Biology subjects

Sha, L.

Publications and source records attributed to Sha, L..

6 recordsLinked to original sources

bioRxiv2025

NLRP3 Inflammasome Exhibits Context-Dependent Roles in Glioblastoma-Astrocyte Crosstalk

Glioblastoma (GBM) is a highly aggressive brain tumor characterized by molecular and cellular heterogeneity and poor patient survival. NLRP3 inflammasome regulates inflammation and cell death with context-specific roles in cancer, but its functions in GBM tumour and astrocyte interactions remain unclear. In this study, we analyzed NLRP3 expression and function in GBM cell lines (LN-229 and LN18), astrocytes (SVG), patient-derived tissues, and organoids. Basal NLRP3 expression was higher in astrocytes than in GBM cells and increased after LPS stimulation, altering astrocyte morphology and NLRP3 subcellular localization. siRNA-mediated silencing of NLRP3 reduced GBM proliferation, migration, and viability in GBM cells, while enhancing proliferation and cytokine secretion in astrocytes, highlighting its context-dependent effects. NLRP3 deficiency exhibited reciprocal cytokine paracrine signaling, with GBM cells releasing elevated levels of IL-6 and TNF-, and astrocytes releasing elevated levels of IL-1{beta}. Glyburide treatment reduced NLRP3 expression and IL-1{beta} release in GBM cells but elevated NLRP3 and IL-1{beta} in astrocytes. Overall, these data reveal NLRP3s dual roles in GBM-astrocyte crosstalk, suggesting cell-type-selective inhibition for therapeutic exploration.

cancer biology↗

bioRxiv2025

NLRP12-mediated Glioblastoma-Astrocyte Cross-talk Promotes Tumor Growth

Nucleotide-binding domain leucine-rich repeat-containing receptors (NLR) are cytosolic pattern recognition receptors that regulate inflammation by sensing pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). NLRP12 is a cytosolic protein with inflammation-promoting and inflammation-attenuating properties. NLRP12 exhibits tumor-suppressive or tumor-promoting effects that may be cancer, cell-type, context-dependent, aided by differences in the microenvironment contributing to the pathophysiology of hepatocellular carcinoma, prostate cancer, colitis-associated cancer, and glioblastoma (GBM). GBM is a grade IV malignant brain tumor with poor patient survival and high tumor recurrence due to a heterogeneous cell population and angiogenesis. Our previous research reported NLRP12 as a potential prognostic marker for GBM with high expression in GBM patient tissues. In the present study, we investigated the role of NLRP12-mediated signalling between GBM and tumor-adjacent astrocytes, using a comprehensive panel of experimental models, including LN-229 GBM and SVG astrocyte cell lines, cell line-derived spheroids, patient-derived primary glioma cells, and patient-derived glioma organoids. We report that NLRP12 deficiency reduces cell proliferation and viability in the LN-229 cells, but increases these parameters in SVG cells. Furthermore, NLRP12-deficient LN-229 cells display attenuated ability to form three-dimensional spheroids, indicating a role of NLRP12 in anchorage-independent growth, which is considered a hallmark of tumorigenicity. Analysis of patient-derived glioma tissue and patient-derived GBM organoids revealed differential expression of NLRP12. Our findings suggest that NLRP12 modulates GBM cell proliferation, viability, and anchorage-independent growth in a cell-context dependent manner. The cell-type-specific roles of NLRP12 may underlie its complex contribution to GBM pathophysiology with implications for therapy and prognosis. SummaryNLRP12, a cytosolic regulator of inflammation, exhibits context-dependent roles in cancer. Its deficiency reduces glioblastoma cell proliferation, viability, and spheroid formation, while enhancing these properties in astrocytes, indicating a cell-type-dependent role for NLRP12 in GBM progression.

cancer biology↗

bioRxiv2025

Repurposing Nelfinavir: AIM2 Inflammasome-Driven Anti-tumor Effects in Glioblastoma

Nelfinavir (NFR), originally developed as an antiretroviral agent for the human immunodeficiency virus, has demonstrated anti-cancer properties across various malignancies; however, its therapeutic potential in Glioblastoma (GBM) remains largely unexplored. In the present study, we investigated the anti-tumor effects of NFR in GBM using a comprehensive panel of experimental models, including established GBM cell lines, GBM cell line-derived spheroids, patient-derived primary glioma cells, and patient-derived glioma organoids. We further evaluated the synergistic potential of NFR in combination with standard chemotherapeutic agents, Carboplatin and Doxorubicin, across these platforms. In vitro analyses revealed that NFR significantly inhibits GBM cell proliferation and induces both apoptotic and necrotic cell death. Mechanistically, we identified activation of the AIM2 inflammasome as a potential additional pathway mediating the anti-tumor effects of NFR. Collectively, our findings highlight NFR as a promising therapeutic candidate for GBM, exerting its effects through anti-proliferative and pro-death mechanisms potentially linked to AIM2 inflammasome activation.

cancer biology↗

bioRxiv2024

GeneLLM: A Large cfRNA Language Model for Cancer Screening from Raw Reads

We present GeneLLM, a novel large language model that offers a transformative approach to non-invasive cancer detection and biomarker discovery by directly interpreting plasma cell-free RNA (cfRNA) sequences. Unlike traditional annotation-dependent methods, GeneLLM operates without prior knowledge, achieving significantly improved multi-cancer detection accuracy. Critically, GeneLLM identifies novel cfRNAs ( pseudo-biomarkers) originating from previously unannotated genomic regions-overlooked by existing methods-offering new therapeutic targets and insights into intercellular communication. This innovative, cost-effective approach bypasses traditional bioinformatics tools, generating novel pseudo-biomarkers that outperform existing methods even with low-depth sequencing data. Consequently, GeneLLM opens new avenues for biomarker discovery and expands our understanding of the extracellular transcriptomes role in cancer development.

bioinformatics↗

bioRxiv2020

Linear integration of sensory evidence over space and time underlies face categorization

Visual object recognition relies on elaborate sensory processes that transform retinal inputs to object representations, but it also requires decision-making processes that read out object representations and function over prolonged time scales. The computational properties of these decision-making processes remain underexplored for object recognition. Here, we study these computations by developing a stochastic multi-feature face categorization task. Using quantitative models and tight control of spatiotemporal visual information, we demonstrate that humans categorize faces through an integration process that first linearly adds the evidence conferred by task-relevant features over space to create aggregated momentary evidence, and then linearly integrates it over time with minimum information loss. Discrimination of stimuli along different category boundaries (e.g., identity or expression of a face) is implemented by adjusting feature weights of spatial integration. This linear but flexible integration process over space and time bridges past studies on simple perceptual decisions to complex object recognition behavior.

neuroscience↗

bioRxiv2018

Psychophysical reverse correlation reflects both sensory and decision-making processes

Goal directed behavior depends on both sensory mechanisms that gather information from the outside world and decision-making mechanisms that select appropriate behavior based on that sensory information. Psychophysical reverse correlation is commonly used to quantify how fluctuations of sensory stimuli influence behavior and is generally believed to uncover the spatiotemporal weighting functions of sensory processes. Here we show that reverse correlations also reflect decision-making processes and can deviate significantly from the true sensory filters. Specifically, changes of decision bound and mechanisms of evidence integration systematically alter psychophysical reverse correlations. Similarly, trial-to-trial variability of sensory and motor delays and decision times causes systematic distortions in psychophysical kernels that should not be attributed to sensory mechanisms. We show that ignoring details of the decision-making process results in misinterpretation of reverse correlations, but proper use of these details turns reverse correlation into a powerful method for studying both sensory and decision-making mechanisms.

neuroscience↗