Pulido, R. | Search bioRxiv

bioRxiv2023

The stress-induced lincRNA JUNI is a critical factor for cancer cell survival whose interactome is a prognostic signature in clear cell renal cell carcinoma

Cancer cells rely on adaptive mechanisms to survive the multiple stressors they encounter, including replication stress, toxic metabolic products and exposure to genotoxic drugs. Understanding the factors involved in these stress responses is crucial for developing effective treatments. Here, we describe a previously unstudied long non-coding RNA (lncRNA), JUNI (JUN-DT, LINC01135), which is regulated by MAPK and responsive to stress. JUNI positively regulates the expression of its neighboring gene JUN, a key transducer of signals that regulate multiple transcriptional outputs. Our findings reveal that silencing JUNI sensitizes cancer cells to chemotherapeutic drugs or UV radiation, and that its prolonged silencing leads to cell death regardless of stress exposure, highlighting the pro-survival importance of JUNI. We identified 57 proteins that interact with JUNI and found that the activity of one of them, the MAPK phosphatase and inhibitor DUSP14, is inhibited by JUNI. This effect results in c-Jun induction following exposure of cancer cells to UV radiation and promotes cellular survival. Although JUNI regulates c-Jun and its downstream targets, the pro-survival effects in cells not exposed to stress are only partially dependent on c-Jun regulation. JUNI expression levels significantly correlate with patients survival across 11 different types of cancer. Interestingly, the correlation of DUSP14 expression levels with patients survival in nine of these tumors is coherently inverse, indicating contradicting effects that are relevant not only for c-Jun induction and cellular survival but also in human cancer. Notably, we observed particularly significant antagonistic correlations in clear cell renal cell carcinoma (ccRCC) (p=5.7E-05 for JUNI and p=2.9E- 05 for Dusp14). In fact, the expression levels of 76% of JUNI-interacting proteins predict the prognosis of ccRCC patients significantly. Furthermore, a combined hazard ratio calculation demonstrates that this gene combination serves as a highly specific prognostic signature for ccRCC. Overall, our findings reveal a new important factor in stress signaling and cellular survival that is involved in ccRCC.

cell biology↗

bioRxiv2023

Leveraging genetic correlation structure to target discrete signaling mechanisms across metabolic tissues

Abstract/IntroductionInter-organ communication is a vital process to maintain physiologic homeostasis, and its dysregulation contributes to many human diseases. Beginning with the discovery of insulin over a century ago, characterization of molecules responsible for signal between tissues has required careful and elegant experimentation where these observations have been integral to deciphering physiology and disease. Given that circulating bioactive factors are stable in serum, occur naturally, and are easily assayed from blood, they present obvious focal molecules for therapeutic intervention and biomarker development. For example, physiologic dissection of the actions of soluble proteins such as proprotein convertase subtilisin/kexin type 9 (PCSK9) and glucagon-like peptide 1 (GLP1) have yielded among the most promising therapeutics to treat cardiovascular disease and obesity, respectively1-4. A major obstacle in the characterization of such soluble factors is that defining their tissues and pathways of action requires extensive experimental testing in cells and animal models. Recently, studies have shown that secreted proteins mediating inter-tissue signaling could be identified by "brute-force" surveys of all genes within RNA-sequencing measures across tissues within a population5-9. Expanding on this intuition, we reasoned that parallel strategies could be used to understand how individual genes mediate signaling across metabolic tissues through correlative analyses of gene variation between individuals. Thus, comparison of quantitative levels of gene expression relationships between organs in a population could aid in understanding cross-organ signaling. Here, we surveyed gene-gene correlation structure across 18 metabolic tissues in 310 human individuals and 7 tissues in 103 diverse strains of mice fed a normal chow or HFHS diet. Variation of genes such as FGF21, ADIPOQ, GCG and IL6 showed enrichments which recapitulate experimental observations. Further, similar analyses were applied to explore both within-tissue signaling mechanisms (liver PCSK9) as well as genes encoding enzymes producing metabolites (adipose PNPLA2), where inter-individual correlation structure aligned with known roles for these critical metabolic pathways. Examination of sex hormone receptor correlations in mice highlighted the difference of tissue-specific variation in relationships with metabolic traits. We refer to this resource as Gene-Derived Correlations Across Tissues (GD-CAT) where all tools and data are built into a web portal enabling users to perform these analyses without a single line of code (gdcat.org). This resource enables querying of any gene in any tissue to find correlated patterns of genes, cell types, pathways and network architectures across metabolic organs.

physiology↗