Biology subjects

Duan, N.

Publications and source records attributed to Duan, N..

9 recordsLinked to original sources

bioRxiv2026

Loss of the Y chromosome drives basal identity and metastatic potential via epigenetic reprogramming in pancreatic cancer

Loss of the Y chromosome (LOY) has been largely unexplored in pancreatic ductal adenocarcinoma (PDAC), and no mechanistic framework previously explained its role in tumor biology. We identify LOY as a recurrent, functionally consequential alteration affecting ~50% of male PDAC patients across two independent cohorts, with strong enrichment in advanced disease and, critically, in the basal subtype, establishing LOY as a clinically actionable biomarker. Comprehensive genomic profiling positions LOY as a distinct genetic event associated with TP53 mutations, implicating it in aggressive tumor evolution. Integrative analyses across bulk tumors, single-cell datasets and CRISPR-engineered PDAC models converge on epithelial-to-mesenchymal transition (EMT) as the dominant LOY-driven program. Y-chromosome deletion alone induces EMT transcriptional states, mesenchymal morphology and enhanced metastatic ability. Mechanistically, LOY disrupts epigenetic regulation through loss of Y-linked chromatin modifiers, reshaping DNA methylation at EMT- and basal-defining loci and reducing chemotherapy sensitivity. These findings establish LOY as a causal genetic driver of PDAC progression and a clinically accessible biomarker with direct therapeutic relevance.

cancer biology↗

bioRxiv2026

Epigenetic and 3D Genome Changes Drive Primary Trastuzumab Resistance in HER2+ Breast Cancer

Primary trastuzumab resistance remains a major challenge in the treatment of HER2-positive breast cancer, with limited therapeutic options highlighting the need for deeper mechanistic insight. Here, integrative analysis of epigenomic and three-dimensional chromatin architecture reveals widespread remodeling of the regulatory landscape in resistant cells. Alterations in promoter-associated histone modifications?particularly H3K4me3 and H3K27me3?together with changes in active enhancer activity and promoter-enhancer interactions, appear to be key drivers of transcriptional reprogramming underlying resistance. As an illustrative example, SGK1 exhibits epigenetic activation in resistant cells, characterized by increased promoter H3K4me3 and enhanced chromatin interactions, consistent with its role in supporting cell survival, proliferation and metastasis. Overall, our findings highlight epigenetic and chromatin structural alterations as central mechanisms of primary trastuzumab resistance, providing a framework for novel therapeutic strategies development.

cancer biology↗

bioRxiv2024

Quantitative mapping of pseudouridines in bacteria RNA

RNA pseudouridylation is one of the most prevalent post-transcriptional modifications, occurring universally across all organisms. Although pseudouridines have been extensively studied in bacterial tRNAs and rRNAs, their presence and role in bacterial mRNA remain poorly characterized. Here, we used a bisulfite-based sequencing approach to provide a comprehensive and quantitative measurement of bacteria pseudouridines. As a proof of concept in E. coli, we identified 1,954 high-confidence sites in 1,331 transcripts, covering almost 30% of the transcriptome. Furthermore, pseudouridine mapping enabled the detection of differentially expressed genes associated with stress response that were unidentified using conventional RNA-seq approach. We also demonstrate that in addition to pseudouridine profiling, our approach can facilitate the discovery of previously unidentified transcripts. As an example, we identified a small RNA transcribed from the antisense strand of tRNA-Tyr which represses expression of distal genes. Finally, we mapped pseudouridines in oral microbiome samples of human subjects, demonstrating the broad applicability of our approach in complex microbiomes. Altogether, our work highlights the advantages of mapping bacterial pseudouridines and provides a tool to study posttranscription regulation in microbial communities.

microbiology↗

bioRxiv2024

Targeting SLC7A11-mediated cysteine metabolism for the treatment of trastuzumab resistant HER2 positive breast cancer

Trastuzumab resistance remains a challenge for HER2 positive breast cancer treatment. Targeting metabolic reprogramming would provide novel insights for therapeutic strategies. Here, we integrated metabolomics, transcriptomics and epigenomics data of trastuzumab sensitive and primary resistant HER2 positive breast cancer to identify metabolic alterations. Aberrant cysteine metabolism was discovered in trastuzumab primary resistant breast cancer at both circulating and intracellular levels. The inhibition of SLC7A11 and cysteine starvation could synergize with trastuzumab to induce ferroptosis. Mechanistically, increased H3K4me3 and decreased DNA methylation enhanced SLC7A11 transcription and cystine uptake in trastuzumab resistant breast cancer. The regulation of epigenetic modifications modulated cysteine metabolism and ferroptosis sensitivity. These results revealed an innovative approach for overcoming trastuzumab resistance by targeting specific amino acid metabolism.

cancer biology↗

bioRxiv2023

Predicting biomarker for the acute pulmonary embolism by using gene ontology and machine learning

Key PointsEarly and Accurate Diagnosis Essential: Acute pulmonary embolism (PE) is a critical condition that demands prompt and precise diagnosis for effective treatment. Limitations of Current Diagnostics: Existing diagnostic methods like Computed Tomography Pulmonary Angiography (CTPA) have certain limitations, leading to the exploration of alternative approaches. Potential of Blood-Based Biomarkers: A recent study focused on identifying blood-based biomarkers for PE. This involved using gene ontology analysis and machine learning methods to analyze gene expression data from both PE patients and healthy controls. Gene Selection and Analysis: The study selected 20 genes for detailed analysis. These included various coagulation factors, fibrinolytic genes, and inflammation markers. Gene Ontology enrichment analysis was performed to understand the biological processes and molecular functions of these genes. Machine Learning for Diagnosis: Supervised machine learning algorithms were utilized to create classification models using the expression levels of these 20 genes. The models demonstrated promising results in distinguishing PE patients from healthy individuals. Acute pulmonary embolism (PE) is a life-threatening condition requiring early and accurate diagnosis. Current diagnostic methods like CTPA have limitations, and a study aimed to identify potential blood-based biomarkers for PE using gene ontology analysis and machine learning methods. Gene expression data of PE patients and healthy controls were obtained from the Gene Expression Omnibus database. A total of 20 genes were selected for further analysis, including coagulation factors F7, F10, F12, fibrinolytic genes PLAT, SERPINE1 and SERPINE2, and inflammation markers SELE, VCAM1 and ICAM. Gene Ontology enrichment analysis was performed to identify biological processes and molecular functions overrepresented among the candidate genes. Supervised machine learning algorithms were applied to build classification models using the expression levels of the 20 genes as features. Nested cross-validation was employed to assess model performance. The RF model achieved the highest area under the receiver operating characteristic curve of 0.89, indicating excellent discrimination between PE patients and controls based on the gene expression signature. Validation in larger cohorts is warranted to clinically translate these findings into a non-invasive diagnostic test for PE.

genomics↗

bioRxiv2022

scFormer: A Universal Representation Learning Approach for Single-Cell Data Using Transformers

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell sequencing has emerged as a promising technique to decode cellular heterogeneity and analyze gene functions. With the high throughput of modern techniques and resulting large-scale sequencing data, deep learning has been used extensively to learn representations of individual cells for downstream tasks. However, most existing methods rely on fully connected networks and are unable to model complex relationships between both cell and gene representations. We hereby propose scFormer, a novel transformer-based deep learning framework to jointly optimize cell and gene embeddings for single-cell biology in an unsupervised manner. By drawing parallels between natural language processing and genomics, scFormer applies self-attention to learn salient gene and cell embeddings through masked gene modelling. scFormer provides a unified framework to readily address a variety of downstream tasks such as data integration, analysis of gene function, and perturbation response prediction. Extensive experiments using scFormer show state-of-the-art performance on seven datasets across the relevant tasks. The scFormer model implementation is available at https://github.com/bowang-lab/scFormer.

bioinformatics↗

bioRxiv2020

A valid protective immune response elicited in rhesus macaques by an inactivated vaccine is capable of defending against SARS-CoV-2 infection

With the relatively serious global epidemic outbreak of SARS-CoV-2 infection, public concerns focus on not only clinical therapeutic measures and public quarantine for this disease but also the development of vaccines. The technical design of our SARS-CoV-2 inactivated vaccine provides a viral antigen that enables the exposure of more than one structural protein based upon the antibody composition of COVID-19 patients convalescent serum. This design led to valid immunity with increasing neutralizing antibody titers and a CTL response detected post-immunization of this vaccine by two injections in rhesus macaques. Further, this elicited immunoprotection in macaques enables not only to restrain completely viral replication in tissues of immunized animals, compared to the adjuvant control and those immunized by an RBD peptide vaccine, but also to significantly alleviate inflammatory lesion in lung tissues in histo-pathologic detection, compared to the adjuvant control with developed interstitial pneumonia. The data obtained from these macaques immunized with the inactivated vaccine or RBD peptide vaccine suggest that immunity with a clinically protective effect against SARS-CoV-2 infection should include not only specific neutralizing antibodies but also specific CTL responses against at least the S and N antigens.

immunology↗

bioRxiv2019

Genome analysis and Hi-C assisted assembly of Elaeagnus angustifolia L., a deciduous tree belonging to Elaeagnaceae

Elaeagnus angustifolia L. is a deciduous tree of the Elaeagnaceae family. It is widely used in the study of abiotic stress tolerance in plants and for the improvement of desertification-affected land due to its characteristics of drought resistance, salt tolerance, cold resistance, wind resistance, and other environmental adaptation. Here, we report the complete genome sequencing using the Pacific Biosciences (PacBio) platform and Hi-C assisted assembly of E. angustifolia. A total of 44.27 Gb raw PacBio sequel reads were obtained after filtering out low-quality data, with an average length of 8.64 Kb. And 39.56 Gb clean reads was obtained, with a sequencing coverage of 75x, and Q30 ratio > 95.46%. The 510.71 Mb genomic sequence was mapped to the chromosome, accounting for 96.94% of the total length of the sequence, and the corresponding number of sequences was 269, accounting for 45.83% of the total number of sequences. The genome sequence study of E. angustifolia can be a valuable source for the comparative genome analysis of the Elaeagnaceae family members, and can help to understand the evolutionary response mechanisms of the Elaeagnaceae to drought, salt, cold and wind resistance, and thereby provide effective theoretical support for the improvement of desertification-affected land.

genetics↗

bioRxiv2018

Elongation during segmentation shows axial variability, low mitotic rates, and synchronized cell cycle domains in the crustacean, Thamnocephalus platyurus

Segmentation in arthropods typically occurs by sequential addition of segments from a posterior growth zone, but cell behaviors producing posterior elongation are not well known. Using precisely staged larvae of the crustacean, Thamnocephalus platyurus, we systematically examined cell division patterns and morphometric changes associated with posterior elongation during segmentation. We show that cell division is required for normal elongation but that cells in the growth zone need only divide ~1.5 times to meet that requirement; correspondingly, direct measures of cell division in the growth zone are low. Morphometric measurements of the growth zone and of newly formed segments suggest tagma-specific features of segment generation. Using methods for detecting two different phases in the cell cycle, we show distinct domains of synchronized cells in the posterior. Borders of cell cycle domains correlate with domains of segmental gene expression, suggesting an intimate link between segment generation and cell cycle regulation.\n\nSummary StatementPosterior growth zone has synchronized cell cycle domains but shows little cell division during segment addition in a crustacean. Dimensions of the shrinking posterior growth zone change at tagma boundaries.

developmental biology↗