ABSTRACT
The nucleolus, the largest subcompartment of the nucleus, stands out from the nucleoplasm due to its exceptionally high local RNA and low DNA concentrations. Within this central hub of nuclear RNA metabolism, ribosome biogenesis is the most prominent ribonucleoprotein (RNP) biogenesis process, critically determining the structure and function of the nucleolus. However, recent studies have shed light on other roles of the nucleolus, exploring the interplay with various noncoding RNAs that are not directly involved in ribosome synthesis. This review focuses on this intriguing topic and summarizes the techniques to study and the latest findings on nucleolar long noncoding RNAs (lncRNAs) as well as microRNAs (miRNAs) in the context of nucleolus biology beyond ribosome biogenesis. We particularly focus on the multifaceted roles of the nucleolus and noncoding RNAs in physiology and tumor biology.
Subject(s)
Neoplasms , RNA, Long Noncoding , Humans , Cell Nucleolus/genetics , Cell Nucleolus/metabolism , Cell Nucleus/metabolism , Ribosomes/genetics , Ribosomes/metabolism , Neoplasms/genetics , Neoplasms/metabolism , BiologySubject(s)
Glioma , Isocitrate Dehydrogenase , Glioma/pathology , Humans , Isocitrate Dehydrogenase/genetics , Mutation/geneticsABSTRACT
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage. VIDEO ABSTRACT.
Subject(s)
Antibodies/therapeutic use , Deep Learning , Diagnosis, Computer-Assisted/methods , Drug Therapy, Computer-Assisted/methods , Neoplasms/pathology , Animals , Humans , MCF-7 Cells , Mice , Mice, Inbred C57BL , Mice, Nude , Mice, SCID , Neoplasm Metastasis , Neoplasms/diagnostic imaging , Neoplasms/drug therapy , Software , Tumor MicroenvironmentABSTRACT
Cellular aspartate drives cancer cell proliferation, but signaling pathways that rewire aspartate biosynthesis to control cell growth remain largely unknown. Hypoxia-inducible factor-1α (HIF1α) can suppress tumor cell proliferation. Here, we discovered that HIF1α acts as a direct repressor of aspartate biosynthesis involving the suppression of several key aspartate-producing proteins, including cytosolic glutamic-oxaloacetic transaminase-1 (GOT1) and mitochondrial GOT2. Accordingly, HIF1α suppresses aspartate production from both glutamine oxidation as well as the glutamine reductive pathway. Strikingly, the addition of aspartate to the culture medium is sufficient to relieve HIF1α-dependent repression of tumor cell proliferation. Furthermore, these key aspartate-producing players are specifically repressed in VHL-deficient human renal carcinomas, a paradigmatic tumor type in which HIF1α acts as a tumor suppressor, highlighting the in vivo relevance of these findings. In conclusion, we show that HIF1α inhibits cytosolic and mitochondrial aspartate biosynthesis and that this mechanism is the molecular basis for HIF1α tumor suppressor activity.
