Bioinformatics and Biology Insights最新文献

Differential gene expression analysis of RNA Sequencing (RNA-Seq) data is crucial for understanding key patterns of gene regulation and enhancing our knowledge of biological processes and diseases. The workflow of this analysis comprises quality control, filtering of low-quality data, alignment, read counting, and final differential analysis. In this case, users often need to manually combine several tools and write multiple scripts to cover the entire pipeline. This fragmented approach is time-consuming and not user-friendly, especially for non-expert users. There is a need for an integrated, automated and accessible solution that unifies the entire analysis process within a single, easy-to-use platform. To address this need, we developed SeqExpressionAnalyser, an R package that provides a web application for interactive differential gene-expression analysis of RNA-seq data, making it accessible to R users for the first time. Built on the Shiny framework, SeqExpressionAnalyser enables users to read FASTQ files and perform analyses, including quality control, filtering, alignment, read counting, and differential expression analysis. The tool generates multiple outputs, including data tables, an HTML report and visualisations. The source code is available on GitHub (https://github.com/sanaeesskhayry/SeqExpressionAnalyser) and is licensed under the GPLv3 license. Also available as a Docker image at https://hub.docker.com/repository/docker/biomix/seq-expression-analyser/general.

Buruli ulcer (BU) is a necrotizing skin disease caused by Mycobacterium ulcerans that produces a virulent lipid toxin, mycolactone, which is detectable in urine. Current diagnostics are time-consuming and require specialized expertise, often leading to delayed diagnosis. This makes it difficult to understand the disease's spread and plan effective interventions. To facilitate early diagnostic biomarker identification, we used computational methods to identify proteins to the antigen lactone, a product of mycolactone hydrolysis, that could be used to develop rapid diagnostic tests (RDTs). Using AutoDock Vina, we performed a virtual screening of 6 proteins against lactone. Four proteins - N-Acyl homoserine lactonases (4G5X), hyperthermophilic Sulfolobus islandicus PLL SisLac (4G2D), phosphotriesterase (2VC5) and quorum-quenching lactonase (6N9I) - showed strong interactions with lactone, with binding energies ranging from -8.9 to -6.0 kcal/mol. Molecular dynamic simulations used to assess the stability of these protein-lactone complexes showed that natural lactonase and promiscuous phosphotriesterase activities (2VC5) and quorum-quenching lactonase GcL (6N9I) were the most stable. In addition, 2VC5 and 4G5X demonstrated the most flexibility. Overall, the proteins 2VC5, 4G2D and 4G5X showed a strong binding affinity, good stability and favourable interactions with lactone. These findings suggest that these proteins could serve as the basis for developing rapid, noninvasive RDTs for BU disease.

Lung adenocarcinoma (LUAD) has emerged as both the most frequently diagnosed malignancy and the predominant contributor to cancer-related mortality worldwide. Current clinical evidence indicates that a significant proportion of LUAD cases exhibit tumor cells characterized by accelerated proliferative activity, which contributes to the aggressive biological behavior. Six microarray data sets were retrieved from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified using the robust rank aggregation (RRA) method. The mRNA and protein levels of selected genes were subsequently validated by quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) and western blot (WB). Short interfering RNA (siRNA)-mediated knockdown combined with EdU incorporation assays was employed to assess proliferation in LUAD cell lines. Chromatin immunoprecipitation (ChIP) assays confirmed that FOXM1 directly regulates the transcription of its target genes. A total of 291 DEGs (133 up-regulated and 158 down-regulated) were identified. Up-regulated genes were significantly enriched in cell-cycle pathways. The FOXM1 exhibited the strongest correlation with these cell-cycle genes and was shown by ChIP-seq to bind to the promoters of 49 of them. TOP2A, MELK, CENPF, NEK2, and KIF20A are the top 5 genes for further analysis in the The Cancer Genome Atlas (TCGA) database. These 5 genes are all highly expressed and show a worse prognosis in LUAD. Cell experiments showed that FOXM1 knockdown only inhibited the expression of CENPF and NEK2. Knocking down either FOXM1 or CENPF can inhibit the proliferation of LUAD cells. Overexpression of FOXM1 promoted CENPF expression and the proliferation of lung cancer cells. The predicted regulatory network of FOXM1 shows significant discrepancies with experimental validation data. Therefore, FOXM1's regulatory role in the cell cycle requires further experimental verification.

Proviral Integration site for Moloney murine leukemia virus-1 (PIM-1) kinase, a serine/threonine kinase overexpressed in various malignancies, plays a critical role in promoting cell survival and proliferation, making it a promising target for anticancer therapy. This study employed an integrated in silico approach to evaluate Lepiotaprocerin derivatives (A to L) from Macrolepiota procera as potential PIM-1 inhibitors. Molecular docking of 12 Lepiotaprocerins revealed Lepiotaprocerin C as the most potent compound, exhibiting superior binding affinity (-11.4 kcal/mol) compared with the reference inhibitor AZD1208. Binding site validation using CASTp, PrankWeb, and blind docking confirmed the ATP-binding pocket as the active cavity. The Lepiotaprocerin C-PIM-1 complex demonstrated enhanced stability during 200 ns molecular dynamics simulations, maintaining low RMSD and strong hydrogen-bond interactions, supported by a favorable Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) binding free energy (-22.0 ± 2.1 kcal/mol). Based on quantitative structure activity relationship (QSAR) analysis, the calculated pIC₅₀ value of Lepiotaprocerin C was 8.67. QSAR modeling (R ² = .74, Q ² = 0.90) confirmed robust predictive capacity, while absorption, distribution, metabolism, and elimination and PerMM analysis indicated favorable pharmacokinetic and permeability profiles. Prediction of Activity Spectra for Substances and toxicity predictions further revealed high antineoplastic potential (Pa = 0.881) and a nontoxic safety profile. These results highlight Lepiotaprocerin C as a promising, stable, and safe inhibitor of PIM-1 kinase, warranting further in vitro and in vivo validation for potential anticancer drug development.

Background: Mutations in isocitrate dehydrogenase 1 (IDH1) and 2 (IDH2) are prevalent drivers of acute myeloid leukemia (AML). While targeted therapies exist, resistance can emerge. This study explored the potential of natural products to identify novel dual IDH inhibitors.

Methods: In silico screening of the COCONUT database was performed using Lipinski's Rule of Five. Pharmacophore modeling identified crucial features for IDH binding. Docking simulations with Glide (Schrödinger) assessed binding affinity, followed by MM-GBSA calculations for free energy estimation. The most promising candidate underwent ADME/T and toxicity analysis. Finally, molecular dynamics (MD) simulations evaluated the stability of protein-ligand complexes and binding interactions, followed by trajectory analysis using dynamical cross-correlation matrix (DCCM) and principal component analysis (PCA).

Results: Ternstroside D (CNP0166496) emerged as a potential dual inhibitor of IDH1 and IDH2 mutations. Docking and MM-GBSA analyses showed strong affinities with IDH1 (-14.2, -84.45 kcal/mol) and IDH2 (-16.8, -60.73 kcal/mol), exceeding those of reference inhibitors GSK321A (-9.6 kcal/mol) and Enasidenib (-8.9 kcal/mol). Key hydrogen-bond interactions with catalytic residues and stable binding during MD simulations support its dual mechanism. ADME/T predictions indicated drug-like properties and a favorable safety profile, highlighting Ternstroside D as a natural scaffold with superior binding compared with existing IDH inhibitors.

Conclusion: This in silico study provides compelling evidence for Ternstroside D (CNP0166496) as a promising dual inhibitor for IDH1 and IDH2 mutations in AML. Furthermore, in vitro and in vivo studies are warranted to validate these findings.

Cervical cancer, induced by human papillomavirus (HPV), ranks as the fourth most prevalent malignancy among women globally. Unfortunately, existing prophylactic vaccines lack therapeutic efficacy. This study aimed to design a multi-epitope vaccine targeting the L1 and E7 proteins of HPV 16, 18, 33, and 45, with both preventive and therapeutic potential. Epitopes predicted using Immune Epitope Database (IEDB) and ABCpred were screened via immunoinformatics tools for antigenicity, immunogenicity, safety, conservancy, population coverage, and homology, and appropriate epitopes were assembled into a vaccine with suitable linkers and a 50-S L7/L12 adjuvant. The modeled and optimized vaccine was immunogenic, antigenic, safe, and displayed favorable physicochemical and solubility properties. Docking studies using ClusPro 2.0 and HDOCK indicated robust interactions between the vaccine and toll-like receptors TLR2/TLR4, and molecular dynamics simulations with Desmond validated the structural stability. Furthermore, molecular mechanics with generalized born and surface area solvation (MM/GBSA) analysis employing HawkDock showed favorable binding free energies of -82.86 and -76.72 kcal/mol, respectively. The vaccine's potential efficacy was demonstrated by C-IMMSIM immune simulations, which revealed robust and long-lasting cellular and humoral responses, and also strong cytokine production. Finally, codon optimization for Escherichia coli K12 using JCat yielded a guanine-cytosine content of 50.69% and a Codon Adaptation Index of 0.97, and in silico cloning into pET28a(+) using SnapGene confirmed high expression potential. Our results indicate that the designed vaccine is a viable candidate for both preventive and therapeutic measures against high-risk HPV, requiring additional laboratory and animal studies.

Noncoding RNAs (ncRNAs) play significant roles in multiple fundamental biological processes, in particular, ncRNAs interactions provide valuable insights into protein synthesis, controlling gene expression, RNA processing, regulation of localization, etc. The dysregulation of ncRNA interaction may cause severe diseases including cancer. Therefore, developing computational methods for investigating ncRNA-protein interaction has become a problem of interest for researchers. In this study, we proposed a novel deep learning (DL) model named RPI-SDA-XGBoost for predicting the interaction between ncRNA and proteins. We utilized the 3-mer conjoint triad feature (CTF) to encode the protein sequence, and the 4-mer frequency to encode the RNA sequence, resulting in the extraction of a total of 599-dimensional vector features. The DL approach is developed based on stack denoising autoencoder (SDA) to discover high-level hidden characteristics from 2 separate networks representing proteins and ncRNAs. Composition of features were fed into XGBoost based meta-learner for the final prediction. Proposed model, RPI-SDA-XGBoost, outperformed most of the individual baseline models and significantly improved the performance on multiple benchmark data sets. We validate the generalization power of the proposed model on five benchmark data sets, namely, RPI_ 369, RP_I488, RPI_1807, RPI_ 2241, and NPInterv2.0. RPI-SDA-XGBoost achieved similar levels of state-of-the-art accuracy on data sets RPI_488, RPI_1807, and RPI_NPInter v2.0. Proposed model achieved the best precision of 87.9% and 94.6% in the largest two data sets RPI_ 2241, and RPI_NPInter v2.0, respectively. We believe the proposed model provides useful direction for upcoming biological research and suggesting more sophisticated computational approaches are warranted in near future for ncRNA protein interaction predictions.

Brugada syndrome (BrS) is a rare cardiac arrhythmia with a complex and largely unexplained genetic basis. In this study, we analysed genomic data from 214 Taiwanese BrS cases and 1316 controls to uncover susceptibility loci using genome-wide association study (GWAS), copy number variation (CNV) analysis, and rare-variant association test (RVAT). Imputation with a population-specific Merged-TWN-panel yielded the highest accuracy across SNP categories. GWAS identified four genome-wide significant SNPs across three loci, including SCN10A, ZNF451, and RP11-510I5, with the ZNF451 locus showing a strong association (OR = 9.845, P = 6.8e-11). The total SNP-heritability for BrS was estimated at 0.18 (SE = 0.20), and SNPs located in the 3 risk loci regions accounted for 0.13 (SE = 0.02) of the phenotypic variance. Functional annotation revealed several regulatory non-coding SNPs, and gene-based analysis confirmed SCN10A as significant. Notably, ZNF451-AS1, a non-coding RNA gene overlapping the ZNF451 region, was identified via RVAT, suggesting that both common and rare variants at this locus contribute to BrS risk. CNV analysis further identified potential case-enriched regions, including a duplication involving HRAS. These findings underscore the importance of population-specific genomic resources and highlight ZNF451 as a key susceptibility locus, bridging both common and rare-variant contributions to BrS.

Background: Leishmaniasis is a neglected tropical disease caused by protozoa of the genus Leishmania, predominantly affecting populations with limited socioeconomic resources. Leishmania (V.) braziliensis is one of the primary etiological agents for cutaneous leishmaniasis (CL) in Brazil. This study aims to evaluate the interactions between IgG antibodies and 10 antigens derived from L braziliensis for diagnostic applications. These antigens were selected using in silico reverse vaccinology approaches, based on previous research conducted by our group. Methods: A total of 124 IgG antibody structures were retrieved from the SAbDab database. Antigen-antibody (Ag-Ab) complexes were subjected to molecular docking analyses using the SnugDock protocol implemented in the Rosetta platform. In parallel, enzyme-linked immunosorbent assays (ELISA) were performed to assess the diagnostic performance of the selected peptides in detecting active CL. Results: Peptides VIII, VI, V, and I showed the most favorable docking scores, indicating a higher predicted binding affinity with IgG. In ELISA assays, sensitivity values ranged from 0% to 96%, whereas specificity varied from 29% to 86%. Peptides III, IV, and V demonstrated the highest sensitivity, achieving values of 96%, 96%, and 94%, respectively. Conclusions: Considering both in silico and in vitro results, peptides IV and V corroborate significatively, demonstrating higher predicted affinity (more negative docking score values) with the set of antibodies (Ab) used in calculations.

Histone deacetylases (HDACs) are essential epigenetic regulators, with HDAC6 overexpression linked to estrogen receptor (ER) activity and breast cancer progression. While several HDAC6 inhibitors have been investigated, their clinical success remains limited due to toxicity and off-target effects, necessitating the discovery of novel, selective inhibitors. This study employs a multi-stage computational approach to identify potent HDAC6 inhibitors for breast cancer therapy. A large-scale virtual screening of 264 834 compounds was conducted, followed by molecular docking, molecular dynamics (MD) simulations (100 ns), molecular mechanics/generalized born surface area (MM/GBSA) binding free energy calculations, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. The HDI-3 emerged as the most promising candidate among replicate simulations, exhibiting a substantially favorable MM/GBSA binding free energy of -130.67 kcal/mol-indicative of strong thermodynamic stability and stronger binding affinity compared to reference inhibitors Trichostatin A and Ricolinostat. Molecular dynamics simulations revealed that HDI-3 maintained structural stability, persistent key interactions with active site residues (ASP649, HIS651, ASP742), and low conformational fluctuations. The ADMET evaluation confirmed HDI-3's favorable pharmacokinetic properties, including optimal bioavailability, non-mutagenicity, and low hepatotoxicity. Essential dynamics and principal component analysis further validated its stable binding profile. While these findings highlight HDI-3 as a selective and pharmacologically viable HDAC6 inhibitor, it is important to acknowledge that the results are entirely computational. Therefore, experimental validation is essential to confirm the compound's efficacy and safety. This integrated computational pipeline provides an efficient strategy to accelerate targeted drug discovery, laying the groundwork for future experimental investigations.