Bioinformatics and Biology Insights最新文献

WNT proteins constitute a highly conserved family of signaling molecules that play an important role in regulating embryonic development and maintaining adult tissue homeostasis. Their diverse biological functions are mediated through multiple signaling pathways, including both canonical β-catenin-dependent and several non-canonical mechanisms. The regulatory activity of WNT proteins is closely linked to their unique structural organization, the presence of N-terminal signal peptides, and posttranslational modifications. In this study, in silico methods were used to analyze the structural features of WNT proteins. Specifically, the isoelectric points, GRAVY scores, aliphatic indices, and instability indices were determined, and correlation analysis was performed to examine relationships between the latter three parameters. In addition, the characteristics of N-terminal signal peptides in WNT family proteins were investigated, with a particular focus on the bioinformatic prediction of N-terminal peptide lengths in the WNT2B protein isoforms. Furthermore, in silico modeling and molecular dynamics simulations were employed to study the tertiary structure of WNT2B and to assess the significance of O-acylation at serine for the behavior of the mature protein in an aqueous environment. Thus, using computational approaches, new data were obtained on the structural features and dynamic properties of this group of regulatory proteins.

Nectin-1/herpes simplex virus glycoprotein D (HSV gD) interaction is crucial to drive herpes simplex virus (HSV) entry. Polyanions are known to show great potential as antivirals. Thus, we explored a peptide-based biotherapeutic approach and, for the first time, evaluated an anionic peptide derived from nectin-1 designed to bind HSV gD. Peptides enriched in acidic and basic residues were selected and computationally modeled using PEP-FOLD3, PROCHECK, ClusPro 2.0, and Desmond. Their antiviral efficacy was tested through virucidal, cell pretreatment, attachment inhibition, entry inhibition, and cytopathic effect (CPE) inhibition assays using a 10 TCID₅₀ (Tissue Culture Infectious Dose 50%) viral dose. Among 4 designed peptides, C1 and C2 showed strong binding to HSV-1 and HSV-2 gD in molecular dynamic (MD) simulations. Peptide C1 exhibited significant virucidal activity (HSV-1: 64.92%, HSV-2: 67.16%), attachment inhibition (HSV-1: 62.03%, HSV-2: 59.38%), and host cell-entry inhibition (HSV-1: 71.37%, HSV-2: 76.28%) at 250 µg/mL concentration. Combination treatment with peptides C1 and C2 at a final concentration of 250 µg/mL (125 µg/mL each) exhibited an additive effect against HSV-1 (68.57%) and HSV-2 (73.37%) infections when tested by CPE inhibition assay. This highlights the potential of HSV gD-targeted anionic peptides for future anti-HSV therapeutics.

Avian coccidiosis, caused by Eimeria protozoa, presents a significant threat to poultry, with Eimeria tenella being particularly harmful due to its impact on the chicken cecum. Growing resistance to current treatments necessitates alternative therapeutic approaches. Consequently, this study employed an immunoinformatics approach to design a multiepitope vaccine targeting E tenella. Key proteins, including the sporulated oocyst TA4 antigen, alkylglycerone-phosphate synthase, and apical membrane antigen-1, were analysed for epitope prediction. Further comprehensive downstream analysis identified 13 MHC class I, 6 MHC class II, and 7 B-cell epitopes, which were linked with suitable linkers. Also, cholera toxin subunit B was incorporated as an adjuvant, creating a 531-amino-acid construct. The vaccine demonstrated favourable predicted antigenicity, non-allergenicity, and stability properties. Molecular docking predicted interaction with toll-like receptor 15, while immune response simulation showed potential induction of various immunocytes, including helper and cytotoxic T-cells, natural killer cells, and immunoglobulins. The vaccine was predicted to promote antigen clearance after the second dose, suggesting strong memory response potential. These findings indicate the designed vaccine could stimulate a potent protective immune response against E tenella infection. However, further in vitro and in vivo validation studies are necessary to confirm the vaccine's efficacy before clinical application in poultry immunization programmes.

Coronary artery disease (CAD) and peripheral artery disease have serious effects on quality of life. Recent studies have shown that peripheral arterial disorders increase a person's risk of developing CAD. The clinical symptoms of these conditions include palpitations, fatigue, confusion, shortness of breath, and, in severe cases, heart failure or stroke. Despite these similarities, many diseases have no known treatments, which raises grave concerns for world health. The study aims to identify common differentially expressed genes (DEGs) between CAD and peripheral arterial disease (PAD) patients and investigate biological pathways, network-based analysis, immune and inflammatory profiling, biomarker identification, and multi-omics integration. This helps in early detection and targeted treatment strategies. The results revealed 48 upregulated DEGs that were shared among CAD and peripheral arterial disorders. Functional enrichment analysis revealed mainly myofibril assembly and actomyosin organization, highlighting muscle development and cellular structure, and KEGG pathway analysis revealed amino acid metabolism pathways. A network of protein-protein interactions (PPIs) was built via the STRING and visualized by Cytoscape plugin, CytoHubba, and MCODE, which led to the identification of 10 hub genes, ie, GLS, CCND2, RBL2, ITGB1, IL6ST, THRB, MBNL1, PDE5A, PROS1, and CACNA2D3. Most of these genes involved in key functions, such as voltage-gated calcium channel trafficking, cGMP degradation, RNA splicing, thyroid hormone receptor beta, IL6ST, and cytokine signaling, play crucial roles. Within this subnetwork, we identified 10 crucial seed nodes that are integral to the network's structure and function. The seed nodes highlight significant regulatory connections, providing insights into the underlying biological mechanisms involved. The transcription factor regulatory network of the hub genes revealed this phenomenon. Finding the important seed nodes in this regulatory network makes their role in maintaining the network's integrity and functionality even more clear. This could lead to new targeted therapies and better ways of managing CAD and peripheral blood disorders.

Automated leaf segmentation pipelines must balance accuracy, scalability, and usability to be readily adopted in plant research. We present an end-to-end deep learning pipeline designed for practical use in plant phenotyping, which we developed and evaluated during a real-world plant growth experiment using Atriplex lentiformis. The pipeline integrates a fine-tuned Mask Region-based Convolutional Neural Network (Mask R-CNN) segmentation model trained on 176 plant images and achieves high performance despite the small training data set (Dice coefficient = 0.781). We quantitatively compare the fine-tuned Mask R-CNN model to Meta AI's Segment Anything Model (SAM) and evaluate natural language prompts using Grounded SAM and the Leaf-Only SAM post-processing pipeline for refining segmentation outputs. Our findings highlight that transfer learning on a specialized data set can still outperform a large foundation model in domain-specific tasks. In addition, we integrate QR codes for automated sample identification and benchmark multiple QR code decoding libraries, evaluating their robustness under real-world imaging conditions like distortion and lighting variation. To ensure accessibility, we deploy the pipeline as a user-friendly Streamlit web application, allowing researchers to analyze images without deep learning expertise. By focusing on practical deployment in addition to model performance, this study provides an open-source, scalable framework for plant science applications and addresses real-world challenges in automation and usability by the end-researcher.

Moyamoya disease (MMD) is a rare, chronic cerebrovascular disorder of uncertain etiology. Although abnormal glucose metabolism has been implicated, the contribution of glycosylation-related genes in MMD remains elusive. In this study, we analyzed 2 transcriptome data sets (GSE189993 and GSE131293) from the Gene Expression Omnibus (GEO) database to identify 723 differentially expressed genes (DEGs) between MMD patients and controls. Intersection genes with known glycosylation-related genes underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We utilized machine learning to select key hub genes, followed by immune cell infiltration and correlation analyses. In-depth immune cell analysis indicated that both CFP and MGAT5B were closely tied to various immune components, suggesting potential crosstalk between glycosylation pathways and immune regulation. Notably, CFP was positively associated with pDCs, HLA, and CCR, whereas MGAT5B correlated with B-cells, check-points, and T helper cells but showed a negative relationship with Tregs, hinting at an immunoregulatory mechanism influencing MMD progression. Motif-TF annotation highlighted csibp_M2095 as the motif with the highest normalized enrichment score (NES: 6.57). Reverse microRNA (miRNA)-gene prediction identified 75 miRNAs regulating these focus genes, along with 126 miRNA-miRNA interconnections. Connectivity Map (Cmap) analysis revealed that Chenodeoxycholic acid, MRS-1220, Phenytoin, and Piceid were strongly negatively correlated with MMD expression profiles, suggesting potential therapeutic candidates. Enzyme-linked immunosorbent assays confirmed elevated CFP and MGAT5B and reduced PTPN11 in MMD, aligning with our bioinformatic findings. Moreover, PTPN11 knockdown in human brain microvascular endothelial cells (HBMECs) significantly enhanced tube formation, indicating a role in vascular remodeling. Collectively, these results emphasize the importance of glycosylation-related genes and immune dysregulation in MMD pathogenesis. These findings broaden our understanding of MMD's underlying mechanisms and underscore the necessity of continued research into glycosylation-driven pathways for improved disease management.

Sirtuin 6 (SIRT6), a member of the class III histone deacetylase (HDAC) family, is crucial for the maintenance of general health and is associated with increased life expectancy and resistance to age-related diseases such as cancer and metabolic disorders. A comparative analysis of the SIRT6 gene in Ashkenazi Jewish (AJ) centenarians and noncentenarian controls found a distinct allele, centSIRT6, enriched in the centenarian group. This allele features 2 linked substitutions, N308K and A313S, and exhibits enhanced functions, including more efficient suppression of LINE1 retrotransposons, improved repair of DNA double-strand breaks, and increased efficiency in cancer cell killing. Notably, centSIRT6 shows lower deacetylase activity but higher mono-adenosine diphosphate (ADP) ribosyl transferase activity compared with the wild-type enzyme. This study used several bioinformatics tools to explore the structural changes caused by the N308K and A313S substitutions in centSIRT6 and to elucidate how these alterations contribute to changes in the enzymatic activities of SIRT6. The results indicate that these mutations reduce the structural flexibility of centSIRT6, thus weakening its interactions with acetyl-lysine but strengthening its interactions with ADP-ribose. This research provides useful information for future experimental studies to further investigate the molecular mechanisms of centSIRT6.

Helicobacter pylori infection of the stomach's epithelial cells is a significant risk factor for stomach cancer. Various H pylori proteins (CagA, GGT, NapA, PatA, urease, and VacA) were targeted to design 2 messenger RNA (mRNA) vaccines, V1 and V2, using bioinformatics tools. Physicochemical parameters, secondary and tertiary structure, molecular docking and dynamic simulation, codon optimization, and RNA structure prediction have also been estimated for these developed vaccines. Physicochemical analyses revealed that these developed vaccines are soluble (GRAVY < 0), basic (pI < 7), and stable (aliphatic index < 80). The secondary and tertiary structure of the vaccines demonstrated robustness. The docking with toll-like receptors (TLRs) revealed that the vaccines have a potential affinity for TLR-2 (V1: -1132.3 kJ/mol, V2: -1093.6 kJ/mol) and TLR-4 (V1: -1042.7 kJ/mol, V2: -1201.2 kJ/mol), and molecular dynamics simulations confirmed their dynamic stability. Structural analyses of V1 (-505.96 kcal/mol) and V2 (-634.92 kcal/mol) mRNA vaccines underscored their stability. In addition, the vaccine showed a considerable rise in the counts of B cells and extended activation of both T cells was also observed for the vaccines, suggesting the potential for long-lasting immunity, and offering enhanced protection against H pylori. These findings not only suggest potential long-lasting immunity against H pylori but also offer hope for the future of stomach cancer prevention. Notably, the study emphasizes the need for subsequent animal and human-based studies to confirm these promising results.

Drug repositioning holds great promise for reducing the time and cost associated with traditional drug discovery, but it faces significant challenges related to data imbalance and noise in negative samples. In this article, we introduce a novel method leveraging high negative oversampling (HNO) to address these challenges. Our approach integrates HNO with advanced techniques such as network-based graph mining, matrix factorization, and Bayesian inference, specifically designed for imbalanced data scenarios. Constructing high-quality negative samples is crucial to mitigate the detrimental effects of noisy negative data and enhance model performance. Experimental results demonstrate the efficacy of our approach in enhancing the performance of drug discovery models by effectively managing data imbalance and refining the selection of negative samples. This methodology provides a robust framework for improving drug repositioning, with potential applications in broader biomedical domains.

With the ability to cause massive epidemics that have consequences on millions of individuals globally, the Chikungunya virus (CHIKV) emerges as a severe menace. Developing an effective vaccine is urgent as no effective therapeutics are available for such viral infections. Therefore, we designed a novel mRNA vaccine against CHIKV with a combination of highly antigenic and potential MHC-I, MHC-II, and B-cell epitopes from the structural polyprotein. The vaccine demonstrated well-characterized physicochemical properties, indicating its solubility and potential functional stability within the body (GRAVY score of -0.639). Structural analyses of the vaccine revealed a well-stabilized secondary and tertiary structure (Ramachandran score of 82.8% and a Z-score of -4.17). Docking studies of the vaccine with TLR-2 (-1027.7 KJ/mol) and TLR-4 (-1212.4 KJ/mol) exhibited significant affinity with detailed hydrogen bond interactions. Molecular dynamics simulations highlighted distinct conformational dynamics among the vaccine, "vaccine-TLR-2" and "vaccine-TLR-4" complexes. The vaccine's ability to elicit both innate and adaptive immune responses, including the presence of memory B-cells and T-cells, persistent B-cell immunity for a year, and the activation of TH cells leading to the release of IFN-γ and IL-2, has significant implications for its potential effectiveness. The CHIKV vaccine developed in this study shows promise as a potential candidate for future vaccine production against CHIKV, suggesting its suitability for further clinical advancement, including in vitro and in vivo experiments.