Glycolytic body (G body) RIP-sequencing
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA608654 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11177826.fastq.gz | Fastqsanger.gz | |||
| SRR11177827.fastq.gz | Fastqsanger.gz | |||
| SRR11177828.fastq.gz | Fastqsanger.gz | |||
| SRR11177829.fastq.gz | Fastqsanger.gz | |||
| SRR11177830.fastq.gz | Fastqsanger.gz |
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Project description:Glycolytic (G) bodies were purified from hypoxic yeast using differential centrifugation and immunoprecipitation by Pfk2-GFP-Flag in order to determine the RNAs that localize to G bodies by determining the enrichment of RNAs copurified with G bodies over both the flow through and total RNA fractions. We find significant overlap between enriched RNAs and RNAs bound by glycolysis enzymes in normoxic conditions. Our study determined that RNA is an integral component of G bodies and is required for G body formation. Specific mRNAs are only slightly enriched in G bodies.
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Project description:Glycolysis is upregulated in cells under specific conditions, such as hypoxia and high energy demand, to achieve the metabolic requirements for continued cell proliferation. However, the mechanism of this increased glycolytic rate remains poorly understood. In the budding yeast Saccharomyces cerevisiae, we discovered that hypoxia induces concentration of many glycolytic enzymes, including the Pfk2p subunit of the ratelimiting enzyme, phosphofructokinase, into a single, non-membrane-bound granule that we define as the "glycolytic body" or "G body". Pfk2p localization to G bodies depends on its N-terminal, intrinsically disordered region. In order to identify factors important for G body formation, we conducted a yeast kinome screen and identified the AMP kinase ortholog, Snf1p, to be required for the localization of multiple glycolytic enzymes to G bodies. Further, proteomic analyses of purified G bodies identified a core set of resident factors, many of which are essential for G body integrity. Cells incapable of forming G bodies in hypoxic conditions display abnormal cell division and produce inviable daughter cells. Conversely, cells that form G bodies show increased glucose consumption and decreased levels of glycolytic intermediates. Importantly, G bodies also form in human, hepatocarcinoma cells upon hypoxic stress. Together, our results suggest that G body formation is a conserved, adaptive response to increase glycolytic output during hypoxia or tumorigenesis.
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Project description:To demonstrate RIPSeeker program that is developed for RIP-seq analyses, we generated RIP-seq data corresponding to the protein CCNT1 in HEK293 cell line using standard RIP-seq protocols described in Zhao et al., (2010). We performed two in-house RIP-seq experiments both for CCNT1 in human HEK293 cells. Briefly, we generated tagged CCNT1 using a triple tag system that supports lentiviral stable expression and mammalian affinity purification (MAPLE) Mak et al (2010). The HEK293 cells stably expressing tagged CCNT1 was purified by M2 agarose beads, followed by RNA extraction by Trizol. The library synthesis was carried out according to the RIP-seq protocol described in Zhao et al., (2010) except that one of the two experiments was done with non-strand-specific sequencing.
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Project description:Background: Pancreatic ductal adenocarcinoma is classically characterized as a glycolytic tumor. However, the extent and implications of metabolic heterogeneity within PDAC remain poorly defined. This study aimed to determine whether glycolytic activity follows a consistent expression pattern across PDAC patients and to explore how metabolic diversity influences therapeutic vulnerability. Methods: We employed spatial transcriptomics in six, ex vivo, primary human PDAC specimens, complemented by single-cell and bulk RNA sequencing, to map glycolytic heterogeneity in PDAC. Patient-derived cell models, representative of distinct glycolytic phenotypes, were used to assess metabolic and antiproliferative responses to glycolytic pathway inhibition. A comprehensive multi-omics strategy—including metabolomics, proteomics, and lipidomics—was integrated through a robust bioinformatics pipeline to dissect pathway-specific variations and integromics. Results: PDAC tumors displayed marked glycolytic heterogeneity, with distinct transcriptional profiles that preserved cell identity and spatial organization. These glycolytic patterns correlated with clinical outcomes, highlighting their potential prognostic value. Functional studies strengthen differential sensitivity to metabolic inhibitors in models with opposite glycolytic activity, demonstrating both the therapeutic relevance of PDAC glycolytic stratification and highlighting the potential of metabolic targeting. Conclusions: Our findings uncover a clinically relevant metabolic heterogeneity in PDAC, offering a novel stratification framework based on glycolytic profiling. This approach may allow the selection of patients for tailored metabolic therapies, advancing precision oncology in pancreatic cancer.
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