Mechanism of KAT2A regulation of H3K36ac in manganese-induced oxidative damage to mitochondria in the nervous system and intervention by curcumin.

Excessive exposure to manganese in the environment or workplace is strongly linked to neurodegeneration and cognitive impairment, but the precise pathogenic mechanism and preventive measures are still not fully understood. The study aimed to investigate manganese -induced oxidative damage in the nervous system from an epigenetic perspective, focusing on the H3K36ac-dependent antioxidant pathway. Additionally, it sought to examine the potential of curcumin in preventing manganese-induced oxidative damage. Histopathology and transmission electron microscopy revealed that apoptosis and necrosis of neurons and mitochondrial ultrastructure damage were observed in the striatum of manganese-exposed rats. manganese suppressed the expression of mitochondrial antioxidant genes, leading to oxidative damage in the rats' striatum and SH-SY5Y cells. With higher doses of manganese, levels of histone acetyltransferase lysine acetyltransferase 2 A (KAT2A) expression and H3K36ac level decreased. ChIP-qPCR confirmed that H3K36ac enrichment in the promoter regions of antioxidant genes SOD2, PRDX3, and TXN2 was reduced in SH-SY5Y cells after manganese exposure, leading to decreased expression of these genes. Overexpression of KAT2A confirms that it attenuates manganese-induced mitochondrial oxidative damage by regulating H3K36ac levels, which in turn controls the expression of antioxidant genes SOD2, PRDX3, and TXN2 in the manganese-exposed cell model. Furthermore, curcumin might control H3K36ac levels by influencing KAT2A expression, boosting antioxidant genes expression, and reducing manganese-induced mitochondrial oxidative damage. In conclusion, the regulation of mitochondrial oxidative stress by histone acetylation may be an important mechanism of manganese-induced neurotoxicity. This regulation could be achieved by reducing the level of H3K36ac near the promoter region of mitochondrial-associated antioxidant genes via KAT2A. Curcumin mitigates manganese-induced oxidative damage in mitochondria and plays a crucial protective role in manganese-induced oxidative injury in the nervous system.

Identification of histone acetylation modification sites in the striatum of subchronically manganese-exposed rats.

Aim: To explore the specific histone acetylation sites and oxidative stress-related genes that are associated with the pathogenesis of manganese toxicity. Methods: We employed liquid chromatography-tandem mass spectrometry and bioinformatics analysis to identify acetylated proteins in the striatum of subchronic manganese-intoxicated rats. Results: We identified a total of 12 differentially modified histone acetylation sites: H3K9ac, H3K14ac, H3K18ac, H3K56ac and H3K79ac were upregulated and H3K27ac, H3K36ac, H4K91ac, H4K79ac, H4K31ac, H2BK16ac and H2BK20ac were downregulated. Additionally, we found that CAT, SOD1 and SOD2 might be epigenetically regulated and involved in the pathogenesis of manganism. Conclusion: This study identified histone acetylation sites and oxidative stress-related genes associated with the pathogenesis of manganese toxicity, and these findings are useful in the search for potential epigenetic targets for manganese toxicity.

Mechanism by which HDAC3 regulates manganese induced H3K27ac in SH-SY5Y cells and intervention by curcumin.

Long-term excessive exposure to manganese can impair neuronal function in the brain, but the underlying pathological mechanism remains unclear. Oxidative stress plays a central role in manganese-induced neurotoxicity. Numerous studies have established a strong link between abnormal histone acetylation levels and the onset of various diseases. Histone deacetylase inhibitors and activators, such as TSA and ITSA-1, are often used to investigate the intricate mechanisms of histone acetylation in disease. In addition, recent experiments have provided substantial evidence demonstrating that curcumin (Cur) can act as an epigenetic regulator. Given these findings, this study aims to investigate the mechanisms underlying oxidative damage in SH-SY5Y cells exposed to MnCl2·4H2O, with a particular focus on histone acetylation, and to assess the potential therapeutic efficacy of Cur. In this study, SH-SY5Y cells were exposed to manganese for 24 h, were treated with TSA or ITSA-1, and were treated with or without Cur. The results suggested that manganese exposure, which leads to increased expression of HDAC3, induced H3K27 hypoacetylation, inhibited the transcription of antioxidant genes, decreased antioxidant enzyme activities, and induced oxidative damage in cells. Pretreatment with an HDAC3 inhibitor (TSA) increased the acetylation of H3K27 and the transcription of antioxidant genes and thus slowed manganese exposure-induced cellular oxidative damage. In contrast, an HDAC3 activator (ITSA-1) partially increased manganese-induced cellular oxidative damage, while Cur prevented manganese-induced oxidative damage. In summary, these findings suggest that inhibiting H3K27ac is a possible mechanism for ameliorating manganese-induced damage to dopaminergic neurons and that Cur exerts a certain protective effect against manganese-induced damage to dopaminergic neurons.

Manganese induces oxidative damage in the hippocampus by regulating the expression of oxidative stress-related genes via modulation of H3K18 acetylation.

Prolonged exposure to manganese (Mn) contributes to hippocampal Mn accumulation, which leads to neurodegenerative diseases called manganese poisoning. However, the underlying molecular mechanisms remain unclear and there are no ideal biomarkers. Oxidative stress is the essential mechanisms of Mn-related neurotoxicity. Furthermore, histone acetylation has been identified as being engaged in the onset and development of neurodegenerative diseases. Therefore, the work aims to understand the molecular mechanisms of oxidative damage in the hippocampus due to Mn exposure from the aspect of histone acetylation modification and to assess whether H3K18 acetylation (H3K18ac) modification level in peripheral blood reflect Mn-induced oxidative damage in the hippocampus. Here, we randomly divided 60 male rats into four groups and injected them intraperitoneally with sterile pure water and MnCl2 ⋅4H2 O (5, 10, and 15 mg/kg) for 16 weeks, 5 days a week, once a day. The data confirmed that Mn exposure down-regulated superoxide dismutase activity and glutathione level as well as up-regulated malondialdehyde level in the hippocampus and plasma, and that there was a positive correlation between these indicators in the hippocampus and plasma. Besides, we noted that Mn treatment upregulated H3K18ac modification levels in the hippocampus and peripheral blood and that H3K18ac modification levels correlated with oxidative stress. Further studies demonstrated that Mn treatment decreased the amounts of H3K18ac enrichment in the manganese superoxide dismutase (SOD2) and glutathione transferase omega 1 (GSTO1) gene promoter regions, contributing to oxidative damage in the hippocampus. In short, our results demonstrate that Mn induces oxidative damage in the hippocampus by inhibiting the expression of SOD2 and GSTO1 genes via modulation of H3K18ac. In assessing Mn-induced hippocampal neurotoxicity, oxidative damage in plasma may reflect hippocampal oxidative damage in Mn-exposed groups.

Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sites.

MOTIVATION: The rapid and extensive transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global health emergency, affecting millions of people and causing an immense socioeconomic impact. The identification of SARS-CoV-2 phosphorylation sites plays an important role in unraveling the complex molecular mechanisms behind infection and the resulting alterations in host cell pathways. However, currently available prediction tools for identifying these sites lack accuracy and efficiency. RESULTS: In this study, we presented a comprehensive biological function analysis of SARS-CoV-2 infection in a clonal human lung epithelial A549 cell, revealing dramatic changes in protein phosphorylation pathways in host cells. Moreover, a novel deep learning predictor called PSPred-ALE is specifically designed to identify phosphorylation sites in human host cells that are infected with SARS-CoV-2. The key idea of PSPred-ALE lies in the use of a self-adaptive learning embedding algorithm, which enables the automatic extraction of context sequential features from protein sequences. In addition, the tool uses multihead attention module that enables the capturing of global information, further improving the accuracy of predictions. Comparative analysis of features demonstrated that the self-adaptive learning embedding features are superior to hand-crafted statistical features in capturing discriminative sequence information. Benchmarking comparison shows that PSPred-ALE outperforms the state-of-the-art prediction tools and achieves robust performance. Therefore, the proposed model can effectively identify phosphorylation sites assistant the biomedical scientists in understanding the mechanism of phosphorylation in SARS-CoV-2 infection. AVAILABILITY AND IMPLEMENTATION: PSPred-ALE is available at https://github.com/jiaoshihu/PSPred-ALE and Zenodo (https://doi.org/10.5281/zenodo.8330277).

m5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation.

BACKGROUND: RNA 5-methyluridine (m5U) modifications are obtained by methylation at the C5 position of uridine catalyzed by pyrimidine methylation transferase, which is related to the development of human diseases. Accurate identification of m5U modification sites from RNA sequences can contribute to the understanding of their biological functions and the pathogenesis of related diseases. Compared to traditional experimental methods, computational methods developed based on machine learning with ease of use can identify modification sites from RNA sequences in an efficient and time-saving manner. Despite the good performance of these computational methods, there are some drawbacks and limitations. RESULTS: In this study, we have developed a novel predictor, m5U-SVM, based on multi-view features and machine learning algorithms to construct predictive models for identifying m5U modification sites from RNA sequences. In this method, we used four traditional physicochemical features and distributed representation features. The optimized multi-view features were obtained from the four fused traditional physicochemical features by using the two-step LightGBM and IFS methods, and then the distributed representation features were fused with the optimized physicochemical features to obtain the new multi-view features. The best performing classifier, support vector machine, was identified by screening different machine learning algorithms. Compared with the results, the performance of the proposed model is better than that of the existing state-of-the-art tool. CONCLUSIONS: m5U-SVM provides an effective tool that successfully captures sequence-related attributes of modifications and can accurately predict m5U modification sites from RNA sequences. The identification of m5U modification sites helps to understand and delve into the related biological processes and functions.

Curcumin protects against manganese-induced neurotoxicity in rat by regulating oxidative stress-related gene expression via H3K27 acetylation.

Long-term manganese exposure causes a neurodegenerative disorder referred to as manganese poisoning, but the mechanism remains unclear and no specific treatment is available. Oxidative stress is widely recognised as one of the main causes of manganese-induced neurotoxicity. In recent years, the role of histone acetylation in neurodegenerative diseases has been widely concerned. curcumin is a natural polyphenol compound extracted from the rhizome of turmeric and exhibits both antioxidant and neuroprotective properties. Therefore, we aimed to investigate whether and how curcumin protects against manganese-induced neurotoxicity from the perspective of histone acetylation, based on the reversibility of histone acetylation modification. In this study, rats were treated with or without curcumin and subjected to long-term manganese exposure. Results that treatment of manganese decreased the protein expression of H3K18 acetylation and H3K27 acetylation at the promoters of oxidative stress-related genes and inhibited the expression of these genes. Nevertheless, curcumin increased the H3K27 acetylation level at the manganese superoxide dismutase (SOD2) gene promoter and promoted the expression of SOD2 gene. Oxidative damage in the rat striatum as well as learning and memory dysfunction were ameliorated after curcumin treatment. Taken together, our results suggest that the regulation of oxidative stress by histone acetylation may be a key mechanism of manganese-induced neurotoxicity. In addition, curcumin ameliorates Mn-induced neurotoxicity may be due to alleviation of oxidative damage mediated by increased activation of H3K27 acetylation at the SOD2 gene promoter.

Research progress of miRNA-disease association prediction and comparison of related algorithms.

With an in-depth understanding of noncoding ribonucleic acid (RNA), many studies have shown that microRNA (miRNA) plays an important role in human diseases. Because traditional biological experiments are time-consuming and laborious, new calculation methods have recently been developed to predict associations between miRNA and diseases. In this review, we collected various miRNA-disease association prediction models proposed in recent years and used two common data sets to evaluate the performance of the prediction models. First, we systematically summarized the commonly used databases and similarity data for predicting miRNA-disease associations, and then divided the various calculation models into four categories for summary and detailed introduction. In this study, two independent datasets (D5430 and D6088) were compiled to systematically evaluate 11 publicly available prediction tools for miRNA-disease associations. The experimental results indicate that the methods based on information dissemination and the method based on scoring function require shorter running time. The method based on matrix transformation often requires a longer running time, but the overall prediction result is better than the previous two methods. We hope that the summary of work related to miRNA and disease will provide comprehensive knowledge for predicting the relationship between miRNA and disease and contribute to advanced computation tools in the future.

Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties.

Dihydrouridine (D) is an abundant post-transcriptional modification present in transfer RNA from eukaryotes, bacteria, and archaea. D has contributed to treatments for cancerous diseases. Therefore, the precise detection of D modification sites can enable further understanding of its functional roles. Traditional experimental techniques to identify D are laborious and time-consuming. In addition, there are few computational tools for such analysis. In this study, we utilized eleven sequence-derived feature extraction methods and implemented five popular machine algorithms to identify an optimal model. During data preprocessing, data were partitioned for training and testing. Oversampling was also adopted to reduce the effect of the imbalance between positive and negative samples. The best-performing model was obtained through a combination of random forest and nucleotide chemical property modeling. The optimized model presented high sensitivity and specificity values of 0.9688 and 0.9706 in independent tests, respectively. Our proposed model surpassed published tools in independent tests. Furthermore, a series of validations across several aspects was conducted in order to demonstrate the robustness and reliability of our model.

NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences.

2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.

RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features.

N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.

Research Progress in Predicting DNA Methylation Modifications and the Relation with Human Diseases.

DNA methylation is an important mode of regulation in epigenetic mechanisms, and it is one of the research foci in the field of epigenetics. DNA methylation modification affects a series of biological processes, such as eukaryotic cell growth, differentiation, and transformation mechanisms, by regulating gene expression. In this review, we systematically summarized the DNA methylation databases, prediction tools for DNA methylation modification, machine learning algorithms for predicting DNA methylation modification, and the relationship between DNA methylation modification and diseases such as hypertension, Alzheimer's disease, diabetic nephropathy, and cancer. An in-depth understanding of DNA methylation mechanisms can promote accurate prediction of DNA methylation modifications and the treatment and diagnosis of related diseases.

Prediction of bio-sequence modifications and the associations with diseases.

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.

Prediction of antioxidant proteins using hybrid feature representation method and random forest.

Natural antioxidant proteins are mainly found in plants and animals, which interact to eliminate excessive free radicals and protect cells and DNA from damage, prevent and treat some diseases. Therefore, accurate identification of antioxidant proteins is important for the development of new drugs and research of related diseases. This article proposes novel method based on the combination of random forest and hybrid features that can accurately predict antioxidant proteins. Four single feature extraction methods (188D, profile-based Auto-cross covariance (ACC-PSSM), N-gram, and g-gap) and hybrid feature representation methods were used to feature extraction. Three feature selection methods (MRMD, t-SNE, and the optimal feature set selection) were adopted to determine the optimal features. The new hybrid feature vectors derived by combining 188D with the other three features all have indicators ranging from 0.9550 to 0.9990. The novel method showed better performance compared with the other methods.

Application and Development of Artificial Intelligence and Intelligent Disease Diagnosis.

With the continuous development of artificial intelligence (AI) technology, big data-supported AI technology with considerable computer and learning capacity has been applied in diagnosing different types of diseases. This study reviews the application of expert systems, neural networks, and deep learning used by AI technology in disease diagnosis. This paper also gives a glimpse of the intelligent diagnosis and treatment of digestive system diseases, respiratory system diseases, and osteoporosis by AI technology.

Progress in the development of antimicrobial peptide prediction tools.

Antimicrobial peptides (AMPs) are natural polypeptides with antimicrobial activities and are found in most organisms. AMPs are evolutionarily conservative components that belong to the innate immune system and show potent activity against bacteria, fungi, viruses and in some cases display antitumor activity. Thus, AMPs are major candidates in the development of new antibacterial reagents. In the last few decades, AMPs have attracted significant attention from the research community. During the early stages of the development of this research field, AMPs were experimentally identified, which is an expensive and time-consuming procedure. Therefore, research and development (R&D) of fast, highly efficient computational tools for predicting AMPs has enabled the rapid identification and analysis of new AMPs from a wide range of organisms. Moreover, these computational tools have allowed researchers to better understand the activities of AMPs, which has promoted R&D of antibacterial drugs. In this review, we systematically summarize AMP prediction tools and their corresponding algorithms used.

Protein Function Prediction: From Traditional Classifier to Deep Learning.

Deep learning demonstrates greater competence over traditional machine learning techniques for many tasks. In last several years, deep learning has been applied to protein function prediction and a series of good achievements has been obtained. These findings extensively advanced our understanding of protein function. However, the accuracy of protein function prediction based upon deep learning still has yet to be improved. In article number 1900019, Issue 12, Zhang et al. construct DeepFunc, a deep learning framework using derived feature information of protein sequence and protein interactions network. They find that implementing DeepFunc for protein function prediction is more accurate than using DeepGO, a similar method reported previously. Meanwhile, they find that the method of combining multiple derived feature information in DeepFunc is much better than the method of using only single derived feature information. Due to its fully exploiting feature representation learning ability, deep learning with more derived feature information will enable it to be a promising method for solving more complicated protein function prediction problems and other bioinformatics challenges. Recent researches have provided some major insights into the value for using deep learning to protein function prediction problem.