Development and application of ribonucleic acid therapy strategies against COVID-19

The Prediction of Recombination Hotspot Based on Automated Machine Learning

Meiotic recombination plays a pivotal role in genetic evolution. Genetic variation induced by recombination is a crucial factor in generating biodiversity and a driving force for evolution. At present, the development of recombination hotspot prediction methods has encountered challenges related to insufficient feature extraction and limited generalization capabilities. This paper focused on the research of recombination hotspot prediction methods. We explored deep learning-based recombination hotspot prediction and scrutinized the shortcomings of prevalent models in addressing the challenge of recombination hotspot prediction. To addressing these deficiencies, an automated machine learning approach was utilized to construct recombination hotspot prediction model. The model combined sequence information with physicochemical properties by employing TF-IDF-Kmer and DNA composition components to acquire more effective feature data. Experimental results validate the effectiveness of the feature extraction method and automated machine learning technology used in this study. The final model was validated on three distinct datasets and yielded accuracy rates of 97.14%, 79.71%, and 98.73%, surpassing the current leading models by 2%, 2.56%, and 4%, respectively. In addition, we incorporated tools such as SHAP and AutoGluon to analyze the interpretability of black-box models, delved into the impact of individual features on the results, and investigated the reasons behind misclassification of samples. Finally, an application of recombination hotspot prediction was established to facilitate easy access to necessary information and tools for researchers. The research outcomes of this paper underscore the enormous potential of automated machine learning methods in gene sequence prediction.

Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review)

Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.

ncRS: A resource of non-coding RNAs in sepsis

Sepsis, a life-threatening condition triggered by the body's response to infection, presents a significant global healthcare challenge characterized by disarrayed host responses, widespread inflammation, organ impairment, and heightened mortality rates. This study introduces the ncRS database (http://www.ncrdb.cn), a meticulously curated repository housing 1144 experimentally validated non-coding RNAs (ncRNAs) intricately linked with sepsis. ncRS offers comprehensive RNA data, exhaustive experimental insights, and integrated annotations from diverse databases. This resource empowers researchers and clinicians to decipher ncRNAs' roles in sepsis pathogenesis, potentially identifying vital biomarkers for early diagnosis and prognosis, thus facilitating personalized treatments.

SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations

Over the years, extensive research has highlighted the functional roles of small nucleolar RNAs in various biological processes associated with the development of complex human diseases. Therefore, understanding the existing relationships between different snoRNAs and diseases is crucial for advancing disease diagnosis and treatment. However, classical biological experiments for identifying snoRNA-disease associations are expensive and time-consuming. Therefore, there is an urgent need for cost-effective computational techniques that can enhance the efficiency and accuracy of prediction. While several computational models have already been proposed, many suffer from limitations and suboptimal performance. In this study, we introduced a novel Graph Neural Network-based (GNN) classification model, called SAGESDA, which is implemented through the GraphSAGE architecture with attention for the prediction of snoRNA-disease associations. The classifier leverages local neighbouring nodes in a heterogeneous network to generate new node embeddings through message passing. The mini-batch gradient descent technique was applied to divide the graph into smaller sub-graphs, which enhances the model's accuracy, speed and scalability. With these advancements, SAGESDA attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 using the standard dot product classifier, surpassing previous related studies. This notable performance demonstrates that SAGESDA is a promising model for predicting unknown snoRNA-disease associations with high accuracy. The SAGESDA implementation details can be obtained from https://github.com/momanyibiffon/SAGESDA.git.

Non-coding RNAs expression in SARS-CoV-2 infection: pathogenesis, clinical significance, and therapeutic targets

The coronavirus disease 2019 (COVID-19) pandemic has been looming globally for three years, yet the diagnostic and treatment methods for COVID-19 are still undergoing extensive exploration, which holds paramount importance in mitigating future epidemics. Host non-coding RNAs (ncRNAs) display aberrations in the context of COVID-19. Specifically, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) exhibit a close association with viral infection and disease progression. In this comprehensive review, an overview was presented of the expression profiles of host ncRNAs following SARS-CoV-2 invasion and of the potential functions in COVID-19 development, encompassing viral invasion, replication, immune response, and multiorgan deficits which include respiratory system, cardiac system, central nervous system, peripheral nervous system as well as long COVID. Furthermore, we provide an overview of several promising host ncRNA biomarkers for diverse clinical scenarios related to COVID-19, such as stratification biomarkers, prognostic biomarkers, and predictive biomarkers for treatment response. In addition, we also discuss the therapeutic potential of ncRNAs for COVID-19, presenting ncRNA-based strategies to facilitate the development of novel treatments. Through an in-depth analysis of the interplay between ncRNA and COVID-19 combined with our bioinformatic analysis, we hope to offer valuable insights into the stratification, prognosis, and treatment of COVID-19.

MetaboliteCOVID: A manually curated database of metabolite markers for COVID-19

In the realm of unraveling COVID-19's intricacies, numerous metabolomic investigations have been conducted to discern the unique metabolic traits exhibited within infected patients. These endeavors have yielded a substantial reservoir of potential data pertaining to metabolic biomarkers linked to the virus. Despite these strides, a comprehensive and meticulously structured database housing these crucial biomarkers remains absent. In this study, we developed MetaboliteCOVID, a manually curated database of COVID-19-related metabolite markers. The database currently comprises 665 manually selected entries of significantly altered metabolites associated with early diagnosis, disease severity, prognosis, and drug response in COVID-19, encompassing 337 metabolites. Additionally, the database offers a user-friendly interface, containing abundant information for querying, browsing, and analyzing COVID-19-related abnormal metabolites in different body fluids. In summary, we believe that this database will effectively facilitate research on the functions and mechanisms of COVID-19-related metabolic biomarkers, thereby advancing both basic and clinical research on COVID-19. MetaboliteCOVID is free available at: https://cellknowledge.com.cn/MetaboliteCOVID.

P450Rdb: a manually curated database of reactions catalyzed by cytochrome P450 enzymes

INTRODUCTION

Cytochrome P450 enzymes (P450s) are recognized as the most versatile catalysts worldwide, playing vital roles in numerous biological metabolism and biosynthesis processes across all kingdoms of life. Despite the vast number of P450 genes available in databases (over 300,000), only a small fraction of them (less than 0.2%) have undergone functional characterization.

OBJECTIVES

To provide a convenient platform with abundant information on P450s and their corresponding reactions, we introduce the P450Rdb database, a manually curated resource compiles literature-supported reactions catalyzed by P450s.

METHODS

All the P450s and Reactions were manually curated from the literature and known databases. Subsequently, the P450 reactions organized and categorized according to their chemical reaction type and site. The website was developed using HTML and PHP languages, with the MySQL server utilized for data storage.

RESULTS

The current version of P450Rdb catalogs over 1,600 reactions, involving more than 590 P450s across a diverse range of over 200 species. Additionally, it offers a user-friendly interface with comprehensive information, enabling easy querying, browsing, and analysis of P450s and their corresponding reactions. P450Rdb is free available at http://www.cellknowledge.com.cn/p450rdb/.

CONCLUSIONS

We believe that this database will significantly promote structural and functional research on P450s, thereby fostering advancements in the fields of natural product synthesis, pharmaceutical engineering, biotechnological applications, agricultural and crop improvement, and the chemical industry.

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations

MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases' biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease.

A First Computational Frame for Recognizing Heparin-Binding Protein

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.

Parameterization of the miniPEG-Modified γPNA Backbone: Toward Induced γPNA Duplex Dissociation

γ-Modified peptide nucleic acids (γPNAs) serve as potential therapeutic agents against genetic diseases. Miniature poly(ethylene glycol) (miniPEG) has been reported to increase solubility and binding affinity toward genetic targets, yet details of γPNA structure and dynamics are not understood. Within our work, we parameterized missing torsional and electrostatic terms for the miniPEG substituent on the γ-carbon atom of the γPNA backbone in the CHARMM force field. Microsecond timescale molecular dynamics simulations were carried out on six miniPEG-modified γPNA duplexes from NMR structures (PDB ID: 2KVJ). Three NMR models for the γPNA duplex (PDB ID: 2KVJ) were simulated as a reference for structural and dynamic changes captured for the miniPEG-modified γPNA duplex. Principal component analysis performed on the γPNA backbone atoms identified a single isotropic conformational substate (CS) for the NMR simulations, whereas four anisotropic CSs were identified for the ensemble of miniPEG-modified γPNA simulations. The NMR structures were found to have a 23° helical bend toward the major groove, consistent with our simulated CS structure of 19.0°. However, a significant difference between simulated methyl- and miniPEG-modified γPNAs involved the opportunistic invasion of miniPEG through the minor and major groves. Specifically, hydrogen bond fractional analysis showed that the invasion was particularly prone to affect the second G-C base pair, reducing the Watson-Crick base pair hydrogen bond by 60% over the six simulations, whereas the A-T base pairs decreased by only 20%. Ultimately, the invasion led to base stack reshuffling, where the well-ordered base stacking was reduced to segmented nucleobase stacking interactions. Our 6 μs timescale simulations indicate that duplex dissociation suggests the onset toward γPNA single strands, consistent with the experimental observation of decreased aggregation. To complement the insight of miniPEG-modified γPNA structure and dynamics, the new miniPEG force field parameters allow for further exploration of such modified γPNA single strands as potential therapeutic agents against genetic diseases.

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.

Antiviral Activity of Oligonucleotides Targeting the SARS-CoV-2 Genomic RNA Stem-Loop Sequences within the 3'-End of the ORF1b

Increased evidence shows vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exhibited no long-term efficacy and limited worldwide availability, while existing antivirals and treatment options have only limited efficacy. In this study, the main objective was the development of antiviral strategies using nucleic acid-based molecules. To this purpose, partially overlapped 6-19-mer phosphorothioate deoxyoligonucleotides (S-ONs) designed on the SARS-CoV-2 genomic RNA stem-loop packaging sequences within the 3' end of the ORF1b were synthetized using the direct and complementary sequence. Among the S-ONs tested, several oligonucleotides exhibited a fifty percent inhibitory concentration antiviral activity ranging from 0.27 to 34 μM, in the absence of cytotoxicity. The S-ON with a scrambled sequence used in the same conditions was not active. Moreover, selected 10-mer S-ONs were tested using different infectious doses and against different SARS-CoV-2 variants, showing comparable antiviral activity that was abrogated when the central sequence was mutated. Experiments to evaluate the intracellular functional target localization of the S-ON inhibitory activity were also performed. Collectively the data indicate that the SARS-CoV-2 packaging region in the 3' end of the ORF1b may be a promising target candidate for further investigation to develop innovative nucleic-acid-based antiviral therapy.

Noncoding RNAs Emerging as Drugs or Drug Targets: Their Chemical Modification, Bio-Conjugation and Intracellular Regulation

With the increasing understanding of various disease-related noncoding RNAs, ncRNAs are emerging as novel drugs and drug targets. Nucleic acid drugs based on different types of noncoding RNAs have been designed and tested. Chemical modification has been applied to noncoding RNAs such as siRNA or miRNA to increase the resistance to degradation with minimum influence on their biological function. Chemical biological methods have also been developed to regulate relevant noncoding RNAs in the occurrence of various diseases. New strategies such as designing ribonuclease targeting chimeras to degrade endogenous noncoding RNAs are emerging as promising approaches to regulate gene expressions, serving as next-generation drugs. This review summarized the current state of noncoding RNA-based theranostics, major chemical modifications of noncoding RNAs to develop nucleic acid drugs, conjugation of RNA with different functional biomolecules as well as design and screening of potential molecules to regulate the expression or activity of endogenous noncoding RNAs for drug development. Finally, strategies of improving the delivery of noncoding RNAs are discussed.