis of metabolic syndrome and cardiac dysfunction, but the mechanisms remain unclear. We hypothesized that suppression of the proinflammatory IKKß/NF-κB pathway in the hypothalamus can improve energy balance and cardiac function in type 2 diabetic (T2D) rats. METHODS: Normal and T2D rats received bilateral hypothalamic arcuate nucleus (ARC) infusions of the IKKß inhibitor SC-514 or vehicle via osmotic minipump. Metabolic phenotyping, immunohistochemical analyses, and biochemical analyses were used to investigate the outcomes of inhibition of the hypothalamic IKKß. Echocardiography and glucometer were used for measuring cardiac function and blood glucose, respectively. Blood samples were collected for the evaluation of circulating proinflammatory cytokines. Heart was harvested for cardiac morphology evaluations. The ARC was harvested and analyzed for IKKß, NF-κB, proinflammatory cytokines, reactive oxygen species (ROS), and NAD(P)H (gp91phox, p47phox) oxidase activity levels and neuropeptides. RESULTS: Compared with normal rats, T2D rats were characterized by hyperglycemia, hyperinsulinemia, glucose intolerance, cardiac dysfunction, as well as higher ARC levels of IKKß, NF-κB, proinflammatory cytokines, ROS, gp91phox, and p47phox. ARC infusion of the IKKß inhibitor SC-514 attenuated all these changes in T2D rats, but not in normal rats. CONCLUSIONS: Our results indicate that the hypothalamic IKKß/NF-κB pathway plays a key role in modulating energy imbalance and cardiac dysfunction, suggesting its potential therapeutic role during type 2 diabetes mellitus.

Central administration of tert-butylhydroquinone attenuates hypertension via regulating Nrf2 signaling in the hypothalamic paraventricular nucleus of hypertensive rats.

Reactive oxygen species (ROS) in the paraventricular nucleus (PVN) play a pivotal role in the pathogenesis of hypertension. Nuclear factor E2-related factor-2 (Nrf2) is an important transcription factor that modulates cell antioxidant defense response against oxidative stress. The present study aimed to explore the efficacy of PVN administration of tert-butylhydroquinone (tBHQ), a selective Nrf2 activator, in hypertensive rats. 16-week-old spontaneously hypertensive rats (SHR) and normotensive Wistar-Kyoto (WKY) rats were used in this study. These rats were chronic bilateral PVN infusion of tBHQ (0.8µg/day), or oxygen free radical scavenger tempol (20µg/h), or vehicle for 2weeks. SHR rats had higher mean arterial pressure (MAP), plasma norepinephrine (NE) levels, and sympathetic nerve activity (RSNA) and lower PVN levels of Nrf2, hemeoxygenase-1 (HO-1), superoxide dismutase-1 (SOD1) and catalase (CAT) as compared with those in the WKY group. Bilateral PVN infusion of tBHQ or tempol significantly reduced MAP, RSNA, plasma NE levels in SHR rats. In addition, tBHQ treatment enhanced the nuclear accumulation of Nrf2 and increased the expression of HO-1, CAT and SOD1 in SHR rats. Furthermore, tBHQ attenuated PVN levels of ROS, the expression of proinflammatory cytokines and restored the imbalance of neurotransmitters in PVN. Knockdown of Nrf2 in the PVN by adeno-associated virus mediated small interfering RNA abrogated the protective effects of tBHQ on hypertension. These findings suggest that PVN administration of tBHQ can attenuate hypertension by activation of the Nrf2-mediated signaling pathway.