Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1291838 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Efficient nucleic acid enrichment is pivotal for deciphering epigenetic modifications and disease biomarkers, yet current methods are constrained by insufficient specificity, poor versatility, and high costs. We developed a universal strategy named ‘Click-IP-Seq’ by leveraging the high-affinity binding between Protein G and Fc region of DBCO-modified IgG. This enabled the directional conjugation of DBCO-IgG with azide-modified nucleic acids via copper-free strain-promoted azide-alkyne cycloaddition (SPAAC) click chemistry, achieving specific capture and enrichment of modified nucleic acids. Firstly, this method efficiently enriched two major DNA modifications, 8-oxo-7,8-dihydroguanine (8-oxo-dG) and 5-hydroxymethylcytosine (5hmC) in model DNA systems. Then, genome-wide distribution of 8-oxodG from human cells and tissues align with previously reports. Finally, employing ‘Click-IP-Seq’, we performed the first comprehensive analysis of 8-oxo-dG spatial distribution and associated biological functions in human colorectal carcinoma tissues. This technology provides a high-specificity and versatile enrichment platform for nucleic acid modifications, which is expected to promote the application of cancer molecular diagnosis.
2025-09-05 | GSE305517 | GEO
Project description:The cGAS-STING pathway plays a central role in controlling tumor progression through nucleic acid sensing and type I Interferon production. Here, we identify Poly(rC) Binding Protein 1 (PCBP1) as a tumor suppressor that amplifies cGAS-STING signaling in breast cancer. Using patient datasets and a transgenic mouse model with conditional PCBP1 knockout in mammary epithelial cells, we show that PCBP1 expression correlates with improved survival, reduced tumor burden, increased type I IFN and ISG expression, and elevated cytotoxic T cell infiltration. Mechanistically, PCBP1 binds cytosine-rich single-stranded motifs via its KH domains and increases cGAS affinity to these nucleic acids. Disruption of the conserved GXXG motif impairs PCBP1's nucleic acid binding and cGAS activation. Although cGAS is a double-stranded DNA sensor with no intrinsic sequence specificity, we uncover that the single-stranded nucleic-acid binding protein PCBP1 enhances cGAS sensing by engaging sequence-specific motifs, acting as an important nucleic acid co-sensor that impairs tumorigenesis.
2025-10-15 | GSE307750 | GEO
Project description:The cGAS-STING pathway plays a central role in controlling tumor progression through nucleic acid sensing and type I Interferon production. Here, we identify Poly(rC) Binding Protein 1 (PCBP1) as a tumor suppressor that amplifies cGAS-STING signaling in breast cancer. Using patient datasets and a transgenic mouse model with conditional PCBP1 knockout in mammary epithelial cells, we show that PCBP1 expression correlates with improved survival, reduced tumor burden, increased type I IFN and ISG expression, and elevated cytotoxic T cell infiltration. Mechanistically, PCBP1 binds cytosine-rich single-stranded motifs via its KH domains and increases cGAS affinity to these nucleic acids. Disruption of the conserved GXXG motif impairs PCBP1's nucleic acid binding and cGAS activation. Although cGAS is a double-stranded DNA sensor with no intrinsic sequence specificity, we uncover that the single-stranded nucleic-acid binding protein PCBP1 enhances cGAS sensing by engaging sequence-specific motifs, acting as an important nucleic acid co-sensor that impairs tumorigenesis.
2025-10-15 | GSE307616 | GEO
Project description:Nucleic Acid Programmable Protein Arrays (NAPPA) Somatic p53 Mutations
| PRJNA312188 | ENA
Project description:The interactions between proteins and nucleic acids have a fundamental function in many biological processes well beyond nuclear gene transcription and include RNA homeostasis, protein translation and pathogen sensing for innate immunity. While our knowledge of the ensemble of proteins binding individual mRNAs in mammalian cells has greatly been augmented by recent surveys, no systematic study on the native proteins of human cells differentially engaging various types of nucleic acids in a non sequence-specific manner has been reported. We designed an experimental approach to cover the non sequence-specific RNA and DNA binding space broadly, including methylation, and test for its ability to interact with the human proteome. We used 25 rationally designed nucleic acid probes in an affinity purification mass spectrometry and bioinformatics workflow to identify proteins from whole cell extracts of three different human cell lines. The proteins were profiled for their binding preferences to the different general types of nucleic acids. The study identified 746 high confidence direct binders, 249 of which were devoid of previous experimental evidence for binding nucleic acids. We could assign 513 specific affinities for sub-types of nucleic acid probes to 219 distinct proteins and to individual domains. The evolutionary conserved protein YB-1, previously associated with cancer and gene regulation, is shown to bind methylated cytosine preferentially conferring YB-1 a potential epigenetic function. Collectively, the dataset represents a rich resource of experimentally determined nucleic acid-specific binding proteins in humans and, indirectly, for other species. Identification of genomic YB-1 binding sites in HEK293 cells
2013-06-18 | E-GEOD-47539 | biostudies-arrayexpress
Project description:Traditional protein engineering methods, such as directed evolution, while effective, are often slow and labor-intensive. Advances in machine learning and automated biofoundry present new opportunities for optimizing these processes. This study devises a protein language model-enabled automatic evolution platform, a closed-loop system for automated protein engineering within the Design-Build-Test-Learn cycle. The protein language model ESM-2 makes zero-shot prediction of 96 variants to initiate the cycle. The biofoundry constructs and evaluates these variants, and feeds the results back to a multi-layer perceptron to train a fitness predictor, which then makes prediction of second round of 96 variants with improved fitness. With the tRNA synthetase as a model enzyme, four-rounds of evolution carried out within 10 days lead to mutants with enzyme activity improved by up to 2.4-fold. Our system significantly enhances the speed and accuracy of protein evolution, driving faster advancements in protein engineering for industrial applications.
2024-12-25 | PXD058768 | Pride
Project description:RNA-RNA interactions (RRIs) are fundamental to gene regulation and RNA processing, yet their molecular determinants remain unclear. In this work, we analyzed several large-scale RRI datasets and identified low-complexity repeats (LCRs), including simple tandem repeats, as key drivers of RRIs. Our findings reveal that LCRs enable thermodynamically stable interactions with multiple partners, positioning them as key hubs in RNA-RNA interaction networks. RNA-sequencing of the interactors of the Lhx1os lncRNA allowed to validate the importance of LCRs in shaping interactions potentially involved in neuronal development. Recognizing the pivotal role of sequence determinants, we developed RIME, a deep learning model that predicts RRIs by leveraging embeddings from a nucleic acid language model. RIME outperforms traditional thermodynamics-based tools, successfully captures the role of LCRs and prioritizes high-confidence interactions, including those established by lncRNAs. RIME is freely available at https://tools.tartaglialab.com/rna_rna.
2025-12-17 | GSE290073 | GEO