Genomics & informatics最新文献

Antimicrobial peptides (AMPs) are universally found in both intracellular and extracellular settings and have significant antibiotic-resistant bacteria are becoming a bigger problem. In medical laboratories, it has shown notable anti-bacterial effectiveness in treating diabetic foot infections and related issues. New medication development frequently targets (AMPs), which are certainly ensuing components of adaptive immune system. The findings of this research employs deep learning to identify antibiotic activity. Numerous computational methods have been established to detect antimicrobial peptides via deep learning algorithms. We introduced a novel deep learning approach called antimicrobial peptides using Capsule Neural Network (AMP-CapsNet) to precisely forecast them and evaluated its efficacy against deep learning and baseline models. AMPs prediction using capsule neural networks, a type of next generation neural network, to build prediction models. Additionally, we utilized Amino Acid Composition (AAC) for effective features encoded method and as well as dipeptide composition (DPC). Every model underwent independent cross-validation and external testing. The findings indicate that the enhanced AMP-CapsNet deep learning model surpassed its counterparts, achieving an accuracy of 97.29% and an AUC score of 98.91% on the test set using with dipeptide Composition (DPC). The proposed AMP-CapsNet demonstrates superior performance of the testing set achieved accuracy 97.29% score with DPC and accuracy 84.42% score with AAC approach. Consequently, the technique we advocate is anticipated to enhance the accuracy of antimicrobial peptide predictions in the future. By producing powerful peptides for medication development and application, this study advances deep learning-based AMP drug discovery approaches. This finding has important ramifications for how biological data is processed and how pharmacology is calculated.

Intercellular mitochondrial transfer (MT) is emerging as a transformative communication axis in cancer biology. Intact mitochondria or mitochondrial components can be exchanged between tumor cells, stromal elements, and immune cells via tunneling nanotubes, extracellular vesicles, cell fusion, or phagocytic uptake. This organelle exchange enables metabolic adaptation by restoring OXPHOS (oxidative phosphorylation), increasing ATP production, and enhancing survival in hostile environments. Conversely, tumor cells also hijack mitochondria from cytotoxic lymphocytes thereby undermining immune function and contributing to immune escape and tumor progression. These converging metabolic exchanges fuel immune evasion, metastatic potential, and resistance to chemotherapy, radiation, and immunotherapy. Cutting-edge tracing tools, including mitochondrial reporter proteins and single-cell mitochondrial genome lineage mapping, have uncovered MT events both in vitro and in vivo. Therapeutic strategies designed to block mitochondrial trafficking, inhibit nanotube formation or vesicle uptake, or enhance immune cell mitochondrial resilience hold promise for tumor sensitization and restoration of antitumor immunity. A deeper understanding of MT provides novel insight into cancer metabolism and intercellular communication, offering a foundation for future therapeutic innovation and potential clinical application as both a biomarker and a therapeutic target.

Background: Thyroid cancer (THCA) is a common malignant tumor of the endocrine system, and significant clinical challenges remain in its diagnosis and prognostic evaluation. This study aims to elucidate the role of AGPAT4 in thyroid cancer by investigating its expression, involvement in metabolic pathways, and potential as a prognostic biomarker.

Methods: We analyzed data from 512 thyroid cancer patients and 279 controls, performed differential expression analysis of AGPAT4 in thyroid cancer, analyzed the gene expression correlation of AGPAT4 in thyroid cancer, and the protein-protein interaction (PPI) network and functional enrichment analysis of AGPAT4 and its differentially expressed genes (DEGs) were constructed. The Kruskal-Wallis test and receiver operating characteristic (ROC) curve analysis were used to investigate the correlation between AGPAT4 expression and clinicopathological characteristics as well as its diagnostic efficacy. Cox regression analysis and Kaplan-Meier analysis were employed to evaluate its prognostic value. Additionally, single-sample gene set enrichment analysis (ssGSEA) was utilized to explore the association between AGPAT4 expression and the level of immune infiltration in the tumor microenvironment.

Results: Our findings revealed that AGPAT4 was significantly downregulated in thyroid cancer (THCA) tissues (P < 0.001), suggesting a potential tumor-suppressive role of AGPAT4 in thyroid cancer. AGPAT4 exhibited robust efficacy in distinguishing tumor tissues from normal tissues, with an area under the receiver operating characteristic curve (AUC) of 0.973. Furthermore, AGPAT4 expression levels were significantly correlated with pathological stage and survival rate (P < 0.05). Kaplan-Meier survival analysis showed that patients with high AGPAT4 expression had better progression-free interval (PFI) (HR = 0.45, P = 0.007). Protein-protein interaction (PPI) network and functional enrichment analyses revealed that AGPAT4 is involved in key pathways associated with thyroid cancer progression. Immune infiltration analysis suggested an association between AGPAT4 expression and immune responses in the tumor microenvironment.

Conclusion: AGPAT4 holds promise as a potential biomarker for the differential diagnosis and prognostic assessment of thyroid cancer, thereby providing a possible reference for the further exploration of therapeutic strategies against this disease.

Fusion genes are key oncogenic drivers in various cancers; however, their role in hepatocellular carcinoma (HCC) remains underexplored. Here, we analyzed RNA-seq data from 68 HCC patients and identified several fusion products where SLC39A14-PIWIL2 stood out a putative driver. Functional assays revealed that the promoter of SLC39A14 potentially drives the overexpression of a truncated PIWIL2 protein (tPIWIL2), which retains its oncogenic MID and PIWI domains, in liver tissues. Both the wild-type and tPIWIL2 were found to interact with oncogenic partners HDAC3 and NME2 through these domains, as demonstrated by structural modeling and molecular dynamics simulations. To disrupt these interactions, we designed novel decoy peptides that potentially competes with both HDAC3 and NME2, effectively inhibiting PIWIL2-driven tumor activity in Huh7, HepG2, SNU449, and SNU398 HCC cell lines. Among the tested candidates, NEP1 markedly suppressed PIWIL2-driven oncogenic activity, and its co-administration with 5-fluorouracil (5-FU) significantly reduced PIWIL2-induced chemoresistance, thereby enhancing therapeutic efficacy. Collectively, these findings establish SLC39A14-PIWIL2 as a novel oncogenic fusion in HCC and highlight fusion protein-targeted peptide therapeutics as a promising avenue for precision treatment in HCC.

Artificial intelligence (AI)-assisted scientific writing is now a common practice in academic publishing, yet concerns persist regarding the authenticity and reproducibility of AI-generated content. While AI tools offer significant advantages, particularly for non-native English speakers who face substantial linguistic barriers in scientific communication, the risk of AI hallucinations and fabricated citations threatens the integrity of scholarly discourse. Journals often require disclosure of the entire AI prompt rather than meaningful intellectual contributions, but this is becoming increasingly impractical as AI prompts are getting longer and more complex. In this paper, I argue that transparency in AI-assisted writing should focus on capturing the author's core research perspective and section-specific key points-the foundational elements that drive meaningful scientific communication. To address this challenge, I developed a web-based tool that implements a human-in-the-loop approach requiring authors to define their research perspective and create detailed outlines with key points before any AI text generation occurs. The tool mitigates AI hallucination by only allowing the use of user-provided citations and generating transparency reports documenting the key elements used for text generation. I validated this approach by writing this paper using the tool itself, demonstrating how the transparency reporting method works in practice. This methodology ensures that AI serves as a linguistic tool rather than a content generator, preserving scientific integrity while democratizing access to high-quality academic writing across linguistic and cultural boundaries.

Background: Imaging-based spatial transcriptomics (ST) enables the quantification of gene expression at single-cell resolution while preserving spatial context, but its utility is limited by small gene panels and challenges in accurate cell segmentation. To address these limitations, we present a graph autoencoder framework that integrates subcellular transcript distribution patterns with cell-level gene expression profiles for enhanced cell clustering in imaging-based ST (SPICEiST).

Results: The clustering performance of SPICEiST was systematically evaluated across several cancer datasets and gene panel sizes. The results demonstrate that SPICEiST consistently outperforms the conventional cell-level gene expression-based methods in distinguishing subtle differences in cell states, as measured by the number of cell clusters and clustering indices, such as the CHI and DBI. Moreover, the findings indicate that SPICEiST can further enhance the performance, even with advancements in cell segmentation, particularly for datasets with small gene panels. Overall, these improvements in cell clustering indices, CHI and DBI, were more pronounced in datasets with small gene panels of around 300 genes, in contrast to those with large panels containing over a thousand genes. Notably, SPICEiST also reveals more spatially intermixed and less compartmentalized cell clusters, a characteristic that better reflects the complex and heterogeneous nature of tumor microenvironments. This effect was especially evident in the datasets with large panels.

Conclusions: These findings highlight the value of leveraging subcellular transcript patterns to overcome the inherent limitations of imaging-based ST, particularly for small gene panels, and may provide new insights into tumor heterogeneity.

Background: Infective endocarditis (IE) is a serious infection of the heart valves, and standard culture methods often miss the bacteria responsible, especially in culture-negative cases. To address this, we used 16S rRNA gene-based next-generation sequencing (NGS) on heart valve tissue. This approach allowed us to map out the bacterial communities present and evaluate their potential role in IE.

Result: We identified six key bacterial genera-Enterococcus, Streptococcus, Coxiella, Staphylococcus, Haemophilus, and Cutibacterium-plus three specific species: Streptococcus troglodytae, Haemophilus parainfluenzae, and Coxiella burnetii. Our co-occurrence analysis showed that these bacteria tend to exist independently within infected valve tissue, with no significant correlations between them.

Conclusion: We detected bacterial taxa, including Cutibacterium and Streptococcus troglodytae. Although S. troglodytae is rarely associated with IE, and Cutibacterium comprises low-abundance bacteria not typically linked to this condition. These findings demonstrate the value of NGS in identifying pathogens that standard culture methods may overlook. As these results are based on computational analyses, further laboratory validation is required. Incorporating NGS into diagnostic protocols may enhance pathogen detection in culture-negative IE and support more targeted treatment and prevention strategies.

Background: Ontology frameworks are essential for organizing complex biological knowledge, such as genes, phenotypes, and pathways, and for ensuring consistent data annotation and retrieval. In biological research, ontologies like the Gene Ontology (GO) and crop-specific trait ontologies (TO) for Oryza sativa (rice) standardize terminology across studies, supporting cross-study comparison and hypothesis generation. However, ontology annotations usually rely on expert manual review of the literature, a process that is accurate but time-consuming, labor-intensive, and difficult to scale as biological data grows. Manual approaches are also prone to inconsistencies and errors. The emergence of large language models (LLMs) such as ChatGPT, DeepSeek, and KIMI, along with curated databases like Rice-Alterome and PubAnnotation, offers new opportunities for semi-automated ontology curation. This study explores how these technologies can be integrated to develop an efficient literature-based curation system for rice trait ontology.

Methods: We developed a curation system that integrates Rice-Alterome-a comprehensive database of rice genomic variations, mutations, and sentence-level literature evidence linked to GO and TO terms with PubAnnotation, an open-source platform for collaborative text annotation. LLMs (DeepSeek and KIMI) were integrated via APIs to automate the extraction, annotation, and validation of trait-related information via prompt engineering. The system was evaluated through use cases designed to demonstrate its performance and functionality compared to manual curation.

Results: The proposed system substantially enhanced the retrieval and organization of literature evidence compared to manual methods. The integrated platform, available through a dedicated website, connects Rice-Alterome, PubAnnotation, and LLMs to streamline ontology curation and evidence discovery. This framework reduces the time domain experts need to locate and validate relevant information and provides interactive tools for users to add, merge, or refine trait annotations. The LLM-driven prompt-based querying also improved the identification of implicit or missing information that may be overlooked during manual curation.

Conclusions: Integrating LLMs with Rice-Alterome and PubAnnotation offers a promising solution for automating rice trait ontology curation. This approach accelerates evidence collection and enhances data consistency and accessibility. Future extensions of this framework will target additional crops such as wheat and maize and focus on refining LLM-based retrieval and annotation mechanisms for broader agricultural genomics applications.

Spatial transcriptomics technologies have significantly enhanced the analysis of gene expression profiles by retaining the spatial information of intact tissue sections and enabling the possibility of a more profound comprehension of tissue structures and cellular relationships. Despite this, most platforms have limited resolution, and at numerous capture spots, multiple signals from various cells are present, requiring deconvolution, a set of computational steps to deduce the underlying cellular composition. Over the last few years, a range of algorithms has been proposed to address this problem, each employing distinct computational principles and processing paradigms. The present review seeks to present a comprehensive analysis of twenty such algorithms, focusing on their methodological foundations. We contrast the underlying computational algorithms, modeling methods, and data processing pipelines that underlie them, and how they deal with external references, noise and sparsity in the data. By drawing out the conceptual as well as technical foundations of each algorithm, we aim to provide researchers a complete and hands-on grasp of the computational landscape of spatial transcriptomics deconvolution. This review is a methodological handbook to enable deep understanding of current deconvolution methods to develop novel strategies and help in selecting or applying these existing tools for different biological contexts.