The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer.

We introduce Promoter-Enhancer-Guided Interaction Networks (PENGUIN), a method for studying protein-protein interaction (PPI) networks within enhancer-promoter interactions. PENGUIN integrates H3K27ac-HiChIP data with tissue-specific PPIs to define enhancer-promoter PPI networks (EPINs). We validated PENGUIN using cancer (LNCaP) and benign (LHSAR) prostate cell lines. Our analysis detected EPIN clusters enriched with the architectural protein CTCF, a regulator of enhancer-promoter interactions. CTCF presence was coupled with the prevalence of prostate cancer (PrCa) single nucleotide polymorphisms (SNPs) within the same EPIN clusters, suggesting functional implications in PrCa. Within the EPINs displaying enrichments in both CTCF and PrCa SNPs, we also show enrichment in oncogenes. We substantiated our identified SNPs through CRISPR/Cas9 knockout and RNAi screens experiments. Here we show that PENGUIN provides insights into the intricate interplay between enhancer-promoter interactions and PPI networks, which are crucial for identifying key genes and potential intervention targets. A dedicated server is available at https://penguin.life.bsc.es/ .

A high-throughput approach to predict A-to-I effects on RNA structure indicates a change of double-stranded content in noncoding RNAs.

RNA molecules undergo a number of chemical modifications whose effects can alter their structure and molecular interactions. Previous studies have shown that RNA editing can impact the formation of ribonucleoprotein complexes and influence the assembly of membrane-less organelles such as stress granules. For instance, N6-methyladenosine (m6A) enhances SG formation and N1-methyladenosine (m1A) prevents their transition to solid-like aggregates. Yet, very little is known about adenosine to inosine (A-to-I) modification that is very abundant in human cells and not only impacts mRNAs but also noncoding RNAs. Here, we introduce the CROSSalive predictor of A-to-I effects on RNA structure based on high-throughput in-cell experiments. Our method shows an accuracy of 90% in predicting the single and double-stranded content of transcripts and identifies a general enrichment of double-stranded regions caused by A-to-I in long intergenic noncoding RNAs (lincRNAs). For the individual cases of NEAT1, NORAD, and XIST, we investigated the relationship between A-to-I editing and interactions with RNA-binding proteins using available CLIP data and catRAPID predictions. We found that A-to-I editing is linked to the alteration of interaction sites with proteins involved in phase separation, which suggests that RNP assembly can be influenced by A-to-I. CROSSalive is available at http://service.tartaglialab.com/new_submission/crossalive.

Identification of long non-coding RNAs and RNA binding proteins in breast cancer subtypes.

Breast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes.

HOTAIRM1 regulates neuronal differentiation by modulating NEUROGENIN 2 and the downstream neurogenic cascade.

Neuronal differentiation is a timely and spatially regulated process, relying on precisely orchestrated gene expression control. The sequential activation/repression of genes driving cell fate specification is achieved by complex regulatory networks, where transcription factors and noncoding RNAs work in a coordinated manner. Herein, we identify the long noncoding RNA HOTAIRM1 (HOXA Transcript Antisense RNA, Myeloid-Specific 1) as a new player in neuronal differentiation. We demonstrate that the neuronal-enriched HOTAIRM1 isoform epigenetically controls the expression of the proneural transcription factor NEUROGENIN 2 that is key to neuronal fate commitment and critical for brain development. We also show that HOTAIRM1 activity impacts on NEUROGENIN 2 downstream regulatory cascade, thus contributing to the achievement of proper neuronal differentiation timing. Finally, we identify the RNA-binding proteins HNRNPK and FUS as regulators of HOTAIRM1 biogenesis and metabolism. Our findings uncover a new regulatory layer underlying NEUROGENIN 2 transitory expression in neuronal differentiation and reveal a previously unidentified function for the neuronal-induced long noncoding RNA HOTAIRM1.

The moonlighting RNA-binding activity of cytosolic serine hydroxymethyltransferase contributes to control compartmentalization of serine metabolism.

Enzymes of intermediary metabolism are often reported to have moonlighting functions as RNA-binding proteins and have regulatory roles beyond their primary activities. Human serine hydroxymethyltransferase (SHMT) is essential for the one-carbon metabolism, which sustains growth and proliferation in normal and tumour cells. Here, we characterize the RNA-binding function of cytosolic SHMT (SHMT1) in vitro and using cancer cell models. We show that SHMT1 controls the expression of its mitochondrial counterpart (SHMT2) by binding to the 5'untranslated region of the SHMT2 transcript (UTR2). Importantly, binding to RNA is modulated by metabolites in vitro and the formation of the SHMT1-UTR2 complex inhibits the serine cleavage activity of the SHMT1, without affecting the reverse reaction. Transfection of UTR2 in cancer cells controls SHMT1 activity and reduces cell viability. We propose a novel mechanism of SHMT regulation, which interconnects RNA and metabolites levels to control the cross-talk between cytosolic and mitochondrial compartments of serine metabolism.

SAMMSON fosters cancer cell fitness by concertedly enhancing mitochondrial and cytosolic translation.

Synchronization of mitochondrial and cytoplasmic translation rates is critical for the maintenance of cellular fitness, with cancer cells being especially vulnerable to translational uncoupling. Although alterations of cytosolic protein synthesis are common in human cancer, compensating mechanisms in mitochondrial translation remain elusive. Here we show that the malignant long non-coding RNA (lncRNA) SAMMSON promotes a balanced increase in ribosomal RNA (rRNA) maturation and protein synthesis in the cytosol and mitochondria by modulating the localization of CARF, an RNA-binding protein that sequesters the exo-ribonuclease XRN2 in the nucleoplasm, which under normal circumstances limits nucleolar rRNA maturation. SAMMSON interferes with XRN2 binding to CARF in the nucleus by favoring the formation of an aberrant cytoplasmic RNA-protein complex containing CARF and p32, a mitochondrial protein required for the processing of the mitochondrial rRNAs. These data highlight how a single oncogenic lncRNA can simultaneously modulate RNA-protein complex formation in two distinct cellular compartments to promote cell growth.