Synchronous T-cell lymphoblastic lymphoma, chronic myelomonocytic leukemia and myeloma with sequential acute myeloid leukemia transformation.
Explore the source record for details and available documents.
Letter↗
Biomedical subjects
Ke Xu
Publications and source records attributed to Ke Xu.
An intricate functional relationship between NuA4 and Sfp1 regulates ribosome biogenesis in response to nutrient availability.
Ribosome biogenesis is a crucial process requiring enormous transcriptional output. In budding yeast, the expression of 138 ribosomal protein (RP) genes and over 200 ribosome biogenesis (RiBi) genes is regulated by an intricate network of factors, including the nutrient-sensitive transcription activator Sfp1 and the NuA4 coactivator/acetyltransferase complex. Nutrient starvation or inhibition of target of rapamycin complex 1 by rapamycin leads to repression of RP and RiBi genes, in part through blocking Sfp1 nuclear localization and NuA4-dependent chromatin acetylation. Here, we demonstrate that Sfp1 physically interacts with NuA4 in a target of rapamycin complex 1-dependent manner. Our results indicate that Sfp1, along with NuA4, regulate the transcription of RiBi and RP genes via distinct mechanisms depending on promoter architectures. Sfp1 promotes histone acetylation at the promoters without affecting NuA4 recruitment. In contrast, NuA4 does impact Sfp1 binding but specifically at two classes of RP genes. Importantly, NuA4 acetylates Sfp1 at lysines 655 and 657, regulating its function. Cells expressing Sfp1 with acetyl-mimicking mutations exhibit increased expression of RiBi genes while RP genes remain stable. However, the same mutants lead to the loss of Sfp1 binding/activity at RiBi genes when cells are under non-optimal growth conditions. Mimicking constitutive acetylation of Sfp1 also limits the transcriptional burst of RP genes upon addition of glucose. Altogether, these results draw an intricate functional relationship between Sfp1 and NuA4 to control ribosome biogenesis, fine-tuning transcription output in different growth conditions.
Saccharomyces cerevisiae Proteins↗
High-Purity Monovalent Functionalization of Carbon Nanotubes.
Single-walled carbon nanotubes (SWCNTs) show promise for probing molecular interactions at single-molecule resolution, yet generating SWCNT populations bearing a single defined functional tag remains challenging because surface functionalization is inherently stochastic. Here, we present a batch-scale strategy to produce predominantly singly tagged SWCNTs by leveraging the stochastic adsorption of single-stranded DNA (ssDNA). Specifically, SWCNTs are dispersed using a mixture of unmodified ssDNA (um-ssDNA) and a minor fraction of modified ssDNA (m-ssDNA) carrying an affinity handle. We developed a probabilistic ssDNA-SWCNT binding model that predicts the distribution of m-ssDNA per nanotube as a function of the input minor-strand fraction p = m-ssDNA/total ssDNA, enabling selection of conditions that maximize single-tag purity. Using magnetic-bead capture via a biotin affinity interaction and subsequent release, we isolate SWCNTs with 97.6% predicted single-tag purity at 2% recovery. Single-molecule fluorescence imaging further supports predominantly single-label occupancy under the model-selected conditions. Thus, this approach provides a general route to SWCNTs bearing a single molecular handle for downstream conjugation and assembly, supporting diverse future applications in SWCNT-based nanotechnologies.
Nanotubes, Carbon↗
stDyer-image improves clustering analysis of spatially resolved transcriptomics and proteomics with morphological images.
MOTIVATION: Spatially resolved transcriptomics (SRT) and spatially resolved proteomics (SRP) data enable the study of gene expression and protein abundances within their precise spatial and cellular contexts in tissues. Certain SRT and SRP technologies also capture corresponding morphology images, adding another layer of valuable information. However, few existing methods developed for SRT data effectively leverage these supplementary images to enhance clustering performance. RESULTS: Here, we introduce stDyer-image, an end-to-end deep learning framework designed for clustering for SRT and SRP datasets with images. Unlike existing methods that utilize images to complement gene expression data, stDyer-image directly links image features to cluster labels. This approach draws inspiration from pathologists, who can visually identify specific cell types or tumor regions from morphological images without relying on gene expression or protein abundances. Benchmarks against state-of-the-art tools demonstrate that stDyer-image achieves superior performance in clustering. Moreover, it is capable of handling large-scale datasets across diverse technologies, making it a versatile and powerful tool for spatial omics analysis. AVAILABILITY AND IMPLEMENTATION: The source code of stDyer-image and detailed tutorials are available at https://github.com/ericcombiolab/stDyer-image.
Proteomics↗
Multi-Omics Biomarker Signatures for Precision Diagnosis and Prognosis in Primary Liver Cancer: A Literature Review.
Primary liver cancer (PLC) is a biologically heterogeneous group of malignancies dominated by hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), and a smaller subset of combined hepatocellular-cholangiocarcinoma (cHCC-CCA), and its clinical burden remains high because current diagnostic and prognostic tools do not adequately capture molecular diversity. Conventional imaging, serum markers, and histopathological assessment remain insufficient for precise early diagnosis, subtype-resolved classification, and outcome stratification, while tissue and liquid biopsy approaches have expanded the range of analytes available for clinical assessment. Recent studies have identified candidate biomarker signatures across genomic, epigenomic, transcriptomic, proteomic, metabolomic, and circulating layers, suggesting that integrated multi-omics profiling may better represent tumor lineage, clonal evolution, immune context, and therapeutic vulnerability than isolated molecular readouts. However, these layers are not equally mature for clinical use: genomic testing is closest to routine therapeutic application in iCCA, plasma methylation assays are advancing for HCC surveillance augmentation, and many proteomic or metabolomic panels remain validation-stage tools. Their clinical value remains constrained by sampling bias, biospecimen-dependent signal loss, assay standardization, cost, and the need for prospective validation across clinically diverse populations. This narrative review critically synthesizes current evidence on multi-omics biomarker signatures for precision diagnosis and prognosis in primary liver cancer and argues that clinically useful signatures should be question-specific, stage-aware, and specimen-aware rather than universal multi-analyte panels.
Humans↗
Putative function and prognostic molecular marker of mast cells in colorectal cancer.
BACKGROUND: The increased demand for markers for colorectal cancer (CRC) highlights the importance of investigating immune cells involved in CRC progression. This study aims to dissect the mast cells in CRC, characterize the role of mast cells in CRC development, coordinate molecular communication between mast cells and malignant cells, and construct and validate a prognostic classification model based on mast cell markers. METHODS: Single-cell transcriptome data of CRC patients were extracted from GSE146771 for cell classification and annotation. The malignant cells were identified by copykat and the communication between mast cells and malignant cells was analyzed by CellChat. Least absolute shrinkage and selection operator (LASSO) regression analysis and Cox regression analysis of mast cell markers were performed in the TCGA-COAD cohort to construct a prognostic classification model. qRT-PCR was performed to detect the mRNA expression of the molecules in the classification model in P815 and MC-9 cells. The co-culture experiment of MC38 and P815 cells were performed in 12-well transwell dish. Wound healing assay and Transwell assay were performed to detect cell migration and invasion. RESULTS: 10,186 high-quality cells in GSE146771 were annotated to 9 cell types. Six markers in mast cells (HDC, GATA2, ASAH1, BTBD19, TIMP1, FAM110A) were selected to construct a classification model. The high-risk score defined showed high infiltration of immunosuppressive cells, including endothelial cells, CAFs, Tregs and high angiogenesis and epithelial-mesenchymal transition (EMT) activities. In the model, HDC were abnormally low expressed in P815 cells, while BTBD19, FAM110A, GATA2, ASAH1 and TIMP1 showed excessive expression in P815 cells. Knockdown of GATA2 in the co-culture system of P815 and MC38 cells blocked cell migration and invasion. CONCLUSION: This study identified the cell types within CRC, elaborated the cellular functions of mast cells in CRC development and their molecular communication to coordinate malignant cells, and highlighted the molecular components and biological features that constitute promising prognostic classification model.
Mast Cells↗
Aberrant DNA methylation of genes regulating CD4+ T cell HIV-1 reservoir in women with HIV.
BACKGROUND: The HIV-1 reservoir in CD4+ T cells (HRCD4) pose a major challenge to curing HIV, with many of its mechanisms still unclear. HIV-1 DNA integration and immune responses may alter the host's epigenetic landscape, potentially silencing HIV-1 replication. METHODS: This study used bisulphite capture DNA methylation sequencing in CD4+ T cells from the blood of 427 virally suppressed women with HIV to identify differentially methylated sites and regions associated with HRCD4. RESULTS: The average total HRCD4 size was 1409 copies per million cells, with most proviruses defective and only a small proportion intact. The study identified 245 differentially methylated CpG sites and 85 regions linked to HRCD4 size, with 52% of significant sites in intronic regions. Genes associated with HRCD4 were involved in viral replication, HIV-1 latency and cell growth and apoptosis. HRCD4 size was inversely related to DNA methylation of interferon signalling genes and positively associated with methylation at known HIV-1 integration sites. HRCD4-associated genes were enriched on the pathways related to immune defence, transcription repression and host-virus interactions. CONCLUSIONS: These findings suggest that HIV-1 reservoir is linked to aberrant DNA methylation in CD4+ T cells, offering new insights into epigenetic mechanisms of HIV-1 latency and potential molecular targets for eradication strategies. KEY POINTS: Study involved 427 women with HIV. Identified 245 aberrant DNA methylation sites and 85 methylation regions in CD4+ T cells linked to the HIV-1 reservoir. Highlighted genes are involved in viral replication, immune defence, and host genome integration. Findings suggest potential molecular targets for eradication strategies.
Humans↗
Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes.
Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.
Humans↗