RNA m6A detection using raw current signals and basecalling errors from Nanopore direct RNA sequencing reads.

MOTIVATION: Nanopore direct RNA sequencing (DRS) enables the detection of RNA N6-methyladenosine (m6A) without extra laboratory techniques. A number of supervised or comparative approaches have been developed to identify m6A from Nanopore DRS reads. However, existing methods typically utilize either statistical features of the current signals or basecalling-error features, ignoring the richer information of the raw signals of DRS reads. RESULTS: Here, we propose RedNano, a deep-learning method designed to detect m6A from Nanopore DRS reads by utilizing both raw signals and basecalling errors. RedNano processes the raw-signal feature and basecalling-error feature through residual networks. We validated the effectiveness of RedNano using synthesized, Arabidopsis, and human DRS data. The results demonstrate that RedNano surpasses existing methods by achieving higher area under the ROC curve (AUC) and area under the precision-recall curve (AUPRs) in all three datasets. Furthermore, RedNano performs better in cross-species validation, demonstrating its robustness. Additionally, when detecting m6A from an independent dataset of Populus trichocarpa, RedNano achieves the highest AUC and AUPR, which are 3.8%-9.9% and 5.5%-13.8% higher than other methods, respectively. AVAILABILITY AND IMPLEMENTATION: The source code of RedNano is freely available at https://github.com/Derryxu/RedNano.

BlockPolish: accurate polishing of long-read assembly via block divide-and-conquer.

Long-read sequencing technology enables significant progress in de novo genome assembly. However, the high error rate and the wide error distribution of raw reads result in a large number of errors in the assembly. Polishing is a procedure to fix errors in the draft assembly and improve the reliability of genomic analysis. However, existing methods treat all the regions of the assembly equally while there are fundamental differences between the error distributions of these regions. How to achieve very high accuracy in genome assembly is still a challenging problem. Motivated by the uneven errors in different regions of the assembly, we propose a novel polishing workflow named BlockPolish. In this method, we divide contigs into blocks with low complexity and high complexity according to statistics of aligned nucleotide bases. Multiple sequence alignment is applied to realign raw reads in complex blocks and optimize the alignment result. Due to the different distributions of error rates in trivial and complex blocks, two multitask bidirectional Long short-term memory (LSTM) networks are proposed to predict the consensus sequences. In the whole-genome assemblies of NA12878 assembled by Wtdbg2 and Flye using Nanopore data, BlockPolish has a higher polishing accuracy than other state-of-the-arts including Racon, Medaka and MarginPolish & HELEN. In all assemblies, errors are predominantly indels and BlockPolish has a good performance in correcting them. In addition to the Nanopore assemblies, we further demonstrate that BlockPolish can also reduce the errors in the PacBio assemblies. The source code of BlockPolish is freely available on Github (https://github.com/huangnengCSU/BlockPolish).

NanoSNP: a progressive and haplotype-aware SNP caller on low-coverage nanopore sequencing data.

MOTIVATION: Oxford Nanopore sequencing has great potential and advantages in population-scale studies. Due to the cost of sequencing, the depth of whole-genome sequencing for per individual sample must be small. However, the existing single nucleotide polymorphism (SNP) callers are aimed at high-coverage Nanopore sequencing reads. Detecting the SNP variants on low-coverage Nanopore sequencing data is still a challenging problem. RESULTS: We developed a novel deep learning-based SNP calling method, NanoSNP, to identify the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. In this method, we design a multi-step, multi-scale and haplotype-aware SNP detection pipeline. First, the pileup model in NanoSNP utilizes the naive pileup feature to predict a subset of SNP sites with a Bi-long short-term memory (LSTM) network. These SNP sites are phased and used to divide the low-coverage Nanopore reads into different haplotypes. Finally, the long-range haplotype feature and short-range pileup feature are extracted from each haplotype. The haplotype model combines two features and predicts the genotype for the candidate site using a Bi-LSTM network. To evaluate the performance of NanoSNP, we compared NanoSNP with Clair, Clair3, Pepper-DeepVariant and NanoCaller on the low-coverage (∼16×) Nanopore sequencing reads. We also performed cross-genome testing on six human genomes HG002-HG007, respectively. Comprehensive experiments demonstrate that NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller in identifying SNPs on low-coverage Nanopore sequencing data, including the difficult-to-map regions and major histocompatibility complex regions in the human genome. NanoSNP is comparable to Clair3 when the coverage exceeds 16×. AVAILABILITY AND IMPLEMENTATION: https://github.com/huangnengCSU/NanoSNP.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sparse reconstruction of sound field using pattern-coupled Bayesian compressive sensing.

Conventional near-field acoustic holography based on compressive sensing either does not fully exploit the underlying block-sparse structures of the signal or suffers from a mismatch between the actual and predefined block structure due to the lack of prior information about block partitions, resulting in poor accuracy in sound field reconstruction. In this paper, a pattern-coupled Bayesian compressive sensing method is proposed for sparse reconstruction of sound fields. The proposed method establishes a hierarchical Gaussian-Gamma probability model with a pattern-coupled prior based on the equivalent source method, transforming the sound field reconstruction problem into recovering the sparse coefficient vector of the equivalent source strengths within the compressive sensing framework. A set of hyperparameters is introduced to control the sparsity of each element in the sparse coefficient vector of the equivalent source strengths, where the sparsity of each element is determined by both its own hyperparameters and those of its immediate neighbors. This approach enables the promotion of block sparse solutions and achieves better performance in solving for the sparse coefficient vector of the equivalent source strengths without prior information of block partitions. The effectiveness and superiority of the proposed method in reconstructing sound fields are verified by simulations and experiments.

Fec: a fast error correction method based on two-rounds overlapping and caching.

The third-generation sequencing technology has advanced genome analysis with long-read length, but the reads need error correction due to the high error rate. Error correction is a time-consuming process especially when the sequencing coverage is high. Generally, for a pair of overlapping reads A and B, the existing error correction methods perform a base-level alignment from B to A when correcting the read A. And another base-level alignment from A to B is performed when correcting the read B. However, based on our observation, the base-level alignment information can be reused. In this article, we present a fast error correction tool Fec, using two-rounds overlapping and caching. Fec can be used independently or as an error correction step in an assembly pipeline. In the first round, Fec uses a large window size (20) to quickly find enough overlaps to correct most of the reads. In the second round, a small window size (5) is used to find more overlaps for the reads with insufficient overlaps in the first round. When performing base-level alignment, Fec searches the cache first. If the alignment exists in the cache, Fec takes this alignment out and deduces the second alignment from it. Otherwise, Fec performs base-level alignment and stores the alignment in the cache. We test Fec on nine datasets, and the results show that Fec has 1.24-38.56 times speed-up compared to MECAT, CANU and MINICNS on five PacBio datasets and 1.16-27.8 times speed-up compared to NECAT and CANU on four nanopore datasets. AVAILABILITY AND IMPLEMENTATION: Fec is available at https://github.com/zhangjuncsu/Fec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The association of sickle cell disorder with adverse outcomes in COVID-19 patients: A meta-analysis.

The aim is to elucidate the relationship between sickle cell disorder and severe COVID-19. We systematically searched the required articles in three electronic databases, extracting and pooling effect sizes (ES) and 95% confidence interval (CI) from each eligible study to evaluate the effect of combined sickle cell disorder on adverse consequences in patients with COVID-19. This meta-analysis included 21 studies. Sickle cell disease (SCD) was a risk factor for mortality (pooled ES = 1.70, 95% CI: 1.00-2.92, p = 0.001), hospitalization (pooled ES = 6.21, 95% CI: 3.60-10.70, p = 0.000) and intensive care unit (ICU) admission (pooled ES = 2.29, 95% CI: 1.61-3.24, p = 0.099) in COVID-19 patients. Patients with SCD had an increased risk of respiratory failure/mechanical ventilation, but a statistical association was not found (pooled ES = 1.21, 95%CI: 0.74-1.98, p = 0.036). There was significant heterogeneity between SCD and death, hospitalization, and respiratory failure/mechanical ventilation. The results of meta-regression of SCD and hospitalization suggested that the tested variables including Area (p = 0.642), study design (p = 0.739), sample size (p = 0.397), proportion of males (p = 0.708), effect type (p = 0.723), whether confounding factors are adjusted (p = 0.606) might not be the source of heterogeneity. In addition, sickle cell trait (SCT) was significantly associated with the mortality (pooled ES = 1.54, 95% CI: 1.28-1.85, p = 0.771) and hospitalization (pooled ES = 1.20, 95% CI: 1.07-1.35，p = 0.519) in patients with COVID-19. But any increased risk of ICU admission/severe (pooled ES = 1.24, 95% CI: 0.95-1.62, p = 0.520) and mechanical ventilation (OR = 1.00, 95%CI:0.59-1.69) in COVID-19 patients with SCT was not observed. Sensitivity analysis demonstrated that the results were robust. The results of the funnel plot and Egger's test did not support the existence of publication bias. Current meta-analysis indicated that sickle cell disorder has a meaningful impact on COVID-19 progression to severe cases and associated deaths. However, further investigations and research to validate the current findings is indispensable.

NeuralPolish: a novel Nanopore polishing method based on alignment matrix construction and orthogonal Bi-GRU Networks.

MOTIVATION: Oxford Nanopore sequencing producing long reads at low cost has made many breakthroughs in genomics studies. However, the large number of errors in Nanopore genome assembly affect the accuracy of genome analysis. Polishing is a procedure to correct the errors in genome assembly and can improve the reliability of the downstream analysis. However, the performances of the existing polishing methods are still not satisfactory. RESULTS: We developed a novel polishing method, NeuralPolish, to correct the errors in assemblies based on alignment matrix construction and orthogonal Bi-GRU networks. In this method, we designed an alignment feature matrix for representing read-to-assembly alignment. Each row of the matrix represents a read, and each column represents the aligned bases at each position of the contig. In the network architecture, a bi-directional GRU network is used to extract the sequence information inside each read by processing the alignment matrix row by row. After that, the feature matrix is processed by another bi-directional GRU network column by column to calculate the probability distribution. Finally, a CTC decoder generates a polished sequence with a greedy algorithm. We used five real datasets and three assembly tools including Wtdbg2, Flye and Canu for testing, and compared the results of different polishing methods including NeuralPolish, Racon, MarginPolish, HELEN and Medaka. Comprehensive experiments demonstrate that NeuralPolish achieves more accurate assembly with fewer errors than other polishing methods and can improve the accuracy of assembly obtained by different assemblers. AVAILABILITY AND IMPLEMENTATION: https://github.com/huangnengCSU/NeuralPolish.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The CRISPR-Cas system as a tool for diagnosing and treating infectious diseases.

Emerging and relapsing infectious diseases pose a huge health threat to human health and a new challenge to global public health. Rapid, sensitive and simple diagnostic tools are keys to successful management of infectious patients and containment of disease transmission. In recent years, international research on Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-related proteins (Cas) has revolutionized our understanding of biology. The CRISPR-Cas system has the advantages of high specificity, high sensitivity, simple, rapid, low cost, and has begun to be used for molecular diagnosis and treatment of infectious diseases. In this paper, we described the biological principles, application fields and prospects of CRISPR-Cas system in the molecular diagnosis and treatment of infectious diseases, and compared it with existing molecular diagnosis methods, the advantages and disadvantages were summarized.

Bacteriological Investigation and Drug Resistance Analysis of Chronic Refractory Wound Secretions.

BACKGROUND: Chronic refractory wounds were common and the treatments were complicated for burn and plastic surgeons. This study was to investigate the bacterial distribution characteristics and bacterial drug resistance of chronic refractory wound secretions. METHODS: The authors retrospectively analyzed 425 patients with chronic refractory wound infection. The results of bacterial culture of wound secretions and drug sensitivity test were retrospectively analyzed. Further, the location area of the wound was divided into 4 regions, and the difference of the bacterial culture results between different regions was analyzed. RESULTS: The wound secretions were cultured into 401 bacterial strains, including 206 gram-positive bacteria strains, accounting for 51.4%, with the highest detection rate of Staphylococcus aureus at 26.2% (105/401). There were 195 gram-negative bacteria strains, accounting for 48.6%, with the highest detection rate of Pseudomonas aeruginosa at 14.2% (57/401). There were 6 fungal strains. The proportion of gram-negative bacteria in the III region of the wound zone was significantly greater than that in the other 3 regions. CONCLUSIONS: The detection rate of gram-positive bacteria and gram-negative bacteria of chronic refractory wound secretions is not much different. However, in the area close to the perineum (III region), gram-negative bacteria is significantly higher, which has a certain reference value for the use of antibiotics in clinical practice. LEVEL OF EVIDENCE: Level 4.

Multi-End Physics-Informed Deep Learning for Seismic Response Estimation.

As a structural health monitoring (SHM) system can hardly measure all the needed responses, estimating the target response from the measured responses has become an important task. Deep neural networks (NNs) have a strong nonlinear mapping ability, and they are widely used in response reconstruction works. The mapping relation among different responses is learned by a NN given a large training set. In some cases, however, especially for rare events such as earthquakes, it is difficult to obtain a large training dataset. This paper used a convolution NN to reconstruct structure response under rare events with small datasets, and the main innovations include two aspects. Firstly, we proposed a multi-end autoencoder architecture with skip connections, which compresses the parameter space, to estimate the unmeasured responses. It extracts the shared patterns in the encoder and reconstructs different types of target responses in varied branches of the decoder. Secondly, the physics-based loss function, derived from the dynamic equilibrium equation, was adopted to guide the training direction and suppress the overfitting effect. The proposed NN takes the acceleration at limited positions as input. The output is the displacement, velocity, and acceleration responses at all positions. Two numerical studies validated that the proposed framework applies to both linear and nonlinear systems. The physics-informed NN had a higher performance than the ordinary NN with small datasets, especially when the training data contained noise.

Therapeutic strategies for chronic wound infection.

Chronic wounds have always been a tough fight in clinical practice, which can not only make patients suffer from pain physically and mentally but also impose a heavy burden on the society. More than one factor is relevant to each step of the development of chronic wounds. Along with the in-depth research, we have realized that figuring out the pathophysiological mechanism of chronic wounds is the foundation of treatment, while wound infection is the key point concerned. The cause of infection should be identified and prevented promptly once diagnosed. This paper mainly describes the mechanism, diagnosis and therapeutic strategies of chronic wound infection, and will put an emphasis on the principle of debridement.

Investigation of the status and influence factors of caregiver's quality of life on caring for patients with chronic wound during COVID-19 epidemic.

The objectives of this study were to investigate the status and influence factors of caregiver's quality of life (QOL) on caring for patients with chronic wound during COVID-19 epidemic. A prospective cross-sectional study of 83 informal caregivers was included. The characteristics of informal caregivers as well as their QOL assessment by the Family Dermatology Life Quality Index (FDLQI) were measured, respectively. Single-factor analysis and multiple regression analysis were carried out to explore the independent influence factors of QOL of caregiver on caring for patient with chronic wound. 62.65% of the caregivers were female with a mean age of (54.24 ± 12.6) years, and 34.9% of the caregivers were parents. The mean FDLQI score was 13.01 ± 7.53 at a high level. The following variables influenced the FDLQI scores of caregivers: self-care ability of patients, patient's satisfaction of home-based wound care, and home-based wound care need of caregivers. The model was able to explain 29.9% of variance in QOL of caregiver (F = 6.561, P = .000, R2  = 0.299, adjusted R2  = 0.253). In conclusion, the impact of chronic wound disease on the QOL of caregivers is heavy during COVID-19 epidemic. Wound professionals are suggested to pay attention to wound care need at home and QOL of caregiver on caring for patients with chronic wound during COVID-19 epidemic and develop tailored wound health education and support programme in order to improve the QOL of caregivers.

A Review of Key Technologies and Trends in the Development of Integrated Heating and Power Systems in Agriculture.

Petroleum agriculture, characterized by mechanization and chemistry, is developing rapidly in China. However, petroleum agriculture has not only brought food safety problems, but also caused great obstacles to the sustainable development of society. In view of the disadvantages of oil agriculture, we provide an upgrading plan for energy systems in agriculture. This work can help reduce carbon emissions and improve food security. We introduce the most advanced technologies in Chinese agricultural development and the technical scope includes new agricultural energy power generation, agricultural energy use and the safe operation of agricultural energy systems. We describe the detailed data of agricultural bioenvironmental and energy engineering to clarify the level of agricultural energy efficiency in China. The overall conclusion of this paper is that the deep integration of agriculture and energy internet has become the development trend of agricultural energy systems.

Correction to: Mild endoplasmic reticulum stress ameliorates lipopolysaccharide-induced neuroinflammation and cognitive impairment via regulation of microglial polarization.

An amendment to this paper has been published and can be accessed via the original article.

DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning.

MOTIVATION: The Oxford Nanopore sequencing enables to directly detect methylation states of bases in DNA from reads without extra laboratory techniques. Novel computational methods are required to improve the accuracy and robustness of DNA methylation state prediction using Nanopore reads. RESULTS: In this study, we develop DeepSignal, a deep learning method to detect DNA methylation states from Nanopore sequencing reads. Testing on Nanopore reads of Homo sapiens (H. sapiens), Escherichia coli (E. coli) and pUC19 shows that DeepSignal can achieve higher performance at both read level and genome level on detecting 6 mA and 5mC methylation states comparing to previous hidden Markov model (HMM) based methods. DeepSignal achieves similar performance cross different DNA methylation bases, different DNA methylation motifs and both singleton and mixed DNA CpG. Moreover, DeepSignal requires much lower coverage than those required by HMM and statistics based methods. DeepSignal can achieve 90% above accuracy for detecting 5mC and 6 mA using only 2× coverage of reads. Furthermore, for DNA CpG methylation state prediction, DeepSignal achieves 90% correlation with bisulfite sequencing using just 20× coverage of reads, which is much better than HMM based methods. Especially, DeepSignal can predict methylation states of 5% more DNA CpGs that previously cannot be predicted by bisulfite sequencing. DeepSignal can be a robust and accurate method for detecting methylation states of DNA bases. AVAILABILITY AND IMPLEMENTATION: DeepSignal is publicly available at https://github.com/bioinfomaticsCSU/deepsignal. SUPPLEMENTARY INFORMATION: Supplementary data are available at bioinformatics online.

Prognostic Significance of ADAM17 for Gastric Cancer Survival: A Meta-Analysis.

Background and objectives: The prognostic role of a disintegrin and metalloproteinase (ADAM) 17 has been widely assessed in gastric cancer. However, the results are inconsistent. We performed a meta-analysis to evaluate the prognostic significance of ADAM17 and its association with clinicopathological parameters. Methods: The databases of PubMed, Web of Science, and Embase were searched for relevant articles published up to April 2020. The reported hazard ratios (HRs) and odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were pooled to evaluate the strength of the association. Stata 12.1 was used to perform statistical analyses. Results: Seven studies, including 1757 patients, were screened for the meta-analysis. Compared with the high ADAM17 expression group, the pooled HR was higher in the low ADAM17 expression group (HR = 2.04, 95% CI 1.66-2.50; I2 = 18.1%; p = 0.299). High ADAM17 expression was also related to the tumor node metastasis (TNM) stages (OR = 4.09, 95% CI 1.85-9.04; I2 = 84.1%; p = 0.000), lymph node metastasis (OR = 3.08, 95% CI 1.13-8.36; I2 = 79.7%; p = 0.007), and ages (OR = 1.65, 95% CI 1.24-2.21; I2 = 0%; p = 0.692) of the gastric patients. Conclusions: This meta-analysis revealed that ADAM17 is a significant biomarker for poor prognosis in gastric cancer.

The Role and Prognostic Significance of Aortopulmonary, Anterior Mediastinal, and Tracheobronchial Lymph Nodes in Esophageal Cancer: Update of the Eighth-Edition TNM Staging System (2018).

BACKGROUND: The eighth edition of TNM staging for esophageal cancer will be implemented at 2018. The stations 5, 6, and 10 lymph nodes (LNs) have been omitted from the regional lymph node map for the new TNM staging. However, the role and prognostic significance of these LN stations were not clear. The purpose of this study was to investigate whether the revised nodal staging is appropriate and to verify the role, prognostic significance, and therapeutic value of these LNs in esophageal cancer. METHODS: The records of patients who underwent esophagectomy for cancer in our department between 2007 and 2013 were retrospectively analyzed. The rate of metastases was calculated for stations 5, 6, and 10 LNs. LN metastasis and patient survival were analyzed. RESULTS: A total of 1637 patients were included. The calculated rate of metastasis to stations 5, 6, and 10 was 3.2%, 2.3%, and 4.9%, respectively. No difference was found in the N stage determined by the seventh and eighth edition N staging systems. The status of station 5, 6, or 10 was not associated with long-term survival according to Cox proportional hazards model analysis. CONCLUSIONS: Metastasis to stations 5, 6, or 10 LNs was infrequent. Omitting of stations 5, 6, and 10 LNs in the eighth edition TNM staging did not influence the accuracy and survival-predicting efficacy. The therapeutic value of lymphadenectomy of stations 5, 6, and 10 was limited. The status of stations 5, 6, and 10 LNs was not associated with long-term survival.

Helicobacter pylori Infection and Psoriasis: A Systematic Review and Meta-Analysis.

Background and Objectives: To perform a systematic review and meta-analysis with the aim of determining the relationship between H. pylori infection and psoriasis. Methods: Pubmed, Embase, China National Knowledge Infrastructure (CNKI), and Web of Science were searched for articles published up to July, 2019. Review Manager 5.3 and Stata 12.0 were used for statistical analyses. Results: The initial database search resulted in 204 articles. Through exclusion and screening, 11 studies involving a total of 1741 participants were finally included in this meta-analysis. The odds ratio (OR) of H. pylori infection rate in the psoriasis group was significantly higher than that in the control group (OR = 1.19, 95% CI 1.15-2.52, P = 0.008). Subgroup analysis showed that no significant difference was detected between the Asia group and the Europe group. As for the methods of H. pylori detection, a statistically significant increase of H. pylori infection in the IgG ELISA test group was detected, compared with the urea breath test group. In addition, analysis based on the severity of psoriasis showed a statistically significant increase of H. pylori infection in moderate and severe psoriasis patients (OR = 2.27; 95% CI: 1.42-3.63, I2 = 27%), but not in the mild psoriasis patients (OR = 1.10; 95% CI: 0.79-1.54, I2 = 0%). Conclusion: H. pylori infection is associated with psoriasis, and psoriasis patients with H. pylori infection have higher Psoriasis Area and Severity Index (PASI) scores. The findings are of considerable significance for the clinical practices.

Effect of tryptase on mouse brain microvascular endothelial cells via protease-activated receptor 2.

BACKGROUND: Mast cells (MCs), the 'first responders' in brain injury, are able to disrupt the blood-brain barrier (BBB), but the underlying mechanism is not well understood. Tryptase is the most abundant MC secretory product. Protease-activated receptor 2 (PAR-2) has been identified as a specific receptor for tryptase, which is abundantly expressed in brain microvascular endothelial cells. The BBB comprises brain microvascular endothelial cells that display specialised molecular properties essential for BBB function and integrity. Therefore, the purpose of the present study was to investigate the effects of tryptase on mouse brain microvascular endothelial cell line bEnd3 and its potential mechanisms of action. METHODS: Induction of mouse brain microvascular endothelial cell activation by tryptase was examined. Then, mouse brain microvascular endothelial cells were pretreated with a PAR-2 antagonist and stimulated with tryptase. Cellular activation, proinflammatory cytokine production, expression of PAR-2, Toll-like receptors (TLRs) and mitogen-activated protein kinases (MAPK), nuclear factor kappa B (NF-kappa B) phosphorylation were assessed. RESULTS: Tryptase upregulated the production of VCAM-1, MMPs (MMP9 and MMP2), TLR4 and TNF-α and downregulated the expression of the tight junction proteins occludin and claudin-5 in mouse brain microvascular endothelial cell. Among the MAPK and NF-kappa B pathway, ERK and NF-kappa B were activated by tryptase. All of these effects could be eliminated by the PAR-2 inhibitor. CONCLUSION: Based on our findings, we conclude that tryptase can trigger brain microvascular endothelial cell activation and proinflammatory mediator release. These findings may further clarify the involvement and mechanism of tryptase in BBB disruption.

Mild endoplasmic reticulum stress ameliorates lipopolysaccharide-induced neuroinflammation and cognitive impairment via regulation of microglial polarization.

BACKGROUND: Neuroinflammation, which ultimately leads to neuronal loss, is considered to play a crucial role in numerous neurodegenerative diseases. The neuroinflammatory process is characterized by the activation of glial cells such as microglia. Endoplasmic reticulum (ER) stress is commonly associated with impairments in neuronal function and cognition, but its relationship and role in neurodegeneration is still controversial. Recently, it was confirmed that nonharmful levels of ER stress protected against experimental Parkinson's disease. Here, we investigated mild ER stress-based regulation of lipopolysaccharide (LPS)-driven neuroinflammation in rats and in primary microglia. METHODS: Male Sprague-Dawley (SD) rats received the intracerebroventricular injection of the ER stress activator tunicamycin (TM) with or without intraperitoneal injection of the ER stress stabilizer sodium 4-phenylbutyrate (4-PBA) 1 h before LPS administration. The levels of neuroinflammation and memory dysfunction were assessed 24 h after treatment. In addition, the effect of mild ER stress on microglia was determined in vitro. RESULTS: Here, we found that low doses of TM led to mild ER stress without cell or organism lethality. We showed that mild ER stress preconditioning reduced microglia activation and neuronal death as well as improved LPS-induced memory impairment in rats. In addition, pre-exposure to nonlethal doses of TM in microglia showed significant protection against LPS-induced proinflammatory cytokine production and M1/2b polarization. However, sodium 4-PBA, a compound that ameliorates ER stress, ablated this protective effect in vivo and in vitro. CONCLUSIONS: Based on our findings, we conclude that the mild ER stress not only limits the accumulation of misfolded proteins but also protects tissues from harmful endotoxemia insults. Therefore, ER stress preconditioning has potential therapeutic value for the treatment of neurodegenerative diseases.