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The DyGAF model, which stands for Dynamic Gene Attention Focus, is specifically designed and tailored to address the challenges in biomarker detection, progression reporting of pathogen infection, and disease diagnostics. The DyGAF model introduced a novel dual-model attention-based mechanism within neural networks, combined with machine learning algorithms to enhance the process of biomarker identification. The model transcended traditional diagnostic approaches by meticulously analyzing gene expression data. DyGAF not only identified but also ranked genes based on their significance, revealing a comprehensive list of the top genes essential for disease detection and prognosis. In addition, KEGG pathways, Wiki Pathways, and Gene Ontology-based analyses provided a multileveled evaluation of the genes' roles. In our analyses, we tailored COVID-19 gene expression profile from nasopharyngeal swabs that offer a more nuanced view of the intricate interplay between the host and the virus. The genes ranked by the DyGAF model were compared against those selected by differential expression analysis and random forest feature selection methods for further validation of our model. DyGAF demonstrated its prowess in identifying important biomarkers that could enrich gene ontologies and pathways crucial for elucidating the pathogenesis of COVID-19. Furthermore, DyGAF was also employed for diagnosing COVID-19 patients by classifying gene-expression profiles with an accuracy of 94.23%. Benchmarking against other conventional models revealed DyGAF's superior performance, highlighting its effectiveness in identifying and categorizing COVID-19 cases. In summary, DyGAF model represents a significant advancement in genomic research, providing a more comprehensive and precise tool for identifying key genetic markers and unraveling the complex biological insights of a disease. The DyGAF model is available as a software package at the following link: https://github.com/hiddenntreasure/DyGAF.

The importance of genomic surveillance strategies for pathogens has been particularly evident during the coronavirus disease 2019 (COVID-19) pandemic, as genomic data from the causative agent, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), have guided public health decisions worldwide. Bayesian phylodynamic inference, integrating epidemiology and evolutionary biology, has become an essential tool in genomic epidemiological surveillance. It enables the estimation of epidemiological parameters, such as the reproductive number, from pathogen sequence data alone. Despite the phylodynamic approach being widely adopted, the abundance of phylodynamic models often makes it challenging to select the appropriate model for specific research questions. This article illustrates the application of phylodynamic birth-death-sampling models in public health using genomic data, with a focus on SARS-CoV-2. Targeting researchers less familiar with phylodynamics, it introduces a comprehensive workflow, including the conceptualisation of a research study and detailed steps for data preprocessing and postprocessing. In addition, we demonstrate the versatility of birth-death-sampling models through three case studies from Germany, utilising the BEAST2 software and its model implementations. Each case study addresses a distinct research question relevant not only to SARS-CoV-2 but also to other pathogens: Case study 1 finds traces of a superspreading event at the start of an early outbreak, exemplifying how simple models for genomic data can provide information that would otherwise only be accessible through extensive contact tracing. Case study 2 compares transmission dynamics in a nosocomial outbreak to community transmission, highlighting distinct dynamics through integrative analysis. Case study 3 investigates whether local transmission patterns align with national trends, demonstrating how phylodynamic models can disentangle complex population substructure with little additional information. For each case study, we emphasise critical points where model assumptions and data properties may misalign and outline appropriate validation assessments. Overall, we aim to provide researchers with examples on using birth-death-sampling models in genomic epidemiology, balancing theoretical and practical aspects.

High-throughput gene expression studies commonly employ pathway analyses to infer biological meaning from lists of differentially expressed genes (DEGs). In toxicology and pharmacology studies, treatment groups are analysed against vehicle controls to identify DEGs and altered pathways. Previously, we empirically quantified false-positive rates of DEGs in gene expression data from pools of vehicle-treated zebrafish embryos to determine appropriate study designs (sample and pool size). Here, the same data were subject to Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) to identify false-positive enriched pathways. As expected, the number of false-positive ORA results was lowest where pool and sample sizes were largest (conditions which also generated the fewest significant DEGs). In contrast, the frequency of GSEA false-positives generated through the fast GSEA (fgsea) algorithm increased with pool and sample size and was highest for simulations that generated 0 DEGs, with ribosomal gene sets significantly enriched with the highest frequency. We describe 2 distinct mechanisms by which GSEA generated these false-positive results, both of which are most likely to generate significant gene sets under conditions where expression differences are particularly low. Finally, GSEA analyses were repeated using 1 alternative GSEA algorithm (CERNO) and 11 different ranking statistics. In almost every analysis, the number of significant results was highest where pool size was highest, with ribosome as the more frequently enriched gene set, suggesting our observations to be generalizable to different implementations of GSEA. These results from zebrafish embryos suggest caution in interpreting any GSEA results in contrasts where there are no DEGs.

Plasmodium falciparum is the most fatal species of malaria parasites in humans. Attempts at developing vaccines against the malaria parasites have not been very successful even after the approval of the RTS, S/AS01 vaccine. There is a continuous need for more effective vaccines including sexual-stage antigens that could block the transmission of malaria parasites between mosquitoes and humans. Low immunogenicity, expression, and stability are some of the challenges of transmission-blocking vaccine (TBV). This study was designed to computationally identify TBV candidates based on fused antigens by combining highly antigenic peptides from prefertilization (Pfs230, Pfs48/45) and postfertilization (Pfs25, Pfs28) gametocytes. The peptides were selected based on their antigenicity, nonallergenicity, and lack of similarity with the human proteome. Two fused antigens vaccine candidates (FAVCs) were constructed using Flagellin Salmonella enterica (FAVC-FSE) and Cholera toxin B (FAVC-CTB) as adjuvants. The constructs were evaluated for their physicochemical properties, structural stability, immunogenicity, and potential to elicit cross-protection across multiple Plasmodium species. The results yielded antigenic peptides, with antigenicity scores between 0.7589 and 1.1821. The structural analysis of FAVC-FSE and FAVC-CTB showed a Z-score of -6.70 and -4.79, a Ramachandran plot of 96.94% and 94.86% with overall quality of 94.20% and 89.85%, respectively. The FAVCs contained CD8⁺, CD4⁺, and linear B-cell epitopes with antigenicity scores between 1.2089 and 2.8623, 0.5663 and 2.4132, and 1.5196 and 2.2212, respectively. Each FAVC generated 6 conformational B-cells. High population coverage values were recorded for the FAVCs. The ability of the FAVCs to trigger immune response was evaluated through an in silico immune stimulation. The low-binding interaction energy that resulted from molecular docking and dynamics simulations showed a strong affinity of FAVCs to Toll-like receptor 5 (TLR5). The results indicate that the FAVC-FSE vaccine candidate is more promising to interrupt P falciparum transmission and provides a baseline for experimental validation.

Breast cancer is a highly heterogeneous disorder characterized by dysregulated expression of number of genes and their cascades. It is one of the most common types of cancer in women posing serious health concerns globally. Recent developments and discovery of specific prognostic biomarkers have enabled its application toward developing personalized therapies. The basic premise of this study was to investigate key signature genes and signaling pathways involved in triple-negative breast cancer using bioinformatics approach. Microarray data set GSE65194 from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus was used for identification of differentially expressed genes (DEGs) using R software. Gene ontology and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses were carried out using the ClueGO plugin in Cytoscape software. The up-regulated DEGs were primarily engaged in the regulation of cell cycle, overexpression of spindle assembly checkpoint, and so on, whereas down-regulated DEGs were employed in alteration to major signaling pathways and metabolic reprogramming. The hub genes were identified using cytoHubba from protein-protein interaction (PPI) network for top up-regulated and down-regulated DEG's plugin in Cytoscape software. The hub genes were validated as potential signature biomarkers by evaluating the overall survival percentage in breast cancer patients.

Conservation of elephants requires physical, chemical, and biological approaches to ensure the protection of these gargantuan pachyderms. One such approach is using orange plants (as biofencing) for the repellence of elephants, which precludes catastrophic events related to the encroachment of elephants into human habitats. Elephants have sensitive olfactory discrimination of plant volatile compounds for foraging and other behavior using G-protein-coupled receptors (GPCRs). However, 2 such receptors are the A2A and A2B receptors mediating olfaction elicited by a host of ligands, including limonene, the main volatile compound in citrus plants, which is hypothesized to be the chief repelling agent. Bioinformatics at the protein and mRNA levels (BLAST/Multiple Sequence Alignments) were employed to explore the multiple expression products of A2B receptors, namely full-length and truncated proteins produced by isoform mRNAs translated from multiple methionines, while the comparison of the limonene-binding pockets of human and elephant A2B receptors and prediction servers [Netphos 3.1; Protter] was used to focus, respectively, on the contacts limonene binding entails and the post-translational modifications that are involved in cell signaling. Finally, the link between limonene and antifeedant activity was explored by considering limonene content on trees that are preferentially foraged or avoided as part of the feeding behavior by elephants. The African bush elephant (Loxodonta africana) possesses a full-length A2A receptor but unlike most mammals, expresses a highly truncated A2B receptor isoform possessing only transmembrane helices 5, 6, and 7. Truncation may lead to higher traffic and expression of the A2B receptor in olfactory interfaces/pathways and aid stronger activation. In addition, all residues in the putative limonene-binding cleft are perfectly conserved between the human and African bush elephant A2B receptors, both full length and truncated. Shallow activation sites require micromolar affinity and fewer side-chain interactions, which is speculated to be the case for the truncated A2B receptor. An N-terminal extremity N-glycosylation motif is indicative of membrane localization of the truncated A2B receptor following accurate folding. A combination of truncation, indels, substitutions, and transcript isoforms are the attributed roles in the evolution of the L. africana A2B receptor, out of which limonene receptivity may be the key. It is also inferred how limonene may act as a dietary repellent/antifeedant to a generalist herbivore, with the documented limonene content being absent in some dietary favorites including the iconic Sclerocarya birrea.

Identifying interactions between transcription factors (TFs) and target genes is crucial for understanding the molecular mechanisms involved in biological processes and diseases. Traditional biological experiments used to determine these interactions are often time-consuming, costly, and limited in scale. Current computational methods mainly predict binding sites rather than direct interactions. Although recent studies have achieved high performance in predicting TF-target gene associations, they still face a significant challenge related to constructing a robust dataset of positive and negative samples. Currently, methods do not adequately focus on selecting negative samples, resulting in incomplete coverage of potential TF-target gene relationships. This article proposes a method to select enhanced negative samples to improve the prediction performance of TF-target gene interactions. Experimental results show that the proposed method achieves an average area under the curve (AUC) value of 0.9024 ± 0.0008 through 5-fold cross-validation. These results demonstrate the model's high efficiency and accuracy, confirming its potential application in predicting TF-target gene interactions across various datasets and paving the way for large-scale biomedical research.

Millions of individuals suffer from chronic infections caused by bacterial biofilms, resulting in significant loss of life. Pseudomonas aeruginosa stands out as a major culprit in causing such chronic infections, largely due to its antibiotic resistance. This pathogen poses a considerable threat in healthcare settings, particularly to critically ill and immunocompromised patients. The persistence of chronic and recurrent bacterial infections is often attributed to bacterial biofilms. Therefore, there is an urgent need to discover novel small molecules capable of efficiently eliminating biofilms independent of bacterial growth. In this project, an in silico drug discovery approach was employed to identify nine halogenated-phenazine compounds as allosteric inhibitors of the LasR protein. The LasR is a key transcription factor that triggers other quorum-sensing systems and plays a crucial role in biofilm formation and activation of virulence genes. By inhibiting LasR, specifically targeting its allosteric site, the dimerization of LasR and subsequent biofilm formation could be prevented. Molecular docking and simulations, coupled with binding energy calculations, identified five compounds with potential as anti-biofilm agents. These compounds exhibited higher binding affinities to the distal site, suggesting their structural capability to interact with allosteric site residues of the LasR protein. Based on these findings, it is proposed that these compounds could serve as promising leads for the treatment of biofilm and quorum-sensing-related infections.

Rhoptry protein 18 (ROP18) is a key virulence factor secreted into host cells during the invasion of Toxoplasma gondii (T. gondii) and plays an important role in the pathogenesis of infection. Due to its potential as a vaccine candidate, this study aimed to characterize several properties of the T. gondii ROP18 (TgROP18) protein to support its inclusion in vaccine formulations. Using a range of bioinformatics tools, we investigated its T-cell and B-cell epitopes, physicochemical properties, subcellular localization, transmembrane domains, and tertiary and secondary structures. Our analysis revealed 48 post-translational modification sites in TgROP18. The secondary structure was composed of 4.87% beta-turns, 38.45% random coils, 42.42% alpha helices, and 14.26% extended strands. Several potential T- and B-cell epitopes were identified on ROP18. The Ramachandran plot of both crude and refined models showed that 85.8% and 95.3% of the amino acid residues, respectively, fell within favored regions, indicating energetically stable conformations. Allergenicity and antigenicity assessments indicated that TgROP18 is a nonallergenic, immunogenic protein. Predictions using the C-ImmSim server suggest that TgROP18 can stimulate humoral and cell-mediated immune responses, based on antibody titers and cytokine profiles following antigen administration. These findings provide baseline data for future investigations focused on the potential of TgROP18 in developing therapeutic strategies against toxoplasmosis.

Introduction: Breast cancer (BC) is a heterogeneous disease involving a network of numerous extracellular signal transduction pathways. The phosphoinositide 3-kinase (PI3K)/serine/threonine kinase (Akt)/mechanistic target of rapamycin (mTOR) pathway is crucial for understanding the BC development. Phosphoinositide 3-kinase, phosphatase and tensin homolog (PTEN), mTOR, Akt, 3-phosphoinositide-dependent kinase 1 (PDK1), FoxO1, glycogen synthase kinase 3 (GSK-3), mouse double minute 2 (MDM2), H-Ras, and proapoptotic B-cell lymphoma 2 (BCL-2) family protein (BAD) proteins are key drivers of this pathway and potential therapeutic targets. Pleurotus ostreatus is an edible mushroom that is rich in flavonoids and phenols that can serve as potential inhibitors of proteins in the PI3K/Akt/mTOR pathway.

Aim: This study evaluated the anticancer properties of P ostreatus through a structure-based virtual screening of 22 biologically active compounds present in the mushroom.

Method: Model optimization was carried out on PI3K, PTEN, mTOR, Akt, PDK1, FoxO1, GSK-3, MDM2, H-Ras, and BAD proteins in the PI3K/Akt/mTOR pathway and molecular docking of compounds/control inhibitors in the binding pocket were simulated AutoDock Vina in PyRx. The drug likeness, pharmacokinetic, and pharmacodynamic features of prospective docking leads were all anticipated.

Result: Several potent inhibitors of the selected key driver proteins in PI3K/Akt/mTOR pathway were identified from P ostreatus. Ellagic acid with binding affinities of -8.0, -8.0, -8.1, -8.2, -6.2, and -7.1 kcal/mol on PI3K, Akt, PDK1, GSK-3, MDM2, and BAD, respectively, had better binding affinity compared with their reference drugs. Likewise, apigenin (-7.8 kcal/mol), chrysin (-7.8 kcal/mol), quercetin (-6.4 kcal/mol), and chlorogenic acid (-6.2 kcal/mol) had better binding affinities to PTEN, mTOR, FoxO1, and H-Ras proteins, respectively.

Conclusion: Ellagic acid, apigenin, luteolin, quercetin, chlorogenic acid, chrysin, and naringenin phytochemicals are seen as the better lead molecules due to their ability to strongly bind to the proteins under study in this pathway. Analogs of these compounds can also be designed as potential drugs.