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Adipocyte-driven unfolded protein response is a shared transcriptomic signature of metastatic prostate carcinoma cells

ABSTRACT: Purpose: To begin understanding molecular processes that enable tumor cells to survive and progress in difficult microenvironments such as bone, we performed unbiased examination of the transcriptome of two different prostate cancer cell lines in the absence or presence of bone marrow adipocytes. Methods: 3’ RNA-seq (QuantSeq 3′ mRNA) was performed. RNA from four biological replicates of PC3 and ARCaP(M) cells cultured alone or in Transwell with adipocytes was collected , and run on an Agilent TapeStation 2200 (Agilent Technologies, Santa Clara, CA) for quality control. Lexogen’s QuantSeq 3’mRNA-seq Library Prep Kit (FWD for Illumina) was utilized for building RNAseq libraries. The barcoded libraries were multiplexed at equimolar concentrations and sequenced with 50 bp single reads on an Illumina HiSeq-2500 run in rapid mode. Data were demultiplexed using Illumina's CASAVA 1.8.2 software and reads were aligned to the human genomes. Differential gene expression analysis was used to detect adipocyte-mediated transcriptome changes. qRT–PCR validation was performed using TaqMan assays. Results: Our RNAseq analyses and subsequent quantitative PCR and protein-based assays reveal that upregulation of Endoplasmic Reticulum (ER) stress and Unfolded Protein Response (UPR) genes is a shared signature between two metastatic prostate carcinoma cell lines of different origin. Pathway analyses and pharmacological examinations highlight the ER chaperone BIP as an upstream coordinator of this transcriptomic signature. Conclusions: Our studies pave the way for additional mechanistic investigations and offer new clues towards effective therapeutic interventions in metastatic disease.

ORGANISM(S): Homo sapiens

PROVIDER: GSE180239 | GEO | 2021/07/17

REPOSITORIES: GEO

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Project description:Spheroids, near spherical multicellular aggregates, are one of the most common types of threedimensional (3D) cell cultures. Despite decades of implementation of spheroid technology in various fields of life science and medical research, no minimal information (MI) guidelines are available to cope with heterogeneity and to stimulate transparency. To cope with this unmet need, we assembled an international consortium to develop the MISpheroID knowledgebase (https://www.mispheroid.org) and interrogation revealed heterogeneity and lack of transparency in published spheroid-related experiments. This steered us to empirically evaluate the impact of cell line, culture medium type, spheroid formation method and spheroid size on complementary spheroid metrics (RNA fingerprints, presence of cell death, ATP content, glucose to lactate conversion, secreted protein signatures, circularity, size and cancer therapy response). We measured media-induced transcriptional variation in lung cancer (A549), colorectal cancer (HCT116), ovarium cancer (SKOV3) and glioblastoma (U87MG) spheroids using RNA sequencing (RNA-seq). These spheroids were formed in ultra-low attachment plates and cultured in 6 different media types (DMEM high glucose, DMEM/F12, RPMI1640, DMEM low glucose, EMEM and MEM) for 5 (HCT116) or 7 (A549, SKOV3 and U87MG) days. RNA extraction was performed on 2 spheroids per condition using the miRNeasy micro kit (217084, Qiagen, Hilden, Germany). RNA-sequencing libraries were prepared from purified RNA using the QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen, Vienna, Austria) according to the manufacturer's instructions, using 27.5ng of RNA that was DNase treated using HL-dsDNA (Arcticzymes, TromsØ, Norway). The individual libraries were quantified by qPCR using the KAPA Library Quantification Kit (Roche, Pleasanton, CA, US) and equimolarly pooled. The pool concentration was measured with Qubit and 1.4pM with 1% PhiX was sequenced on a NextSeq 500 (Illumina, San Diego, CA, US) using a high-output 1x75 run. Reads were mapped to the human genome using Tophat and gene expression counts were generated using HTSeq. This data was used to perform Principal Component Analysis (PCA) and Gene Set Enrichment Analysis (GSEA).

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Adipocyte-driven unfolded protein response is a shared transcriptomic signature of metastatic prostate carcinoma cells

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