Pub Date : 2023-10-24DOI: 10.2174/0113892029264886231016050547
Louie Cris Lopos, Urbashi Panthi, Igor Kovalchuk, Andriy Bilichak
Abstract: Wheat, a crucial crop for the pursuit of food security, is faced with a plateauing yield projected to fall short of meeting the demands of the exponentially increasing human population. To raise global wheat productivity levels, strong efforts must be made to overcome the problems of (1) climate change-induced heat and drought stress and (2) the genotype-dependent amenability of wheat to tissue culture, which limits the success of recovering genetically engineered plants, especially in elite cultivars. Unfortunately, the mainstream approach of genetically engineering plant protein-coding genes may not be effective in solving these problems as it is difficult to map, annotate, functionally verify, and modulate all existing homeologs and paralogs within wheat’s large, complex, allohexaploid genome. Additionally, the quantitative, multi-genic nature of most agronomically important traits furthers the complications faced by this approach. miRNAs are small, noncoding RNAs (sncRNAs) that repress gene expression at the post-transcriptional level, regulating various aspects of plant growth and development. They are gaining popularity as alternative targets of genetic engineering efforts for crop improvement due to their (1) highly conserved nature, which facilitates reasonable prediction of their gene targets and phenotypic effects under different expression levels, and (2) the capacity to target multiple genes simultaneously, making them suitable for enhancing complex and multigenic agronomic traits. In this mini-review, we will discuss the biogenesis, manipulation, and potential applications of plant miRNAs in improving wheat’s yield, somatic embryogenesis, thermotolerance, and drought-tolerance in response to the problems of plateauing yield, genotype-dependent amenability to tissue culture, and susceptibility to climate change-induced heat and drought stress.
{"title":"Modulation of Plant MicroRNA Expression: Its Potential Usability in Wheat (Triticum aestivum L.) Improvement","authors":"Louie Cris Lopos, Urbashi Panthi, Igor Kovalchuk, Andriy Bilichak","doi":"10.2174/0113892029264886231016050547","DOIUrl":"https://doi.org/10.2174/0113892029264886231016050547","url":null,"abstract":"Abstract: Wheat, a crucial crop for the pursuit of food security, is faced with a plateauing yield projected to fall short of meeting the demands of the exponentially increasing human population. To raise global wheat productivity levels, strong efforts must be made to overcome the problems of (1) climate change-induced heat and drought stress and (2) the genotype-dependent amenability of wheat to tissue culture, which limits the success of recovering genetically engineered plants, especially in elite cultivars. Unfortunately, the mainstream approach of genetically engineering plant protein-coding genes may not be effective in solving these problems as it is difficult to map, annotate, functionally verify, and modulate all existing homeologs and paralogs within wheat’s large, complex, allohexaploid genome. Additionally, the quantitative, multi-genic nature of most agronomically important traits furthers the complications faced by this approach. miRNAs are small, noncoding RNAs (sncRNAs) that repress gene expression at the post-transcriptional level, regulating various aspects of plant growth and development. They are gaining popularity as alternative targets of genetic engineering efforts for crop improvement due to their (1) highly conserved nature, which facilitates reasonable prediction of their gene targets and phenotypic effects under different expression levels, and (2) the capacity to target multiple genes simultaneously, making them suitable for enhancing complex and multigenic agronomic traits. In this mini-review, we will discuss the biogenesis, manipulation, and potential applications of plant miRNAs in improving wheat’s yield, somatic embryogenesis, thermotolerance, and drought-tolerance in response to the problems of plateauing yield, genotype-dependent amenability to tissue culture, and susceptibility to climate change-induced heat and drought stress.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.2174/0113892029265046231011100327
Ezgi Man, Serap EVRAN
abstract: Epigenetic changes play an important role in the pathophysiology of autoimmune diseases such as allergic asthma, multiple sclerosis, lung diseases, diabetes, cystic fibrosis, atherosclerosis, rheumatoid arthritis, and COVID-19. There are three main classes of epigenetic alterations: post-translational modifications of histone proteins, control by non-coding RNA and DNA methylation. Since histone modifications can directly affect chromatin structure and accessibility, they can regulate gene expression levels. Abnormal expression and activity of histone deacetylases (HDACs) have been reported in immune mediated diseases. Increased acetylated levels of lysine residues have been suggested to be related to the overexpression of inflammatory genes. This review focuses on the effect of HDAC modifications on histone and non–histone proteins in autoimmune diseases. Furthermore, we discuss the potential therapeutic effect of HDAC inhibitors (HDACi) used in these diseases.
{"title":"Deacetylation of Histones and Non-histone Proteins in Inflammatory Diseases and Cancer Therapeutic Potential of Histone Deacetylase Inhibitors","authors":"Ezgi Man, Serap EVRAN","doi":"10.2174/0113892029265046231011100327","DOIUrl":"https://doi.org/10.2174/0113892029265046231011100327","url":null,"abstract":"abstract: Epigenetic changes play an important role in the pathophysiology of autoimmune diseases such as allergic asthma, multiple sclerosis, lung diseases, diabetes, cystic fibrosis, atherosclerosis, rheumatoid arthritis, and COVID-19. There are three main classes of epigenetic alterations: post-translational modifications of histone proteins, control by non-coding RNA and DNA methylation. Since histone modifications can directly affect chromatin structure and accessibility, they can regulate gene expression levels. Abnormal expression and activity of histone deacetylases (HDACs) have been reported in immune mediated diseases. Increased acetylated levels of lysine residues have been suggested to be related to the overexpression of inflammatory genes. This review focuses on the effect of HDAC modifications on histone and non–histone proteins in autoimmune diseases. Furthermore, we discuss the potential therapeutic effect of HDAC inhibitors (HDACi) used in these diseases.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135322662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.2174/0113892029270191231013111911
Jianhua Jia, Xiaojing Cao, Zhangying Wei
Introduction: N4 acetylcytidine (ac4C) is a highly conserved nucleoside modification that is essential for the regulation of immune functions in organisms. Currently, the identification of ac4C is primarily achieved using biological methods, which can be time-consuming and laborintensive. In contrast, accurate identification of ac4C by computational methods has become a more effective method for classification and prediction Aim: To the best of our knowledge, although there are several computational methods for ac4C locus prediction, the performance of the models they constructed is poor, and the network structure they used is relatively simple and suffers from the disadvantage of network degradation. This study aims to improve these limitations by proposing a predictive model based on integrated deep learning to better help identify ac4C sites. Methods: In this study, we propose a new integrated deep learning prediction framework, DLCac4C. First, we encode RNA sequences based on three feature encoding schemes, namely C2 encoding, nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Second, one-dimensional convolutional layers and densely connected convolutional networks (DenseNet) are used to learn local features, and bi-directional long short-term memory networks (Bi-LSTM) are used to learn global features. Third, a channel attention mechanism is introduced to determine the importance of sequence characteristics. Finally, a homomorphic integration strategy is used to limit the generalization error of the model, which further improves the performance of the model. Results: The DLC-ac4C model performed well in terms of sensitivity (Sn), specificity (Sp), accuracy (Acc), Mathews correlation coefficient (MCC), and area under the curve (AUC) for the independent test data with 86.23%, 79.71%, 82.97%, 66.08%, and 90.42%, respectively, which was significantly better than the prediction accuracy of the existing methods. Conclusion: Our model not only combines DenseNet and Bi-LSTM, but also uses the channel attention mechanism to better capture hidden information features from a sequence perspective, and can identify ac4C sites more effectively.
{"title":"DLC-ac4C: A Prediction Model for N4-acetylcytidine Sites in Human mRNA Based on DenseNet and Bidirectional LSTM Methods","authors":"Jianhua Jia, Xiaojing Cao, Zhangying Wei","doi":"10.2174/0113892029270191231013111911","DOIUrl":"https://doi.org/10.2174/0113892029270191231013111911","url":null,"abstract":"Introduction: N4 acetylcytidine (ac4C) is a highly conserved nucleoside modification that is essential for the regulation of immune functions in organisms. Currently, the identification of ac4C is primarily achieved using biological methods, which can be time-consuming and laborintensive. In contrast, accurate identification of ac4C by computational methods has become a more effective method for classification and prediction Aim: To the best of our knowledge, although there are several computational methods for ac4C locus prediction, the performance of the models they constructed is poor, and the network structure they used is relatively simple and suffers from the disadvantage of network degradation. This study aims to improve these limitations by proposing a predictive model based on integrated deep learning to better help identify ac4C sites. Methods: In this study, we propose a new integrated deep learning prediction framework, DLCac4C. First, we encode RNA sequences based on three feature encoding schemes, namely C2 encoding, nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Second, one-dimensional convolutional layers and densely connected convolutional networks (DenseNet) are used to learn local features, and bi-directional long short-term memory networks (Bi-LSTM) are used to learn global features. Third, a channel attention mechanism is introduced to determine the importance of sequence characteristics. Finally, a homomorphic integration strategy is used to limit the generalization error of the model, which further improves the performance of the model. Results: The DLC-ac4C model performed well in terms of sensitivity (Sn), specificity (Sp), accuracy (Acc), Mathews correlation coefficient (MCC), and area under the curve (AUC) for the independent test data with 86.23%, 79.71%, 82.97%, 66.08%, and 90.42%, respectively, which was significantly better than the prediction accuracy of the existing methods. Conclusion: Our model not only combines DenseNet and Bi-LSTM, but also uses the channel attention mechanism to better capture hidden information features from a sequence perspective, and can identify ac4C sites more effectively.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135779408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}