Current Genomics - Volume 24, Issue 3, 2023

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: posttranslational 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 non128;“histone proteins in autoimmune diseases. Furthermore, we discuss the potential therapeutic effect of HDAC inhibitors (HDACi) used in these diseases

Background: The gypsy moth (Lymantria dispar L., Lepidoptera: Erebidae) is a worldwide pest of trees and forests. Lymantria dispar nucleopolyhedrovirus (LdMNPV) belongs to the Baculoviridae family and is an insect virus specific to gypsy moth larvae. In this study, we describe the complete genome sequences of three geographically diverse isolates, H2 (China), J2 (Japan), and T3 (Turkey), of Lymantria dispar multiple nucleopolyhedrovirus (LdMNPV). Methods: The genomes of isolates H2, J2, and T3 were subjected to shotgun pyrosequencing using Roche 454 FLX and assembled using Roche GS De Novo Assembler. Comparative analysis of all isolates was performed using bioinformatics methods.Results: The genomes of LdMNPV-H2, J2, and T3 were 164,746, 162,249, and 162,614 bp in size, had GC content of 57.25%, 57.30%, and 57.46%, and contained 162, 165, and 164 putative open reading frames (ORFs 137;¥ 150 nt), respectively. Comparison between the reference genome LdMNPV-5/6 (AF081810) and the genomes of LdMNPV-H2, J2, and T3 revealed differences in gene content. Compared with LdMNPV-5/6, ORF5, 6, 8, 10, 31, and 67 were absent in LdMNPVH2, ORF5, 13, and 66 were absent in LdMNPV-J2, and ORF10, 13, 31, and 67 were absent in LdMNPV-T3. In addition, the gene encoding the mucin-like protein (ORF4) was split into two parts in isolates H2 and T3 and designated ORF4a and ORF4b. Phylogenetic analysis grouped isolates H2 and J2 in a different cluster than isolate T3, which is more closely related to the Turkish and Polish isolates. In addition, H2 was found to be closely related to a South Korean LdMNPV isolate. Conclusion: This study provided a more detailed overview of the relationships between different geographic LdMNPV isolates. The results showed remarkable differences between groups at the genome level.

Background: Recent studies on CRISPR/Cas9-mediated gene editing in Schistosoma mansoni have shed new light on the study and control of this parasitic helminth. However, the gene editing efficiency in this parasite is modest.Methods: To improve the efficiency of CRISPR/Cas9 genome editing in schistosomes, we used lentivirus, which has been effectively used for gene editing in mammalian cells, to deliver plasmid DNA encoding Cas9 nuclease, a sgRNA targeting acetylcholinesterase (SmAChE) and a mCherry fluorescence marker into schistosomes.Results: MCherry fluorescence was observed in transduced eggs, schistosomula, and adult worms, indicating that the CRISPR components had been delivered into these parasite stages by lentivirus. In addition, clearly changed phenotypes were observed in SmAChE-edited parasites, including decreased SmAChE activity, reduced hatching ability of edited eggs, and altered behavior of miracidia hatched from edited eggs. Next-generation sequencing analysis demonstrated that the lentiviral transductionbased CRISPR/Cas9 gene modifications in SmAChE-edited schistosomes were homology-directed repair predominant but with much lower efficiency than that obtained using electroporation (data previously published by our laboratory) for the delivery of CRISPR components.Conclusion: Taken together, electroporation is more efficient than lentiviral transduction in the delivery of CRISPR/Cas9 into schistosomes for programmed genome editing. The exploration of tactics for enhancing CRISPR/Cas9 gene editing provides the basis for the future improvement of programmed genome editing in S. mansoni.

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.

Background: Viruses have high mutation rates, facilitating rapid evolution and the emergence of new species, subspecies, strains and recombinant forms. Accurate classification of these forms is crucial for understanding viral evolution and developing therapeutic applications. Phylogenetic classification is typically performed by analyzing molecular differences at the genomic and sub-genomic levels. This involves aligning homologous proteins or genes. However, there is growing interest in developing alignment-free methods for whole-genome comparisons that are computationally efficient.Methods: Here we elaborate on the Chaos Game Representation (CGR) method, based on concepts of statistical physics and free of sequence alignment assumptions. We adopt the CGR method for classification of the closely related clades/lineages A and B of the SARS-Corona virus 2019 (SARS-CoV-2), which is one of the fastest evolving viruses.Results: Our study shows that the CGR approach can easily yield the SARS-CoV-2 phylogeny from the available whole genomes of lineage A and lineage B sequences. It also shows an accurate classification of eight different strains and the newly evolved XBB variant from its parental strains. Compared to alignment-based methods (Neighbour-Joining and Maximum Likelihood), the CGR method requires low computational resources, is fast and accurate for long sequences, and, being a K-mer based approach, allows simultaneous comparison of a large number of closely-related sequences of different sizes. Further, we developed an R pipeline CGRphylo, available on GitHub, which integrates the CGR module with various other R packages to create phylogenetic trees and visualize them.Conclusion: Our findings demonstrate the efficacy of the CGR method for accurate classification and tracking of rapidly evolving viruses, offering valuable insights into the evolution and emergence of new SARS-CoV-2 strains and recombinants.