RNA‐sequencing profiles hippocampal gene expression in a validated model of cancer‐induced depression

Abstract

To investigate the pathophysiology of cancer-induced depression (CID), we have recently developed a validated CID mouse model. Given that the efficacy of antidepressants in cancer patients is controversial, it remains unclear whether CID is a biologically distinct form of depression. We used RNA-sequencing (RNA-seq) to investigate differentially expressed genes (DEGs) in hippocampi of animals from our CID model relative a positive control model of depressive-like behavior induced with chronic corticosterone (CORT). To validate RNA-seq results, we performed quantitative real-time RT-PCR (qRT-PCR) on a subset of DEGs. Enrichment analysis using DAVID was performed on DEGs to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and biological process gene ontologies (GO:BP). qRT-PCR results significantly predicted RNA-seq results. RNA-seq revealed that most DEGs identified in the CORT model overlapped with the CID model. Enrichment analyses identified KEGG pathways and GO:BP terms associated with ion homeostasis and neuronal communication for both the CORT and CID model. In addition, CID DEGs were enriched in pathways and terms relating to neuronal development, intracellular signaling, learning and memory. This study is the first to investigate CID at the mRNA level. We have shown that most hippocampal mRNA changes that are associated with a depressive-like state are also associated with cancer. Several other changes occur at the mRNA level in cancer, suggesting that the CID model may represent a biologically distinct form of a depressive-like state.

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“…Several mRNA targets previously reported to be related to cancer- and drug-induced depression were affected by peripheral tumor in the present study, demonstrating parallel outcomes to those previously reported in SC tumor-bearing mice,20 with these effects being amplified in IF tumor-bearing mice. In particular, GluRs and glutamatergic modulators present on microglia (glutamate ionotropic receptor NMDA type subunit 2C [Grin2c], glutamate ionotropic receptor AMPA type subunit 4 [Gria4], gamma-aminobutyric acid type A receptor alpha 3 subunit [Gabra3], and glutamate metabotropic receptor 4 [Grm4]) were reduced across Pexidartinib- and control-treated IF and SC tumor groups, with the exception of increased Grin2c mRNA in IF tumor-bearing mice relative to sham.…”

“…Isolated hippocampi were dissociated, preserved in RNAlater, and total RNA was isolated from each sample using the RNeasy Kit (Qiagen), as described previously 20. Briefly, eluates were DNase-treated using the DNA-free Kit (DNase Treatment and Removal Reagents; Ambion) and RNA quality/purity was quantitated using spectrophotometric analysis at OD 260 /OD 280 .…”

“…Relative mRNA levels were quantified using qPCR, adopted from previous reports 20. Briefly, cDNA was synthesized from 400 ng total RNA via reverse transcription using Superscript III kit (Life Technologies) and qPCR assays were performed for each target and reference gene per sample with SsoAdvanced Universal SYBR Green Supermix (BioRad).…”

“…Our lab recently developed a validated murine model of cancer-induced depression (CID) using 4T1 carcinoma cells, in which subcutaneous (SC) tumor burden was associated with increased depressive-like symptoms 19. RNA-sequencing and real-time RT-PCR analysis of hippocampi revealed considerable overlap between cancer-induced and positive control corticosterone-induced depressive states 20. Several of the validated hippocampal mRNA targets associated with the depressive-like state in tumor-bearing mice were ionotropic and metabotropic glutamate receptors (GluRs) and glutamatergic modulators,20 many of which are expressed or secreted by microglia (reviewed in Murugan et al21).…”

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“…27 Analysis was carried out using a web-based platform, the Galaxy Project, as previously described. 25,26 Briefly, the FastQC tool, Tophat, Cufflinks, and Cuffmerge were applied to evaluate the quality of sequencing, align reads to the human GRCh38/hg38 assembly, create assembled transcripts, and create a transcriptome assembly, respectively. Cuffdiff was then used to assess the transcript abundance in fragments per kilobase of transcript per million mapped reads (FPKM) and to identify the differentially expressed genes (DEGs) by merging the transcriptome assembly with individual aligned reads created by Tophat.…”