Biology subjects

Wenzel, A. T.

Publications and source records attributed to Wenzel, A. T..

8 recordsLinked to original sources

bioRxiv2025

Interferon Restores Antigen Presentation and Sensitizes Medulloblastoma to T Cell Killing

Medulloblastomas are commonly considered immunologically cold and refractory to immunotherapy. One contributing factor to their low immunogenicity is impaired antigen presentation, which allows tumor cells to escape from cytotoxic T cells. Here we use a syngeneic mouse model of medulloblastoma to study the role of CD8+ T cells in medulloblastoma growth. We demonstrate that despite low expression of MHC Class I on tumor cells, depletion of CD8+ T cells accelerates tumor growth, whereas adoptive transfer of tumor-reactive CD8+ T cells prolongs survival. These anti-tumor effects rely on T cells secreting interferon gamma (IFN{gamma}), which induces MHC class I on tumor cells and facilitates tumor cell killing by T cells. Notably, this response is essential for CD8+ T cell-mediated tumor attack, as blocking IFN{gamma} signaling in vivo abrogates MHC class I induction and eliminates the beneficial effect of T cells. Importantly, delivering IFN{gamma} directly into tumors via convection-enhanced delivery (CED) enhances CD8+ T cell-mediated killing of tumor cells and significantly prolongs survival in tumor-bearing mice. These studies highlight the importance of T cells in controlling brain tumor growth and the value of IFN{gamma} as an adjuvant for T cell-based immunotherapy.

cancer biology↗

bioRxiv2025

Single Nuclei-Derived Molecular Subtypes of Gastrointestinal Stromal Tumors Correlate with Clinicopathologic Features and Predict Clinical Outcomes

Historically, gastrointestinal stromal tumor (GIST) has been subtyped by oncogenic driver mutations. However, tumors with the same mutational profile can have variable biology. To further explore the impact of molecular diversity on GIST biology, we performed single nucleus RNA sequencing on 16 primary GIST and utilized an integrated single cell atlas of the normal GI tract composed from multiple publicly available datasets to identify six distinct GIST cell states. We then statistically estimated the relative abundances of these profiles in bulk transcriptomic data. These were used to define six common GIST molecular subtypes based upon one or two predominant tumor cell states. We found that these molecular subtypes correlate with tumor locations, mutational profiles, and patient outcomes, and validated these subtypes in an independent international cohort. These molecular subtypes have the potential to be used for clinical prognostication for patients with GIST, identifying new therapeutic targets, and studying the cell of transformation of GIST.

cancer biology↗

bioRxiv2024

Data driven refinement of gene expression signatures for enrichment analysis

Gene set enrichment methods measure biological process or pathway activation in gene expression data by testing coordinate up- or down-regulation of pathway members in a ranked list of genes. These methods rely on curated, annotated gene sets whose members coordinate expression is an indicator of a process or state. We therefore developed the Molecular Signatures Database (MSigDB), a collection of expertly annotated gene sets. While using, enhancing, and expanding MSigDB, we have observed that some gene sets can lack coordinate expression, especially those derived from canonical pathways. To address this challenge, we developed gene set refinement (GSR), a data-driven approach leveraging large-scale multi-omics compendia to extract context-specific sets, deconvolve heterogeneity, and reveal multiple downstream signaling. We applied this method to address cancer biology questions, and demonstrated successful, targeted refinement of existing MSigDB gene sets.

bioinformatics↗

bioRxiv2024

MICAL2 Is a Super Enhancer Associated Gene that Promotes Pancreatic Cancer Growth and Metastasis

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid cancers and thus identifying more effective therapies is a major unmet need. In this study we characterized the super enhancer (SE) landscape of human PDAC to identify novel, potentially targetable, drivers of the disease. Our analysis revealed that MICAL2 is a super enhancer-associated gene in human PDAC. MICAL2 is a flavin monooxygenase that induces actin depolymerization and indirectly promotes SRF transcription by modulating the availability of serum response factor coactivators myocardin related transcription factors (MRTF-A and MRTF-B). We found that MICAL2 is overexpressed in PDAC and correlates with poor patient prognosis. Transcriptional analysis revealed that MICAL2 upregulates KRAS and EMT signaling pathways, contributing to tumor growth and metastasis. In loss and gain of function experiments in human and mouse PDAC cells, we observed that MICAL2 promotes both ERK1/2 and AKT activation. Consistent with its role in actin depolymerization and KRAS signaling, loss of MICAL2 expression also inhibited macropinocytosis. Through in vitro phenotypic analyses, we show that MICAL2, MRTF-A and MRTF-B influence PDAC cell proliferation, migration and promote cell cycle progression. Importantly, we demonstrate that MICAL2 is essential for in vivo tumor growth and metastasis. Interestingly, we find that MRTF-B, but not MRTF-A, phenocopies MICAL2-driven phenotypes in vivo. This study highlights the multiple ways in which MICAL2 impacts PDAC biology and suggests that its inhibition may impede PDAC progression. Our results provide a foundation for future investigations into the role of MICAL2 in PDAC and its potential as a target for therapeutic intervention.

cancer biology↗

bioRxiv2024

Profiling ranked list enrichment scoring in sparse data elucidates algorithmic tradeoffs

Gene Set Enrichment Analysis (GSEA) is a method for quantifying pathway and process activation in groups of samples, and its single sample version (ssGSEA) scores activation using mRNA abundance in a single sample. GSEA and ssGSEA were developed for "bulk" samples rather than individual cell technologies such as microarrays and bulk RNA-sequencing (RNA-seq) data. The growing use of single cell RNA-sequencing (scRNA-seq) raises the possibility of using ssGSEA to quantify pathway and process activation in individual cells. However, scRNA-seq data is much sparser than RNA-seq data. Here we show that ssGSEA as designed for bulk data is subject to some amount of score uncertainty and other technical issues when applied to individual cells from scRNA-seq data. We also show that a ssGSEA can be applied robustly to "pseudobulk" aggregate groups of a few hundred to a few thousand cells provided appropriate normalization is used. Finally, in comparing this approach to other ranked list enrichment methods, we find that the UCell method is most robust to sparsity. We have made the aggregate cell version of ssGSEA available as a Python package and GenePattern module and will also modularize UCell for use on GenePattern as well.

bioinformatics↗

bioRxiv2023

NMFClustering: Accessible NMF-based clustering utilizing GPU acceleration

SummaryNon-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible in silico research for non-programmers. Availability and ImplementationNMFClustering is freely available on the public GenePattern server at https://genepattern.ucsd.edu. Code for the NMFClustering is available under a BSD style license on github at https://github.com/genepattern/nmf-gpu. ContactTed Liefeld, jliefeld@cloud.ucsd.edu Supplementary InformationSupplementary data are available at Bioinformatics online and at https://datasets.genepattern.org/?prefix=data/test_data/NMFClustering/.

bioinformatics↗

bioRxiv2022

Inactivation of histone chaperone HIRA unmasks a link between normal embryonic development of melanoblasts and maintenance of adult melanocyte stem cells

Histone chaperone HIRA is thought to play a role in both early development and aging, but little is known about connections between the two processes. Here, we explore this relationship using a lineage-specific knockout mouse model, TyrCre::Hirafl/fl, in which HIRA is deficient in the pigmentary system consisting of embryonic melanoblasts, postnatal melanocytes and melanocyte stem cells (McSCs). Hira knockout leads to reduced melanoblast numbers during embryogenesis, but wild type numbers of melanocytes at birth, normally functioning juvenile and young adult McSCs, and only a very mildly hypopigmented first hair coat. However, on closer analysis, Hira knockout melanocytic cells of newborn mice exhibit molecular markers characteristic of cell aging and proliferative deficits. As they age, TyrCre::Hirafl/fl mice display marked defects in McSC maintenance and premature hair graying. Importantly, these defects are only observed when HIRA is inactivated during embryogenesis, not post-natally. This genetic model illustrates how normal embryonic development lays the foundation for maintenance of adult tissue specific stem cells and so suppression of degenerative phenotypes of aging.

developmental biology↗

bioRxiv2020

What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data

Large-scale omics experiments have become standard in biological studies, leading to a deluge of data. However, researchers still face the challenge of connecting changes in the omics data to changes in cell functions, due to the complex interdependencies between genes, proteins and metabolites. Here we present a novel framework that begins to overcome this problem by allowing users to infer how metabolic functions change, based on omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. We then used genome-scale metabolic networks to define gene modules responsible for each specific metabolic task. We further developed a framework to overlay omics data on these modules to predict pathway usage for each metabolic task. The proposed approach allows one to directly predict how changes in omics experiments change cell or tissue function. We further demonstrated how this new approach can be used to leverage the metabolic functions of biological entities from the single cell to their organization in tissues and organs using multiple transcriptomic datasets (human and mouse). Finally, we created a web-based CellFie module that has been integrated into the list of tools available in GenePattern (www.genepattern.org) to enable adoption of the approach.

systems biology↗