Biology subjects

Feng, E.

Publications and source records attributed to Feng, E..

4 recordsLinked to original sources

bioRxiv2024

Lysosomal LRRC8 complex regulates lysosomal pH, morphology and systemic glucose metabolism

The lysosome integrates anabolic signalling and nutrient-sensing to regulate intracellular growth pathways. The leucine-rich repeat containing 8 (LRRC8) channel complex forms a lysosomal anion channel and regulates PI3K-AKT-mTOR signalling, skeletal muscle differentiation, growth, and systemic glucose metabolism. Here, we define the endogenous LRRC8 subunits localized to a subset of lysosomes in differentiated myotubes. We show LRRC8A regulates leucine-stimulated mTOR, lysosome size, number, pH, and expression of lysosomal proteins LAMP2, P62, LC3B, suggesting impaired autophagic flux. Mutating a LRRC8A lysosomal targeting dileucine motif sequence (LRRC8A-L706A;L707A) in myotubes recapitulates the abnormal AKT signalling and altered lysosomal morphology and pH observed in LRRC8A KO cells. In vivo, LRRC8A-L706A;L707A KI mice exhibit increased adiposity, impaired glucose tolerance and insulin resistance characterized by reduced skeletal muscle glucose-uptake, and impaired incorporation of glucose into glycogen. These data reveal a lysosomal LRRC8 mediated metabolic signalling function that regulates lysosomal activity, systemic glucose homeostasis and insulin-sensitivity.

cell biology↗

bioRxiv2024

A prostate cancer gastrointestinal transcriptional phenotype may be associated with diminished response to AR-targeted therapy

BackgroundProstate cancer is a heterogenous disease, but once it becomes metastatic it eventually becomes treatment resistant. One mechanism of resistance to AR-targeting therapy is lineage plasticity, where the tumor undergoes a transformation to an AR-indifferent phenotype, most studied in the context of neuroendocrine prostate cancer (NEPC). However, activation of additional de- or trans-differentiation programs, including a gastrointestinal (GI) gene expression program, has been suggested as an alternative method of resistance. In this study, we explored the previously identified GI prostate cancer phenotype (PCa-GI) in a large cohort of metastatic castration-resistant prostate cancer (mCRPC) patient biopsy samples. MethodsWe analyzed a dataset of 634 mCRPC samples with batch effect corrected gene expression data from the West Coast Dream Team (WCDT), the East Coast Dream Team (ECDT), the Fred Hutchinson Cancer Research Center (FHCRC) and the Weill Cornell Medical center (WCM). Survival data was available from the WCDT and ECDT cohorts. We calculated a gene expression GI score using the sum of z-scores of genes from a published set of PCa-GI-defining genes (N=38). Survival analysis was performed using the Kaplan-Meier method and Cox proportional hazards regression with endpoint overall survival from time of biopsy to death of any cause. ResultsWe found that the PCa-GI score had a bimodal distribution, identifying a distinct set of tumors with an activated GI expression pattern. Approximately 35% of samples were classified as PCa-GI high, which was concordant with prior reports. Liver metastases had the highest median score but after excluding liver samples, 29% of the remaining samples were still classified as PCa-GI high, suggesting a distinct phenotype not exclusive to liver metastases. No correlation was observed between GI score and proliferation, AR signaling, or NEPC scores. Furthermore, the PCa-GI score was not associated with genomic alterations in AR, FOXA1, RB1, TP53 or PTEN. However, tumors with MYC amplifications showed significantly higher GI scores (p=0.0001). Patients with PCa-GI tumors had a shorter survival (HR=1.5 [1.1-2.1], p=0.02), but this result was not significant after adjusting for the liver as metastatic site (HR=1.2 [0.82-1.7], p=0.35). Patients with PCa-GI low samples had a better outcome after androgen receptor signaling inhibitors (ASI, abiraterone or enzalutamide) than other therapies (HR=0.37 [0.22-0.61], p=0.0001) while the benefit of ASI was smaller and non-significant for PCa-GI high samples (HR=0.55 [0.29-1.1], p=0.07). A differential pathway analysis identified FOXA2 signaling to be upregulated PCa-GI high tumors (FDR = 3.7 x 10-13). ConclusionsThe PCa-GI phenotype is prevalent in clinical mCRPC samples and may represent a distinct biological entity. PCa-GI tumors may respond less to ASI and could offer a strategy to study novel therapeutic targets.

cancer biology↗

bioRxiv2023

Targeting the MR1-MAIT Cell Axis Improves Vaccine Efficacy and Affords Protection against Viral Pathogens

Mucosa-associated invariant T (MAIT) cells are MR1-restricted, innate-like T lymphocytes with tremendous antibacterial and immunomodulatory functions. MAIT cells also sense and respond to viral infections in an MR1-independent fashion. However, whether they can be directly targeted in immunization strategies against viral pathogens is unknown. We addressed this question in multiple wild-type and genetically altered but clinically relevant mouse strains using several vaccine platforms against influenza viruses, poxviruses and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We demonstrate that 5-(2-oxopropylideneamino)-6-D-ribitylaminouracil (5-OP-RU), a riboflavin-based MR1 ligand of bacterial origin, can synergize with viral vaccines to expand MAIT cells in multiple tissues, reprogram them towards a pro-inflammatory MAIT1 phenotype, license them to bolster mainstream virus-specific CD8+ T cell responses, and potentiate heterosubtypic antiviral protection. Repeated 5-OP-RU administration did not render MAIT cells anergic, thus allowing for its inclusion in prime-boost immunization protocols. Mechanistically, tissue MAIT cell accumulation was due to their robust proliferation, as opposed to altered migratory behavior, and required viral vaccine replication competency, Toll-like receptor 3 (TLR3) and cell-autonomous type I interferon receptor signaling. Furthermore, the observed phenomenon was manifest in young and old female and male mice, and could also be recapitulated in a human cell culture system in which peripheral blood mononuclear cells were exposed to replicating virions and 5-OP-RU. In conclusion, although viruses and virus-based vaccines are devoid of the riboflavin biosynthesis machinery that supplies MR1 ligands, targeting MR1 enhances the efficacy of vaccine-elicited antiviral immunity. We propose 5-OP-RU as a non-classic but potent and versatile vaccine adjuvant against respiratory viruses.

immunology↗

bioRxiv2022

Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study

Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50,000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially-resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected and preprocessed Visium spatial transcriptomics data (17,943 genes at up to 5,000 spots per patient sampled in a honeycomb pattern) from tissue across four stage-III matched colorectal cancer patients. We compare and prototype several convolutional, Transformer, and graph convolutional neural networks to predict spatial RNA patterns under the hypothesis that the transformer and graph-based approaches better capture relevant spatial tissue architecture. We further analyzed the models ability to recapitulate spatial autocorrelation statistics using SPARK and SpatialDE. Overall, results indicate that the transformer and graph-based approaches were unable to outperform the convolutional neural network architecture, though they exhibited optimal performance for relevant disease-associated genes. Initial findings suggest that different neural networks that operate on different scales are relevant for capturing distinct disease pathways (e.g., epithelial to mesenchymal transition). We add further evidence that deep learning models can accurately predict gene expression in whole slide images and comment on understudied factors which may increase its external applicability (e.g., tissue context). Our preliminary work will motivate further investigation of inference for molecular patterns from whole slide images as metastasis predictors and in other applications.

pathology↗