BACKGROUND AND OBJECTIVE

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the causative agent of COVID-19 has significantly challenged the public health landscape in late 2019. After almost 3 years of the first ever SARS-CoV-2 case, the World Health Organization (WHO) declared the end of this global health emergency in May 2023. Although, despite the subsequent drop of COVID-19 cases, the SARS-CoV-2 infection still exhibited multiple waves of infection, primarily attributed to the appearance of new variants. Five of these variants have been classified as Variants of Concern (VOC): Alpha, Beta, Gamma, Delta, and the most recent, Omicron. Therefore, the development of methods for the timely and accurate detection of viral variants remains fundamental, ensuring an ongoing and effective response to the disease. This study aims to evaluate the feasibility of the application of an in-house approach in genomic surveillance for the detection of SARS-CoV-2 variants using in silico designed primers.

METHODS

The primers used for the study were particularly designed based on conserved regions of certain genes in the virus, targeting distinct mutations found in known variants of SARS-CoV-2. Viral RNA extracts from nasopharyngeal samples (n=14) were subjected to quantitative and qualitative tests (Nanodrop and AGE). Selected samples were then analyzed by RT-PCR and amplicons were submitted for sequencing. Sequence alignment analysis was carried out to identify the prevailing COVID-19 variant present in the sample population.

RESULTS

The study findings demonstrated that the in-house method was able to successfully amplify conserved sequences (spike, envelope, membrane, ORF1ab) and enabled identification of the circulating SARS-CoV-2 variant among the samples. Majority of the samples were identified as Omicron variant. Three out of four designed primers effectively bound into the conserved sequence of target genes present in the sample, revealing the specific SARSCoV-2 variant. The detected mutations characterized for Omicron found in the identified lineages included K417N, S477N, and P681H which were also identified as mutations of interest. Furthermore, identification of the B.1.448 lineage which was not classified in any known variant also provided the potential of the developed in-house method in detecting unknown variants of COVID-19.

CONCLUSION

Among the five VOCs, Omicron is the most prevalent and dominant variant. The in-house direct PCR product sequencing surveillance (DPPSS) method provided an alternative platform for SAR-CoV-2 variant analysis which is accessible and affordable than the conventional diagnostic surveillance methods and the whole genome sequencing. Further evaluation and improvements on the oligonucleotide primers may offer significant contribution to the development of a specific and direct PCRbased detection of new emerging COVID-19 variants.

Sars-cov-2 ; Polymerase Chain Reaction ; Dna Primers ; Oligonucleotide Primers ; Computer Simulation ; Conserved Sequence ; Coronavirus ; Covid-19 ; Disease ; Emergencies ; Evaluation Studies As Topic ; Genes ; Genome ; Global Health ; Health ; Identification (psychology) ; Infection ; Infections ; Membranes ; Methods ; Mutation ; Oligonucleotides ; Organizations ; Population ; Public Health ; Rna ; Rna, Viral ; Sars Virus ; Sequence Alignment ; Severe Acute Respiratory Syndrome ; Syndrome ; Viruses ; Whole Genome Sequencing ; World Health Organization

OBJECTIVES: This study aimed to explore the active components, potential targets, and mechanism of Cnidii Fructus in the treatment of periodontitis with osteoprosis through network pharmacology, molecular docking, and molecular dynamics simulation technology. METHODS: The main chemical constituents and targets of Cnidii Fructus were screened using the TCMSP and SwissTargetPrediction databases, as well as literature reports. Targets of periodontitis and osteoporosis were predicted using different databases. The intersection targets of Cnidii Fructus, periodontitis, and osteoporosis were obtained using Venny 2.1. The protein-protein interaction network was formed on the STRING platform. Cytoscape 3.9.1 was used to construct the active component-intersection target interaction network, perform the topological analysis, and screen key targets and core active components. Furthermore, the Metascape database was used to perform gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis on the intersection targets. The top five key targets and core active components were selected as receptor proteins and ligand small molecules. Discovery Studio 2019 was used to dock ligands and receptors and visualize the docking results. Molecular dynamics simulation was conducted using Gromacs2022.3 to assess the stability of the interactions between the core active components and the main targets. RESULTS: A total of 20 potential active ingredients of Cnidii Fructus were screened, and 116 targets of Cnidii Fructus were obtained for treating periodontitis and osteoporosis. GO and KEGG analyses of the 116 targets showed that Cnidii Fructus may play a therapeutic role through the phosphoinositide 3-kinase-protein kinase B (PI3K-Akt) and advanced glycation end products-receptor for advanced glycation end products (AGE-RAGE) signaling pathways. Molecular docking showed that the core constituents were well bound to the main targets. Molecular dynamics simulations confirmed the stability of the Diosmetin-AKT1 complex system. CONCLUSIONS The preliminary discovery of the potential molecular pharmacological mechanism of Cnidii Fructus extract in the targeted treatment of periodontitis with osteoporosis through a multi-component, multitarget, and multi-pathway approach can serve as a theoretical foundation for future drug-development research and clinical application.
Molecular Docking Simulation ; Molecular Dynamics Simulation ; Network Pharmacology ; Periodontitis/complications* ; Drugs, Chinese Herbal/chemistry* ; Osteoporosis/complications* ; Humans ; Protein Interaction Maps ; Cnidium/chemistry*

OBJECTIVES: This study aimed to explore the potential target and molecular mechanism of Eclipta prostrata L-Ligustrum Lucidum Ait (EPL-LLA) in the treatment of periodontitis by using network pharmacology and molecular docking technology, and to explore its biocompatibility, regulatory effects on inflammatory factors, and antioxidant acti-vity through in vitro experiments. METHODS: The active components and potential targets of EPL-LLA were screened and predicted through a variety of databases, and the intersection of EPL-LLA and periodontitis targets was selected. The protein interaction network (PPI) was analyzed by the string platform. The Metascape database was used for gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. The active ingredients from the top 6 degrees were docked with the core targets, and the results of binding energy were visualized. An in vitro cell model was established to evaluate the biocompatibility, modulation of inflammatory factors, and antioxidative effects of EPL-LLA through cell counting kit-8 (CCK-8), quantitative real-time polymerase chain reaction (qRT-PCR) and 2',7'-Dichlorodihydrofluorescein diacetate (DCFH-DA) fluorescent probe assays. RESULTS: Screening revealed 13 active components in EPL corresponding to 220 potential targets, 10 active components in LLA corresponding to 283 potential targets, and 1 643 periodontitis-related targets, with 91 shared targets among the three. GO analysis of the shared targets yielded 5 271 entries, while KEGG enrichment analysis indicated involvement in 253 signaling pathways. Molecular docking confirmed stable binding between the top 6 active components and core targets. CCK-8 assays demonstrated good biocompatibility of EPL-LLA at concentrations 0.02 mg/mL (P<0.05). qRT-PCR showed that EPL-LLA reduced the mRNA expression of pro-inflammatory factors in macrophages stimulated by Porphyromonas gingivalis lipopolysaccharide while upregulating anti-inflammatory factor mRNA expression (P<0.05). DCFH-DA fluorescence probe assays confirmed the reactive oxygen species (ROS)-scavenging capacity of EPL-LLA (P<0.05). CONCLUSIONS EPL-LLA may treat periodontitis through multi-component, multi-target, and multi-pathway mechanisms, providing a theoretical basis for further research on its therapeutic potential.
Periodontitis/drug therapy* ; Molecular Docking Simulation ; Eclipta/chemistry* ; Humans ; Protein Interaction Maps ; Ligustrum/chemistry* ; Antioxidants/pharmacology* ; Drugs, Chinese Herbal/therapeutic use* ; Network Pharmacology

Flavonoids are key bioactive components for evaluating the pharmacological activities of Chimonanthus praecox. Exploring the potential flavonoids and pharmacological mechanisms of C. praecox lays a foundation for the rational development and efficient utilization of this plant. This study employed ultra-performance liquid chromatography-tandem mass spectrometry-based widely targeted metabolomics to comprehensively identify the flavonoids in C. praecox. Network pharmacology was employed to explore the bioactive flavonoids and their mechanisms of action. Molecular docking was adopted to validate the predicted results. Finally, the content of bioactive flavonoids in different varieties of C. praecox was measured. The widely targeted metabolomics analysis identified 387 flavonoids in C. praecox, and the flavonoids varied among different varieties. Network pharmacology predicted 96 chemical components including 19 bioactive compounds, 181 corresponding targets and 2 504 disease targets, among which 99 targets were shared by the active components and the disease. Thirty-three core targets were predicted, involving 229 gene ontology terms and 99 pathways (P≤0.05), which indicated that the flavonoids components of C. praecox exhibited pharmacological activities including antioxidant, anti-inflammatory, antimicrobial, and antiviral activities. Topological analysis screened out five core components (salvigenin, laricitrin, isorhamnetin, quercetin, and 6-hydroxyluteolin) and five core targets (SRC, PIK3R1, AKT1, ESR1, and AKR1C3). The predicted bioactive flavonoids from C. praecox stably bound to key targets, which indicated that these flavonoids possessed potential bioactivities in their interactions with the targets. The flavonoids in C. praecox exerted pharmacological activities in a multi-component, multi-target, and multi-pathway manner. The combined application of metabolomics and network pharmacology provides a theoretical basis for in-depth studies on the pharmacological effects and mechanisms of C. praecox.
Flavonoids/metabolism* ; Network Pharmacology ; Metabolomics/methods* ; Molecular Docking Simulation ; Calycanthaceae/chemistry* ; Tandem Mass Spectrometry ; Drugs, Chinese Herbal/chemistry*

Computational modeling is an invaluable tool for mechanism analysis, directed engineering, and rational design of biological parts, metabolic networks, and even cellular systems. It can provide new technological solutions to address biological challenges at different levels and has become a central focus of research in biomanufacturing. In the computational modeling of proteins, which are the key parts in biological systems, the traditional physics-based methods (computer software and mathematical model) have been widely used to study the physical and chemical processes in the functioning of proteins, and have thus been recognized as a powerful tool for understanding complex biological systems and guiding experimental designs. As the scale of computational modeling continues to expand, traditional modeling techniques face difficulties in balancing computational accuracy and speed. In recent years, the explosive growth of biological data has made it possible to construct high-performance artificial intelligence (AI) models, which brings new opportunities to the computational modeling of proteins, and the AI-enhanced physics-based computational modeling technologies have emerged. This combined strategy not only incorporates the chemical knowledge and established physical principles but also is powerful in data processing and pattern recognition, which greatly improves the computational efficiency and prediction accuracy, as well as possesses stronger interpretation ability, transferability, and robustness. The AI-enhanced physics-based computational modeling technologies have already shown great potential and value in biocatalysis, paving a new way for the future development of biomanufacturing.
Artificial Intelligence ; Proteins/chemistry* ; Computer Simulation ; Software ; Computational Biology/methods*

Transcription factor (TF)-based biosensors have been widely applied in metabolic engineering, synthetic biology, metabolites monitoring, etc. These biosensors are praised for the high orthogonality, modularity, and operability. However, most natural TFs with weak responses and low specificity still demand optimization for desired performance in applications. Herein, we comprehensively summarize the recent advances in the engineering and optimization of TF-based biosensors with the assistance of computational simulation and artificial intelligence. This review includes the regulatory protein engineering aided by protein structure prediction and ligand binding simulation and the regulatory protein responses predicted by a mathematical model obtained from machine learning of mutagenesis data. In comparison with conventional tools, computational simulation and artificial intelligence enable more accurate and rapid design and construction of biosensors. Thus, these technologies will greatly promote the development of novel biosensors for applications.
Biosensing Techniques/methods* ; Transcription Factors/metabolism* ; Artificial Intelligence ; Protein Engineering/methods* ; Computer Simulation ; Synthetic Biology ; Machine Learning

As green, sustainable, and environmentally friendly material processing processes using biological cells or enzymes to achieve substance conversion, bioprocesses play an increasingly important role in biomanufacturing. It is difficult to optimize bioprocesses because of the complex relationship at multiple levels and multiple scales. The knowledge of mesoscale behaviors is the key to understanding the dynamics of bioprocesses and to sort out the complex relationships of parameter variations in the spatial-temporal domain. Mesoscale numerical simulation paves a way for understanding these phenomena, and the integration of artificial intelligence (AI) and mesoscale simulation offers new vitality into the optimization of bioprocesses. This article reviews the progress in mesoscale simulation and AI optimization of bioprocesses and discusses the possible development directions, aiming to promote the development of this field.
Artificial Intelligence ; Biotechnology/trends* ; Computer Simulation

The inappropriate utilisation of the agricultural insecticide lambda-cyhalothrin (LCT) has the potential to result in residues that compromise food safety and human health. Respiratory exposure represents a major route of LCT contact in humans. Nevertheless, its deleterious effects on the respiratory system remain inadequately characterized. It is imperative to elucidate the potential relationship and mechanisms by which lung cancer, a significant malignant neoplasm of the respiratory system, is associated with exposure to LCT. The objective of this study is to utilise bioinformatics methodologies to screen and analyse the key target molecules affected by LCT in the occurrence of lung cancer, and their mechanisms of action. Specifically, network toxicology methods were employed to identify core targets of LCT-induced lung cancer. Subsequently, functional annotation to delineate associated cellular pathways, and finally, molecular docking to simulate binding modes between LCT and shared core targets. Core target screening identified 50 targets for large cell lung cancer, 54 for small cell lung cancer, 29 for lung squamous cell carcinoma, and 28 for lung adenocarcinoma, with EGFR, HSP90AA1, JUN, CCL2, MYC, CXCL8, and HSPA4 shared in all subtypes. Functional annotation revealed that LCT-triggered oncogenic pathways predominantly involved ubiquitination, chemotaxis, and tumor immune signaling. Molecular docking demonstrated spontaneous binding of LCT to core targets mediated by hydrogen bonds and π-cation interactions. These results establish a theoretical framework for evaluating LCT-associated risks of lung cancer and respiratory system damage.
Lung Neoplasms/metabolism* ; Pyrethrins/toxicity* ; Humans ; Insecticides/toxicity* ; Nitriles/toxicity* ; Molecular Docking Simulation

Objective To screen the anti-CD22-specific nanobodies to provide a basis for immunotherapy agents. Methods The naive phage nanobody library was constructed and its diversity was analyzed. Three rounds of biotinylated streptavidin liquid phase screening were performed by using biotinylated CD22 antigen as the target, and the sequence of nanobodies against CD22 were identified by ELISA and gene sequencing. Results The capacity of the constructed naive phage nanobody library was 3.89×10⁹ CFU/mL, and the insertion of effective fragments was higher than 85%. Based on this library, seven anti-human CD22 nanobodies were screened, and the amino acid sequence comparison results showed that the overall similarity was 70.34%, and all of them were hydrophilic proteins. The results of protein-protein complex docking prediction showed that the mimetic proteins of the five nanobody sequences could be paired and linked to CD22, and the main forces were hydrophobic interaction and hydrogen bonding. Conclusion This study provided a basis for the study of chimeric antigen receptor T cells targeting CD22, successfully constructed the natural phage nanobody library and obtaining five anti-CD22-specific nanobodies.
Camelus/immunology* ; Single-Domain Antibodies/chemistry* ; Peptide Library ; Humans ; Animals ; Sialic Acid Binding Ig-like Lectin 2/genetics* ; Amino Acid Sequence ; Molecular Docking Simulation

BACKGROUND: Retinal ganglion cell (RGC) death caused by acute ocular hypertension is an important characteristic of acute glaucoma. Receptor-interacting protein kinase 3 (RIPK3) that mediates necroptosis is a potential therapeutic target for RGC death. However, the current understanding of the targeting agents and mechanisms of RIPK3 in the treatment of glaucoma remains limited. Notably, artificial intelligence (AI) technologies have significantly advanced drug discovery. This study aimed to discover RIPK3 inhibitor with AI assistance. METHODS: An acute ocular hypertension model was used to simulate pathological ocular hypertension in vivo . We employed a series of AI methods, including large language and graph neural network models, to identify the target compounds of RIPK3. Subsequently, these target candidates were validated using molecular simulations (molecular docking, absorption, distribution, metabolism, excretion, and toxicity [ADMET] prediction, and molecular dynamics simulations) and biological experiments (Western blotting and fluorescence staining) in vitro and in vivo . RESULTS: AI-driven drug screening techniques have the potential to greatly accelerate drug development. A compound called HG9-91-01, identified using AI methods, exerted neuroprotective effects in acute glaucoma. Our research indicates that all five candidates recommended by AI were able to protect the morphological integrity of RGC cells when exposed to hypoxia and glucose deficiency, and HG9-91-01 showed a higher cell survival rate compared to the other candidates. Furthermore, HG9-91-01 was found to protect the retinal structure and reduce the loss of retinal layers in an acute glaucoma model. It was also observed that the neuroprotective effects of HG9-91-01 were highly correlated with the inhibition of PANoptosis (apoptosis, pyroptosis, and necroptosis). Finally, we found that HG9-91-01 can regulate key proteins related to PANoptosis, indicating that this compound exerts neuroprotective effects in the retina by inhibiting the expression of proteins related to apoptosis, pyroptosis, and necroptosis. CONCLUSION AI-enabled drug discovery revealed that HG9-91-01 could serve as a potential treatment for acute glaucoma.
Animals ; Glaucoma/metabolism* ; Neuroprotective Agents/pharmacology* ; Mice ; Receptor-Interacting Protein Serine-Threonine Kinases/metabolism* ; Artificial Intelligence ; Retinal Ganglion Cells/metabolism* ; Disease Models, Animal ; Molecular Docking Simulation ; Mice, Inbred C57BL ; Male