Frontiers in bioinformatics最新文献

t-distributed Stochastic Neighbour Embedding (t-SNE) is a cornerstone for visualizing high-dimensional biological data, where each high-dimensional data point is represented as a point in a two-dimensional map. However, this static map provides no information about the stability of the visual layout, the features that influence it, or the impact of uncertainty in the input data. This work introduces a computational framework that allows one to extend the standard t-SNE plot by visual clues about the stability of the t-SNE embedding. First, we perform a sensitivity analysis to determine feature influence: by combining the Implicit Function Theorem with automatic differentiation, our method computes the sensitivity of the embedding w.r.t. the input data, provided in a Jacobian of first-order derivatives. Heatmap-visualizations of this Jacobian or summarizations thereof reveal which input features are most influential in shaping the embedding and identifying regions of structural instability. Second, when input data uncertainty is available, our framework uses this Jacobian to propagate error, probabilistically quantifying the positional uncertainty of each embedded point. This uncertainty is visualized by augmenting the plot with hypothetical outcomes, which display the positional confidence of each point. We apply our framework to three diverse biological datasets (bulk RNA-seq, proteomics, and single-cell transcriptomics), demonstrating its ability to directly link visual patterns to their underlying biological drivers and reveal ambiguities invisible in a standard plot. By providing this principled means to assess the robustness and interpretability of t-SNE visualizations, our work enables more rigorous and informed scientific conclusions in bioinformatics.

Background: Triple-negative breast cancer (TNBC) is defined by the absence of ER, PR, and HER2 expression. This limits the targeted therapies, resulting in poor clinical outcomes. Identifying the molecular targets that can be regulated through miRNAs and natural compounds offers a potential therapeutic platform.

Methods: We combined transcriptomic profiling with miRNA target prediction to identify genes regulated by miR-30a-5p and assess their interaction with the green tea polyphenol, epigallocatechin gallate (EGCG). Differentially expressed genes (DEGs) from TCGA-TNBC datasets and miRNA targets from miRDB, TargetScan, and miRTarBase were screened for common genes. Then, the protein-protein interaction and network topology analyses were performed to identify key hub genes. Molecular docking and simulation were carried out with the four key genes against EGCG.

Results: Data integration yielded 393 overlapping genes and identified ten hub genes- RRM2, KIF11, ANLN, CDC20, CCNA1, AGO2, YWHAZ, DTL, SKP2, and PCNA. Pathway enrichment showed that all these hubs are involved in cell cycle and mitotic regulation, which was associated with poor TNBC prognosis. Mutation profiling revealed high alteration rates in KIF11, ANLN, CDC20, and YWHAZ, with increased missense mutations and C>T transitions. Molecular docking and simulations identified YWHAZ as the most favorable and structurally stable EGCG-binding target, compared to the other three key genes.

Conclusion: The results emphasizes that EGCG has strong binding affinity towards YWHAZ, revealing that miR-30a-EGCG targets TNBC synergistically through cell-cycle-mediated pathways. The findings give rational support for miRNA-guided phytochemical-based TNBC therapeutic development.

RHO family GTPases are key regulators of cancer-related processes such as cytoskeletal dynamics and cell migration, proliferation, and survival. Despite this, a comprehensive understanding of RHO signaling alterations across tumors is still lacking. In this study, we present a pan-cancer analysis of 484 genes encoding RHO GTPases, regulators, proximal effectors, distal downstream signaling elements, and components of their proximal interactomes using data from over 10,000 tumor samples and 33 tumor types present in The Cancer Genome Atlas (TCGA). In addition, we have utilized available data from genome-wide functional dependency screens performed in more than 1,000 gene-edited cancer cell lines. This study has uncovered positively selected mutations in both well-known and previously uncharacterized RHO pathway genes. Transcriptomic profiling reveals widespread and tumor-specific differential expression patterns, with some of them correlating with copy number changes. Interestingly, certain regulators exhibit consistent expression profiles across tumors opposite to those predicted from their canonical roles. Co-expression and gene set enrichment analyses highlight coordinated transcriptional programs involving some RHO GTPase pathway genes and their linkage to key cancer hallmarks, including extracellular matrix reorganization, cell motility, cell cycle progression, cell survival, and immune modulation. Functional screens further identify context-specific dependencies on several deregulated RHO GTPase pathway genes. Altogether, this study provides a comprehensive map of RHO GTPase pathway alterations in cancer and identifies new oncogenic drivers, expression-based signatures, and therapeutic vulnerabilities that could guide future mechanistic and translational research in this area.

Objective: Knee osteoarthritis (KOA) is a prevalent chronic degenerative joint disease that causes chronic pain and mobility restrictions in the elderly, significantly impacting quality of life. Current treatments focus on symptom relief, lacking effective interventions targeting the underlying mechanisms. Understanding KOA's molecular mechanisms and identifying key pathogenic genes are essential for developing targeted therapies.

Methods: Gene expression data from KOA patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to reveal the associated biological processes and signaling pathways. Protein-protein interaction (PPI) network analysis and Gene Ontology-based semantic similarity calculations were used to identify hub genes. Gene Set Variation Analysis (GSVA) assessed enrichment in KOA-related pathways. Immune infiltration analysis (CIBERSORT) assessed the immune cell distribution in KOA tissues. Finally, hub gene expression changes were validated using the IL-1β-treated CHON-001 cell model and real-time quantitative PCR (RT-qPCR).

Results: A total of 3,290 upregulated and 2,536 downregulated DEGs were identified. GO and KEGG enrichment analyses revealed these genes were primarily involved in extracellular matrix remodeling, transmembrane transport, and inflammation-related pathways. Key hub genes, including HSPA5, FOXO1, and YWHAE, were identified. GSVA showed that these genes were significantly enriched in multiple KOA-associated signaling pathways. Immune infiltration analysis revealed significant differences in the levels of six immune cell types in KOA tissues, which were associated with the hub genes expression. In CHON-001 cell, the expression levels of GRB2, IKBKG, and HSPA12A were upregulated, whereas YWHAE, HSPB1, and DCAF8 were downregulated, consistent with the tissue samples.

Conclusion: This study identified key pathogenic genes and their regulatory pathways in KOA, highlighting their potential role in disease progression via inflammation and immune modulation. These findings provide insights for developing targeted therapeutic strategies for KOA.

The goal of bioengineering in synthetic biology is to redesign, reprogram, and rewire biological systems for specific applications using standardized parts such as promoters and ribosomes. For example, bioengineered micro-organisms capable of cleaning up environmental pollution or producing antibodies de novo to defend against viral pandemics have been predicted. Artificial Life (ALife) facilitates the design and understanding of living systems, not just those found in nature, but life as it could be, while synthetic biology provides the means to realize life as it can be engineered. Despite significant advances, the synthesis of evolving, adaptable, and bioengineered problem-solving ALife has yet to achieve practical feasibility. This is primarily due to limitations in directed evolution, fitness landscape mapping, and fitness approximation. Thus, currently synthetic (biological) ALife does not continue to evolve and adapt to changing tasks and environments. This is in stark contrast to current digital based ALife that continues to adapt and evolve in simulated environments demonstrating the dictum of life as it could be. We posit that if the bioengineering (on-demand design) of problem solving ALife is to ever become a reality then open issues pervading the directed evolution of synthetic ALife must first be addressed. This review examines open challenges in directed evolution, genetic diversity generation, fitness mapping, and fitness estimation, and outlines future directions toward a hybrid synthetic ALife design methodology. This review provides a novel perspective for a singular (hybridized) evolutionary design methodology, combining digital (in silico) and synthetic (in vitro) evolutionary design methods drawn from various bioengineering, digital and robotic ALife applications, while addressing highlighted directed evolution deficiencies.

TNBC is an aggressive and various subtype of breast cancer, notable by the lack of specific oestrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), consequential in limited treatment options and poor prognosis. Kinesin Family Member C1 (KIFC1), a mitotic motor protein critical for centrosome clustering and spindle formation, has critical role in TNBC progress. In this situation, natural compounds were explored as probable inhibitors of this protein. we utilized molecular docking, ADMET profiling, density functional theory calculations, molecular dynamics simulations, MM/GBSA binding free energy analysis, and principal component analysis to thoroughly evaluate binding affinity, stability, and drug-likeness property of natural compounds against KIFC1. Of the 36,900 compounds utilized, five natural compounds were carefully chosen for further assessment. All five compounds Fosfocytocin, Molybdopterin Compound Z, 5-amino-2-(3-hydroxy-13-methyltetradecanamido) pentanoic acid, TMC-52A, and Muscimol exhibited significant inhibitory efficacy against KIFC1. These compounds demonstrated persistent interactions with critical residues and had advantageous binding properties in computational evaluations. The results collectively indicate their potential as effective inhibitors for targeting KIFC1 in forthcoming studies. These data collectively identify all five natural compounds as possible inhibitors of KIFC1. Nonetheless, their effectiveness and safety must be confirmed through in vivo and in vitro study prior to consideration for clinical application.

Single-cell RNA sequencing (scRNA-seq) has generated a rapidly expanding collection of public datasets that provide insight into development, disease, and therapy. However, researchers lack an end-to-end solution for seamlessly retrieving, preprocessing, integrating, and analyzing these data because existing tools address only isolated steps and require manual curation of accessions, metadata, and technical variability, known as batch effects. In this study, we developed Celline, a Python package that executes an entire workflow using a single-line commands per step. Celline automatically gathers raw single-cell RNA-seq data from multiple public repositories and extracts metadata using large language models. It then wraps established tools, including Scrublet for doublet removal, Seurat and Scanpy for quality control and cell-type annotation, Harmony and scVI for batch correction, and Slingshot for trajectory inference, into one-line commands, enabling seamless integrative analyses. To validate Celline-acquired data quality and the integrated framework's practical utility, we applied it to 2 mouse brain cortex datasets from embryonic days 14.5 and 18. Technical validation demonstrated that Celline successfully retrieved data, standardized metadata, and enabled standard analyses that removed low-quality cells, annotated 11 major cell types, improved integration quality (scIB score +0.22), and completed trajectory analysis. Thus, Celline transforms scattered public scRNA-seq resources into unified, analysis-ready datasets with minimal effort. Its modular design allows pipeline extension, encourages community-driven advances, and accelerates the discovery of single-cell data.

Background: Lung adenocarcinoma (LUAD) is the predominant histological subtype of lung cancer, representing a major contributor to cancer mortality rate marked by a high frequency of mutations and intricate interactions between multiple signalling pathways.

Objective: Here we explore the role of NOTCH1 associated Single nucleotide polymorphisms (SNPs) IDR and PTM in LUAD progression. Although the NOTCH1 expression is downregulated, it has been validated as an important prognostic marker because of its complex biological roles under specific conditions.

Methods: With the aid of In silico tools we predicted and identified the deleterious SNPs. The Molecular Docking and dynamics simulations (MDS) were conducted to characterize these mutations.

Results: A total of 43 deleterious SNPs were found in the sequential SNP analysis with 13 SNPs resulted deleterious and damaging effects. The stabilizing SNPs such as S1464I, A1705V and T1602I are found within the conserved and functional domains of NOTCH1. In addition, 1660-2555 sequence region of the PEST domain was recognized as an Intrinsically Disordered Region (IDR) with a score of above 0.5. Moreover, the presence of the two phosphodegrons (SCF_FBW7_1 at 2129-2136 and SCF_FBW7_2 at 2508-2515) along with the Post Translational Modification (PTM) such as o-linked glycosylation and Phosphothreonine within the IDR region, PEST and conserved domains suggest functional significance in LUAD progression.

Conclusion: In conclusion our research highlights the potential regulatory role of identified SNPs, PTMs, and the functional domains of Notch1, particularly the PEST domain and IDR, in pathophysiology of LUAD particularly through the crosstalk of the EMT signalling.

Introduction: With increased drug resistance and tumor heterogeneity accounting for limited therapeutic strategies, gastric cancer remains one of the major causes of cancer-related mortality around the globe. Targeting the components of the tumor microenvironment (TME) has become a promising therapeutic strategy due to their crucial roles in cancer cell proliferation, progression, and metastasis. One of the limitations of the previously identified therapeutic targets is their limited applicability to a broader patient population.

Methods: This study aims to identify (TME)-related therapeutic targets using an integrated bioinformatics and molecular docking approach that involves a larger number of datasets to cover a broader cohort of gastric cancer patients. It analyzed multiple publicly available transcriptomic datasets using Robust Rank Aggregation (RRA) meta-analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to identify significant hub genes. Furthermore, protein-protein interaction (PPI) network analyses, conducted using multiple methods such as Cytohubba topology analysis and ClusterONE module analysis, refined the potential therapeutic candidates. Functional enrichment analyses were performed to identify vital genes involved in TME interactions and ECM remodeling.

Results: The enriched genes were validated for their significant dysregulation in the Cancer Genome Atlas gastric adenocarcinoma dataset (TCGA-STAD) and three independent GEO datasets to ensure differential expression across distinct cohorts. Genes with consistent dysregulation were used in survival analyses across TCGA and two GEO datasets to prioritize hub genes with prognostic significance. Finally, a targeted literature survey ensured the exclusion of previously targeted genes, and molecular docking analyses conducted using phytocompounds identified potential therapeutic leads with strong affinities for the identified targets.

Discussion: This integrated approach revealed notable, promising targets in the TME and natural compounds for developing potential personalized therapeutic strategies in gastric cancer.

Nipah virus (NiV) is a zoonotic pathogen that causes recurrent outbreaks with considerable implications for public health. Viruses engage host kinases to phosphorylate viral proteins, aiding replication and host disruption. Identifying NiV phosphoproteins and their host kinases is therefore critical for understanding the mechanism of infection and developing therapeutics. We performed kinase-substrate phosphomotif analysis based on prior studies and employed computational tools to identify putative phosphosites in NiV proteins and corresponding host kinases. Redundancy analysis highlighted key kinases capable of phosphorylating multiple NiV proteins and high-potential viral substrates. Integration with human-viral protein-protein interaction data revealed human kinase substrate proteins in human that interact with NiV proteins, while conservation analysis assessed phosphosites across nine NiV proteins in various strains. The functional significance of the identified and predicted viral substrates and their corresponding host kinases was further validated through in silico docking and molecular dynamics simulation (MD). Motif-based kinase-substrate analysis identified 51 human kinases predicted to target 1180 phosphorylation sites across nine NiV proteins, including key human kinases such as Eukaryotic elongation factor 2 kinase [EEF2K], Haploid germ cell-specific nuclear protein kinase [HASPIN], Mitogen-activated protein kinase 9 [MAPK9], Microtubule-associated serine/threonine-protein kinase 2 [MAST2], and Spleen tyrosine kinase [SYK], with the potential to phosphorylate multiple sites across NiV proteins. Using computational prediction tools, we identified several potential phosphorylation sites on NiV proteins, along with their corresponding candidate human kinases. In silico docking revealed interactions between EEF2K and both the NiV Fusion Glycoprotein and NiV Phosphoprotein (P), MAPK9 with the NiV Matrix Protein, and HASPIN with NiV RNA-dependent RNA polymerase. MD simulations of the EEF2K-NiV Fusion Glycoprotein complex confirmed the stability of this interaction. Leucine-rich repeat serine/threonine-protein kinase 2 [LRRK2], HASPIN, MAST2, and EEF2K were the human kinases predicted to phosphorylate experimentally validated sites on NiV nucleocapsid (N), P, and W proteins. Furthermore, through an extensive literature review, we investigated the therapeutic potential of targeting these kinases using known inhibitors and identified compounds that could potentially be repurposed as antiviral agents against NiV infection. Our findings indicate that EEF2K phosphorylates key NiV proteins at conserved phosphosites across variants, underscoring the pathogenic significance of kinases in NiV infection and their potential as therapeutic targets.